POLITICS BY OTHER MEANS
Science and Religion in
the Twenty-First Century
A Metanexus Imprint
Copyright © 2010 by William Grassie.
Library of Congress Control Number: 2010901676
ISBN: Hardcover 978-1-4500-3849-2
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1. Epiphany on the New Jersey Turnpike ......................................................... 11
Religion by Other Means ...................................................................... 15
2. Ten Reasons for the Constructive Engagement of Science and Religion .... 17
3. Metanexus: The Very Idea ........................................................................... 23
4. Beyond Intelligent Design, Scientiﬁ c Debates, and Cultural Wars ............. 32
5. Which Universe Do You Live In? ................................................................ 36
6. Toward a Constructive Theology of Evolution ............................................ 39
7. Universalism and Particularism: Judaism in an Age of Science .................. 60
8. Resources and Problems in Whitehead’s Process Metaphysics ...................72
Peace by Other Means .......................................................................... 85
9. Sleepless in Tehran ....................................................................................... 87
10. Universal Reason: Science, Religion, and the Foundations of
Civil Societies ............................................................................................ 100
11. Science, Religion, and the Bomb ............................................................... 108
12. Engaged Contemplation for a Troubled World .......................................... 117
13. Leeches on the Road to Enlightenment ..................................................... 133
14. Nationalism, Terrorism, and Religion: A Biohistorical Approach ............. 142
15. Entangled Narratives: Competing Visions of the Good Lie ...................... 158
Evolution by Other Means ................................................................. 185
16. Biocultural Evolution in the Twenty-ﬁ rst Century ..................................... 187
17. Useless Arithmetic and Inconvenient Truths ............................................. 207
18. Rereading Economics: New Economic Metaphors for Evolution ............. 218
19. Post-Darwinism: The New Synthesis .........................................................231
20. Eating Well Together: Donna Haraway’s Companion Species Manifesto .... 245
21. In the Heavens As It Is on Earth: Astrobiology and the Human Prospect .... 259
22. A Thought Experiment: Envisioning a Civilization Recovery Plan .......... 271
23. Millennialism at the Singularity: The Limits of Ray Kurzweil’s
Exponential Logic ...................................................................................... 280
Postscript .............................................................................................. 301
24. All My Relations: The Challenge Ahead ................................................... 303
Index ................................................................................................................. 315
17. Useless Arithmetic and Inconvenient Truths
This paper was originally published on Metanexus, 2007.03.26
A Review of Useless Arithmetic: Why Environmental Scientists Can’t Predict the
Future by Orrin H. Pilkey and Linda Pilkey-Jarvis, Columbia University Press,
2007. ISBN 0-231-13212-3
My story begins with the intriguing title of a new book—Useless Arithmetic:
Why Environmental Scientists Can’t Predict the Future. The authors are a father
and daughter team. The father is Orrin H. Pilkey, an emeritus professor of geology
at Duke University’s Nicolas School of the Environment. He lives in Hillsborough,
North Carolina. The father is a prolific author and expert in shoreline developments.
The daughter, Linda Pilkey-Jarvis, is also a geologist. She hails from McCleary,
Washington, working in Washington State’s Department of Ecology, managing the
state’s oil spill programs.
The book is a delight to read. The Pilkeys recount dozens of scientific
vignettes, unfolding like detective stories, of scientists gone astray, lost following
their predictive models to unexpected consequences and tragic failures. As the
Pilkeys make clear, science has not been very successful in predicting or managing
environmental changes. The problems, they argue, are inherent in any attempt
to model complex natural and human systems. Predictions from any computer
simulations of any complex reiterative dynamic processes are not worth the binary
code they were written in, nor the supercomputers they were run on. The book reads
like a series of parables, each illustrates what Whitehead meant by “the Fallacy of
Misplaced Concreteness.” The problem is endemic to all modeling of any complex
environmental or human process.
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Chapter four alone should be required reading for anyone concerned with the
debate over climate change. To address the larger question, the authors begin by
pulling on the string of sea level change. Readers get a brief tutorial on eustatic
and isostatic changes in sea level. Eustatic variations are changes in the volume of
liquid water in the Earth’s oceans, more or less depending on the amount of glaciated
ice, atmospheric water, and geologically bounded water captured in aquifers, lakes,
soil, and rock. Isostatic changes in sea levels are dramatic geological changes in the
contours of Earth’s ocean basin, increasing or decreasing the volume of the ocean
containers. When the ocean basin is smaller, global sea levels rise everywhere.
The ocean cup runneth over unto all of the continents. Or as the case may be in the
reverse, sea levels can also drop dramatically.
These dynamics and others have been at work on the Earth since its beginning.
Major climate changes in the past have been caused by wobbles in the Earth’s axis
of rotation. Indeed, the magnetic poles have even flipped—south becomes north and
north becomes south. Our orbit around the sun is also ever so slightly out of kilter.
Our sun too is dynamic, sometimes overly exuberant in bathing the Earth with excess
solar energy, and sometimes too little. In addition, there are disruptions caused by
volcanic activity and terrestrial impacts. And life itself is also an important part of
the story, like the invention of photosynthesis or the formation of large hydrocarbon
deposits hundreds of millions of years ago. All of these can dramatically impact
global climate and maybe even your vacation plans this summer.
Climate change is hardly front-page news for geologists; climate change is the
whole story from beginning to end. Geologists read this story from the text of rock, mud,
water, ice, and air, in the half-lives of radioactive isotopes, in the orientation of magnetic
sediments, in geological deposits, in the traces of ancient glaciers, mountain ranges,
canyons, fossils, bygone oceans, and tectonic plates. The 4.5 billion years old Earth story
is one of continuous and dramatic metamorphoses on a time scale difficult to imagine,
unless, of course, you happen be a geologist—or in this case, two geologists.
This is the backdrop to the Pilkeys’ exploration of useless arithmetic in the
current debate on anthropogenic global climate change. Their message undermines
everyone and every position in the current global debate about global climate
change. The book came out before the release of the 2007 Intergovernmental Panel
on Climate Change (IPCC) Report, but we do get a careful analysis of the 2001
IPCC Report. Perhaps this section can be updated in future releases of the book,
even though there is no problem in extrapolating from 2001 to 2007, unlike some
of the other extrapolations discussed in their book.
There are about fifteen major climate models used by scientists around the
world. Favored are bottom-up models, involving a long chain of events and very
complicated computer simulations running on supercomputers. This approach
uses a great aggregation of models, and models of models, all the way up. In
other words, it is models all the way down too. The assumption here is that the
more variables included in the metamodel, the better the metamodel. Another
17. USELESS ARITHMETIC
approach, the minority view, favors top-down models, focusing only on larger
systems—simplify, averaging, estimating, testing, but not presuming to include
every potentially relevant variable. Predicting future sea levels, of course, is only
one piece of the climate puzzle. Up or down, the Pilkeys profess:
What a daunting task faces those who choose to predict the futures of
the sea-level rise! We have seen that the factors affecting the rate are
numerous and not well understood. Even if our understanding improves,
the global system simply defies accurate and quantitative prediction
because of its complexity.
Their argument is not whether our climate cup is half-full or half-empty.
Geologists have a different perspective on time. Their earthy timescale is some
4.5 billion years. All rock is ultimately metamorphic rock. And this includes the
concrete, steel, and glass monuments of human engineering and architecture built
in cities around the world. Imagine my beloved New York City, and every other at
some point in the future, crushed under mile-thick glacier ice, or perhaps absorbed
back into the molten core of the Earth through normal plate tectonics, or perhaps
someday under the ocean. A geologist knows it is only a matter of time—hot and
cold, sea levels up and down, round and round the sun—before there are dramatic
changes on our restless and creative planet. Maybe this will happen soon, maybe
suddenly, and maybe not for a long time, at least relative to the scale of human life,
but it will happen, if the past is any guide.
The American Petroleum Institute and Dick Cheney should take no pleasure
in the Pilkeys’ thorough challenge to the global climate change prediction industry.
Anthropogenic climate change may be a real concern. And furthermore, the same
types of modeling errors and unknowns presumably also call into question industry
models of global petroleum reserves. The Pilkeys’ real argument is that no scientist
can offer cogent predictions of the Earth’s climate—too hot, too cold, or just right. No
matter how much data is collected, no matter how sophisticated the computer program,
no matter how powerful the supercomputer employed to run the simulation. Complex
natural systems cannot be modeled in a way that generates useful predictions. There
are too many variables, too many feedback loops between variables, and the system is
dynamic in ways that we do not understand and cannot represent mathematically.
In the case of climate change, a short list of variables and feedback loops
the absorption of CO• 2 by the ocean,
the heat exchange between the oceans and the atmosphere,•
the effect of cloud cover,•
variations in the Earth’s albedo,•
ocean current circulation,•
POLITICS BY OTHER MEANS
local climate perturbations,•
long-term climate cycles arctic ice melt,•
release of methane from melting artic tundra,•
health of phytoplankton,•
variations in amounts and types of precipitation, and•
many more confounders large and small.•
Any of these variables could accentuate or ameliorate climate change and could
do so with runaway dynamics. The authors are leaning agnostic to pessimistic on
the prospects for near-term climate change (resulting from anthropogenic causes).
It may not be all that bad. It may even be worse. We have no way of knowing, in
spite of the $2 billion-per-year industry funded by the U.S. government to study
climate change. The Pilkeys use strong words to criticize these expenditures:
Assumption upon assumption, uncertainty upon uncertainty, and
simplification upon simplif ication are combined to give an ultimate and
inevitably shaky answer, which is then scaled up beyond the persistence
time to make long-term predictions of the future of sea level rise. Aside
from the frailty of the assumptions, there remains ordering complexity: the
lack of understanding of the timing and intensity of each variable. (82)
The authors advocate instead a qualitative methodology that settles with
tendencies, directions, and magnitudes of change. A supercomputer is not required
to document actual glacial declines around the world over the last few decades.
Before-and-after photographs from a tourist camera of Muir Lake, Alaska from
1941 and 2004 provide compelling evidence for major changes. Over twenty
years of space telemetry and ground observations in Antarctic give us disturbing
short-term trends. Over a three-year period, the West Antarctic Ice Sheet lost
thirty-six cubic miles of ice per year. The complete melting of the West Antarctic
Ice Sheet alone would produce a thirteen-foot global sea level rise (78). Maybe
you should rebook that summer vacation after all.
The Pilkeys certainly seem to think that global climate change is a serious
problem. It is just that “A serious societal debate about ‘solutions’ can never occur
so long as modelers hold out the probability, just around the corner, of accurate
projections of future climates and sea levels” (86). There will be no accurate
Along with their scathing critique, the authors do manage a backhanded
compliment to climate change modelers, at least by way of a negative comparison
to their own guild in applied geology. They write:
The publications of this diverse international group (IPCC) are filled
with painfully long discussions about error, uncertainties, and missing
17. USELESS ARITHMETIC
data. The objectivity of these global change modelers stands in stark
contrast to the arrogance of the coastal engineers or the overconfidence
of ground water modelers (79).
It is not that mathematical predictions are always impossible—far from it. At
one point, the authors quote reassuringly the New York Times for June 7, 2004:
In New York City sunrise will be at 5:25 am. Eastern time on Tuesday,
and Venus is to begin leaving the solar disc at 7:06 am, when the sun is
17 degrees above the horizon. The planet’s final contact with the sun’s
edge should occur about 7:26 am when the sun is 20 degrees high. There
will be another transit on June 6, 2012 . . .”
It is comforting that some things can be known with certainty. I can plan on
another transit of Venus in 2012. Predictive success is thought to be the sine qua non
in most science, technology, and engineering fields. Regularity and reproducibility
have traditionally been seen as one of the hallmarks of science. I count on it every
time I log onto this computer, get on an airplane, or take an elevator to the fortieth
floor. In some domains, however, science is going to need to let go of prediction.
Two things have changed:
1. The rise of complexity and
2. The rise of computation.
Environmental and human processes have always been complex. This is not
new. It is just that now we have a lot more insights and background information.
We know a lot more of the details, so we are compelled by the known facts at every
turn to ask more and more complex questions. This is true in many disciplines, but
for the Pilkeys, it is the key to understanding our human power in affecting major
environment changes by our actions. For instance, they launch the first chapter
showing how industrial f ishing wiped out the North Atlantic cod fisheries, in spite
of mathematical models predicting levels for maximum sustainable yields.
The complexity challenge also arises because of the availability of the computer.
Every scientific discipline has been dramatically changed over the last twenty years
by the availability of computers. Scientists can now collect enormous datasets, query
the datasets, and run computer simulations. Without computers, there would be an
epistemic bias toward asking simpler questions and ignoring questions that were
thought to be beyond the capabilities of science.
POLITICS BY OTHER MEANS
Climate change is only one of two dozen different kinds of quantitative
modeling projects that the Pilkeys discuss in their book. Each example demonstrates
failures of quantitative modeling, including:
maximum sustainable yield and the Atlantic cod fishery,•
plans for storing highly radioactive nuclear waste in Yuka Mountain,•
invasive weed species,•
1972 Club of Rome Report, • Limits to Growth,
McNamara’s management of the Vietnam War,•
abandoned pit mines water toxicity,•
forecasting on Wall Street,•
EPA secondhand smoke studies•
Lord Kelvin and the age of the Earth•
soil erosion on sandy coasts•
Brown Tree Snakes on the Island of Guam•
We also get a thorough introduction to Orrin Pilkey’s specialty—developed
shorelines, treated in two chapters and the appendix. These should be required
reading for anyone living in the coastal communities on any of the seven seas.
Already in the second chapter, the Pilkeys begin to develop a typology
for modeling. This comes with a long list of common modeling errors. This
genealogy of models—mathematical, applied, quantitative, qualitative, statistical,
epidemiology, simulations, analytic, numerical, static, dynamic, conceptual—are
all discussed with an eye to how the model employed can distort our understanding
of reality. Other sources of reality distortion result from computer coding, uncertain
debugging, and quality assurance in computer programming, algorithmic biases
based on important assumptions, situational bias, model-tweaking, pessimist and
optimist biases, advocacy and politically correct biases. All of this, compounded
and confounded by increasing complexity, causes us to often ask the wrong
questions. We don’t look back. I will refer to these as tragic errors, distortions
that arise because we are imperfect humans being. We are finite and mortal. We
There are other sets of modeling distortion. Let’s call these complexity
errors, because these errors result from the nature of complexity itself. Our
models necessarily make assumptions about partially known and unknown
relationships, expressed in ordering complexity with different valences,
17. USELESS ARITHMETIC
intensities, and vectors. There are negative and positive feedback, linear and
nonlinear systems, deterministic or probabilistic strategies. So too, I might add
exponentially more datasets, but also exponentially more models. It’s models
all the way down.
Complex models often exhibit sensitivity. This means that when some small
variable is changed, the system changes dramatically. Complex models can
exhibit sensitivity to initial conditions, variations in guiding assumptions, and
minor modifications in ordering the parameters. The Pilkeys remind us that “the
sensitivity of the parameters in the equation is what is being determined, not the
sensitivity of the parameters of nature.”(25) The italics is theirs, so let me restate
There are two problems that need to be solved in every model of complexity.
First, what is the ordering of complexity in the system, the timing, and intensity of
different parameters? And second, how does one best “rerepresent” this ordering
of these parameters and complexities mathematically on a computer? Algorithms
need to be imagined. Relationships defined. Data collected. Data analyzed. Values
assumed. Code written. Models tested. Simulations run. And all of this—the
algorithms, the lines of code, sets of data, computer storage and processing—have
all been growing exponentially over the last three decades. But, and this is what
the Pilkeys are emphasizing, as a simulation leading to predictions, the computer
model is only simulating and testing itself. The computer rerepresentation is not
“run” on the actual complex natural phenomena.
The Pilkeys show that substituting mathematics for nature is itself a source
of errors in modeling nature. What is most illuminating are the varied ways that
models are corrupted and misguided. What is the impact of substituting laboratory
measurements for nature? What happens when we scale up short-term predictions
into long-term predictions? What happens when one chooses and omits different
parameters in a model of nature? What we do not know about initial conditions
in a model of nature? What happens with the intrusion of forces from outside of a
particular model of nature?
The Pilkeys are advocates of qualitative modeling, which at best can be used
only to predict general directions of change and possible magnitudes. Qualitative
modeling will not presume to offer a numerical answer with a range of error. The
approach asks why, how, and what if. Qualitative modeling can also use large
datasets, computer simulations, and lots of arithmetic, but they used to explore
different scenarios, contingencies, and normative relationships. At the end, there is
also humility and uncertainty, multiple scenarios, and no hard-and-fast predictions.
The authors offer the following chart, in other words, a model of modeling.
POLITICS BY OTHER MEANS
Scenario Planning Strategic Planning or
Qualitative input Quantitative input
Exploits uncertainties Minimizes uncertainty
Long-range planning Short-term planning
Multiple answers Single answer
Planning for the future Predicting the future
Hypothetical events Predetermined goals
The bad news about complex predictions is that we don’t know anything and we
can’t know anything—not about future climate change, not about storing radioactive
waste over eons, not about managing declining f isheries or invasive weed species.
Science is butting its head against more and more complexity horizons, my term,
not theirs. Science discovers complexity horizon that it cannot cross, but cannot yet
accept. This is not a problem that can be solved with bigger datasets, more code,
more powerful supercomputers, and less flawed and politicized science.
We cannot look over this horizon of complexity, in part, because we are mortal
humans with normal human problems. We do not have a God’s eye view of the world
and ourselves. This means that science will always be distorted by political and economic
interests, the culture and personalities of the scientists at that time. Even if we could
minimize all of these “externalities,” science is still confronted with the problem of
complexity itself. When the phenomenon is networked, reiterative, nonlinear, creative,
then prediction will not work.
The Pilkeys focus on environmental changes, but I suspect that many scientists
are on similar wild-goose chases when it comes to hope for understanding
and controlling complex genetic systems, developmental biology, cognitive
neurosciences, and a whole slough of other phenomena. Complexity is not just more;
it is something new. There are known limits to computational complexity. There are
known limits to science. And the really creative processes in nature and by humans
in nature tend to be complex distributed systems, not amenable to deterministic
modeling. This is the greatest challenge for science today. It is also a challenge to
any applied bioethics or environmental ethics, because the consequences of actions
cannot be known in advance.
Again science produces lots of useful and reliable predictions. Mathematical
modeling works well enough with simpler systems, like plotting the motion of
the stars and planets in the evening sky or designing a modern bridge with stress
17. USELESS ARITHMETIC
engineering of concrete and steel under variable loads and conditions. Multiply the
variables, however, adds a lot feedback loops and grows the complexity of a system,
and suddenly predictive modeling becomes an exercise in futility. Predictive modeling
cannot yield valid predictions for any complex natural and human-related processes.
This is truly the Earth shattering story, which really should be on the front page of
the New York Times, not to mention Fox News. This story is about the approaching
limits of science, at least a certain kind of science.
After goring so many sacred cows, it is perhaps understandable that the Pilkeys
resist the temptation to move into metaphysics, philosophy, and applied ethics.
These iconoclasts have already gotten themselves into a lot of hot water with their
colleagues. One conclusion to be drawn is that humanity is now thrust willy-nilly
into the role of managing the Earth, not that we really know what we are doing.
The Pilkeys advocate a qualitative modeling approach, which aspires to predict
mere tendencies, directions, and magnitudes of changing systems. After all of the
qualifications and caveats though, I am not sure qualitative modeling has much
more to offer in the way of certainty, comfort, or a clear plan of action. The future
will always be shrouded in a cloud of uncertainty.
And that is the bad news enumerated in Useless Arithmetic. Humans will never
have the complete know-how, even though we certainly have increasing can-do.
Humans have themselves become an important variable in the future evolution of
the planet. This book offers no comfort or consolation. The Pilkeys offer no hard
and fast predictions.
The good news is that we live and think in a networked universe. Our environment
is networked, as are our networked bodies with our networked brains in our networked
culture. Let’s call it a metanexus. You and I are surrounded by, constituted by, and are also
ourselves dynamic components within all kinds of complex distributed systems. These
systems transcend us and form us, even as we also participate in their transformation.
The universe is metanexus all the way down. These complex distributed systems exhibit
creative intelligence, even elegance, though not unfailingly to our benefit. Still some
amazement and gratitude are evoked. This seems like a promising point of departure
for a new theology of nature based on a rather different understanding of nature (and
science). I also find it hopeful that science has known theoretical and practical limits.
Do not get me wrong. Push the mechanistic, reductionist, and predictive envelope as
far as possible. Without the skeptics like the Pilkeys, however, there would be no way
of escaping from “misplaced concreteness.”
Science must now recognize that there are nonreducible emergent, transcendent
systems, which seem to constitute many of the most interesting and creative
phenomena in our contextual universe—ecosystems, genomics, brains, and culture.
No amount of mathematical modeling, computer simulations, reiterative databases,
and paradigm filtering will get us beyond this horizon of complexity. We may hope
that an “Invisible Hand,” reputably at play in free economic markets to the maximum
benefit of all, is also at play in the free evolution of technology, culture, and the planet.
POLITICS BY OTHER MEANS
We won’t know for certain, but the very hope itself now becomes a variable in our
future modeling and doings.
None of this relieves us of the risks and responsibilities of taking action. We
have to make choices. We have to project desirable outcomes. Let us try to model,
design, and build for sustainable and better futures. Expect adaptation. Think
How should governments, business, and citizens respond to the real and/or
perceived threat of global climate change? The Pilkeys don’t really say. Perhaps
the question is as perplexing as asking how one would plan for and respond to a
dramatic nonanthropogenic climate change? Still I wish they had been more explicit
in their recommendations for the stray business leaders, elected leaders, and eclectic
citizens who might pick up this book.
For my part, we need to deemphasize climate change and look at other
variables. There are many compelling arguments for radically reducing fossil-fuel
consumptions. These reasons do not depend on prognostications of climate models.
Reducing fossil-fuel consumption will improve local environmental air and water
quality. It will increase health, safety, and quality of life. It will slow resource
depletion. Reducing fossil-fuel consumption can improve the bottom line for
individuals, corporations, and entire economies. There are also important national
security interests at risk, if we do not dramatically reduce fossil-fuel consumption.
We don’t need a global climate change scare, in order to justify, rationalize, or
motivate, what should already be obvious and sound public and private policy. It is in
the best interest of the United States and the world to dramatically reduce fossil-fuel
consumption, especially through increased efficiency, while also developing
alternative energy sources. I wonder whether the Pilkeys would agree. After reading
their chapter on nuclear waste storage, I doubt they would be enthusiastic about
increasing nuclear power production as one of those alternative strategies. Again,
the authors leave us hanging, perhaps intentionally.
Useless Arithmetic is a book that should be adopted widely in college courses
because professors and students both need to read it. It is directly relevant in
departments of engineering, environmental science, economics, public policy,
medicine, sociology, psychology, history of science, law schools, computer science,
and applied mathematics. I would also add departments of philosophy, religion,
and theology, who have a vested interest in understanding the content, practices,
limits, and interpretations of science.
In the end, the qualitative modeling advocated by the Pilkeys will also fail to
make useful predictions. Perhaps their approach offers more understanding with
less explanation. When they do fail, they will do so humbly and with multiple
scenarios in their back pocket. This may not be very satisfying. Remember that
17. USELESS ARITHMETIC
humans are being asked to make major political and economic decisions in response
to an unknown threat of anthropogenic climate change. And that is just the tip of
the iceberg, so to speak, of the many and varied complex ways that humans and
The Pilkeys call for an adaptive management. To this, we might add adaptive
epistemology. This strategy is the most potentially transformative take-home from
the book, but very few examples are offered. It would be nice if they developed
adaptive management and adaptive epistemology with lots of specific examples.
How do corporations, governments, and people actually implement an adaptive
management strategy? How would scientists practice adaptive epistemology?
Perhaps their next book will offer successful case studies, the lessons learned, the
successes counted, and the adaptations made. We need a lot more successful case
studies in the world today.
Barrow, John D. Impossibility: The Limits of Science and the Science of Limits.
New York: Oxford University Press, 1999.
Harel, David Computers Ltd.: What They Really Can’t Do. New York: Oxford
University Press, 2000.
Kelly, Kevin. Out of Control: The New Biology of Machines, Social Systems, and
the Economic Wo r l d . New York: Addison-Wesley, 1994.
Malthus, Thomas Robert (1766-1834). An Essay on the Principle of Population.
1798, 18032 1826, 1830 ed. London: J. Johnson, 1798.
Pilkey, Orrin H., and Linda Plkey-Jarvis. Useless Arithmetic: Why Environmental
Scientists Can’t Predict the Future. New York: Columbia University Press,
Sober, Elliot, and David Sloan Wilson. Unto Others: The Evolution and Psychology
of Unselfish Behavior. Cambridge: Harvard University Press, 1999.