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Springer Proceedings in Complexity
Georgi Yordanov Georgiev
JohnM. Smart
ClaudioL.FloresMartinez
MichaelE.Price Editors
Evolution,
Development
and Complexity
Multiscale Evolutionary Models of
Complex Adaptive Systems
The Evo Devo Universe Community:
Why This Volume Exists
The Evo Devo Universe (EDU) research and discussion community (http://
EvoDevoUniverse.com) is a small international group of scholars with broad
interdisciplinary interests in multiscale complexity. The EDU community explores
Biologically-Inspired Complexity Science and Philosophy (BICS&P) as applied
to the universe and its many complex subsystems. What unites our community
is the recognition that all complex systems are in a continual tension between
stochastic, creative, and unpredictable processes, which we may call evolutionary,
and convergent, constraining, and predictable processes, which we can call
developmental. We also recognize that all of our most complex systems engage
in replication and inheritance and are subject to many forms of selection.
Within the biological sciences, one community that exemplifies this perspective
is the field of evolutionary developmental (evo-devo) biology. Evo-devo biology
seeks to understand how developmental processes evolved and how they both
constrain and enable the generation of evolutionary novelty under replication and
selection. As both evolutionary novelty and developmental constraint are found
in all complex replicators, it is an open question, of potential interest to all
scholars, whether an “evo-devo” perspective will prove particularly fundamental
to understanding multiscale complexity and, perhaps, to the universe itself.
If we live in an evo-devo universe, one whose complexity has self-organized
recursively, in a manner at least roughly analogous to organic systems, our universe
may embody at least two fundamental telos (drives, purposes, goals): to evolve
(create, vary, diverge, experiment) and to develop (conserve, converge, generate
hierarchy, undergo a predictable life cycle). We can further imagine that both of
these drives are subject to yet unclarified forms of selection and adaptation. If
our universe indeed has this degree of functional similarity to living systems and
replicates in some multiversal environment, we can predict that development at
all system scales (organismic, ecological, biogeographic, cultural, technological,
universal, etc.) will act as a constraint on the creation of evolutionary novelty at
all system scales, just as it does in biology. Likewise, we can expect that evolu-
tionary processes, via preferential replicative selection, will continually and slowly
change future development, again, at all scales. By analogy with the evolutionary
v
vi The Evo Devo Universe Community: Why This Volume Exists
developmentof two genetically identical twins, many of our cosmology models now
predict that two parametrically identical universes would each exhibit unpredictably
separate and unique “evolutionary” variation over their lifespan and, at the same
time, a broad set of predictable “developmental” milestones and shared structure
and function between them.
We may ask: In what other ways does our universe appear to be an evolutionary
developmental system? What other forms of evolutionary novelty and developmen-
tal constraint are missing from our current models? How do cyclic unpredictability
and predictability interact in complex systems within our universe, from stars to
chemistry to life, and what generic selection functions apply? What models suggest
our universe itself may replicate and be selected upon in some extrauniversal
environment?
The majority dynamical view in science, both in the modern evolutionary syn-
thesis and in modern complexity science, is one of ever-increasing unpredictability,
contingency, and diversity in our extant complex systems. But developmental
thinking, and emerging evidence of convergent evolution, argues the opposite, that
our universe is simultaneously winnowing toward a few implicit past and future
forms and functions, at all scales, constrained to do so by initial conditions, universal
laws, functional optima, and statistical determinism. Always looking for both
creative and divergent, and constraining and convergent, physical and informational
processes, and asking how they interact and simultaneously contribute to adaptation,
is an important shift in perspective. We hope you find it valuable for your own work.
To summarize, we think taking an evo-devo approach can help us address a few
of the “missing links” in complexity research today. If this view proves valid, there
is much we will continue to learn from the biological and complexity sciences, and
their philosophical bases, that will inform our models of complex replicators at all
scales. A better understanding of organic development, for example, may inform
our understanding of galactic development. A better understanding of convergent
evolution in biology may inform our understanding of it in society and technology.
A better informational and functional understanding of the origin of life may help
us to understand the origin of other complex replicators, including our universe
itself. Many other insights, unclear to us today, may be gained. We hope this volume
conveys some of the great excitement and opportunity ahead.
Evolutionary Development: A Universal
Perspective
John M. Smart
1 Definition and Overview
“Evolutionary development,” “evo devo” or “ED,” is a term that can be used as a
replacement for the more general term “evolution,” whenever any scholar thinks that
both experimental, creative, contingent, stochastic, and increasingly unpredictable
or “evolutionary” processes and conservative, convergent, statistically deterministic
(probabilistically predictable), or “developmental” processes, including replication,
may be simultaneously contributing to selection and adaptation in any autopoetic
(self-reproducing) complex system.
The hyphenated “evo-devo” is commonly used for living systems, most promi-
nently in evo-devo genetics and epigenetics, and the unhyphenated “evo devo” can
be used for the theory of any potentially replicating and adapting complex system
(star, prebiotic system, gene, cell, organism, meme (concept), behavior, technology),
whether living or nonliving. Occasionally, the hyphenated term “eco-evo-devo” is
used to place evo-devo biology within ecological systems with their own evo devo
dynamics (e.g., Pigliucci 2007; Gilbert et al. 2015). This is controversial, since
the conventional neo-Darwinian Modern Synthesis does not recognize multi-level
selection, and maintains that ecological dynamics are secondary to species com-
petition. But the rise of theoretical and systems ecology and its models, including
ecological energetics, panarchy, and ascendancy, can be viewed as supporting the
Support: No institutional support or grant was used for this work.
J. M. Smart ()
Acceleration Studies Foundation, Los Gatos, CA, USA
Naval Postgraduate School, Monterey, CA, USA
e-mail: ejsmart@nps.edu
© Springer Nature Switzerland AG 2019
G. Y. Georgiev et al. (eds.), Evolution, Development and Complexity,
Springer Proceedings in Complexity, https://doi.org/10.1007/978-3-030-00075-2_2
23
24 J. M. Smart
idea that ecologies themselves both evolve and develop. Finally, future evo devo
models may require what Lucia Jacobs refers to as “cog-evo-devo” (Jacobs 2012),
the recognition that both information and cognition evolve and develop, are causal
agents in the dynamics of complex replicators, and are increasingly important in
determining their future as higher intelligence emerges. Inspired by the work of
evo-devo biologists, evo devo systems theorists look for processes of evolutionary
creativity and developmental constraint in any autopoetic complex systems, at any
scale.
Evo devo systems theory thus redefines the much-used but increasingly multi-
causal term “evolution,” to restrict evolutionary process to contingent, stochastic,
information-creative, experimental, diversifying, and nonhierarchical processes of
system change, when we are referring to evolution within the boundaries of any
proposed autopoetic system. These processes are the dynamical and informational
opposite of the predictable, information-conservative, convergent, unifying, and
hierarchical processes of “development,” which work to replicate and maintain
that system. Redefinitions of long-used words are never a popular choice, but this
redefinition is potentially clarifying for autopoetic dynamics, from the perspective of
information theory. If evolutionary processes necessarily generate new information,
and developmental processes conserve and build upon old information, and we can
determine “new” or “old” only in relation to the life cycle of the system under
analysis, we may have a useful new perspective on both dynamicsand their intrinsic
predictability to observers within any self-reproducing system.
When we apply these definitions to the life cycle of an individual organism,such
as a frog, we can observe evolutionary, information-creative processes in such events
as stochastic gamete production, and in the stochastic cellular microarchitecture
in any specific frog. Simultaneously, we can observe developmental, information-
conservative processes in any replicative dynamics, informatics, and morphology
that we empirically observe in all frogs of a specific species. Both evolutionary and
developmental processes can thus be empirically differentiated in any living com-
plex system via these definitions. Both processes are presumably fundamental to
adaptation, and the ways each system encodes representations (models, intelligence)
of itself and its environment.
Again, within any particular autopoetic system, evo devo models redefine the
word “evolution”to refer specifically to variety-generating,experimental,divergent,
and other “soon-unpredictable” processes that generate combinatorial explosions
of contingent possibilities. They use the word “development” to refer to variety-
reducing, conservative, convergent, and other “statistically predictable” processes
that manage replication. These developmental processes are intrinsically predictable
if you have the right models and sufficient computation capacity, the right perspec-
tive (often a collective, big picture, or long-term view) or if you have empirical
experience, having seen a prior life cycle of the developing system in question (a
cell, a tree, a human, a stellar system, a galaxy, a universe).
Independently, and via similar reasoning, some scholars occasionally use the
term “evolutionary development” as a replacement for “evolution” as it juxtaposes
two dynamically and informationally opposing concepts – “random,” unguided
Evolutionary Development: A Universal Perspective 25
Darwinian evolution and nonrandom, guided development – and thus is a more
conservative and humble descriptive term to use when one is uncertain whether the
change one is talking about is random or directional. For either reason, in addition
to occasional early use by physicists (Turchin 1977) and origin of life scholars
(Oparin 1968), a small but growing group of ecologists (Salthe 1993), biologists
(Losos 2017), paleontologists (Conway-Morris 2004), theoretical biologists (Reid
2007), cosmologists (Munitz 1987), complexity theorists (Levin 1998), and systems
theorists (Smart 2008) find it valuable to use the “evolutionary development” term.
The start of the journal Evolution & Development in 1999 signaled rise of evo-
devo biology to a formal subdiscipline. Evo devo systems science and philosophy
presently has no journal. If it did, Evolution, Development, and Complexity,the
name we use for this volume, would be a reasonable title. This would be a
journal within which the complexity science and systems theory of such topics as
universal Darwinism, evo devo cosmology, evo-devo biology, eco-evo-devo, cog-
evo-devo, living systems theory, technological evo devo, artificial and biological
intelligence, hierarchy theory, accelerating change, and related topics might be
modeled and critiqued. Biologically-Inspired Complexity Science and Philosophy
(BICS&P) would be reasonable title for our emerging field itself. BICS&P is the
self-description of our Evo Devo Universe research and discussion community
(EvoDevoUniverse.com).
2 Two Polar Categories and Tensions
Tabl e 1(Smart 2008) introduces sets of two polar (equal and opposite) word pairs
that can be associated with evolutionary and developmental processes in a range
of complex systems. As you look them over, think of all the events, processes,
and systems you have previously described with these words. These and similar
words, and the concepts behind them, are often useful starts at categorizing social,
economic, and technological events and processes into one of two camps.
Some systemic processes operate by chance, others by necessity, and some by
both. Some processes are random, others predestined. Some events are indeter-
minate, others predetermined. Some processes are segregating, others integrating.
Some act bottom-up, others are top-down. Some systems appear to be branching,
others funneling. Some changes look reversible, others irreversible. Some are
generating novelty, others conserving sameness. Some are exploring possibilities,
others running into constraints. Some promote variability, others stability. Some
degrade hierarchies, others create hierarchy. In the organization, good foresight and
strategy requires a continual balance between divergent (innovative, experimental)
and convergent (predictive, conservative) thinking. We can see these twin tensions,
and their mixture, in all the ways humans use for knowing the world.
In the twentieth century, we learned that even our scientific laws fall neatly into
these two categories. From our reference frame, not only have we discovered deter-
ministic (developmental) types of laws that precisely describe the far future, like
26 J. M. Smart
Tab le 1 Common evo and
devo terms Evolution Development
Unpredictability Predictability (statistical)
Chance Necessity
Indeterminacy Determinism
Random Destined
Divergent Convergent
Reversible (long-term) Irreversible (long-term)
Possibilities Constraints
Varie t y / M a ny Unity/Monism
Variability Stability
Uniqueness Sameness
Transformation Transmission
Accidental Self-organizing
Bottom-up Top-down
Local Global
Immaturity Maturity
Individual Collective
Instance Average
Short-term Long-term
Reductionism Holism
Analysis (breaking) Synthesis (joining)
Amorphous Hierarchical/Directional
Innovative Conservative
Creativity (of novelty) Discovery (of constraint)
Period-doubling/Chaos Period-halving/Order
Experimental Optimal
Dispersion Integration
Dedifferentiation Differentiation
STEM recombination STEM compression
Nonergodicity Ergodicity
Innovation Sustainability
Belief (unproven) Knowledge (verified)
Source: ASF
the equations of classical mechanics and relativity, we’ve also found stochastic and
statistical (evolutionary) physical laws, like quantum mechanics, thermodynamics,
and nuclear physics.
We have learned we can view physical and informational systems as either
deterministic or stochastic, depending on the analytical reference frame we adopt.
Deterministic laws are highly conserved and predictable at the individual level
(i.e., the laws of motion for individual objects), yet become unpredictable at the
collective/emergent level (i.e., the N-body problem in physics). Stochastic laws are
random, novel, and creative at the individual level (the quantum state or entropy of
Evolutionary Development: A Universal Perspective 27
any particular system, the decay of any particular nucleus), and yet are probabilis-
tically predictable at the collective level. We see a simple example in radioactive
half-life, and more complex examples in non-equilibrium thermodynamics, self-
organized criticality, and phase transition thermodynamics. Such factors as the
reference frame of the observer with respect to the system, the scale at which they
are observing the system, and the duration of observation relative to a (presumed)
autopoetic cycle all seem to influence the ease and extent of predictability in nature.
Many social, economic, and political processes historically alternate between
unpredictable and divergent (evolutionary) and predictable and convergent (devel-
opmental) phases (cf. Vermeij 2009). For every social issue, we can find processes
simultaneously generating “evolutionary” variety and “developmental” conver-
gence, in comparative analyses of different cities, counties, states, countries, or
regions. For example, regarding economic inequality, we find great “evolutionary”
variations, country by country, in the levels and quality of social services available
to each citizen and in the cycles of increasing or decreasing inequality. Yet we also
find a long-term “developmental” trend of predictably increasing total economic
inequality (relative and absolute rich-poor divides) the greater the flow rates of
capital, goods, and information in any societies we analyze (Bejan and Errera
2017). The two opposing perspectives and tensions of evolution and development
(unpredictability or predictability) appear to be equally fundamentally useful ways
to view the world.
Use of the paradox-containing evo devo term also communicates our humility
and ignorance when we are asked whether evolutionary (divergent, contingent) or
developmental (convergent, inevitable) processes are presently dominating in any
system or environment.We usually don’t know which processes are most in control
of physical or informational dynamics, at first glance. Careful study, modeling,
and data collection may be required to see where any complex system is presently
headed, process by process.
3 Contingency Versus Inevitability: The Two Extremes
of Scientific and Societal Bias
One might think that the existence of inevitable, developmental physical and
informational processes of change is as obvious, in modern scientific practice and
philosophy, as are unpredictable contingent, evolutionary processes of change. It
has been more than 300 years since universally inevitable celestial mechanics was
elucidated (Newton 1687), a century and a half since we discovered the second
law of thermodynamics (Clausius 1851), and a century since Einstein reformulated
mechanics into an even more general inevitabilist framework (Einstein 1915),
allowing us a deeper understanding of both space-time and energy-matter and
predicting such still incompletely understood emergent phenomena as black holes,
28 J. M. Smart
and perhaps even dark energy,via the cosmological constant. These and many other
physically well-characterized processes are developmental constraints within which
all of life’s stochastic evolutionary processes must occur. Surely such examples
must lead us to realize that there are likely to be many other statistically predictable
macrotrends and inevitable emergences in life, society, and technology,waiting to be
discovered and measured empirically, and eventually more rigorously characterized
by physical and informational theory and simulation.
Unfortunately, there is presently a strong practitioner and philosophical bias
against inevitabilist thinking in most scientific, technical, economic, political, and
cultural communities, particularly since the rise of chaos theory and nonlinear
science in the 1970s and of subjectivist postmodernism in our academic institutions
in the late twentieth century. Humanity is guilty of periods and domains of overap-
plied developmentalist thinking, as in the various clockwork universe models of the
eighteenth and nineteenth centuries (most famously, Laplace 1812). More recently,
many Western nations overapplied reductionist and logical positivist thinking in our
think tanks, corporate strategy, and government plans in the mid-twentieth century.
The above brief history illustrates that our dominant societal biases have tended
to each of two extremes (contingency or inevitability in various human futures) in a
chaotic and cyclic dynamic. A few decades or generations hence, perhaps after some
particularly predictive scientific or technical advance, we may again swing to the
opposite extreme, and adopt an overly developmentalist bias, at least in particular
scientific or societal subcultures.
A more adaptive position, rather than swinging to extremes, might be to
recognize that both predictable and unpredictable processes are always occurring
within any complex system, and to try to better understand each. We can identify
at least a few inevitable (developmental) processes, as well as many contingent
(evolutionary) processes in any complex system we analyze, including the universe
as a system. Chaos theory and sensitive dependence on initial conditions apply
to some universal processes, but certainly not to all processes. Even our modern
philosophy of science, while it acknowledges a “contingentist” and “inevitabilist”
debate with respect to the results of scientific experiments (cf. Martin 2013) does
not yet acknowledge that both positions are always true, in any complex system,
from different perspectives, as we will discuss. More disturbingly, modern science
and complexity theory also rarely ask how each apparently fundamental process
interrelates, and how each must contribute to selection and adaptation.
Today, we are primarily contingentists, and so we are biased to under-recognize
and under-seek statistically inevitable processes of change, and there are social
blocks and professional costs to significant inevitablist thinking in the social and
technical sciences. For one example of the costs of this bias, consider the following
potential sociotechnicaldevelopmentalprocess, one that I study and find particularly
important. We can characterize a “general Moore’s law” of exponentially growing
computational capacity per dollar, observable since the 1890s at least (Kurzweil
1999), involving exponential growth in performance and resource efficiency the
Evolutionary Development: A Universal Perspective 29
further our computing processes move into meso-, nano-, and quantum-scale realms
(Smart 2000). This computational performance and efficiency acceleration via
physical miniaturization appear to be a developmental macrotrend in human history,
one likely to occur on all Earthlike planets, within some predictable stochastic
envelope, if those planets harbor intelligent technology-using life, regardless of their
political economies and cultures.
This example of one very socially relevant potential statistical inevitability, still
poorly characterized in our physical and informational theory, has been largely
ignored by academic and complexity science communities alike. Only futurists
like myself, and a handful of philosophers and social theorists, seem interested in
writing about it and asking about its causal dynamics. I have followed the literature
in this area since the late 1990s, and I can assure you that the number of funded
science or engineering researchers considering this process is minuscule,even today.
Contingentist bias, in my view, is the simplest likely explanation for this state of
affairs.
The Santa Fe Institute, a leading US complexity science research organization,
tried at least three times (2009–2011) to get the NSF to fund a Performance Curve
Database (PCDB) project (see http://pcdb.santafe.edu/), simply to collect better
data on predictable exponential trends in technological performance efficiency, to
aid in empirical and theoretical models of these fascinating and still accelerating
processes. The requested modest funds were denied, and the SFI postdoc leading
the grant applications, Bela Nagy, a personal friend, left his scientific career soon
afterward, in part due to his disillusionment with the conservative funding priorities
of Big Science. The PCDB remains unfunded today, and I know of no other similar
project yet in any nation. Perhaps collecting data on technical exponentials, the
fastest-changing and most economically and socially disruptive processes in human
society today, wasn’t considered a high enough priority for the grantors, due to our
current scientific and societal bias toward a primarily contingentist view of social
change. Perhaps also, NSF politicians didn’t want the controversy of being seen
as aiding the inevitablist perspective (see Kurzweil 1999,2005) on scientific and
technical acceleration. I do not know the details, but would be curious to see a
causal study done.
Given the reality of contingentist bias, those who write about technological
development or accelerating change from a macrohistorical perspective today are
often pejoratively labeled as technophiles, utopians, or positivists, when all they
are trying to do is establish that both unpredictable evolutionary paths, wherein
we must exercise our free moral choice, and predictable yet causally opaque
developmental processes, like technical acceleration, and destinations, like societal
electrification, digital computers, or machine intelligence, appear to coexist in
our complex universe. Our ability to see not only evolutionary change but also
simultaneous processes of ecological, societal, technical, global, and universal
development suffers greatly as a result.
30 J. M. Smart
4 The VCRIS Model of Natural Selection in Autopoetic
Systems
If we wish to understand natural selection in autopoetic systems, both living and
nonliving, we must better characterize dynamical change and develop better theories
of information and intelligence. The VCRIS (“vee-kriss”) evo devo conceptual
model (Smart 2017a) may be a useful, small step toward these challenges,especially
when contrasted to the classic VIST model (variation, inheritance, selection,
time/cumulative replication, Russell 2006) of dynamics offered by traditional
evolutionary theory. The VCRIS model proposes that three sets of physical and
informational dynamics must be modeled to understand and predict the outcomesof
natural selection in autopoetic systems. The first two are fundamentally oppositional
processes, and the third arises from their interaction. These are:
1. Variational or “evolutionary” processes that generate, maintain, and manage
diversity, divergence, and experiment. When we observe them from within any
autopoetic system, these processes grow increasingly unpredictable over time.
2. Convergent or “Developmental” processes that attract, constrain, and guide the
system through hierarchical stages of form and function. When observed from
within any autopoetic system, these processes grow increasingly predictableover
time.
3. “Evo Devo” processes that are Replicative, with Inheritance of informational and
physical parameters, under Selection for adaptation. Selection can favor either or
both “evolutionary” (variational) or “developmental” (convergent) dynamics in
the replicator, depending on context, and these two processes are particularly
fundamental ways to understand selection. Adaptation, in turn, depends on the
encoding of information (intelligence) in three places: Replication (organism,
autocatalytic) processes, Inheritance (seed, gene, parametric) processes, and
Selection (environment) processes (Fig. 1).
Fig. 1 The VCRIS
conceptual model of natural
selection in autopoetic
systems. Variation
(evolutionary process) and
convergence (development),
operating under replication
and inheritance, can be
viewed as fundamental
physical and informational
dynamics that mediate
selection in self-reproducing
systems. (Source: ASF)
Evolutionary Development: A Universal Perspective 31
In the VCRIS model, physical and informational processes that change unpre-
dictably in successive replication cycles, to generate, maintain, and manage Variety,
are in fundamental tension and oppositionwith physical and informational processes
that change predictably in successive replication cycles, and thus generate, maintain,
and manage Convergence. V and C are the first two terms in the VCRIS model,
as these two oppositional processes are proposed as “root perspectives” in any
model of physical and informational change in autopoetic systems, including our
universe itself, if it is a replicating and adaptive system, as various theorists
have proposed (Smolin 1992,1997,2004; Vaas 1998;Vidal2010;Price2017).
Standard evolutionary theory offers no model of this fundamental opposition, of
the inheritance and tension between two classes of informational-physical initiating
parameters (evo and devo) at every scale at which replication occurs, including gene,
epigene, organism, group, niche, environment, and universe.
If our universe is an autopoetic system, the VCRIS model offers us a new term
to understand selection, a term that juxtaposes two fundamental binaries, those
things that change and those that converge to stay the same, in any replication
cycle. “Unpredictable predictable” is a term a physicist might favor, yet evolutionary
development (evo devo) seems more precise, as it uses our model of replication (life
cycle) as a way to define those things that predictably stay the same, in prior and
parallel life cycles.
In toy cellular automata universe models, like Conway’s Game of Life, the spa-
tiotemporally repetitive structures and dynamics that we see in each successive game
(replication cycle) can be defined as predictable, convergent, and developmental.
Such reliably emergent structures and dynamics are robust to variation in most of
the game’s initial conditions (occupied configurations within the initiating matrix),
yet they are also finely sensitive (finely tuned) to be critically dependent on a few of
those conditions, such as the rules of the automata. The morphology and dynamics
of other emergent structuresin this game are essentially unpredictableand divergent
and can be thought of as sources of evolutionary variety within the game. See
Poundstone 1985 for an account of Conway’s game from a universal perspective.
In real-world systems, such as individual living organisms, we can observe that
the features of two genetically identical twins that look the same are (in theory)
predictable, convergent, and developmental. The morphological, dynamical, and
functional features that are stochastically different, which include their fingerprints,
brain wiring, organ microarchitecture, and many (not all) of their ideas and behav-
iors, are unpredictable, variety-generating (within bounds), and “evolutionary,”
in an evo devo model. Most dynamical processes in two identical twins, when
we observe them at the molecular scale, appear stochastic and evolutionary. It
is only when we look at the twins from across the room (a great increase in
observational space and time, from the molecular perspective) that we see a subset
of developmental similarities. We will discuss this as the 95/5 rule in the next section
and then consider how it may apply to the universe as an autopoetic system.
In living systems, Selection always appears to involve a majority of “tree-like”
evolutionary processes (think of Darwin’s “tree of life”) driven by Variation, and a
minority of “funnel-like” developmental processes (any cyclically stable attractors
32 J. M. Smart
in phenospace) driven by Convergence. From the perspective of information theory,
the first process generates new information, and the latter conserves old information,
expressed in a prior cycle. These two informational and dynamical processes
appear to work both cooperatively and competitively with each other, in service to
adaptation. Consider how Replicating organisms are sometimes driven to variation,
and sometimes to convergence in both their systems and subsystems. Inheritance
units (seeds, genes) sometimes duplicate (think of gene duplication) and vary, and
sometimes converge (with gene loss). Selection in the environment sometimes
favors creation of phenotypic diversity, and sometimes favors convergence to a
particular dominant phenotype. In the VCRIS model, evo and devo (variational
and convergent) replication and inheritance under selection are the root source of
adapted order.
Perhaps most promisingly, from my perspective, the RIS terms at the center of
the model allow us to think of information, learning, and intelligence, all processes
that may be central to the maintenance of autopoesis, from three separate systems
perspectives, that of the replicator (organism, as an autocatalytic system), the
inheritance system (informational parameters that guide variation and convergence),
and the selective environment (environmental conditions). This seems particularly
appropriate, and a clue toward a better autopoetic information theory, as all
sufficiently complex organisms(such as any metazoans with culture) appear to store
the fruits of their learning and intelligence in these three, partially decomposable
systems. In other words, we can say that adapted intelligence (encoded information)
in any evo devo system always appears opportunistically partitioned between three
complex actors, Seed (inherited parameters), Organism (autocatalytic replicator),
and selective Environment (SOE partitioning). Intelligence is never resident in only
one of these actors. It always straddles all three (Smart 2008).
For a basic example of Environmental intelligence partitioning, genes use
historically metastable features of the local environment to reliably guide the
evolving and developing organism to its future destinations. Much information
for embryo construction is not specified in the genome, but in the replication-
stable features of the environment. For a more complex example, metazoans
externalize their intelligence in “niche construction” of their local environment, to
make it more co-adapted (Odling-Smee et al. 2003). This process is also called
“stigmergy” by scholars (Heylighen 2008,2016). Niche construction/stigmergy is a
key informational process that appears to grow with the complexityof the replicator.
It presumably exerts selective pressure toward certain forms of variation and of
convergence, in ways not yet well characterized in evo devo theory.
Consider also that environments may also replicate, on some higher systems
level, just as organisms and seeds replicate. This happens, for example, when we
replicate an urban architecture or idea-complex (like capitalism or democracy) in
global society, when stars replicate, when continents drift apart, or if our universe
itself replicates. In this model, our selective environment is much more similar to
an organism, one fated to produce a new seed or seeds in special high-complexity
locales, than is commonly understood in complexity theory.
Evolutionary Development: A Universal Perspective 33
We may also use the VCRIS model to gain a new perspective on another
long-used term in the complexity literature, self-organization. Self-organization
is typically defined as the emergence of “spontaneous” order from a previously
apparently disordered system. When a complexity theorist uses the term self-
organization, they are calling attention to poorly understood, partly hidden ordering
processes. In the VCRIS model, these ordering processes must be partly evo-
lutionary (via inherited mechanisms of variation) but largely developmental (via
inherited mechanisms of convergence).Both ordering processes interact to produce
an autocatalytic life cycle (replication), and both appear to require inheritance
factors that are selected upon. These five VCRIS processes, then, are the key ones
we must strive to better understand in any autopoetic system.
To understand self-organization, we must find the hidden evolutionary (to some
extent) and developmental (to a major extent) dynamics that have been tuned into
the initial and boundary conditions of the replicating system, as a result of selection
that occurred upon that system in previous autopoetic cycles. For example, when
we randomly cut up viral DNA and proteins in a petri dish, and place those
molecular fragments in another dish, many fragments will appear to “self-organize”
(spontaneously form structure), at a rate much greater than chance. They do so
because those molecules have become finely tuned, under prior selection, to use
physically and informationally metastable features of the universal environment to
produce both contingent evolutionary variety and deterministic developmental order
(self-assembly), using processes of both bottom-up and top-down causation. In an
evo devo universe, such classical self-organization discoveries as Rayleigh–Bénard
convectionand the Belousov–Zhabotinskyreaction can be called previously hidden,
now understood forms of evolutionary developmental ordering. Once we have the
appropriate model, such order is no longer spontaneous but becomes predictable, in
a broad range of environmental conditions.
If our universe is an autopoetic system, it too must have many such hidden
evolutionary and developmental ordering processes at work as well, most of which
we do not yet model well. Complexity theorists who argue that self-organization
under far-from equilibrium conditions is as much a source of biological complexity
as genetic variation and natural selection can be classified as universal evolutionary
developmentalists, though they may not self-describe with this term. See Jantsch
(1980), Haken (1984), and Kauffman (1993) for three promising yet still early
theoretical efforts exploring self-organization from a universal evo devo frame.
Self-organization is thus a helpful term to remind us that both evolutionary
and developmental processes are occurring in any autopoetic system, and I will
use it in that sense in this article. At the same time, it should be most helpful
to use the full set of VCRIS terms as our models improve, as we should be able
to model replication, inheritance, and selection in evolutionary and developmental
terms. Again, in the VCRIS model of selection in autopoetic systems, adaptive
processes are not called “evolutionary” but rather “evolutionary developmental” or
evo devo, to remind us they are always a balance between diverging and converging
dynamical processes. This small change in terms helps to correct a bias of standard
models, which ignore or minimize convergence, particularly at the level of the
34 J. M. Smart
universal environment. Even today, the topic of convergent evolution (apparent
planetary, biogeographic, and ecosystem development) remains controversial and
understudied in evolutionary (developmental) biology. This neglect is no longer
acceptable, in my view.
Many biologists today would argue that macroevolutionary dynamics are over-
whelmingly contingent, diversity generating, and unpredictable. So it is a small
change in definition for us to restrict the term evolution to “only” such processes,
within any autopoetic system. Many evolutionary biologists might not like that
restriction, but from my perspective, evolutionary biology today offers a view
of life and selection that is dangerously incomplete. It has long neglected the
physical and informational roles of development in macroevolutionary change,
and developmental processes in the selective environment. Fortunately, evo-devo
biology is rehabilitating development as a process in living systems. We can hope
this will lead us to better see development in the universe as well.
Finally, if autopoesis turns out to be the most efficient and effective way to
generate advanced complexity that is both intelligent and stable to time and change,
as I presently believe but cannot prove, then it seems most parsimonious to expect
both that our future AI must be autopoetic (evo devo) in nature, and that our universe
itself is an autopoetic system. Our reality may be, as Rod Swenson (1992) argues,
autopoetic “turtles all the way down.”
5 Evolutionary Development in Organisms: The 95/5 Rule
Since the mid-1990s, the interdisciplinary field of evolutionary developmental, or
“evo-devo” biology has emerged to explore the relationship between evolutionary
and developmental processes at the scale levels of single-celled and multicellular
organisms (Steele 1981,1998; Jablonka and Lamb 1995,2005;Raff1996; Sander-
son and Hufford 1996; Arthur 2000; Wilkins 2001;Hall2003; Müller and Newman
2003; Verhulst 2003; West-Eberhard 2003; Schlosser and Wagner 2004; Carroll
2005; Callebaut and Rasskin-Gutman 2005). Evo-devo biology includes such issues
as:
• How developmental processes evolve
• The developmental basis for homology (similarity of form in species with a
common ancestor)
• The process of homoplasy (convergent evolution of form and function in species
with unique ancestors)
• The roles of modularity and path dependency in evolutionary and developmental
process
• How the environment impacts evolutionary and developmental process.
Conceptual and technical advances in scientific disciplines including compar-
ative phylogenetics, morphology and morphometrics, and statistics are allowing
better insights into the evolutionary relationships among organisms, and inferences
Evolutionary Development: A Universal Perspective 35
about how developmental processes influence those relationships. The best work
in evo-devo recognizes that natural selection is a net subtractive process. Natural
selection generates increasing physical diversity, as seen in ever-growingevolution-
ary “trees,” but, at the same time, an even greater reduction in potential physical
diversity (Johnson 2011).
The fundamental role of evolution can be hypothesized as cumulative mech-
anisms that generate experimental (“good bet”) types of diversity, to improve
the odds of survival under environmental selection. Evolutionary systems har-
ness stochasticity in an increasingly information-driven and intelligent way as
organic complexity grows, but evolutionary innovation itself is largely unpredictable
(Shapiro 2011; Noble 2017). Living systems continually sense their internal states
and environment, and they react to catastrophe and stress with bursts of such poorly
predictable, information-driven innovation, a pattern some evolutionary biologists
call punctuated equilibrium (Eldredge and Gould 1972).
The fundamental role of development can be hypothesized as cumulative mech-
anisms that conserve and execute a small subset of (in-principle) predictable
processes that have worked in the past to guarantee replication, under a range
of chaotic internal and external environmental conditions. Developmental systems
encode future-predictive probabilistic models of themselves and their environment,
models which we assume follow the rules of Bayesian probability in nervous
systems and presumably even in single-celled organisms. Developmental prediction
(a convergent form of “intelligence”) is generated from special initial conditions
(developmental genes), tuned via informational constancies that exist in genes,
developing organisms, and the environment.
The theory of facilitated variation (Gerhart and Kirschner 2005,2007), in which
the genetic processes in living systems are assumed to sort into two groups, a
conserved core, which regulate critical elements of development and physiology,
and a set of changing genetic elements, whose variation is “facilitated” by the
conserved core, presumably in ways that both reduce the lethality of experimental
change and increase the utility of genetic variation (“experiments”) subsequently
retained by populations, is a model consistent with this view. In evo devo language,
the conserved core are conserved developmental genetic, allelic, and epigenetic
processes, and evolutionary genetic processes are those that facilitate genetic,
allelic, and epigenetic variation within and across generations. Such processes
presumably act in tension with and opposition to each other in very fundamental
informational and dynamical ways.
In this model, natural selection can be argued to be a composite of two more
fundamental kinds of selection. Evolutionary selection biases the system toward
potentially useful, intelligence-guided innovation and disorder when needed, and
developmental selection biases the system toward convergences and order that have
historically allowed complexity conservation and replication. In this view, we must
see both of these selective and often opposingprocesses, apparently at work at many
scales in every system that replicates, to truly understand biological change. For
example, we should be able to identify both structure- or function-divergent and
structure- or function-convergent classes of gene flow operating between species
36 J. M. Smart
in the terrestrial biosphere, via such processes as genetic drift and horizontal (or
lateral) gene transfer. Such transfer is well documented in Prokarya, and has been
greatly facilitated by viruses in Eukarya (Zimmer 2015).
One clarifying feature features of developmental selection is that it is always crit-
ically dependent on a small subset of control parameters (in biology, developmental
genes and other regulatory molecules). While about half of metazoan genes are
expressed in such processes as organ development,less than 20% of these (thus less
than 10% of all genes) are substantially regulated during expression (Yi et al. 2010).
A further subset of our genome, roughly 5% of DNA in human, mouse, and rat, is
highly conserved across these and other metazoan species. This 5% of our genome
typically cannot be changed without stopping, or causing major deleterious effects
to, processes of development. The majority of this highly conserved DNA, 3.5% of
our genome, is noncoding, yet presumably also constrains functional expression
(Wagman and Stephens 2004). A subset of this conserved DNA is sometimes
referred to as the developmental genetic toolkit (DGT), or less accurately, the evo-
devo gene toolkit. These genes include the Hox genes which determine animal
body plans, and they often involve initial symmetry breaking choices in spatial,
dynamical, and informational form and function that commit the organism to a
particular developmental path. Thus a subset of all metazoan genomes have become
very finely tuned, over many past replications, for the production of complex, path-
dependent modularity, hierarchy, and life cycle. Presumably, the other 95% of these
genomes can change and generate diversity without such immediately deleterious
effects.
Thus all genomes can be categorized into two groups, of conserved and non-
conserved genes, and we can propose that all highly conserved genes which are
also highly tuned (highly sensitive to change, with deleterious effect) are the core
constraints on development itself. I call this observation the 95/5 rule, and have
found early evidence for it in replicative systems at a wide variety of scales (Smart
2008). The rule proposes that some small subset of developmental parameters are
always top-down causal, involving essentially one-way information flow (in this
case, developmentalgenes to organism). Theycan no longer be easily changed, they
can only be added to, as organisms get more complex. The remainder of the genome
can be considered evolutionary, whether it controls evolutionary or developmental
process, as all of those genes can be altered by two-way information flow with the
environment, with feedback. But per the 95/5 rule, a small and highly tuned set of
top-down constraints must always exist, in any evo devo system.
There are a variety of levels of biological hierarchy at which evo devo concepts
can be applied, and evo-devo biologists believe developmental processes and genes
must themselves act to constrain evolutionary processes, in ways not yet understood
by traditional evolutionary theory (notably the neo-Darwinian Modern Synthesis),
and that both evolutionary diversity and developmental constraint are important to
understanding long range “macrobiological” change (Pigliucci 2007; Pigliucci and
Müller 2010). Evo-devo genetics and epigenetics are rapidly improving fields, and
they promise to improve our understanding of such complex yet central topics as
biological constraint, adaptation, intelligence, and convergent evolution.
Evolutionary Development: A Universal Perspective 37
6 The Riddle of Development and the Challenge
to Cosmology
There is nothing in science more magnificent and more mysterious than biological
development, including genetic, embryonic, organismic, and psychological devel-
opment. How is it that developing organisms can reliably converge on far-future
form and function (from the molecular perspective), under chaotic and variable
environmental conditions? How is this done with just a small percentage of highly
conserved developmental genes? Development employs stochastic, contingent,
and selectionist processes, presumably ranging from quantum to macroscopic
scales, in service to statistically deterministic, modular, hierarchical and cyclic
emergent change, from conception to organism, and from organism to reproduction,
senescence, and death (recycling) (Salthe 2010). Our mathematical models of
development are incomplete today, but they continue to make progress. Our models
of evolution, of randomgenetic reassortment and selection in populations, are much
more advanced.
Development also involves teleology, or the assumption of goal-driven, end-
seeking behavior, including successful replication. For these and other reasons, most
scientists have focused on the idea that our universe may be evolving, while ignoring
the idea that it may also be developing. This oversight, more than any other, has
motivated the creation of the EDU community. The great challenge to cosmology
today is to change this state of affairs, to learn from biology to better understand
universal change.
Biologically inspired hypotheses for cosmological change offer us a number of
predictive models of the dynamics and large-scale properties of the universe. This is
necessary for establishing the potential value of ED as an explanatory approach,
but only falsifiable predictions can establish (or negate) its legitimacy. Unfortu-
nately, falsifiability is not easy in our present level of cosmological understanding.
Whenever these hypotheses appear (and we shall see some below) we may need to
investigate many details before concluding that that the hypothesis is impossible or
unfeasible. In such circumstances, it is best not to jump to negative conclusions on
the basis of the greater familiarity that science has had to date with mechanistic,
bottom-up reductionism.
Ever since Plato, scholars have occasionally compared our universe, in some
ways, to a living organism.If evo devo models are correct, this organicist philosophy
may be true in part, but we should also expect this analogy to be both overly and
poorly applied at first. Fortunately, the rise of bio-inspired design, and the recent
successes of bio-inspired approaches in deep machine learning, are showing the
value of generalizing organic structure and function to other substrates. As our
understanding of biological development grows, and we gain the ability to predict
developmental outcomes in embryogenesis via partial dependence on top-down
parameters like developmental genes, our understanding of causality will improve,
and so too will our cosmology. Fortunately, a subset of scholars continue to call
for more holistic and top-down approaches to understanding universal change (e.g.,
38 J. M. Smart
Vidal 2010; Ellis et al. 2012; Ellis 2015; Adams et al. 2016). The great challenge
we have is in learning how to blend our best top-down and bottom-up perspectives.
Predictability, convergence, and constraint help explain our universe, but these
concepts only take us so far. Consider symmetry. Discovering hidden symmetries
underlying physical reality has been tremendously helpful in building our standard
model of physics, allowing us to understand conservation laws, Maxwell’s equa-
tions, the electroweak interaction, and predict fundamental particles like the charm
quark. Exploration of the symmetries of very high dimensional shapes, like the
Lie groups, may uncover a constraining relationship between our universe’s forces
and particles. But our attempts to use supersymmetry to arrive at a single “theory
of everything” for our universe, and to make verifiable predictions in our particle
accelerators, have stalled. I do not expect such a single theory exists, and would
predict that supersymmetry, or any other fully top-down, constraint-based model,
will never be enough to explain reality.
Our universe also seems to use unpredictability, divergence, and freedom just
as fundamentally. Besides quantum theory, two other useful physical theories,
eternal inflation and string theory, each offer a mathematics of diversity and
unpredictability, in which our universe is just one in a multiverse of possible
universes, and ourfundamental parameters alone cannotfully specify all the features
of this universe. Some scholars propose that these new multiverse models imply we
live in an “accidental” universe (Lightman 2011), and that our ability to understand
our universe in terms of fundamental principles, at a level below this essential
randomness, is, like a fully deterministic (non-statistical) understanding of quantum
theory, an objective we will never achieve.
I am sympathetic to both of these views, and expect each will continue to
make progress, while each alone will remain incomplete. If we live in an evo devo
universe, where universal dynamics and informatics have proceeded something like
biology has, from simple to more complex, over many past cycles, then something
like the 95/5 rule should apply. Our universe will increasingly be understood as
both “accidental” and “purposeful.” While the vast majority of our universe’s
mathematics will have this random, accidental, and evolutionary looking nature, we
will also continue to discover a growing subset of top-down, constraining processes
that guide our universes critical processes of development, including complexity
and intelligence production, conservation, and replication. Some combination of
environmental selection (for adaptation) and self-selection (for intelligence) should
apply. We need to get smart enough to see both classes of process, and ask how they
each relate to and support the growth of useful (intelligent, adapted) complexity.
Like living systems, our universe broadly exhibits both stochastic and determin-
istic components, in all historical epochs and at all levels of scale (Miller 1978). It
has a definite birth and it is inevitablysenescing toward heat death. The idea that we
live in an “evo devo universe,” one that has self-organized over past replications
both to generate multilocal evolutionary variation (preselected diversity) and to
convergently develop and pass to future generations selected aspects of its accumu-
lated complexity (“intelligence”), is an obvious hypothesis. Living systems harness
stochastic evolutionary processes to produce novel developments, especially under
Evolutionary Development: A Universal Perspective 39
stress, in a variety of systems and scales (Noble 2017). If our universe is an adaptive
replicator, it makes sense that it would do the same. Yet very few cosmologists
or physicists, even in the community that theorizes universal replication and the
multiverse, have entertained the idea that our universe may be both evolving and
developing (engaging in both evolutionary innovation and goal-driven, teleological,
directional change and a replicative life cycle).
There is a reasonable frequencyof discussion, in the cosmology and astrophysics
literature, of the idea of universal evolution. But none of it takes an evo devo
approach. We find plenty of random, Monte Carlo models of change, applied to our
universe’s initial conditions (e.g., various chaotic inflationary multiverse models;
Linde 1992), but no models in which adaptive complexity and modeling intelligence
emerge via evolutionary development in replicating universes in the multiverse,
just as it does in all living replicators, and in several nonliving ones, such as
hierarchical prebiotic chemistries on the path to RNA and hierarchical populations
of increasingly chemically complex stars. Even our best current models of universal
replication, like Lee Smolin’s cosmological natural selection, do not yet use the
concept of universal development, or refer to development literature, or to any
theories of intelligence. Yet intelligence and its causal implications are an emergent
property of all organic replicators, and if our universe is a replicator it is reasonable
to expect universal intelligence must be accounted for in our future cosmology, as
we will describe.
Organisms are evolutionary, and most of their genes recombine and change to
generate diversity, but they are also developmental, and a small subset of their
DNA, on the order of 5% per the 95/5 rule, cannot be changed without disastrous
effects on development. As previously mentioned, this DNA has become veryfinely
tuned, over many past replications, for the production of complex, path-dependent
modularity,hierarchy, and life cycle in all complex metazoans.
In the same fashion, a handful of our universe’s fundamental parameters appear
breathtakingly finely tuned, in their mathematical values, for producing stable, long-
lived, complex universes (Barrow and Tipler 1986; Rees 1999; Smolin 2006,2012).
If our universe is an adaptive replicator, under some sort of selection (either self-
selection or environmental selection), the most parsimonious explanation for our
universe’s incredible developmental fine-tuning would be past universal replication,
with both optimization and path dependency of developmental parameters (con-
served inheritance) aiding in universal complexity survival and adaptation. Virtually
all known or proposed intrauniversal complex adaptive systems are replicators,
with the exception of galaxies, which presumably replicate as dependents on their
universes (Smart 2008), so it is conceptually the simplest to infer that the universe
is also such a system, in my view.
In living systems, developed properties like intelligence, immunity, and morality
strongly alter previously locally contingent environmental selection processes
toward organism improvement and survival. See Corning 2018 for a nuanced
argument that synergy (cooperative competition, interdependence) is central to
adaptive selection in all intelligent systems. If our universe is a replicating system
under selection, it is a reasonable hypothesis that aspects of its internal adaptive
40 J. M. Smart
complexity, intelligence, immunity, and morality may not only be evolutionary
(stochastic, unpredictable) but also developmental (fine-tuned, predictable) as well.
Yet at present, the scientists exploring the fine-tuned universe problem presently
do not consider explanations in terms of universal development. Instead, we find
fine-tuning research disproportionately dominated by intelligent design creationists
championing the idea of fine-tuning as “evidence for God,” leading to much
confusion in professional and lay circles.
Perhaps as a result, the field remains professionally controversial for orthodox
science, and a minority of astrophysicists, seeking to debunk theists, argue that fine-
tuning, beyond the weak anthropic principle (observer-selection effects) doesn’t
exist (Adams 2008; Stenger 2011; Carroll 2016). But since the anthropic principle
was first clearly articulated in cosmology (Carter 1974), another community of
scientists have offered their own reasonable evidence that such tuning appears baked
into our standard model of physics and empirically observed cosmology. In recent
decades, fine-tuning explanations are commonly done via appeal to the multiverse.
Among multiverse models, the hypothesis of universal evolutionary development
offers a naturalistic explanation for fine-tuning that is homologous to biological
fine-tuning. It deserves elucidation and critique.
The primary bias that exists in our cosmological models today is not observer
selection bias, which is real but overrated. The primary bias at present is our
failure to consider the concept of universal development, the idea that our universe’s
special initial conditions and stunning internal complexity are likely self-organized,
via evolutionary development, just as our initial conditions and complexity have
self-organized in all living systems. If our universe is a replicator, then evo devo
self-organization is the most parsimonious explanation for the surprising levels of
fine-tuning, massive parallelism, and fitness for life we find in our universe, not
randomness alone, and not “design.” See the fine-tuned universe hypothesis – early
evidence for universal evolutionary development below for further discussion.
In the meantime, our leading theories of universal change are presently missing
the concept of evolutionary development. If ouruniverse is an evo devo system, then
cosmologists, astrophysicists, geochemists, planetary scientists, astrobiologists,
information theorists, philosophers, Big Historians, anthropologists, sociologists,
and scholars of long-range biological, social, and technological change will have to
update their models of the future. For more on this perspective, see Smart (2015).
7 Do We Live in an Evo Devo Universe? The EDU
Hypothesis
All replicating complex systems can be viewed from two fundamentally different
perspectives. When we look at the system up close, whether it is a star, a prebiotic
chemistry, a cell, or an organism, we see much that is locally unpredictable. Yet
when we observe the same system either at a larger scale, or over a longer time
Evolutionary Development: A Universal Perspective 41
Fig. 2 Acorn and oak tree.
(Source: Pixabay)
frame, long enough to see its replication cycle, we see much about it that is
predictable – even when we don’t yet know any of the math or causal forces behind
its predictability.
Think of an acorn. Once you’ve seen one acorn grow into an oak tree (Fig. 2), you
learn that the shape of the acorn seed tells you that it will make an oak tree, with its
characteristic leaves, morphology, and behavioral proclivities. Once you’ve planted
more than one acorn, you know, in advance, that most of the structural and molecular
details of each oak tree will remain contingent, “random,” and unpredictable. But
you also know much about its future that is predictable. That predictability, in
biology, is called development. The unpredictable, diversity-generating parts we can
call evolution, in an evo devo model.
If our universe is a replicating system, it is very much like an oak tree, moving
from a highly defined initial seed, to a very flexible, undifferentiated, and totipotent
embryo, as we see in our universe’spregalactic era, then to an increasingly specified
and constrained set of outcomes, like the increasingly terminally differentiated
structure of the oak tree, or the terminally differentiated tissue types that emerge
in a developing embryo. Some scholars have represented the latter as a “tree of
differentiation” (Fig. 3), a developmental counterpart to the evolutionary “tree of
42 J. M. Smart
Fig. 3 A developmental
“tree of differentiation.”
(Source: ASF)
life.” The more we learn about the shape of the seed that created our universe, its
nurturing environment (multiverse), and the “organism” itself, the more we’ll know
about both our evolutionary futures – what will stay unpredictable, and about our
developmental future – what predictable and constraining “portals” and “terminal
destinies” lie ahead, both for us and for all intelligent life.
The massive scale and isotropy (parallelism) of our particular universe, and its
severe migration and communication constraints, can also be suspected, given the
presumably sharply limited complexity of each local intelligence (constrained by
physical law), to have been self-organized by the universe to maximize the local
evolutionary variety of each intelligence prior to contact (Smart 2008). If universal
intelligence plays a nonrandom role in universal replication, as it does in living
systems, a bio-inspired case for the emergence of our kind of massively parallel
yet apparently intelligence-compartmentalized universe can be made, as well as
the prediction that a mechanism must exist for all end-of-universe intelligences to
eventually be able to compare and contrast their computationally incomplete yet
usefully locally unique models of reality. If future intelligences can survive a black
hole transition, a number of arguments can be made that black holes themselves
Evolutionary Development: A Universal Perspective 43
may uniquely offer such a merger and selection mechanism, in what I call the
transcension hypothesis for universal intelligence (Crane 1994; Harrison 1995;
Smart 2008,2012;Vidal2008,2016).
Our intelligence can take these multiscale and macrotemporal views on our
reality, even as we are physically stuck in one small corner of our universe. All the
universe’s most complex bits are curiously isolated, by astronomical distances, and
thus each is constrained to follow its own unique evolutionary path toward common
developmental destinations. When viewed from a cosmic perspective, we can also
see that our computers are rapidly becoming the new leading local intelligence on
our planet. They may soon (perhaps even this century) exceed us in their general
adaptiveness, immunity, and intelligence. Such intelligences may be immune to
environmental catastrophe, able to exist in near space, fully independent of our
planet’s nurturing environment. Using nuclear fusion technology, they would not
even require our sun for energy. Using quantum computation, these intelligences
might even function best in the cold environment of space. This accelerating
transition to a new level of hierarchical complexity (and presumably, consciousness)
may predictably occur on all planets that harbor intelligent biological life.
The evo devo universe (EDU) hypothesis proposes that our universe has two
fundamental drives, to evolve (vary, diverge, create, experiment) and to develop
(converge on a predictable, information-conservative hierarchy and life cycle). In
the VCRIS model, the adaptive intelligence of any replicating complex system lives
in, and is opportunistically partitioned between, at least three physical and informa-
tional actors: the initiating Seed, the Organism, and the selective Environment.
The cosmological natural selection (CNS) hypothesis (Smolin 1992,1997,
2004), in which our universe replicates via black holes, with random reassortment
of fundamental cosmological parameters at each replication, is one such evo devo
model. In CNS, black holes can be considered the “seeds,” the universe the
“organism,” and the multiverse the “environment.” CNS remains controversial (see
Vaas 1998 for one review of questions to be resolved). See Gardner and Conlon
(2013) for an evolutionary biological approach to CNS using the Price equation to
model selection for black hole replication. In my view, CNS as a model for an evo
devo universe is an auspicious start, but has at least two shortcomings(Smart 2008).
First, CNS currently predicts that universes which replicate via black holes would
select for a maximum of progeny (black holes), when real biological replicators
always balance replication fecundity with other adaptive goals, including the
resource cost to add somatic complexity to the organism (the universe, in this case).
In a biologically analogous evo devo model, the qualities of the soma (universe),
of the seeds (black holes) and of the environment itself (multiverse) can all be
modified, both randomly (evo) and predictably (devo), and increasingly intelligently
in more complex replicators, to make the system more adaptive. Some critics of
CNS who state that our universe doesn’t appear to maximize black hole production
have assumed this insight makes the theory invalid, when in fact, any adaptive theory
would rarely argue for black hole maximization.
Second, CNS has a very incomplete selection function, which does not yet
account for intelligence (modeling ability) at any level. CNS assumes a random
44 J. M. Smart
reassortment of our universe’s fundamental parameters at the replication step, but
this model is not appropriate even for the simplest biological replicators, as all living
systems encode a kind of world modeling (intelligence) in both their evolutionary
and developmentalgene complexes. Genesreassort nonrandomly, based on develop-
mental constraints. In higher complexity systems like human civilizations, consider
the way ideas replicate in communities of brains. Idea replication is not random, but
is increasingly selected by the intelligences responsible for modifying and passing
them on. As internal intelligence grows in any replicator, it seems increasingly hard
to neglect, in any good model of selection.
In my view, a theoretical framework we can call CNS with Intelligence (CNSI)
(Smart 2008;Price2017) will be necessary, if we are to use CNS to causally explain
the roles of life and intelligence in our universe in coming years. Adding intelligence
to our selection function allows us to consider to what extend the parameters of
any seed have been self-selected (e.g., for greater capacity to simulate, and to
engage in replication, independent of multiversal environment) by the growing
evolutionary and developmental intelligence of the replicator itself. I will offer one
such speculative model (Five goals of complex systems, Smart 2017b) for such self-
selection later. At the same time, we must also consider if and how our universe’s
parameters have been environmentally selected, in some specific multiverse context,
as we would do in a conventional Darwinian view of selection. For more on CNS and
CNSI, see the EDU wiki page “cosmological natural selection (fecund universes)”
(Wikipedia 2008, and Smart and Vidal 2008–2017).
If our universe has these general similarities to living systems, and is subject
to selection, in some fashion, either self-selection or selection in the multiversal
environment, we can predict that development at all system scales (organismic,
ecological, biogeographic, cultural, technological, universal, etc.) will act as a
constraint on evolution at all system scales. Likewise, we can expect that evolution,
via preferential replicative selection, will continually and slowly change future
development, again at all scales.
I expect a future information-centric theory of adaptation will find a number
of evo devo processes (goals, values, drives, abilities) that are widely shared
by complex systems. I can imagine, but not validate, one such speculative evo
devo model, which we will see later. If we live in a noetic (information and
intelligence-accumulating) universe, we may need such normative (goal and value-
based) models to understand the way the growth of information and modeling
abilities change complex environments. Modern hypotheses on how top-down
causal information (Walker et al. 2017) and niche-constructing intelligence
(Odling-Smee et al. 2003; Heylighen 2016; Noble and Noble 2017) constrain and
direct innovation and selection in biology are an important start in this direction.
The better we understand the evo and devo roles for information-drivenprocesses
in bioadaptation (and today we often do not) the better we may understand their
adaptive role for the universe as a replicator. When we discover and validate
evolutionary process and structure, we can better describe innovation possibilities
for complex systems in our universe. Likewise, when we find and model devel-
opmental process, we can predict or guess developmental constraints on those
Evolutionary Development: A Universal Perspective 45
systems, and where they are striving to go. Most auspiciously for our moral and
intellectual lives, we can better understand more of the evo and devo “purposes” or
“telos” for ourselves, our societies, and the universe. We can recognize our natural
drives to pursue both evolutionary goals (e.g., to create/innovate/experiment) and
developmental goals (to conserve/sustain/discover), and seek to harness these two
apparently fundamental processes to greater individual, organizational, and societal
adaptiveness (Smart 2017a).
8 The Fine-Tuned Universe Hypothesis: Early Evidence
for Universal ED
The fine-tuned universe hypothesis (Rees 1999,2001) can be best understood as
an important and early example of universal evolutionary development. In most
organisms, you can change many genes and generate phenotypically different
organisms, but they will still develop. We can call those “evolutionary” genes.
But there is a subset of genes that are highly conserved in evolutionary history,
and highly resistant to change. Nudge them just a bit, and you don’t get viable
development.
In the same way, while our universe and multiverse simulation capacity are still
emerging (Fig. 4), and our physical and informationaltheories are not yet complete,
we know that among the known 26 or so fundamental parameters of our universe,
most can be changed and simulations will still produce viable universes (Smolin
1997). We can call these the universe’s “evolutionary” parameters in its initiating
“seed” or “genome” in an evo devo model. At the same time, there are a special
subset of parameters that seem improbably precisely tuned (one, the cosmological
constant, apparently even to 120 orders of magnitude), to work with the other
finely tuned parameters to produce universes capable of rich internal complexity
and longevity.
Fig. 4 Universe systematics
must exhibit both evo and
devo processes when viewed
from a multiverse perspective,
if our universe is an evo devo
system. (Source: ASF)
46 J. M. Smart
When we nudge any of these precisely tuned parameters in our simulations, we
don’t get viable universes. We can call those “developmental” parameters, in an
evo devo model. If our universe replicates, they seem homologous to the small
subset of developmental genes in organisms. Edit any of those parameters and
you never get viable organisms. They’ve been self-organized, over vast numbers of
previous cycles, to work together to conserve the developmental forms, functions,
hierarchies, and life cycle of the organism.
The proposition that our universe’s laws are finely tuned for various evo devo
outcomes can be made in a variety of ways. These claims can be consideredvarious
forms of the anthropic principle, and anthropic reasoning its field of inquiry. In its
most useful variation, the anthropic principle is the idea that our universe’s initial
conditions and laws seem improbably biased toward the production of intelligent
observers (Barrow and Tipler 1986). But there is a more fundamental bias that
must be considered when evaluating fine-tuning models, the bias that results from
observer-selection effects. Is the particular kind of physical and mathematical
universe we live in logically necessary, if there are intelligent observers around to
ask questions about it?
We will discuss anthropicselection effects in detail in a later section, but for now,
let us make just one potentially useful observation. It does seem plausible that we
must have a quantum universe in order to have a universe with observers. So that
particular level of observer-selection bias may necessarily exist, at least. In perhaps
the best-known “weirdness” of quantum physics in the standard (“Copenhagen”)
interpretation of the wave function, we observers alter physical reality (quantum
states) by the manner in which we choose to observe them. But the apparent
necessity for quantum physics in our observable universe in no way tells us that
fine-tuning doesn’t exist. If anything, it could point to necessity of some version
of the “co-evolution” between universe and observers, perhaps as first sketched by
John Archibald Wheeler (1977,1988). The kind of quantum physics we have, and
its relation to the rest of our physics, may be tuned for the necessary emergence
of not just observers, but of intelligent observers, of “mind.” Quantum physics
doesn’t presently integrate fully with other physical and mathematical features of
our universe, such as the fundamental parameters, general relativity, symmetry,
information, and meaning (whatever that is). So we don’t know yet what our most
fundamental universal theories are. As we don’t yet have the ability to definitively
answer such questions at present, the fine-tuning debates will continue, and continue
to be productive.
Most physicists were strongly opposed to the teleological (purposeful, direc-
tional) idea of fine-tuning when the Barrow and Tipler book emerged in 1986.
Today, many leading physicists, like Leonard Susskind (2006)andStevenWein-
berg (2007), now argue that multiverse models offer us the simplest explanation
(principle of parsimony)for the mathematically improbablelevels of fine-tuning we
find in several of our fundamental physical constants, and as a result, in the strengths
and nature of the four forces in our Standard Model of Physics. So we have seen a
shift of many leaders in the physics community toward multiverse explanations of
fine-tuning, and thus an implicit recognition that our universe’sapparent fine-tuning
is a real problem that must be addressed.
Evolutionary Development: A Universal Perspective 47
In perhaps the most dramatic example presently known, the empirically observed
value of our universe’s cosmological constant appears to be tuned to one part in ten
to the power of one hundred andtwenty. In current models, any imperceptibly small
change in that constant would lead to either near-immediate collapse or destructive
inflation of the early universe. Likewise, Planck’s constant, the gravitational con-
stant, the neutron-proton mass difference, the strengths of electromagnetism, weak
and strong nuclear forces, the masses of particles in early inflation after the Big
Bang, and several other aspects of our physical universe appear fine-tuned for the
production of long-lived universes that support high levels of emergent complexity.
Assuming other values of these constants are possible and would lead to alterna-
tive universes, there are many improbable transitions and architectures to explain,
including the special subatomic physical resonance (quantum “fine-tuning”) we
call the triple-alpha process, famously predicted by astronomer Fred Hoyle (1954),
which produces an abundance of carbon and oxygen in our particular universe.
Recent calculations (Meissner 2013) continue to support the hypothesis of fine-
tuning in the fundamental parameters of quantum chromodynamics and quantum
electrodynamics for this fortuitous result to occur. Carbon, oxygen, and a handful of
other elements (HNPS, and some metal cofactors) are developmental portals (unique
gateways on the molecular phase space landscape) to redox organic chemistry,
which is the structural and energetic foundation of life.
For another potential fine-tuning example, consider the way dark matter and a
smattering of older Population II stars, often presenting as globular clusters, form an
elliptical “halo” galactic superstructure, one that allows newer Population I stars to
precipitate into elegant planar spiral and elliptical galaxies. The newer stars’ rotation
and metallicity gradients are apparently created and maintained by this halo dark
matter distribution across vast ranges of space and time, giving the mature form of
our most chemically complex galaxies the rough appearance of a complex biological
development, like an ovarian follicle. Consider also the curiously scale-free and
organic looking appearance of the large-scale structure of matter distribution in our
universe. We also can suspect a variety of improbably life-generating conditions
on the early cooling Earth, whose geochemistry may be catalytically optimized for
life’s emergence, including a predictable distribution of mineral cofactors able to
catalyze the rTCA cycle on metal-rich planets around Population I stars, perhaps one
of several critical preconditions for life (Smith and Morowitz 2016). Life appears to
have arrived on Earth almost as soon as our crust sufficiently cooled.
Consider also the curiously biphasic nature of Earth’s crust. It consists of
a denser, continually recycling oceanic crust, which regulates CO2and other
atmospheric parameters via plate tectonics, and a lighter, continually floating
continental crust, which offers a stable nursery for the growth of land-based life.
Stable continents, in turn, may be a developmental portal for the first emergence of
complex social mimicry, language and tool use on any Earthlike, given the many
physical advantages of air over water for such forms of social intelligence growth.
We can also identify, with varying degrees of controversy, several life-stabilizing
biogeohomoeostatic features (e.g., the Gaia hypothesis) in various atmospheric and
ocean properties on our current Earth (Volk 2003). All these tunings and others may
48 J. M. Smart
Fig. 5 Accelerating energy flow density control in our most complex (and rapidly learning)
systems, in universal history. (Figure: Smart 2008. Data: Chaisson 2001)
be necessary for robust phase transitions to higher complexity, in special domains of
space and time, allowing life to emerge, diversify, persist, and grow more complex
and generally intelligent at its leading edge of evolutionary development.
But there’s even more to explain, because not only does our universe support
improbably high levels of emergent complexity and mind, it supports an even more
improbable condition of continuously accelerating complexification in special envi-
ronments, an acceleration that seems increasingly self-stabilizing under periodic,
and often catalyzing, episodes of selective catastrophe (Smart 2000,2008,2012).
Konrad Lorenz (1977) was an early advocate of the view that both energy transfer
and information processing must work together to create the mode and tempo of
biological change. If our universe is a replicator, we can expect both physical and
informational causes are needed to explain its accelerative aspects as well. Chaisson
(2001), Aunger (2007a,b), and others have proposed that it is the increasingly
intelligent control of energy flow that drives structural-functional acceleration in
our universe. Chaisson has estimated exponentially increasing energy flow density
(free energy flow per gram or volume) in a special subset of complex adaptive
systems over universal time (Fig. 5). Processes like galactic structure formation,
Evolutionary Development: A Universal Perspective 49
stellar nucleosynthesis, and redox organic chemistry are themselves accelerative,
in free energy flow measures, over previous complex systems, and each may
be developmental portals (unique gateways) to further structural and functional
complexification and intelligence growth. Life’s accelerating complexification, in
turn, has reliably produced a variety of social tool using species, and in humans,
accelerating intelligence, immunity, and (though it is often debated) morality in
recent millennia.
Curiously, our leading technology, digital computers, have a free energy density
control rate that is now at least a millionfold faster than our biological neurons. This
differential has grown exponentially over our “Moore’s law” era of computing, and
may grow by many additional orders of magnitude as we shift to future even more
miniaturized, dense, and complex architectures and technologies including massive
parallelism, single electron transistors and optical and quantum computing. I call
this process of accelerating complexification “STEM compression” (Smart 2002,
2008,2012), with “compression” referring to predictable growth in both physical
and informational density and efficiency of critical spatial, temporal, energetic, and
material (STEM) metabolic, effector, and thinking processes in our most dominantly
adaptive systems over time. I consider energy flow density acceleration (e.g.,
Chaisson 2001) to be just one example of this apparent universal trend. Furthermore,
now that our leading computers are using biologically inspired algorithms, and
are developing increasingly general forms of intelligence, the adaptive goals they
learn from their environment should be similarly accelerated, particularly if we can
intelligently aid this apparently natural process. Later in this paper, I will propose
five learnable goals (abilities, drives, ends, telos) that seem particularly universally
adaptive and self-stabilizing for intelligent complex systems, if they are built from
both evolutionary and developmental processes.
Empirically, this record of growing internal control and self-stabilization, and
increasingly general adaptivenessof our most complex systems, which we can argue
exists in the geophysical processes of Earth, in life, and in human civilization and
our leading technology, seems unlikely in a randomly generated universe. Why
hasn’t our universe been far more disruptive to our general record of accelerating
complexification? Some kind of developmental immunity may exist, tuned into
every autopoetic system, including our universe’s genes, soma, and environment,
if our universe replicates with inherited characteristics, and if the acceleration of
complexity and/or intelligence has had some past adaptive (selective) value.
In biological systems, we presumably need ever more sophisticated processes
of immunity and (in more complex systems) of morality to stabilize growing
individual intelligence. If we come to understand the inevitability of these processes
in biological systems, we may come to understand them in replicating cultures
and their technologies as well. Early developmentalist models of social immunity
(stability) and morality (virtue) were championed by such 19th theorists as August
Comte (1844) and Herbert Spencer (1864). In the twentieth century, the priest-
paleontologist Pierre Teilhard de Chardin (1955) was perhaps their most famous
advocate. In recent years, a few psychologists have offered us statistical arguments
that both the average severity and average frequency of global social violence have
50 J. M. Smart
substantially declined over human history, even as our potential for committing
acts of violence at scale, via science and technology, has steadily grown (Pinker
2010). Causal models for this decline are still lacking, but Pinker is clearly arguing
for both an evolutionary and developmental morality, and I believe our collective
morality must grow predictably in nuance, force, and scale on all Earthlike planets
if intelligence is to be stabilized as complexity accelerates.
We may also need to explain the impressive simplicity and comprehensibility
of (most of) the mathematics that underlies nature. According to Leslie and
Kuhn (2013), Gottfried Leibniz (1686) was perhaps the first to argue that while
some mathematical equation could be found to fit any curve one might draw, the
vast majority of the set of possible curves and equations would be exceedingly
complex. Similarly to Leibniz, Vilenkin (2006) argued that one would expect
“horrendously large and cumbersome” mathematics underlying a typical randomly
derived universe in a multiverse ensemble. Yet the applied mathematics and physics
that our minds can understand seems unreasonably effective for both scientific
modeling and technological development (Wigner 1960).
We should also explain why our universe appears to use massive parallelism
in its production of intelligent civilizations, and keeps them spatially separated
for the majority of their evolutionary development. This is related to the Fermi
paradox, namely, the observed absence of extraterrestrial intelligent beings and their
artifacts in our past light cone (Lem 1971;Brin1983;Webb2015), even though
we are likely to have emerged one to three billion years later than other Earthlike
planets further in on our galactic habitable zone (Lineweaver et al. 2004). Although
many hypotheses have been suggested for this curious absence, a large subset of
these hypotheses require some form of fine-tuning ( ´
Cirkovi´
c2009). It’s as if the
universe seeks to promote maximum evolutionary diversity in each civilization,
while developmentally guiding each of them to a future in which their evolutionary
learning might be instantly shared (Smart 2012). In short, there’s a lot of apparent
tuning that needs explaining.
Of course, there is also a lot of waste and danger and randomness in our universe
as well. The vast majority of physical systems in our universe are simple and
dead, not complex or adaptive, and there are catastrophes and danger everywhere.
Intelligent life may be so hard to produce that our universe may need to evolve
an entire galaxy of stars to develop one intelligent planet, on average. Evolution
on Earth has seemed equally wasteful and violent, if we focus on all the species
that have disappeared, rather than the intelligence, morality, and immunity that
have survived and grown. In the far future, our Milky Way galaxy and Andromeda
are destined to crash into each other, obliterating their beautiful spiral structures.
Observing all this apparent waste, danger, and chaos has led many astrophysicists,
including Neal DeGrasse Tyson (2006), to argue that fine-tuning doesn’t exist.
Besides the desire to avoid the idea of a purposeful universe, and its historically
theistic implications, scientists commonly seem to reject the idea of fine-tuning via
two ways: misperception and mischaracterization. Let us consider each of these
latter issues now, and propose an alternative description, the partially fine-tuned
universe hypothesis, to try to reduce these problems. In my view, modeling fine-
Evolutionary Development: A Universal Perspective 51
tuning in evo devo terms, in both physical and informational dimensions, and
simulation testing it in both organisms and universes, is a core challenge science
must address if it is to properly critique universal fine-tuning models.
9 The Partially Fine-Tuned Universe: Intelligence Is a Weak
Selector, Not a Designer
Misperceptions can commonly cause us to reject fine-tuning, if we examine complex
systems from inappropriate perspectives and scales. Consider a few examples:
• If it turns out to be true that it takes a galaxy of stellar “experiments” to produce
just one (or a few) intelligent civilizations per galaxy, on average, that looks
extremely wasteful and random (evolutionary) at the solar system scale, but
simultaneously convergent and predictable (developmental) when we view the
same process at the universal scale. A system that reliably produces hundreds of
billions of something may very well be fine-tuned for that end.
• Intelligence in living systems looks very fragile and endangered (evolutionary)
at the species scale, but very robust and accelerative (developmental) when
viewed from the ecosystem or planetary scale (Heylighen 2008). For example,
we can presume that very little of the “conserved core” of developmental genetic
intelligence (Gerhart and Kirschner 2005) in Earth’s species pool was eliminated
by any of the major past catastrophes and extinctions that Earth’s ecosystem
has experienced. Instead, those catastrophes appear to have pruned back the
evolutionary variety, created new exploration space, and catalyzed powerful new
punctuations of evolutionary innovation (new phenotypic or sociotechnological
morphology and function), while increasing immunity to further disruptions
of the same type, shortly after each major catastrophe (K-T event, Permian
extinction, Ice Ages, Toba event, many others). I call this process natural security,
in general terms, or the catalytic catastrophe hypothesis, in relation to specific
catastrophic events (Smart 2008,2018). This hypothesis has been explored in
biological systems by Gerhart and Kirschner 2005, Bhullar 2017 and many
others. In social systems, economist Nick Taleb calls it antifragility (Taleb 2012).
A general kind of immune learning appears to have operated throughout life’s
long history on our planet, as a central stabilizer of accelerating change.
• We can see chaos and randomness in galaxies colliding billions of years from
now, as Tyson (2006) emphasizes. But if both low-intelligence universes (via
CNS) and higher-intelligence civilizations (via the transcension hypothesis)
use black holes (either to “randomly” produce new universes in CNS, or to
do intelligence-guided replication in the transcension hypothesis), then future
galaxy collisions long after many of the universe’s black holes are created looks
like normal aging and recycling of an evo devo system after it has aged past
replicative maturity. All complex living systems are developmentally fated to
senesce and be recycled. What looks fine-tuned, from that life cycle perspec-
52 J. M. Smart
tive, is that galaxies are stable for the billions of years necessary to produce
complex life, and that mechanisms for universal replication and civilization
communication (e.g., black holes, in the CNS and transcension hypotheses) have
self-organized to be fecund in our universe.
• We can focus on how easy it is for planets to be outside a galactic or stellar
habitable zone, and (in our solar system) become greenhouse hells like Venus,
or lose their plate tectonics and atmospheres and dry up like Mars (taking an
evolutionary perspective on planetary science), or we can consider the marvel
of the apparently robust (developmental) existence of both habitable zones and
Earthlike planets in our galaxy, and the unique features of Earthlikes as a cradle
for life. Water-bearing Earthlike planets and yellow-white suns may be univer-
sally unique developmental portals (accelerative gateways) for life. Yellow-white
stars like our Sun have their peak irradiance in the visible light range, optimal for
water-based, photosynthetic life (Fig. 6). Hotter (blue) stars radiate much more
in the dangerous, high energy range, and colder (red stars) radiate more in the
infrared range, and with far lower specificity (their peak irradiance curve is much
flatter). Our Sun’s particular spectral type and our Earth’s plentiful water vapor,
water, and strong magnetic field efficiently shield life from radiation arriving
outside our Sun’s most useful range. Our plate tectonics, oceans and clouds,
carbon and nitrogen cycles, and ecosystem itself stabilize many other features of
our nurturing environment (Volk 2003). Why is our Sun-Earth energy transfer
and geophysics so apparently co-adapted for the generation and buffering of
life processes? Either of two kinds of observation selection would seem to be
involved. Either the law of large numbers explains these local conditions (an
evolutionary observer selection explanation), or some of the physical parameters
in our universe have become biased toward the production and protection of life
(an evolutionarydevelopmental observer selection explanation). We must predict
that the vast majority of planets would still be expected to be barren in an evo
devo universe, as biological evolution always requires massive and “wasteful”
stochastic variety to find new developmental optima. But we should also expect
some improbable fine-tuning, in a small subset of parameters (the “5%”), for the
robust emergence of a special class of life-supporting planets. Without taking
this evo devo perspective, we might predict that self-aware life would typically
emerge, on average, in both a much less efficient and less safe ecogeophysical
environment.
Mischaracterizations, arising from incorrect models, can also cause us to reject
fine-tuning. Perhaps the most common mischaracterization comes from focusing
only on the evolutionary processes of adaptation, to the exclusion of the develop-
mental processes. That can happen when we view a system from only one scale or
perspective, as we have just described. But there is another mischaracterization that
comes with the assumption, surprisingly common among fine-tuning critics, that
fine-tuning must be extensive if intelligence is involved in universe replication. But
as we’ll argue now, if replicating universes are anything like replicating organisms,
then extensive fine-tuning by any finite intelligence, whether internal or external to
our universe, seems an unsupportable and non-naturalistic assumption.
Evolutionary Development: A Universal Perspective 53
UV
2.5
2
1.5
1
0.5
0
250 500 750
O2
O3
H2O
H2O
H2O
Atmospheric
absorption bands
Sunlight at sea level
5778K blackbody
Sunlight without atmospheric absorption
CO2H2OH2O
1000 1250
Wavelength (nm)
Spectrum of Solar Radiation (Sun)
Spectrum of Liquid Water Absorption (Earth)
Irradiance (W/m2/nm)
1500 1750 2000 2250 2500
109
108
107
106
105
104
103
102
101
100
10-1
10-2
10 nm 100 nm
Ultraviolet VIS Near IR Mid IR Far IR EHF
1 mm 10 mm
Wavelength
Absorption (1/m)
100 mm 1 mm 10 mm
Visible Infrared
Fig. 6 Our highly energetically efficient Sun-Earth energy transmission and buffering system. The
absorption spectrum for atmospheric water is very similar to liquid water, depicted. (Image sources:
Wikipedia)
54 J. M. Smart
If we live in an evo devo universe, it can only be a partiallyfine-tuned universe,as
the production of very limited and partial fine-tuning is how intelligence has always
interacted with replicating, complex adaptive systems in biology, and with itself as
a replicator. At best, intelligence always functions as an aid to selection in complex
systems, and never as an omniscient or all-powerful designer.
What I call CNS with Intelligence (CNSI) (Smart 2008,2012), or what my EDU
colleague Clement Vidal calls Cosmological Artificial Selection (CAS) (Vidal 2008,
2010) is the hypothesis that intelligence, and its ability to simulate more and less
adaptive futures, must play some useful role in the replication of universes. These
hypotheses do not argue that intelligence can rationally design future universes,
but rather that universes that self-organize intelligence are somehow more adaptive,
in a nonrandom fashion, than universes that don’t. In other words, some kind of
multiversal selection occurs, in which intelligence, at both the very fundamental
“genetic” level of self-replicating universal parameters, and at its higher levels,
which includes conscious beings able to develop science and engineering, plays
a nonrandomly beneficial role. That’s the core hypothesis.
Even without the math it is easy to induce, in any biological replicator, that there
is likely to be a nonrandom adaptive value to the emergence of general intelligence,
of immunity (defensive intelligence), and of various forms of interdependence
(collective intelligence, social morality, positive sum games), the latter starting with
kin. We can imagine many circumstances when each of these computational systems
that encode models of self-, others, and environment have adaptive value. These
intelligence systems (general intelligence, immunity, and interdependence) may
be present, in some fashion, in all complex adaptive systems. If we also consider
evolutionary innovation and developmental sustainability as forms of intelligence,
including the mix of stochastic and predictable genetic processes that generate our
minds, we can imagine at least five potentially universal processes of intelligence,
as we will discuss later (Smart 2017b). We can also identify multiple forms of
intelligence (genetic, cellular, collective, neurological, societal, technological, etc.)
on Earth, in living systems and their creations. Why should intelligence not also be a
central property of the universe as a complex system, if it in fact is a self-replicator,
existing in some larger environment (the multiverse)?
Yet we must also recognize that all simulations that any intelligence can do, either
within our outside our universe, must be sharply finite and constrained, rapidly
unable to predict most of the multivariate nonlinear dynamics and informatics of
any complex adaptive system, the farther we extrapolate it to the future, or the
more we include its evolutionary (vs. developmental) mechanisms. All such systems
quickly become combinatorially explosive in their potential complexity, and all
real intelligences are limited in their physical and computational complexity. With
respect to the special case of logical-mathematical provability, this concept is as old
as Gödel’s Incompleteness Theorem (1931), and incompleteness seems intrinsic to
the nature of informational complexity itself (Chaitin 1992; Calude and Jürgensen
2005).
The informational incompleteness of all intelligence, along with the inability
to have perfect knowledge (simulation capacity) of initial conditions and all the
Evolutionary Development: A Universal Perspective 55
relevant laws, may also explain why all complex actors with mind have “free will”
(unpredictability to self), even under the most informationally ideal conditions.
Philosophers since Lucas (1961) have tried to relate free will to Gödel’s theorem,
but such a relation starts by preassuming the physical universe conforms to Gödel’s
conditions for mathematics (it may not). At present, the full informational and
physical nature of (our self-experienced) free will, and of conscious decision-
making, remain a mystery to be solved.
Nevertheless, what we know and can guess so far about intelligence argues that
any “design” that real intelligences can do, of their future selves and environments,
will be highly limited. It does not seem defensible to imagine that end-of-
universe or extrauniversal intelligences might be omniscient or Godlike, if they are
physically real and we live in an evo devo universe, and they also seem unlikely
to have the capacity to create “anything” out of “nothing.” It is illuminating that
“Why there is anything rather than nothing?” or, alternatively, “Why did anything
begin?” is sometimes called “Question Zero” in physics. It may always remain a
metaphysical question to real intelligences. Perhaps one of the most useful clues to
its metaphysical nature is that the concept of nothing itself, just like the concept of
infinity, while very useful in our mathematics, may be only an informational, not
a physical concept (Aguirre 2016). Real intelligences may be forever stuck within
the (evo devo) system (supporting universal environment) that they find themselves
emerging within, a system that has its own physical and informational laws and
constraints, only some of which are likely to be modifiable.
Discovering our universe’s parameters and laws, and learning how to manipulate
them to improve adaptation under selection, but always in finite and limited ways,
seems to me to be the central benefit of intelligence in living systems. All living
systems, while they possess some level of intelligence, still have many vestigial
systems and errors and maladaptations in them which are beyond their control or
even understanding. This should be true for our universe as well, if it is an evo devo
system.
My current intuition for what an end-of-universeintelligence might be able to do
with respect to “design” of future universes would be to alter some of the coupling
constants which influence the developmental characteristics of the next universe,
presumably to raise the probability that it will be complexity, life, and intelligence
friendly. They might do this, for example, if intelligences in this universe can use
their intelligence, and the laws of physics, to produce black holes which create other
universes, and if the coupling constants can pass through the singularity of a black
hole into another universe, as some physicists have postulated (Smolin 1992;Crane
1994). But notice that this kind of “universe engineering,” though it is simulation-
guided, is not the rationally engineered universes idea of James Gardner (2003,
2007). Gardner assumes that end of universe intelligences could change any of the
constants, and might have extensive foreknowledge and control of the consequences
of those changes. I would equate this view with intelligent design, which we shall
discuss later. It sounds like non-naturalistic theology, not science.
Instead, tinkering with the values of our universe’s coupling constants, in a way
that might produce even more life- and intelligence-friendly universes, seems likely
56 J. M. Smart
to be analogous to what human genetic “engineers” do today when we alter genes
in “designer” organisms. What we are actually doing is making intelligence-guided
engineering guesses at what will be more adaptive, and some of the most critical
conserved genes are beyond our ability to tweak, without killing the organism.
Our foreknowledge of these complex evolutionary systems must always be limited
the further ahead we look. An honest assessment would be that we are not really
“engineering” or “designing” new organisms, but are instead making our best
experimental guesses, based on our finite simulation capacity and knowledge,
working within the evo devo framework we have inherited, at what might be more
adaptive.
Any hypothetical universe “design” would have to work the same way, in an
evo devo universe. It would be a process of partly intelligence-guided selection,
and partly unknowable experiment. It is not accurate to call such an undertaking
by the word design. When we are talking about bottles and bridges, and other
nonautonomous systems, it makes sense to use the word design. But the more com-
plex adaptive and internally intelligent the system gets, the more the unpredictable
evolutionary aspects of the system overwhelm the predictable developmental parts.
Once we get to the “design” of things like living organisms, or new deep learning
computers, or future universes, it makes more sense to call this process selection
than it does design.
Gardening future universes using our own best science and intelligence would be
directly analogous to the artificial selection we humans do on our domestic plants
and animals, a process Darwin discussed at length in Origin of Species (1859). This
is why Clement Vidal prefers the term Cosmological Artificial Selection to describe
what this process might look like to any future intelligences that become competent
enough to “engineer” intelligence-influenced black holes (if those are the seeds of
new universes, per CNS), or to otherwise aid in the production of future universes.
In sum, there are at least five important levels of evo devo-related partial fine-
tuning models that should be critiqued in future fine-tuning debates:
1. Level I. Our universe appears fine-tuned (self-organized) for the emergence
of complex, long-lived universes and black holes (Smolin 1997; Rees 1999;
Gardner and Conlon 2013).
2. Level II. Our universe appears fine-tuned for the fecundemergence of G-, K-, and
M-class stars and biological life (Henderson1913; Barrow et al. 2008;Lewisand
Barnes 2016).
3. Level III. Our universe may be fine-tuned for the fecund and accelerating
emergence of intelligent life (Piel 1972; Sagan 1977;Moravec1979;Dick1996;
Kurzweil 1999,2005).
4. Level IV. Our universe may be fine-tuned for the fecund emergence of intelligent
life, which can then produce new universes (Crane 1994; Harrison 1995).
5. Level V. Our universe may be fine-tuned for the fecund and accelerating emer-
gence of increasingly innovative,intelligent, immune, interdependent (defending
evo devo values), and sustainable forms of complex life (Smart 2008,2012,
2017b).
Evolutionary Development: A Universal Perspective 57
If this analogy between replicating organisms and universes holds up, models
like Smolin’s CNS, in some variation that also includes intelligence (CNSI), will
continue to gain theoretical and empirical support. The better we understand and
can simulate the operation of evolutionary and developmental parameters in living
systems, the better we should be able to understand and simulate them in universes
as well. Both look like finite and replicating systems, in an evo devo model.
10 The Riddle of Convergent Evolution: Limited Forms
Most Beautiful
Convergent evolution is evidence or argument for physical attractors in the phase
space of dynamical possibility which guide and constrain contingently adaptive
evolutionary processes into statistically predictable future-specific structure or
function, in a variety of physical and informational environments. When we look
at evolutionary history, species morphologyor function is often seen to converge to
particular “archetypal forms and functions” in a variety of environments.
Such attractors have been called deep structure, guiding evolutionary process
in predictable ways, regardless of local environmental differences. Organismic
development depends on specific initial conditions (developmental genes in the
“seed”), the emergence of hierarchies of modular structure and function in the
unfolding organism, and persistent constancies (physical and chemical laws, stable
biomes) in the environment.Likewise, some examples of convergent evolution may
be best characterized as ecological, biogeographical, stellar-planetary, or universal
evolutionary development (ED) if their emergence can be modeled, after adjusting
for observer selection bias, to depend on specific universal initial conditions,
emergent hierarchies, and environmental constancies.
A famous example of convergence is found in eyes, which appearto have evolved
on Earth from different genetic lineages to work similarly (function as sensors
for nervous systems) in all species possessing sight, and in the case of camera
eyes, to also look very similar (form) in both vertebrate and invertebrate species,
like humans and octopi (humans famously have a blind spot, however, as our
eyes evolved via a different evolutionary developmental history than invertebrates.
See Ogura et al. 2004). One can easily advance the argument that, in universes
of our type, eyes, though first created by a process of evolutionary contingency,
become a developmental archetype, an adaptive optimization for the great majority
of multicellular species in Earthlike environments.
Presumably, the previously rapidly changing “evolutionary” gene groups that led
to eye creation become part of an increasingly conserved “developmental” genetic
toolkit for all eye-possessing species in environments where eyes are adaptive.
Eventually, due to both path dependency and emergent hierarchies, some subset
of these gene groups should be incapable of being changed without preventing
development itself. Proving such genetic convergence arguments with evidence and
theory is of course more difficult, yet it is a fertile area of investigation today.
58 J. M. Smart
Charles Darwin ended his foundational text on evolution, On the Origin of
Species (Darwin 1859) with a well-known phrase, predicting “endless forms most
beautiful” continuing to evolve. But as George McGhee describes in a well-
titled book, Convergent Evolution: Limited Forms Most Beautiful (McGhee 2011),
preexisting physical and informational optima in our particular universe mandate
that only a very limited subset of forms and functions will ever emerge in
biological evolution. Evolutionary development always grows morphological and
functional diversity, and especially rapidly under stress, but developmental control
and optimization makes it a net subtractive and constraining process, relative to its
theoretical potential. Creative evolutionary process is continually reconverging to
developmentaloptima, driven there by functional (environmental) and developmen-
tal (genetic) constraints. Better understanding and modeling convergence is one of
the great and underappreciated challenges of modern evolutionary biology.
11 Less-Optimizing Convergence (LOC) Versus Optimizing
Convergence (OC)
In our mostly chaotic, contingent, and deeply nonlinear universe, we can predict
that the vast majority of examples of convergent evolution will not be driven by
the evolving system’s discovery of some hidden general optimization function in
parameter space, like the eye archetype, but rather, the discovery of many less-
valuable and less-permanent optima that do not lead to higher complexity, yet
may still be required for the universe’s evolutionary development. To understand
convergence, we will need some kind of evo devo-guided general optimization
theory. Let’s consider two necessary features of that theory now.
1. We can predict that any optimization that occurs must be on a continuum, from
highly optimizing convergence, which we will refer to simply as optimizing
convergence (OC), conferring advantage in all the most competitive and complex
environments, to a wide variety of other cases, which we can refer to collectively
as less-optimizing convergence (LOC). LOC cases would include convergence
that offers only some temporary or local adaptive advantage, to just a few
specific species, or in some subset of specialized or less-complex environments,
convergence that offers no advantage, or convergence that is deleterious but
not fatal. Names for a few general classes of LOC cases have been offered by
scholars, including passive convergence, parallel evolution, etc.
2. Optimizing convergence can occur via both physical and informational pro-
cesses. Physically, we might see greater efficiency of employment of physical
resources, as in Bejan’s constructal law, or greater density of employment of
physical resources for offense or defense, the escalation hypothesis (Vermeij
1987). Informationally, we might see efficiency or density gains via informa-
tional substitution for physical processes, what Fuller called ephemeralization,
or greater general intelligence (modeling ability), greater immunity, or a more
Evolutionary Development: A Universal Perspective 59
Fig. 7 Adaptive landscapes
allow both local and global
optima. (Source: ASF)
useful collective morality, offering more general and persistent adaptation to a
wider range of environments than previous strategies. Intelligence also offers the
ability to modify environments to suit the organism, what biologists call niche
construction or stigmergy, as humans, social insects, and many other species
do either in limited forms or extensively today. To understand OC, we will
need a theory of optimization that tells us when a physical or informational
advantage is likely to be more generally adaptive, particularly in the most
complex, competitive, and rapidly changing environments (Fig. 7). We also
need to know whether there are any other paths that can lead, in a competitive
timeframe, toward a competitively superior new form of adaptiveness. If not,
then we may have discovered a developmental portal, a global optimum that
represents a bottleneck, a singular pathway toward greater adaptation at the
leading edge of local complexity. Organic chemistry, RNA, photosynthesis, and
oxidative phosphorylation are all potential examples of portals that all universal
life must pass through first, on the way toward greater adaptive complexity.They
may be the only global optima on their landscapes, at the relevant timeframes,
that will allow the creation of vastly greater adaptive complexity.
Another complication of optimizing convergence at the leading edge of com-
plexification is that over time, it must occur within an increasingly limited set
of evolutionary possibilities, as increasing developmental genes and processes
at the leading edge will progressively limit the evolutionary possibility space
within any particular inheritance system. Processes like heterochrony, neoteny,
and gene duplication (Wagner 2003) can temporarily reverse generally growing
genetic constraint, but only the invention of a new class of inheritance system,
in a metasystem inheritance transition (e.g., self-replicating genes in organisms
inventing self-replicating ideas in brains, inventing self-replicating algorithms in
technology), seems able to lead to large new regimes of evolutionary innovation
(Turchin 1977).
If we live in a noetic (information accumulating and intelligence-centric) uni-
verse, nervous systems would surely qualify as OC. Based on neurotransmitter
and genomic differences, Flores-Martinez (2017) argues that nervous systems
were convergently invented three different times, by comb jellies, jellyfish, and
60 J. M. Smart
bilaterians. But only in a small subset of prosocial, tool-using, land-based vertebrate
bilaterians do we see a strong trend toward runaway brain size. OC is clearly
multifactorial for developmental transitions to more rapid, more stable, and more
complex evolutionary regimes (e.g., cultural evolution).
Consider eyes again. As with nervous systems, which are particularly helpful
in complex environments, we can make a plausible case that eyes, at one point in
time, became a necessary functional adaptation in the most complex environments.
Andrew Parker’s light switch theory (In the Blink of an Eye 2003) proposes that
the development of vision in Precambrian animals directly caused the Cambrian
explosion. Critics have observed problems with the timing, and that complex
eyes may instead be a consequence of rapid body plan complexification, rather
than a generator of new selection pressure for complexification. Either way, this
is a fascinating theory, as it implies a necessary coevolution of intelligence and
morphological and functional complexity. Once they emerged, it is easy to argue
eyes were an evolutionary ratchet, and that all visible animals in the most complex
environments would soon need them, or a handful of other uniquely effective
defensive strategies, to survive.
Many other examples of OC can be proposed, in the most physically and
informationally complex, and rapidly changing, environments on Earth, including
the necessary emergence of eukaryotes, oxidative phosphorylation, multicellularity,
bioluminescence, nervous systems, bilateral symmetry, jointed limbs, opposable
thumbs, tool and language use on land (much faster-improving than aqueous
environments), culture, and technology, including machine intelligence.
To make a few intelligence-related predictions in OC, I suspect that grasping
limbs and tree niches on land are an early developmental portal (optimized
convergence and phase transition in collective intelligence) leading to complex
tool use on Earthlike planets, as tree swinging and grasping limbs offer an ideal
training ground for complex, predictive brains, and as tool use and construction in
air offer far greater mechanical advantage than in water. I am also a fan of Dale
Russell’s Dinosauroid hypothesis (Russell and Séguin 1982), which argues that
the bilaterian tetrapodal humanoid form, which includes two locomotion and two
prehensile (grasping) appendages, may be an optimizing convergence (minimum
viable solution set) for becoming the most generally intelligent (and largest brain
to body weight) land-dwelling bilaterian. I have also predicted that competitive-
cooperative tool use on land, in the manner employed by early humans with
Oldowan axes, is likely to be a universal developmental portal to runaway collective
intelligence in bilaterians, as that environment seems to offer such strong selection
pressures for generally adaptive defensive and offensive capacities, by contrast to
animals that cannot collectively employ such “game-changing” early offensive and
defensive tools as stone axes, clubs, and fire (Smart 2015).
Future science will need better theories of complexity, complexification, and
optimization, to deeply understand such candidates for evolutionary convergence,
and to distinguish the much greater variety of examples of less-optimized conver-
gence from the most highly optimized forms.
Evolutionary Development: A Universal Perspective 61
12 Optimizing Convergence as Accelerating and Stabilizing
ED on Many Scales
When convergence is viewed from the perspective not of the evolving species, but
from some larger system scale (the biogeography, the planet, the universe) we can
view optimizing convergent evolution as a process of not simply evolution, but of
evolutionary development (ED), an ED that continually accelerates and stabilizes its
complexity in special domains of space and time.
When we claim a convergence process is an example of ED, we are not only
claiming that some kind of general optimization is occurring. We are also claiming
that some kind of evolutionary developmental process, with both “random” and
creative evolutionary search, and predictable convergence, directionality, hierarchy,
modularity, life cycle, and perhaps other features found in biological development,
is being followed, at some larger systems level. Consider embryogenesis. Viewed
from the perspective of the individual actors (molecules), we see mostly stochastic,
divergent, and contingent processes. As we zoom outward to larger and longer
spatiotemporal scales, we can also see a few convergent, hierarchical, and life
cycle processes. To view optimizing convergence as not simply evolution, but as
evolutionary development, we often must take these wider scale views, as in the
following examples:
• Galactic and universal change offers many potential examples of not only evo-
lutionary but apparent developmental change, as we have discussed. Curiously,
the evolutionary development of complexity seems strongly accelerative, with
increasingly rapid complexity transitions in increasingly local spatial domains
(Smart 2008). As a high school student contemplating this trend in 1972, I
recognized the logical limit of that process was the black hole. The first of these
puzzling objects, Cygnus X-1, had been discovered just the year previously, in
1971.
• Stellar-Planetary-Astrobiological change offers more examples. When we look
down from early universal change to the stelliferous era, and the genesis of our
life-permissive planet and its star, astrophysical theory tells us that the way stars
have replicated, and chemically complexified, through three different populations
over billions of years, has been not only evolutionary (a variety of randomly
arrived at star and planet types and distributions) but evolutionary developmental,
involving a progressive drive to complexification in a predictable subset of
types. Many astrobiologists and planetologists argue that a subset of chaotic and
nonlinear (“evolutionary”) stellar-planetary change has reliably led, with high
probability and massive parallelism, to G-(and perhaps some K- and M-) class
stars and Earthlike planets ideal for the development of archaeal (geothermal
vent) life and, from there, to prokaryotes and eukaryotes. See Nick Lane’s The
Vital Question (2016) and Smith and Morowitz’s The Origin and Nature of Life
on Earth (2016) for two such stories.
62 J. M. Smart
• Biogeography and Ecology offer more examples of not only evolutionary but
apparent developmental change. In biogeography, we find scaling laws, like
Copes rule, and biogeographic laws like Foster’s rule and Bergmann’s rule, with
their predictable processes of optimizing convergent evolution, or evolutionary
development. The famous convergence of form seen in placental and marsupial
mammals, on separate continents, offers another example of not just evolution,
but biogeographic ED. For many more examples, including intelligence traits,
see Conway-Morris (2004,2015), McGhee (2011), Losos (2017) and our list of
examples of convergent evolution (Wikipedia 2012, and Smart and Chattergee
2012–2017) in species morphology and function. In ecologies, we can iden-
tify predictable patterns of ecological change, including ecological succession,
ascendancy, and panarchy.
• Culture, Science, and Technology change offers yet more examples. When we
look above individual cultures and do cross-cultural comparisons, we find many
examples of developmental features at the leading edge of competitiveness,
including parallel invention and/or convergent development of archetypal sci-
entific and technological inventions like fire, language, stone tools, clubs, sticks,
levers, written language, mathematics, hydraulic empires for our first great cities,
wheels, electricity, computers, artificial neural networks, etc. In each of these
cases, a high-order convergence has occurred. These and other specific examples
of cultural change look not only evolutionary, but evolutionary developmental
(ED). Once these archetypes and algorithms exist, there’s no going back, for
any culture seeking to stay on the leading edge of physical and informational
complexification, and general adaptiveness. Some form of technologically-
mediated global superorganism (see Heylighen 2007) is another planet-scale
development that might be necessary to regulate future state, corporate, and
individual technological misuse and rivalries. We also find many examples of
developmental constraint laws that operate in social and economic systems, like
scale laws (West 2017; Bejan and Zane 2013) and more generally, the least action
principle (Georgiev et al. 2015).
In each of these rough hierarchies of complexity, our universe is not only
generating local variation, creativity, and difference; it is also developing toward a
small set (in our present understanding) of currently predictable destinations. While
there is much about cosmogony that remains unclear, we know that dark energy
is accelerating complex galactic groups away from each other, that total entropy
increases, and that an increasing fraction of the mass-energy of our universe will
end up in black holes. The better we understand the conservative and predictable
features of our universe, and can distinguish them from creative and unpredictable
ones, the better we may understand evolutionary and developmental processesat all
scales.
There are two additional curious features of this developmental trajectory, two
unexplained phenomenawe can observe across all of these complex systems, which
must now be mentioned. Understanding and modeling them are among the greatest
challenges of modern science:
Evolutionary Development: A Universal Perspective 63
1. The first is the ever-faster complexification rates seen in the historical record
of the most physically and informationally complex locations in our universe,
since the emergence of G-class stars, Earthlike planets, and almost simultane-
ously, on our planet, life. This acceleration was famously summarized in Carl
Sagan’s metaphor of the Cosmic Calendar. Ever since August, on this calendar
metaphor, leading-edge complexity environments have become exponentially
faster, more complex, and more intelligent, on average, on Earth. Sagan said
this phenomenon, which we can call acceleration studies, was an understudied
area of science, in need of better understanding. See Sagan’s The Dragons of
Eden (1977) for his original account and Heylighen (2008) for more recent
work. It is my hope that better models of early universe and astrophysical,
chemical, biological, psychological, social, economic, technological, and other
evolutionary development will help us understand our universe’s emergence
record of ever-faster and more physically and informationally complex local
environments.
2. The second is the increasingly informationally stable (developmentally immune,
antifragile) nature of complexity in ever more complex environments. In prehis-
tory, species could easily be destroyed by environmental change. But once we
began recording and simulating our world in nonbiological substrates, human-
technological culture has gotten better every year at recording, simulating,
and recreating both biological and cultural information (Malone 2012). As a
result, such information has become far more resilient to catastrophe (Smart
2008; Taleb 2012; Dartnell 2014). There is something about mind, culture,
science, and technology that makes the information it produces more stable
to destruction via environmental fluctuations. Perhaps a growing intelligence
typically provides increasingly useful sets of adaptive strategies for survival.
Some kind of nonlinear input-to-state stability (a form of Lyapunov stability)
may emerge as intelligence’s potential to moderate environmental inputs grows.
Perhaps the most intelligent collectives develop not only greater immunity but
greater morality (both have been proposed as subtypes of intelligence). This latter
view is controversial, given recent human history with advanced technology,
but there are good early arguments for it as well (Pinker 2010). Perhaps
it is simply that increasing intelligence allows progressively more durable
(informationally immune) forms of cultural memory to be developed (Malone
2012). The best descriptor of local intelligence’s ever-growing immunity may
be niche construction (environmental engineering), of which memory is just
one form. Niche construction has afforded humanity the ability to move our
core complexity to increasingly time-stable architectural environments (books,
villages, cities, computers), but these are nothing compared to what may soon
come. Several scholars have argued that humanity appears just a few decades
away from being able to port its essential complexity, in body and mind, into a
technological substrate (substrate shift). Such postbiological entities seem likely
to be vastly more stable to destruction via any imaginable universal process, and
far more redundant, than today’s bio-dependent culture, science, and technology.
Such entities should be able to harness (and do intelligence-guided experiments
64 J. M. Smart
with) molecular nanotech, fusion energy, and perhaps even subatomic processes
(femtotech), and should no longer require either planets or functioning stars to
maintain their existence (Forward 1980;Smart2008;Davies2010; Rees 2015).
Due to accelerating change, such stable new entities also seem likely to arrive
much earlier, in cosmic time, than most of us would presently expect or predict.
Both of these features, our potentially developmentally guided acceleration and
our progressive forms of informational stability, suggest that today’s currentmodels
of existential risks are likely to be overstating the near-term risk to our species of
many apparent species-threatening events (for a detailed review of such risks, see
Bostrom and ´
Cirkovi´
c(2008)). The time for which such risks actually threaten our
informational complexity seems to be rapidly decaying. We appear to be on the edge
of entering a far more stable substrate for life and intelligence, in a cosmologically
insignificant fraction of future time.
13 “Tape of Life” (Identical Earths) Experiments:
Simulating Ecogeophysical ED
If life emerges on two similar Earthlike planets, either in reality or in a sufficiently
accurate simulation test, then by definition we can predict that the evolutionary
aspects will almost always turn out differently in the two environments, and the
developmental aspects will turn out the same. This is called the “Tape of Life”
experiment, and it is commonly discussed in the philosophy ofbiology and by some
of the more systems-oriented evolutionary (developmental) biologists.
Beginning in the 1970s, leading evolutionary theorist Stephen Jay Gould (1977,
2002) famously predicted that little of life’s functions and morphologies on another
similar Earth would turn out the same as those presently found on our Earth.
He expected a few broad similarities, in kingdoms and some phyla, but most
species would turn out very differently, in his view. Beginning in the 1990’s,
Simon Conway-Morris (1998,2004,2015) famously argued the opposite, that
most functions and many morphologies would turn out the same, optimized for
replication and adaptation in this particular Earth environment. We may aptly
call this an evolutionary developmental perspective on Earth’s history (Fig. 8). In
the decades since, some biologists and most astrobiologists have migrated from
Gould’s to Conway-Morris’s camp, though the dividing line between predictable
and unpredictable processes of change remains a productive and contentious debate.
In recent years, there has been a surge of studies of evolutionary convergence,
motivated by such wide-ranging questions as the structure of the protein space to
experimental evolution to evolutionary genetics to ergodicity in biophysics to the
attempted “neo-Gouldian” developmental account of homology versus homoplasy
(Dryden et al. 2008; Turner 2011; Lobkovsky and Koonin 2012; Pearce 2012;
Powell 2012; Orgogozo 2015; Powell and Mariscal 2015; McLeish 2015; O’Malley
and Powell 2016; Louis 2016). Roughly speaking, most of these new results
Evolutionary Development: A Universal Perspective 65
Fig. 8 Cartoon of a developmental Earth. Many Earthlikes, with stochastic differences (not
depicted), would make an evo devo cartoon. (Source: ASF)
are strongly supportive of convergence – in more or less radical form – as the
key feature of macroevolution. For example, Dryden et al. (2008)andMcLeish
(2015) argue that the accessible part of the genomic space is much smaller
than conventional combinatorial wisdom suggests, and that evolution may have
actually explored most of it by now. This is a powerful idea. Consider that a
fully explored (statistically repetitive and no longer creative) phase space may be
a necessary but not sufficient condition of all developmental portals (complexity
transitions), to make such transitions appropriate guides(checks, funnels, gateways)
to evolutionary exploration.
Convergent evolution, at all universal scales, can be productively modeled as a
pull of attractors, and if those attractors are subject to replication and selection,
as a process of evolutionary development. Such modeling should work, to varying
degrees, whether we are describing physical, chemical, genetic, organismic, species,
ecosystem, organizational, cultural, or technological evolutionary development.
Perhaps the simplest phraseto encompass all these and other types is “universal evo-
66 J. M. Smart
lutionary development.” Applied to the universe, evo devo theory argues that both
universal evolution (useful diversity) and universal development (useful similarity)
must be aspects of any universal biology that some scientists and systems theorists
(Flores-Martinez 2014; Mariscal 2016) are seeking. Though we seek simplicity in
our models, discussing either unpredictableor predictable processes alone will lead
to insufficient views of how adaptation actually occurs. We must learn how they
blend, and when those blends are adaptive.
We must also recognize that just as in biological evo devo, our science and
simulation skills will be insufficiently advanced to predict many of the develop-
mental similarities (“convergent evolutionary developments”) that emerge between
two parametrically identical universes, two Earthlike planets, two similar but
biogeographically separated continents, two highly similar cities or organizations,
two genetically identical twins, or even two dividing cells.
Fortunately, the latter examples, and others, have happenedmany times on Earth.
So we can look to these “natural experiments” to better understand processes of
development, at all scales. As our science and simulation capacity gets better, we
can also develop better and more predictive models of how our physical universe
evolved and developed.
In a few of our more advanced biotechnological prosthetics (e.g., cochlear and
vision implants, even hippocampal “chips”), our software and hardware models
are good enough to substitute for the biological system without significant loss of
function. We can hope that this intelligence substitution will also serve us as we
learn to simulate universes in our future computers as well.
If so, we will increasingly be able to predict and validate ED hypotheses in
at least two major ways. By discovering more natural experiments, at all scales,
and by simulating the emergence of those experiments, at a level sufficient for the
simulation to substitute for the physical process.
14 “Tape of the Cosmos” (Identical Universes) Experiments:
Simulating Universal ED
Let’s look now at convergent evolution on the largest scale we can presently
imagine: our universe. In Carl Sagan’s famous Cosmic Calendar metaphor of change
(1977,1980)(Fig.9), we see that earlier stages of hierarchical evolutionary devel-
opment, involving the emergence of large-scale structure, galaxies, and stelliferous
and planetary change, are highly isomorphic and convergent, across the universe.
Simply looking at the night sky shows us these amazing levels of convergence. In
the last century, physicists have worked out many of the reasons this convergence
is evolutionary developmental. It is written into the initial conditions and emergent
laws of our particular universe.
Are the observable morphological, functional, and informational features of
our universe that have clearly accelerated on Earth since the emergence of life,
as depicted from August afterward in the Cosmic Calendar metaphor, also found
Evolutionary Development: A Universal Perspective 67
Fig. 9 The Cosmic Calendar: 13.7 billion years of universal history on a 12-month calendar.
(Source: Eric Fisk, Wikipedia)
convergently throughout the universe? Is this convergence on multilocal complexity
acceleration in our universe strong, happening with high frequency, as a develop-
mental process, or is it random and happening weakly, as an evolutionary process?
In other words, should we expect Earthlike acceleration in a multitude of special
environments, such as those found on habitable planets around G-class stars? These
are questions of universal ED. Astrophysicists and astrobiologists hope to answer
such questions, by theory, simulation, and observation, in coming years.
Today we can conduct primitive simulation tests (“simulation experiments”) to
explore the divergences and convergences we see in two model universes, but our
science remains incomplete, and our cosmological simulations, both in their phys-
ical and informational dimensions, still do not capture all the reality they attempt
to model. Fortunately, our experiments in simulating evo-devo phylogenetics in
biology (Fig. 10) may lead the way to better simulations of all kinds of evo devo
systems. If we live in an evo devo universe, our simulation experiments will get
ever more predictive in their developmental components, and they’ll eventually
convince eventhe most die-hard believers in contingency that we havea set of highly
constrained futures ahead of us.
68 J. M. Smart
Fig. 10 Phylogenetic tree (simulation experiment). (Source: ASF)
Consider genetically identical twins. Most molecular and tissue-level aspects
of two genetically identical twins look different when you view them up close
(different fingerprints, organ microstructure, ideas, etc.). Those are “evolutionary”
differences in an evo devo model. They are locally unique in myriad ways, either
because the twins genetic systems aren’t capable of ensuring perfect identicalness,
or because there are adaptive (e.g., immunity) advantages to this local diversity.
Genes are not a blueprint, but a recipe for building local complexity in a way that
allows contingent local diversity, yet is also robust enough to local molecular chaos
that each twin is reliably guided toward a set of useful far-future destinations in
structure and function. All the aspects of the two genetically identical twins that
turn out the same, we call “developmental.”
Now consider that if our universe replicates, and its emergent features and
intelligence undergo some form of self-selection or selection in the multiverse, this
twin model helps us to define evo devo terms like universal evolution (variation
between universes) and universal development (similarity between universes).
Cosmology models typically assume that if our multiverse had two parametrically
identical universes (universes with the same fundamental parameters and initial and
boundary conditions), some aspects of those universes would turn out the same and
some would turn out differently. Astrophysics guides our universe toward future-
varying (evolutionary) and future-determined (developmental) form and function,
at the same time. Both evo and devo processes, and a recognition of the adaptive
value of both evolutionary variation and developmental conservation, would seem
to be necessary to any accurate simulation.
Evolutionary Development: A Universal Perspective 69
15 Physical and Informational Adaptation: Autopoesis
and Intelligence
Autopoesis is a term introduced by Chilean biologists Humberto Maturana and
Francisco Varela (1973/1980) to describe the chemistry of living cells. It became
popular with a few systems theorists in the late twentieth century to describe the
capacity of some complex systems to self-reproduce and self-maintain. Autopoesis
scholars seek to find general systems rules applicable to any stably self-reproducing
complex systems, including not only living systems, but stars, the chemical origin
of life, and ideas, behaviors, algorithms, organizational rulesets, and technologies
in culture. Implicit to autopoetic models is the idea that a better information theory,
including a theory of cumulative embodied and adaptive cognition (intelligence)
in the replicator, its inheritance system, and its environment, will be necessary to
understand dynamical change in complex systems. See Varela et al. 1974; Maturana
and Varela 1987; Mingers 1995; Luhmann 2003/2013;Luisi2003; Bourgine and
Stewart 2004 for some autopoetic models. Mingers (1995) offers a particularly good
introduction to rules, drivers, and research questions regarding autopoetic chemical,
biological, social, and technological systems, though even this excellent work does
not consider the universe as an autopoetic system.
While they have made little scientific progress to date, autopoetic models are
focused on what we might call the right questions: the physical and informational
sources of adaptation in self-producing, autocatalyticsystems, and the ways adapta-
tion changes over time in environments which are, in the most likely presumption,
replicating, autocatalytic complex systems as well. At the least complex end of
the spectrum, all stable replicating systems depend on the emergence of some set
of predictable action-reaction couplings to their environment. Stars are autopoetic
systems, dependent on physical action-reaction processes. Moving up the chain
of information-production rate density (a form of complexity), a variety of self-
reproducing prebiotic systems (clays, RNA, protein polymers) are dependent on
not only physical but also chemical action-reaction processes afforded by the
complexifying Earth environment. On the path to life, certain self-replicating
chemical systems developed autocatalytic protometabolic networks (Kauffman
1993), and some developed sensory-motor cognitive Bayesian (predictive) chem-
ical networks, including memory networks, observable in single-celled organisms
like Paramecium,Amoeba,orStentor (Bray 2011). At some point, gene-protein
regulatory networks also emerged, and lipid cellularization. A subset of cells
developed multicellularity, another subset developed specialized neural networks,
and a subset of those, self-awareness. Understanding these increasingly complex
set of adaptations, and the necessary emergence of life and mind as adaptations in
certain environments, is one of the main challenges of modern science.
Scholars in such complementary fields as the origin of life (Smith and Morowitz
2016;Pross2014), computational astrobiology (Pohorille 2012; Forgan et al.
2017), artificial life and information theory (Adami 2016), evolutionary escalation
(Vermeij 1987), top-down causation (Walker et al. 2017), and evo-devo theory (see
70 J. M. Smart
above) have all made progress in recent decades in understanding how successful
cumulative replication changes the replicator, its seeds, and its local environment.
Continued progress in such fields, especially in intelligence and information theory,
will be critical to developing better models of adaptation.
Neo-Darwinian models of evolutionary adaptation, such as the adaptive land-
scape theory of Sewall Wright (1932) seek to model adaptation as phenotypic fitness
to the environment, in some genotypic, morphological, or functional parameter
space. Such models, while they have been tremendously useful, are also deeply
incomplete, as they do not allow that the environment itself may change in
predictable and highly nonrandom ways over time, as the growth of intelligence
influences local environments, as described in the phenomenon of stigmergy/niche
construction (Odling-Smee et al. 2003). They also do not consider that the selective
environment itself may be both evolving and developing over time, changing the
nature of selection and adaptation.
But if our universe itself is a replicator, as the evo devo universe hypothesis pro-
poses, then it too is a selective environmentthat is not just evolving (experimenting,
diversifying), it is also developing (complexifying, and engaged in a life cycle).
From the perspective of biological evo-devo theory, much of this environmental
complexification is both constraining, directional, progressive, and in-principle
predictable, just as biological development is in-principle statistically predictable
(though not always so in practice). If the universe is an evo devo replicator, at
least some kinds of local environmental complexification will function to protect
the replication and self-maintenance of the system (the universe). Any evolutionary
(experimental, creative, contingent) activity that occurs within a developing organ-
ism must be increasingly constrained as that organism develops, in service to the
organism’s self-replication and self-maintenance. If our universe is an evolutionary
developmental system, the local adaptive landscape must constantly be shifting
toward certain developmental attractors, as evo devo complexity grows in certain
local environments.
As with intrauniversal replicators, mechanisms that guide and protect universal
replication may be very simple action-reaction and maximally energy-dissipative
physical and informational processes, such as those that statistically guarantee
stellar replication via star formation feedback in the nebular hypothesis (Krumholz
and McKee 2005), or the reductive tricarboxylic acid (rTCA) cycle, a proposed
universal intermediary metabolism (Smith and Morowitz 2004). The rTCA cycle
generates the five fundamental precursors to all biosynthesis (acetate, pyruvate,
oxaloacetate, succinate, and α-ketoglutarate), and may be a maximal free energy
dissipator in high energy flow environments, like geothermal vents. The rTCA
cycle can be catalyzed by inorganic mineral cofactors. When run in reverse, the
rTCA cycle is the oxidative Kreb’s cycle, central to all life. After the rTCA phase
transition occurred in a local environment, presumably further phase transitions
allowed combination with another cycle, oxidative phosphorylation, and an energy
harvester, photosynthesis, to store energy for the first cells. It is possible energy
storage wasn’t part of the first metabolism, as some photosynthetic bacteria use
the rTCA cycle (Kreb’s in reverse) to produce carbon compounds (Smith and
Evolutionary Development: A Universal Perspective 71
Morowitz). Note that we’ve still left out DNA-guided protein synthesis, which is
an information producing and environmental simulation system, and the transition
that would merge it with metabolism, if we are going to describe the origin of life
in evo devo terms. No wonder it is such a complex puzzle at present.
How advanced universal developmental processes may be, and how deeply
they structure cascades of non-equilibrium phase transitions, may depend on the
prior degree of universal replication, and the strength and nature of intra- and
extrauniversal selection. At some point, the analogy with developmental genes in
living organisms may apply, in which tuned parameters guide the emergence of
planetary-scale social and technological processes that are functionally similar to
biological intelligence, immunity, and morality. Such top-down causal informational
mechanisms could be an integral part of our universe’s self-maintaining processes.
From a functional perspective, mind might inevitably emerge in a universal repli-
cator, just as it has in biological replicators, if intrauniversal intelligence plays any
usefully nonrandom role in universal replication and selection.
In summary, evo-devo biology may offer us the most complex and rigorous
model for understanding not only convergent evolution in universal evolutionary
development, but how adaptation itself must change in a universe that is itself
a replicator. Once certain critical biological advantages, like eyes, emerge and
are strongly adaptive in an environment, the majority of the most successful
complex replicators in those environments may have to employ that advantage.
Once certain critical technological advantages, like digital computers and machine
intelligence, emerge and are adaptive in an environment, a subset of replicators
(individuals, organizations, societies) must use those technologies if they wish to
remain at the leading edge of adaptation. In this view only evo-devo biology, and
its successive processes of molecular, genetic, physiological, and psychological
evolutionarydevelopment,offer us a sufficiently complex analogy for understanding
how adaptation may change in the universe, if it too is a self-reproducing, self-
maintaining evo devo system.
16 Evo Devo Models Require Progress in a Variety
of Theories, Especially of Intelligence
If universal evolutionary development is occurring, future science must show that
each successive environment in the developmental hierarchy inherits certain initial
conditions and physical constancies from the environment that preceded it, back to
the birth of the universe, and that some of these initial conditions and constancies
act to predictably constrain the future dynamics of each successive environment, to
some degree. Such constraints have been called developmental attractors (or more
commonly,just attractors) by a variety of scholars. If they are the only such attractors
on the adaptive landscape at that level of complexity and timescale, in a universe
where accelerating complexification is possible, and such acceleration results in
72 J. M. Smart
local dominance of the most rapidly improving systems, then I think it is clarifying
to call them developmental portals (gateways, checks, bottlenecks) as well (Smart
2016a). For specific examples, G-, K-, and M-class stars and organic chemistry may
be necessary portals to planets capable of generating life. Fats, proteins, and nucleic
acids may be necessary portals to cells. Eyes may be necessary portals to higher
nervous systems. Tree niches (which support complex prediction),and animals with
grasping appendages, language, and technology use may be necessary portals to
human civilization acceleration, etc.
From an adaptive landscape (phase space) perspective, if ED is occurring, as
the evolutionary “search” landscape gets more diverse and locally complex, certain
portions must convert into developmental funnels, then portals. These portals must
also work together to periodically produce a metasystem transition (a higher level
of order or control), a new level of ED hierarchy. Both the landscape’s tendency to
produce funnels/portals as complexity emerges, and the number of portals (lower
is generally better) are two obvious ways to maintain developmental control in any
evolutionary (chaotic, creative, locally unpredictable) system.
It is widely agreed that physical complexification, and such riddles as the
origin of life, must be described by non-equilibrium thermodynamics as a coupled
cascade of phase transitions in energy degradation and information production.
As Smith and Morowitz (2016) state, each emergence (phase transition) in the
development of hierarchy creates new simplifying constraints and logic, and there
is a floor and a ceiling of environmental complexity for which those constraints
apply. Reductionism can be very successfully applied at each level to discover its
internal constraints (laws of chemistry, biology, etc.). The holism problem comes at
the transitions (portals) between hierarchies. Some combination of bottom-up (evo,
atomistic) and top-down (devo, holistic) parameters are involved, but how this works
in any transition still remains unclear.
Evo-devo genetic and epigenetic models, as they seek to differentiate develop-
mental and “evolutionary” gene fitness landscapes, will have to incorporate phase
space models and (wherever there is high dimensional reduction) landscape models
as our theory, tools, and data advance. Unfortunately, there are many problems
with current adaptive and fitness landscape models in depicting the hyperspace of
structure and function, and as critics of the adaptive landscape metaphor point out
(Kaplan 2008) few models incorporate any concept of probability of movement
across the landscape. In useful landscape models, potential portals would have
to emerge as persistent, and theoretically globally optimal peaks (or in a more
thermodynamically useful depiction, valleys) on adaptive fitness landscapes. These
models will eventually have to evolve into network-based models with search
basins and portal paths, which include both “evolutionary” tangles of similar-
fitness landscapes and portals (Crutchfield and van Nimwegen 2002)aswellas
regions that use portals to predictably transition to globally optimal, hierarchical
and developmental forms, landscape locations offering the greatest resource (space,
time, energy,matter) efficiency or density of adaptation.
Another field that will help evo devo models advance will be protein folding,
which already use funnel (portal) landscapes to depict 2D to 3D transformation of
Evolutionary Development: A Universal Perspective 73
protein structure, involving both energy minimization and information production
or conservation, a key example of biomolecular evolutionary development. In evo
devo models, alternative chemistries for life, periodically sought by astrobiologists
(see Goodwin et al. 2014) if they continue to be undiscovered by observation or
simulation, would be more evidence indicating a universe with a high level of ED
(self-organizing) constraint on the life transition. Science fiction authors and origin
of life theorists have been imagining them for decades, but so far we haven’t found
any particularly credible alternatives, in my view. Such constraint (only one physico-
chemical portal for the life transition being accessible in reasonable astronomical
time, see Koonin 2007), if it exists, might be due to strong or weak multiversal
selection for life and intelligence with both evo and devo properties, over many past
cyclings of our universe.
In addition to better simulation capacity, progress in any theory of evolutionary
development will require better:
1. Complex systems theory – Seeing the appropriate system and scale at which ED
is occurring, and any information-dependent processes (goals, drives) that may
operate all in complex adaptive systems. I offerone such speculative model (Five
goals of complex systems) in Smart 2017b.
2. Evo-devo theory – Better understanding organismic ED, modularity, reaction-
diffusion systems, gene-protein regulatory networks, intelligence, immunity,
morality, and other ED features of living systems, both individually and as
collectives. This will require advances in evo-devo genetics and epigenetics,
theoretical morphology, paleontology, evolutionary (developmental) biology and
psychology,anthropology, sociology, economics, and many other fields.
3. Adaptation theory – Moving beyond the MES (modern evolutionary synthesis)
to evo devo models, including self-selection (intelligence) and self-organization
(development) as sources of adapted order.
4. Optimization theory – Reliably differentiating less-optimized convergence
(LOC) and optimized convergence (OC) in the emerging study of convergent
evolution, via better definitions, tools, data, models, and optimization functions.
5. Acceleration theory – Understanding accelerating change, in ED terms. When
it happens as a physical process, acceleration always seems to involve both
densification and miniaturization of critical adaptive processes in complex
systems. Speculative proposals like the transcension hypothesis (Smart 2012)
and the stellivore hypothesis (Vidal 2016) extrapolate accelerating densification
trends in adaptive systems to their universal limit, a black hole. Acceleration also
happens via informational or computational processes as well. For that we may
need a better intelligence theory.
6. Intelligence theory (cog evo devo) – The Baldwin effect is the recognition,
beginning with James M. Baldwin in 1896, that learned behavior affects an
organism’s reproductive success. It is a modest start in understanding learning
and intelligence in evo devo systems, but we must go much farther. The better
we understand the evo and devo roles for cognitive processes in adaptation (and
today we often do not) the better we may understand the roles of intelligence
74 J. M. Smart
Fig. 11 Five goals (abilities, values, drives, telos) of complex systems, a possible evo devo
self-selection function for the growth of adaptive intelligence. In this cartoon, interdependence
(collectively shared feelings, values, preferences) is proposed as the central (most evo devo mixed)
goal of complex systems (Smart 2017b)
for the universe as a replicator. I can imagine (Smart 2017b) at least five goals
of complex systems, innovation, intelligence, interdependence, immunity, and
sustainability, each of which may be considered a form of intelligence (Fig. 11).
All of these goals may be self-selecting in evo devo systems, and their interaction
a primary driver of adaptation, as follows:
6.1. Intelligence as innovation (exploratory intelligence) – Evolutionary process
is the hallmark of this type of intelligence. As Shapiro 2011 and others
propose, living systems harness stochasticity to generate selectable variety
(experiments, possible futures), particularly under stress or after catastro-
phe. They seek to do this in increasingly clever (“good bet”) ways, the
smarter they become. Evolutionary innovation is nonrandomly guided by
intelligence, particularly in the “next adjacent” action and feedback cycle.
At the same time, the complexity generated becomes rapidly unpredictable
the farther ahead any intelligence looks.
6.2. Intelligence as intelligence (representation intelligence) – Most fundamen-
tally, intelligence is a process of informational representation of environ-
mental reality (Fischler and Firschein 1987). Informational representation
(modeling) can be argued to be a dominantly divergent, evolutionary
process. Our neural models conform to regularities of their environments,
but they also generate astounding numbers of exploratory representations,
only a fraction of which are universal (predictable) or adaptive. Think of
imagination, fiction, or theoretical math, most of which has no known
application. Being “intelligent” is also no guarantee of being adaptive.
Evolutionary Development: A Universal Perspective 75
Indeed, those with too much of this single ability may be maladaptive.
The finite nature of all intelligence (its computational incompleteness)
also strongly argues that massive parallelism is a fundamental adaptive
evolutionary strategy. All models are wrong, but some are useful. Massive
parallelism, and regular selection on that parallel variety, seems key to how
genes, neural nets, populations, and civilizations get more adaptive.
6.3. Intelligence as interdependence (empathic-ethical intelligence) – Organ-
isms engage in positive sum games, rules and algorithms (morality, ethics),
involving not just self- and world-modeling but collective competition and
cooperation, coordinated by other-modeling and other-feeling (empathy).
Complex interdependent organisms develop culture, which evolves and
develops independently from the individual, both faster and more resiliently,
and allows them to view and optimize outcomes from either personal or
group perspectives(which may conflict). A variety of universal evolutionary
and developmental ethics (algorithms that protect collective adaptation
and intelligence) may apply to all complex cultures. For more on how
emergent synergies (interdependences) may have driven major transitions
in evolutionary development, see Corning and Szathmáry 2015.
6.4. Intelligence as immunity (security intelligence) – Organisms use many
strategies for differentiating self from other, and passively and actively
countering degradation and predation. Chronic stress and stress avoidance
both weaken immunity, but right-sized cyclic stress and catastrophes both
build immune system capacity and accelerate evolutionary innovation.
Taleb’s concept of antifragility argues this for organizations, as does
the catalytic catastrophe hypothesis. If there are universally discoverable
security architectures and strategies (many ways to fail, only a few ways
to survive), as I suspect, then immunity can be classed as a dominantly
convergent and developmental process.
6.5. Intelligence as sustainability (predictive intelligence) – Developmental
process itself is the hallmark of this type of intelligence. Organisms use
their intelligence not just to explore possible (innovation, intelligence)
and preferable (interdependent, immune) futures, but to build predictive,
and presumably Bayesian, models of probable futures. A subset of these
predictive models are encoded in an organisms developmental genes, in
emergent properties of their soma, in their environment, and in more
complex organisms, culture. The growth of knowledge, common sense,
science, and all the processes of development that predict, but do not
protect (immunity) can all be considered sustainability. These processes
grow “truth” and understanding. This form of intelligence is in constant
tension with innovation, which can rapidly cause both poorly understood
and dangerous forms of complexity to emerge.
6.6. Intelligence substitution – Understanding precisely when information, or
a computational process, can substitute for a physical process, and either
retain or improve adaptiveness for the system under study. Some scholars
call this dematerialization, or ephemeralization. Along with densification,
76 J. M. Smart
dematerialization seems to be a central driver of accelerating complexifica-
tion (Smart 2016b).
6.7. Intelligence partitioning – Adaptation and intelligence always exist in three
interacting subsystems: seeds (with evo and devo initial conditions), organ-
isms (which engage in a life cycle), and the selective Environment (some
scholars call this ambient intelligence). Because of niche construction
or stigmergy (intelligence always alters its local environment, in minor
or major ways, changing adaptive pressures), environments essentially
replicate along with seeds and organisms (think of the replication we see
in city structure and function) and are a full partner with organisms and
seeds in the ED of intelligence.
7. Hierarchy theory – Seeing the ED trajectory for the system as a whole. Stan
Salthe’s work on subsumptive hierarchies is an excellent example. Hierarchy
theory (Salthe 1985,1993,2012) tells us how each new hierarchy is in some
sense more free and in another more constrained than the latter. While we
traditionally think of intelligence in an evolutionary role (increasing diversity and
options), hierarchies tell us the ways that new “higher” systems are also more
developmentally constrained than the ones from which they emerged. Using
Volk’s concept of “combogenesis” (Volk 2017), we can think of chemistry as
both a set of new freedoms (to space- and time-efficiently create new adaptive
structure and function) and new constraints on the local dynamics of physical
laws. Biology locally enables and constrains chemistry, society locally enables
and constrains biology, and so on. In a physical universe, such nesting and
accelerating hierarchies must have a limit, a point at which further evolutionary
development cannot proceed within this universe (Smart 2008,2012).
8. Information theory – Convergent evolution in biology can be modeled as the
result of networks made up by biomolecules or other agents that are organized
and structured by information hierarchies emerging via top-down causation. The
emergence of modularity and of functional equivalence classes in subroutines –
both in biological and technological systems – can be explained via such infor-
mation hierarchies. Top-down causation describes the process whereby higher
levels of emergent informational organization in structural hierarchies constrain
the dynamics of lower levels of organization. In a typical reductionist paradigm
it is assumed that purely physical effects entirely determine the dynamics of
lower levels of organization and, by extension, at higher levels as well. But an
emerging school of investigators hypothesize that the transition from non-life
to life, abiogenesis, requires a top-down transition in causation and information
flow (Flack 2017;Walkeretal.2017).
9. Life cycle theory – Seeing the full replicative cycle of the developing system.
If we can predict the remaining stages of the life cycle in any system, aided by
comparisons with other evo devo systems, we can see its developmental futures,
in broad outline at least. Its evolutionary futures, of course, remain intrinsically
unpredictable at the same time. Both predictable and unpredictable processes are
perennially found in complex systems, whether an organism, a culture, a star, a
galaxy, or a universe.
Evolutionary Development: A Universal Perspective 77
Building better hypotheses and theory of evolutionary and developmental pro-
cesses will be an immense amount of work. But this path may be the only viable way
forward (a conceptual developmental portal) to fully understanding such scientific
challenges as convergent evolution, galactic development, and the origin of life. If
validated, the benefits we stand to gain, via better collective foresight, also seem
comparatively immense.
17 Observation Selection Effects: The Challenge of Assessing
Them in Evo Devo Terms
Any form of reasoning about traits or properties which constitute observers, or that
are logically or physically necessary for the existence of observers, is subject to
observation selection effects and biases. The importance of these selection effects
and biases has only recently begun to be fully appreciated. For example, the number
of small bodies’ (asteroids and comets) impacts in Earth’s history is constrained by
our existence at the present time through the “anthropic shadow” effect (´
Cirkovi´
c
et al. 2010).
Several detailed reviews of observation selection effects exist (for example, see
Bostrom 2002). Observer selection arguments and models are often used to critique
the fine-tuned universe hypothesis. Unfortunately, our cosmological models remain
quite primitive, so it is easy to argue that either fine-tuning or observer selection
bias is more important in such models. But even more fundamentally, as I have
argued before (Smart 2008) all observer selection models in common use depend
on a random observer self-sampling assumption, a random distribution of possible
universes, or some other random, Monte Carlo-style mathematical framework in
their evaluations. In other words, they assume we live in an essentially random,
evolutionary universe, and this is a major limit to their utility.
Consider that if we actually live in an evo devo universe, such math must itself
be incorrect. If our universe is not just randomly (contingently) evolving, but it also
nonrandomly developing, then some subset of its critical probability distributions
(informational and dynamical) will continually be skewed in the direction of the uni-
verse’s developmental trajectory, given its special initial conditions and constraints.
As complexity and hierarchy grow in local environments, those environments will
further bias a special subset of locally constraining, nonrandom developmental
processes to occur. Such developmental biases may be why accelerating complex-
ification occurs in special environments (acceleration bias) and why spontaneous
abortions (miscarriages) are so frequent early in gestational development, but so
rare late in development, with miscarriage frequency in humans declining from
40% of pregnancies at conception to 0.1% of pregnancies at 42 weeks of gestation
(Rosenstein et al. 2012). Presumably, the more developmentally complex both
the fetus and the gestational environment become, the less often that any random
perturbations of a standard size or duration are disruptive (stability bias).
78 J. M. Smart
Math that describes an increasing developmental bias toward both acceleration
and toward growing informational stability during complexification in special
environments is the kind of math we may need to properly model developmental
processes, and to properly understand complex observers. Such observers are not
random, they are privileged, in some proportion to their complexity. We surely
do not live in an anthropic universe, if by that we mean one self-organized for
the end purpose of producing biological humans. But we may well live in a
noetic (intelligence-centric) universe, self-organized to produce accelerating and
increasingly stable intelligent observers, as a central adaptive strategy for the
universe itself. Biological humans may well be one of a long chain of developmental
purposes, both an important and a transitory intelligence substrate (Hoyle 1983;
Gardner 2007).
Even the frequency of evolutionary convergence,versus presumably much more
commonplace evolutionary contingency in biological change, is a complex issue we
don’t yet agree upon. In the Signor-Lipps Effect, because rarer species are much
less common in the fossil record, and the record itself is so sparsely sampled, rarer
species will seem to disappear long before their actual time of extinction, simply due
to chance. This makes the timing and speed of mass extinctions, and the ancestry
of specific genera both much harder to determine from paleontology alone. As one
consequence, quick and dramatic extinctions can go undetected, as they may look
gradual due to selective sampling of a poor record, a classic observation selection
effect (Signor and Lipps 1982).
Unfortunately, even the consequences of known biases like this are not clear.
On one hand, if extreme ecological perturbations have overturned entire faunas via
mass extinction events at much greater rates than we presently appreciate, and more
rapidly switching Earth’s macroevolutionary regimes (Jablonski 1986), then the
contingency that life is subject to due to intrinsic evolutionary (diversity-generating)
mechanisms may be less than is currently theorized. Our environment,not evolution
itself, may be our greatest source of contingency. On the other hand, it has long
been argued that environmental catastrophes themselves act as a major (and perhaps
even the primary) catalyst for evolutionary innovation (Gerhart and Kirschner 2005;
Bhullar 2017). We know that immune systems depend on catastrophe and hormesis
to get stronger, and evolutionary gene complexes may also depend on catastrophe
and chaotic stress to innovate. Without periodic catastrophe, some biologists have
argued that evolutionary contingency must steadily reduce with time (Salthe 1993;
Shapiro 2011). In stable niches, stabilizing genes presumably gain the upper hand
over innovating genes. At other times, evolutionary innovation itself has provided
the catastrophic environmental change, spurring further evolutionary innovation,
as in the Great Oxygenation Event, causing massive dieoffs due to oxygen-
excreting cyanobacteria, the End-Ediacaran extinction of large sessile organisms
due to the emergence of mobile sighted animals during the Cambrian Explosion,
and the Permian-Triassic extinction, perhaps precipitated by the emergence of
Methanosarcina, a methane-synthesizing archaea (Ray 2017).
Evolutionary Development: A Universal Perspective 79
Geerat Vermeij has offered a particularly interesting and relevant argument
supporting high frequency of convergence in evolution based on the logical
asymmetry between singular and multiple events in the incomplete terrestrial fossil
record (Vermeij 2006). If some specific character, call it Z, demonstrably evolved
multiple times in different lineages in different ecological conditions, this is clearly
an argument for the convergent nature of Z. However, if Z evolved only once
to the best of our knowledge, it is still not an argument for uniqueness, since
the incompleteness of our record may hide repeated instances of the independent
evolution of Z. This logical asymmetry has intriguing consequences when a wider
set of various important innovative characters scattered over all of the history of
life are analyzed from a Bayesian point of view. Vermeij was able to show that the
alleged singular innovations tend to be either more ancient or to appear in small
clades. Small clades, in turn, are more invisible in the fossil record if located in the
more distant past. In other words, purportedly unique innovations in small clades,
or in the distant past, may be only the latest or the dominant instances of convergent
evolutionary events, with most past convergences hidden from our view.
Vermeij concludes that few innovations are ever truly unique: “Purportedly
unique innovations either arose from the union and integration of previously
independent components or belong to classes of functionally similar innovations.
Claims of singularity are therefore not well supported by the available evidence.
Details of initial conditions, evolutionary pathways, phenotypes, and timing are
contingent, but important ecological, functional, and directional aspects of the
history of life are replicable and predictable” (Vermeij 2006, p. 1804). Insofar as key
evolutionary innovations are largely determined by universal principles of physics
and economics, they will lead to widely-to-universally useful designs. This is the
classic view of environment-driven convergence.
18 Ambiguity of the Word “Evolution” and the Modern
Evolutionary Synthesis
In the scientific literature, the term “evolution” is used to describe any process
of growth or change that involves the accumulation of historical information,
in either living or nonliving complex systems (Meyers 2009). When we restrict
the term to refer to biology, and modern forms of Darwinian evolution, it is
used to describe cumulative inherited change, via descent with modification from
preexisting organisms. A classic conceptual model of Darwinian evolution, often
taught in undergraduate classes, is the acronym VIST. Evolutionary change is
proposed to happen via Variation, with Inheritance, and (Natural) Selection, over
long amounts of Time (Russell 2006).
80 J. M. Smart
While it is a good start, there are three basic problems with the VIST model:
1. VIST does not explicitly consider the concept of development, and of develop-
mental genes and processes, which act in opposition to processes of variation
within the organism. Developmental genes and processes are those that keep
the organism on a convergent, conservative life, and reproduction cycle. Their
fundamental role is Convergence, funneling the organism toward a series of
future-specific states. Variation, within the organism or within the environment,
is the “enemy of development.” It must be overcome by Convergence, if the
organism is to develop in a predictable way. Unfortunately, both classical Dar-
winism and modern evolutionary theory deprioritize the influence of organismic
development on macrobiological change.
2. VIST does not explicitly consider cumulative Replication, and its growing infor-
mational constraints on organizational change, in any substrate, over cumulative
life cycles. Replication is implicitly considered as the factor of “Time” in the
classic VIST model. But it is not Time that causes biological change. Organic
change occurs via cumulative and increasingly ergodic cycles of Replication (of
the organism), within any substrate, as guided by Inheritance factors (genes,
brains, and other information carriers, or “seeds”), and Selection (in the envi-
ronment). In all three of these interacting systems (organisms, seeds, and the
environment) we find processes of Variation (evolutionary processes) and Con-
vergence (developmental processes), working together in service to adaptation.
Considered together, these five factors give us the VCRIS model of change.
After variation and convergence themselves (what changes, and what doesn’t),
replication is the next most fundamental process we should acknowledge in
any model of the self-organization of complex adaptive systems. Whether we
are discussing replicating suns creating organic chemistry, replicating chemicals
creating cells, replicating cells creating organisms, replicating organisms creating
ideas, or replicating ideas creating self-replicating machines, we must recognize
that the most complex forms of adaptation, learning, and intelligence always
require replication, inheritance, and life cycle, in some kind of “organism”
(system).
3. VIST doesn’t recognize that the natural environment may itself be not only
evolving (creating unpredictable experimental variety, by our definition above)
but also developing, if our universe is itself a replicator. As a result, the Modern
Evolutionary Synthesis, our current standard in biological investigations, is
biased toward the idea of an “accidental” universe, and “random” experimen-
tation and diversity as the primary (or in some views, exclusive) cause of
macrobiological change.
Evo devo models, whether in biology or in other replicating systems, help us
correct the biases of both the original Darwinian VIST view of evolution (Fig. 12,
white oval), and of modern evolutionary theory (Fig. 12, light gray oval), both of
which view diversification as the primary source of adaptiveness. Each of these
views ignores or minimize the converging, conserving role of development, and
the possibility of development on scales far larger than the organism. An evo devo-
Evolutionary Development: A Universal Perspective 81
Fig. 12 Conceptual schematic of Darwinian evolution (1859, white oval), the modern evolution-
ary synthesis (post-1940, light gray oval) and current concepts in the coming extended evolutionary
synthesis (post-2000, dark gray oval). (Source: Pigliucci and Müller 2010, with permission)
centric perspective (Fig. 12, dark gray oval, for the case of living systems) will allow
us to see that complex adaptive systems must harness both unpredictable, divergent
evolutionary stochasticity and predictable, convergent developmental destiny and
life cycle in search of greater adaptiveness and that these two sets of mechanisms
act in productive opposition to and tension with each other, at every scale. Evo devo
models allow us to see evo devo self-organization as the natural source of adapted
complexity and causal order in all successfully replicating systems, which we must
come to understand from both physical and informational perspectives.
As we’ll discuss shortly, understanding self-organization also shows us why
challenges to Darwinism that have been launched by groups like the “intelligent
design” community are more in line with supernatural belief, not science. They
are typically motivated by belief in an “intelligent designer.” But if the universe
replicates, as several cosmologists propose, parsimony and evidence both argue that
evo devo self-organization, via many past replications in a selective environment,
not intelligent design, is the source of the intelligence we see.
After we have done our best to adjust for observer-selection effects, we still see
many highly unreasonable examples of adaptedness for complexification, in the
laws and processes of our universe as a system. The phenomenon of accelerating
change, evolutionary constraint laws (like the constructal law and various scaling
laws), terminal differentiation of morphological complexity, the fine-tuned universe
hypothesis, the presumed fecundity of Earthlike planets, the collective morality of
82 J. M. Smart
social animals, and the Gaia hypothesis (in a more rigorous form) all come to mind.
To explain such unreasonable adaptedness for complexification in our universe we
should think first of replicative self-organization under selection, not design. After
all, such self-organization is our best model for the source of the intelligence that is
reading this page, right now.
As we come to understand the complex phenomenon of convergent evolution,
on myriad system levels (physical, chemical, genetic, morphological, functional,
algorithmic, cognitive, technological, etc.), we will rectify the historical biases
that the Modern Evolutionary Synthesis (MES) has perpetuated with respect to
our presumably living in a “random,” “directionless,” and “purposeless” universe.
To do this, we will need what Pigliucci and Müller (2010) and in a particularly
comprehensive review, Laland et al. (2015) call an Extended Evolutionary Synthesis
(EES). I expect this synthesis must include evo-devo and evo devo perspectives, a
better theory of intelligence, better science and simulations, and more.
A large and well-funded group exploring an EES, led by evolutionary biologists
Kevin Laland and Tobias Uller, can be found at ExtendedEvolutionarySynthe-
sis.com. Another group working on an EES, led by biochemist and molecular
biologist James Shapiro and physiologist Denis Noble, can be found at The Third
Way of Evolution. This latter website is admirable, but not entirely error-free. As
biologist Jerry Coyne points out in a post at the Richard Dawkins Foundation, the
web editor of the Third Way website, Raju Pookottil, who does not have biology
training, once argued that life “carries the hallmarks of design.” That is a useful
critique (see my section on the Fallacy of Intelligent Design below) but Coyne’s
post also ignores the scientific contributions of the many eminent meta-Darwinist
scholars listed at the website. In exploring an EES, both poor evolutionary thinking
and ultraorthodoxy with respect to the modern synthesis must be avoided.
“Ultra-Darwinists” like Coyne and Dawkins have attracted this label whenever
they advocate, with a confident certainty, the position that contingent evolutionary
selection (neo-Darwinism) can be the only force driving macrobiological change.
Though Darwinism has deep evidence behind it, and its models appear to aptly
describe the vast majority of (divergent and contingently convergent) organic
change, they also seem insufficient to explain a small subset of phenomena
that appear developmental and universally convergent. That subset includes the
accelerating development of intelligence, and the increasingly nonrandom guidance
of evolutionary innovation in intelligent systems (Smart 2008). In a similar way,
the vast majority of change we can sample at the molecular scale in biology seems
locally stochastic, randomly selectionist, and diversity generating (“evolutionary”),
yet a small subset, as we have proposed, also seems deterministic, convergent, and
predictively selectionist (“developmental”), particularly when viewed from larger
or longer-range spatial, temporal, energetic, and material (STEM) scales. Better
defining and empirically discovering that subset seems a reasonable next step in
evo devo inquiries.
Evolutionary Development: A Universal Perspective 83
19 The Fallacy of Intelligent Design as an Explanation
for Adapted Complexity
Religious belief is deeply valuable to many of us, and religious communities are
globally important social institutions. Religion is humanity’s first effort at universal
moral philosophy, greatly predating Greek natural philosophy, and religion often
ventures first into areas of moral prescription where science cannot yet easily tread.
History shows that religious community has provided invaluable guidance and
public benefit for millennia, and that all of our most socially successful religions are
continuously reforming their beliefs and practices to be congruent with accelerating
scientific knowledge.
Yet a key insight in the philosophy of science is that everyintelligence is woefully
finite and incomplete relative to both the current and future complexity of physical
and informational reality. Thus we all must live with our own pragmatic sets of
unproven beliefs, and many of us will seek communities that share those beliefs. It
seems inevitable that self-aware artificial intelligences, if and when they eventually
emerge, must also evolve and develop their own set of religious beliefs (read:
philosophies of universal purpose, meaning and value), as there will remain many
areas of reality about which they will know little. Fortunately, freedom of religious
practice and freedom from religious discrimination are bedrocks of all modern
democracies. Besides the traditional religions, atheism, agnosticism, possibilianism,
universism (my own belief), and others are all belief systems that offer valuable,
unproven beliefs about metaphysical reality.
As good practitioners of science and natural philosophy, all of us should attempt
to make our unproven beliefs explicit and public, and seek to test them with
evidence and experiment in whatever partial ways we can. When we feel we
cannot separate our unproven beliefs from the practice of science, we should
declare our influences. Unfortunately a number of scholars in the intelligent design
community do not do this, and their religious belief has led some members of
these communities to a variety of objectionable political acts, like seeking “equal
treatment” for their evidence-poor hypotheses in the science classrooms of our
public high schools. Given the intelligent design community’s position on mixing
religion and science, and not declaring their supernatural beliefs, we do not welcome
scholars affiliated with the Discovery Institute or other intelligent design or creation
science communities within the Evo Devo Universe (EDU) research community,
and non-naturalistic discussion of religious belief is outside the scope of our
community.
Given that there are fully naturalistic, evolutionary developmental explanations
for the sources of “design” we see in living systems, and given the sharply limited
value of all known physical intelligence, the concept of intelligent design, as it is
generally proposed, appears fallacious. We must recognize that adapted intelligence
has always had a useful but very minor influence on processes of selection in VCRIS
systems. A crucial insight is that no physical intelligence ever becomes “Godlike” in
its ability to predict either its own or its environmental future. We must acknowledge
84 J. M. Smart
that all our present attempts to “rationally design” our own environment, including
our genetically modified organisms, must be more accurately characterized as
intelligence-guided guesses at more adaptive forms and functions. Human science
and engineering are always evo devo process, like all other natural processes.
They might be 95% bottom-up creativity/experiment/serendipity,and 5% top-down
discovery/optimization/prediction, if we were to guess a very rough ratio. They are
never a fully top-down or future-omniscient“design.” The universeand all evo devo
systems are far too chaotic and contingent to allow such omniscient foresight.
If we live in an evo devo universe, it is easy to argue that our future must
continue to become rapidly computationally opaque to any finite and physical
beings, the further ahead they look into their own futures.Combinatorial explosions
of possibilities and contingencies, both in the universe itself and in our own mental
processes, must always limit our foresight. No matter how advanced we become, any
intelligences generated by this universe, or its ancestors, seem destined to remain
evo devo “gardeners” as opposed to omniscient engineers, finite beings with “free
will” (self-unpredictable evolutionary futures) not gods.
Supernaturalism takes many forms, some quite subtle. Even otherwise deeply
insightful works, like EDU scholar and complexity theorist James Gardner’s
Biocosm (2003), which some critics read as an attempt to “split the difference”
between a God-created and self-organized universe, run into trouble when they
speculate that our universe may have been rationally constructed (read: intelligently
designed) by “godlike” entities in a previous cycle. Such models simply don’t
fit with all materialist experience to date with respect to intelligence’s role in
replicating systems within our own universe.
Just as life’s incredibly adapted complexity self-organized over many evo devo
cycles, and just as everything that is complex and adaptive inside our universe
is a replicating system, it is most parsimonious to assume that our universe is a
replicating evo devo system as well. If it is, its evo devo intelligence will always
remain a limited and incomplete aid to selection, not a “godlike” designer. We
may think a highly adapted design offers evidence of a designer (Paley 1802)but
this argument has been exhaustively refuted by the rise of evolutionary theory with
respect to biological systems (Darwin 1859), and we should expect it to be defeated
for an evo devo universe as well.
If our universe replicates, either in isolation or as part of a fractally replicat-
ing multiverse, as some cosmologists propose, evolutionary developmental self-
organization underselection seems the simplest explanationfor such curious univer-
sal features as our improbably fine-tuned initial conditions, the robust emergence of
adaptive complexity and intelligence, our improbably self-correcting geophysical
environment, our continually accelerating complexity on Earth (Sagan’s “Cosmic
calendar” metaphor), even under periodic catastrophe, and other puzzling aspects
of our complexity emergence story so far. We have no need to invoke supernatural
entities to explain such phenomena, and we have found no credible evidence, in our
five hundred year epic of science advancement, for an intelligent designer.
Evolutionary Development: A Universal Perspective 85
20 Research Questions
• How do we best improve our physical and informationaltheories of unpredictable
evolutionary and predictable developmental process?
• What improvements to complex systems theory, evo-devo theory, adaptation
theory, optimization theory, acceleration theory, intelligence theory, hierarchy
theory, life cycle theory, and other topics will help us better define, delineate,
and compare evo and devo process in all replicating complex systems?
• How can we better define evolutionary and developmental process as sources of
intelligence, in seeds (containing initiating evo and devo parameters), organisms,
and environments?
• What evo and devo goals (purposes, telos) can we discover for intelligent
complex systems?
• To what extent can we find modularity, reaction-diffusion systems, and other
features of organismic ED in ecosystem ED? In biogeographical ED? In stellar-
planetary ED? In galactic ED?
• What empirical and statistical tools and tests can help us to infer developmental
processes in biology, based on past experience with other organisms,when we do
not have the capacity to simulate development causally? Can we use those tools
and tests to help us infer hierarchy and life cycle in the universe as well?
• How do we best improve our models, simulations, and tests, especially falsifica-
tion tests, for the universe as an evo devo system?
Acknowledgments The author thanks Evo Devo Universe co-directors Clement Vidal, Georgi
Yordanov Georgiev, Michael Price, and Claudio Flores-Martinez for helpful critiques. Special
thanks go to EDU member Milan ´
Cirkovi ´
c who offered extensive constructive feedback on the
earliest version of this paper. Thanks also to Anthony Aguirre, John Leslie, Denis Noble, Rüdiger
Vaas and Tyler Volk for key insights, and to Carlos Gershenson and the CCS committee for
approving our satellite on Evolution, Development, and Complexity at CCS2017, where these and
other ideas were discussed.
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