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Partial connectivity increases cultural accumulation within groups

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  • Institute for Advanced Study in Toulouse

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Complex technologies used in most human societies are beyond the inventive capacities of individuals. Instead, they result from a cumulative process in which innovations are gradually added to existing cultural traits across many generations. Recent work suggests that a population's ability to develop complex technologies is positively affected by its size and connectedness. Here, we present a simple computer-based experiment that compares the accumulation of innovations by fully and partially connected groups of the same size in a complex fitness landscape. We find that the propensity to learn from successful individuals drastically reduces cultural diversity within fully connected groups. In comparison, partially connected groups produce more diverse solutions, and this diversity allows them to develop complex solutions that are never produced in fully connected groups. These results suggest that explanations of ancestral patterns of cultural complexity may need to consider levels of population fragmentation and interaction patterns between partially isolated groups.
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Partial connectivity increases cultural accumulation
within groups
Maxime Derex
a,1
and Robert Boyd
b
a
Institute of Human Origins, Arizona State University, Tempe, AZ 85287; and
b
School of Human Evolution and Social Change, Arizona State University,
Tempe, AZ 85287
Edited by Adam T. Powell, Max Planck Institute for the Science of Human History, Jena, Germany, and accepted by the Editorial Board February 2, 2016
(received for review September 21, 2015)
Complex technologies used in most human societies are beyond
the inventive capacities of individuals. Instead, they result from a
cumulative process in which innovations are gradually added to
existing cultural traits across many generations. Recent work
suggests that a populations ability to develop complex technolo-
gies is positively affected by its size and connectedness. Here, we
present a simple computer-based experiment that compares the ac-
cumulation of innovations by fully and partially connected groups of
thesamesizeinacomplexfitnesslandscape.Wefindthatthepro-
pensity to learn from successful individuals drastically reduces cultural
diversity within fully connected groups. In comparison, partially con-
nected groups produce more diverse solutions, and this diversity al-
lows them to develop complex solutions that are never produced in
fully connected groups. These results suggest that explanations of
ancestral patterns of cultural complexity may need to consider levels
of population fragmentation and interaction patterns between par-
tially isolated groups.
cultural evolution
|
innovation
|
population size
|
social network
|
technological trajectory
P
eople everywhere rely on technology for their survival (1). In
even the simplest foraging societies, essential tools are be-
yond the inventive capacities of individuals; they result from a
cumulative process by which innovations are gradually added to
existing cultural traits across many generations (2). Recent work
suggests that a populations ability to develop complex technol-
ogies is positively affected by its size and connectedness (35).
Large interaction networks allow individuals to learn from many
others, and theory predicts that increased opportunities to learn
socially reduces the rate of cultural loss and increases the rate at
which people improve existing cultural traits. This prediction is
supported by evidence from both field and laboratory studies (5
10). However, some authors have been reluctant to embrace this
idea, pointing out discrepancies between measures of population
size and observed cultural complexity (1114). It seems likely that
factors other than social network size affect the evolution of cul-
tural complexity, and these factors may obscure the effect of the
population size under some conditions.
One possibility is that social network structure matters as well.
Economists have recently investigated how levels of connected-
ness in networks affect a groups ability to solve problems of
varying complexity (15, 16). In agreement with previous work in
cultural evolution, they found that high levels of connectivity help
groups to solve simple tasks. However, they also suggest that well-
connected networks perform poorly at solving complex tasks (15,
16). The complexity of a problem can be represented by mapping all
possible solutions onto a measure of performance, which results in
what has been called a fitness landscape (17). Simple problems are
associated with smooth landscapes, each with a unique optimum
that will be reached by local exploration from any point in the
landscape. By contrast, complex problems are associated with rug-
ged landscapes with multiple peaks of different heights. In rugged
fitness landscapes, a propensity to learn from successful models can
cause the entire population to converge rapidly on a suboptimal
peak (18). Thus, a well-connected group will quickly reach a local
optimum that may not be the highest one.
In the context of cultural evolution, this work suggests that
well-connected populations might not exhibit the most complex
cultural repertoires. Technologies typically arise from a cumu-
lative process that operates through incremental changes within
path-dependent technological trajectories and by combining traits
that have evolved along different trajectories (1921). Screws, for
example, are relatively simple artifacts that can be improved in-
crementally by using better materials, modifying head shape, or using
a different kind of thread. However, one may also combine screws
with unrelated cultural traits to produce radically new technologies.
The bench vise, for instance, results from the combination of a screw
with a lever, and the wheel barrow combines a lever with a wheel
(22). More recently, combinations of levers, pulleys, cranks, ropes,
and toothed gears resulted in the production of early machines that
were used for milling grains, irrigation, construction, and time-
keeping (23). The production of such innovations strongly depends
on cultural diversity because more cultural traits provide more
combinatorial opportunities. Thus, well-connected populations may
be less likely to produce complex technologies because the ability to
learn from the most successful models can reduce fitness landscape
exploration and cultural diversity.
To address this question, we used an experimental task in
which individuals had to discover successive innovations to produce
a virtual remedy and stop the spread of a virus. To make the process
of cultural accumulation realistic, we specified that innovations were
contingent upon earlier discoveries and resulted from incremental
Significance
The remarkable ecological success of the human species has
been attributed to our capacity to overcome environmental
challenges through the development of complex technologies.
Complex technologies are typically beyond the inventive ca-
pacities of individuals and result from a population process by
which innovations are gradually added to existing cultural traits
across many generations. Recent work suggests that a pop-
ulations ability to develop technologies is positively affected by
its size and connectedness. Here, we present an experiment
demonstrating that partially connected groups produce more
diverse and complex cultural traits than fully connected groups.
This result suggests that changes in patterns of interaction be-
tween human groups may have created propitious conditions
for the emergence of complex cultural repertoires in our
evolutionary past.
Author contributions: M.D. designed research; M.D. performed research; M.D. and R.B.
analyzed data; and M.D. and R.B. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission. A.T.P. is a guest editor invited by the Editorial
Board.
1
To whom correspondence should be addressed. Email: maxime.derex@gmail.com.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
1073/pnas.1518798113/-/DCSupplemental.
www.pnas.org/cgi/doi/10.1073/pnas.1518798113 PNAS Early Edition
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EVOLUTIONANTHROPOLOGY
improvement or recombination of different traits (19, 24). Players
were provided with six active ingredients that could be associated
in groups of three to create a remedy. Whereas all 56 possible
triads provided players with a measure of remedy efficiency, two
triads provided players with a new active ingredient (A
1
and B
1
;
Fig. 1). Each of these new ingredients allowed players to create
new, more rewarding triads simulating incremental improvements.
The opportunity for players to discover two different innovations
from the same initial set of ingredients simulated cultural di-
vergence. By allowing players to produce specific triads, early
innovation events opened alternative path-depe ndent trajecto-
ries: Triads using A
1
allowed players to discover the new in-
gredient A
2
, whereas B
1
-based triads allowed players to discover B
2
.
The same principle applied to the discovery of third-level in-
gredients, after which players had to combine innovations from both
A and B path-d epende nt trajectories to progress further in the
landscape. For example, the discovery of one of the two fourth-
level innovations required triads involving both A
3
and B
3
.
Participants were placed within groups of six. Each participant
had 72 trials to refine his/her own remedy and maximize his/her
cumulative score. We compared two different treatments. In the
fully connected condition, players were provided with information
about the remedies produced by the five other group members after
each trial. In the partially connected conditio n, players were
members of a partially connected network of three subgroups of two
players. Players could observe the solutions of the other player within
their subgroup, and, occasionally, different groups were connected
by the movement of individuals between groups.
We predicted that individuals in fully connected groups would
tend to converge on the same solution, making them unable to
generate new traits by combining solutions fro m different
path-dependent trajectories. We expected individuals in partially
connected groups to be more likely to progress along different path-
dependent trajectories, and thus exhibit more diverse cultural rep-
ertoires, including innovations resulting from the combination of
traits arising from different evolutionary pathways.
Results
We found that fully connected groups were able to discover A
3
(50% of groups) and B
3
(33.3%), but none of the fully connected
groups were able to discover both A
3
and B
3
. In comparison,
91.7% of partially isolated groups discovered A
3
, 66.7% discov-
ered B
3
, and 58.3% discovered both (Fig. 2). Additionally, every
partially connected group that reached both third-level innova-
tions combined them together and produced some fourth-level
innovations. In three of the five partially connected groups that
failed to reach fourth-level innovations, the three subgroups hap-
pened to produce innovations along the same pathway, thereby
A
B
Fig. 1. Path-dependent trajectories. The six initial ingredients were randomly divided into two types unknown to players. These ingredients could be as-
sociated using a three-slot apparatus. Within each type, ingredients were randomly assigned one of three possible values. Ingredients positively interacted
with ingredients from the same type and negatively with ingredients from the other type, which created an initial fitness landscape with two possible optima
with the same value. The discovery of one of these optima provided players with a new ingredient (A
1
or B
1
). A
1
and B
1
were assigned the same value
(superior to those values associated with the initial ingredients) and positively or negatively interacted with the same number of randomly chosen ingredients
(the interaction pattern of A
1
was the reverse of the interaction pattern of B
1
). As a result, A
1
and B
1
allowed players to produce specific and more rewarding
triads (A
1
-based and B
1
-based triads). Scores associated with superior-level innovations were attributed according to the same principle, and the most re-
warding A
i
-based and B
i
-based triads provided players with a new ingredient (A
i+1
and B
i+1
, respectively). A
3
and B
3,
however, were assigned specific
properties and strongly and positively interacted with each other. As a result, the most rewarding A
3
-based triad (that provided players with A
4
) required B
3,
and, recip rocally, the most rewarding B
3
-based triad (that provided players with B
4
) required A
3
.
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www.pnas.org/cgi/doi/10.1073/pnas.1518798113 Derex and Boyd
preventing any recombination. The two others did not reach both
third-level innovations by the end of the experiment. Consistent
with predictions from theoretical work (16), partially isolated groups
did not perform as well in the short run as fully connected groups,
but performed better in the long run than fully connected groups
(Fig. 3).
Discussion
Our experimental results show that partially connected groups
produce more diverse solutions and this diversity leads to the
development of complex solutions that are never observed in
fully connected groups. Individuals in fully connected groups
tend to converge to the same solution in early stages of the in-
novation process, and this convergence limits their ability to develop
complex solutions in later stages. Participants had a strong pro-
pensity to learn from successful individuals and adopted new ben-
eficial ingredients in one of the two trials that followed their
discovery in almost 70% of cases. This propensity to learn from
successful individuals promoted the discovery of new solutions that
are built upon older ones (i.e., within the same path-dependent
trajectory). However, as innovations accumulate, well-connected
groups get locked into a particular pathway because the higher
payoffs associated with more refined traits discourage individuals
from exploring alternative, lower payoff solutions. This lock-in
effect is illustrated by the fact that fully connected groups pro-
duced their best solutions after only 35 trials on average and did
not develop alternative solutions. These results are consistent with
the arguments that early innovation events drive the subsequent
direction of change and create powerful exclusion effects. Progress
along one path-dependent trajectory hinders progress along other
trajectories (19, 25, 26).
The same payoff-driven learning strategies were observed within
partially connected groups, but initial isolation enhanced explora-
tion and often caused subgroups to progress along alternative path-
dependent trajectories. Then, contacts between subgroups that had
evolved along different pathways allowed groups to combine dif-
ferent solutions to develop increasingly complex solutions (Fig. 4).
These results suggest that larger and more connected pop-
ulations do not necessarily exhibit higher cultural complexity. Pre-
vious theoretical work suggested that the effects of population size
and connectedness on cultural evolution should be similar because
higher levels of connectedness allow information to flow within
large networks and should prevent cultural loss. Our results suggest
that increased connectedness can limit cultural accumulation when
the landscape is more rugged and improvement relies on recom-
bining elements from different path-dependent trajectories. Under
these c ircumstances, decreased connectedness leads to greater
cultural diversity between groups, which can enhance t he e vo-
lution of technological complexity. It is, however, important to
note that in the present exp eriment, acquisition of cultural
traits was straightforward. In more realistic situations, high
levelsoffragmentationshouldexposesmallandisolatedgroups
to higher rates of cultural loss (37) and reduce the rate at which
innovations appear within groups (as shown by lower rates of
short-run accumulation in partially connected groups; Fig. 4).
Thus, outside the laboratory, there is probably an optimal level of
connectedness that balances cultural loss and cultural diversity.
Our experiment also suggests that changes in patterns of in-
teraction between populations could have been critical in our
evolutionary past. Contacts between previously isolated groups
could have brought different skills and cultural traits together
and may have led to increased cultural complexity. Interestingly,
the Upper Paleolithic period is characterized by a significant
increase in both technological and cultural complexity. Several
lines of evidence suggest that more frequent contacts between
populations may have taken place during this period. First, the
archeological record indicates more regular use of body deco-
rations (e.g., shell beads, teeth, ivory, ostrich egg shells), which
are thought to serve between-group signaling functions (2729).
Second, the expansion of interaction networks is suggested by
the emergence of long-distance flows of tools and raw materials
(29). Our results suggest that the development of these expanded
interaction networks may help explain the rapid increase in
technical complexity observed during this period, including rapid
shifts in core reduction techniques; use of bone, antler, and ivory
in production of tools; and the invention of improved hunting
technologies (29). Because group size and changes in interaction
patterns are both expected to affect cultural evolution, it may be
Fig. 2. Probability of producing third-level innovations. Fully connected
groups were able to discover either A
3
or B
3,
but none of them were able to
discover both. In comparison, partially connected groups were able to pro-
duce both in 58.3% of cases. The higher probability of discovering A
3
over B
3
across treatments may be due to individuals propensity to associate warm-
colored ingredients (Fig. 1).
Fig. 3. Remedy score across time. Fully connected groups outperformed
partially connected groups in the short run before being trapped on local
optima. Contact events that took place from trial 36 allowed partially con-
nected groups to benefit from cultural diversity and escape local optima.
Error bars show 95% confidence intervals.
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EVOLUTIONANTHROPOLOGY
hard to disentangle these effects properly. However, assuming
that long-distance flows of raw materials and personal ornaments
can provide information about degree of population structuring,
it should be possible to investigate the extent to which changes in
the pattern of interaction among members of different pop-
ulations affected cultural complexity in our evolutionary past.
The effect of connectedness may explain discrepancies be-
tween inferred population size and observed cultural complexity.
For example, Powell et al. (5) used molecular data to estimate
when different regions of the world reached the same population
density as Europe at the start of the Upper Paleolithic. They
report that the crossing of the density threshold coincided with
the appearance of markers of modern behavior in some regions,
but not others. Our results suggest that population size alone is
not sufficient to predict cultural complexity. We predict that
populations with more markers of group identity should exhibit
more complex cultural traits than populations of the same size
that have fewer markers of group identity.
Our results seem to be at odds with the results of a previous
experimental study of problem solving, which found that more
connected groups were better able to solve a task associated with
a rugged landscape than less connected ones (30). However, this
study was based on a fitness landscape that was very different from
the fitness landscape assumed in our experiment. In the task used
by Mason and Watts (30), participants searched for the most re-
warding position on a 2D map, and every landscape position was
accessible from every other position in the landscape. Even if
groups got trapped on a suboptimal peak, single nonlocal moves
could reveal m ore rewarding solutions. Although this kind of
landscape is likely to apply to some optimization problems, it
does not ca pture the cumulative nature of the technological
process. Hi storians of technolo gy a nd economists have de-
scribed how innovations are contingent upon earlier discov-
eries (19, 24), which means that innovations create new, more
rewarding and previousl y unreachable pos itions in the land-
scape. Our experiment ca ptures this dynamic b ecause a s soon
as groups start along a path-dependent trajectory, nonl ocal
moves become unprofitable.
Our experiment illustrates how networks of partially isolated
groups connected by occasional migration events can outperform
fully connected networks of the same size. It is, however, worth
noting that other network structures may affect exploration and
cultural diversity in a similar way. Theoretical work by econo-
mists, for example, typically compares networks composed of
permanent links with different levels of clustering rather than oc-
casionally connected groups. These studies indicate that more
clustering leads to more thorough exploration of the design space,
which suggests that high levels of isolation might not be necessary
to allow connected groups to evolve on different pathways. In
theory, fragmentation should only play a role when the rate at
which innovations spread to other groups is lower than the rate at
which groups produce innovations. Weakly connected groups, for
example, may not progress along different trajectories if the rate of
evolution is low because innovations will spread among groups
before alternative solutions are produced. We saw this case in our
experiment. In the partially connected treatment, two groups had
subgroups that initially progressed along different trajectories but
then converged to the same path-dependent trajectory after contact
Fig. 4. Example of partially connected group scores and associated progress along trajectories. Subgroup 1 initially progressed along the B trajectory (red
line), whereas subgroups 2 and 3 progressed along the A trajectory (dark blue line). Subgroup 1 benefited from the visit of a subgroup 3 member (vertical
blue bar) and acquired A
1
before reaching A
3
after visiting subgroup 2 (vertical green hatched bar). Subgroup 2 reached B
3
following the same visit, whereas
the first contact with subgroup 3 (vertical blue hatched bar) did not result in any improvements for either subgroup because they previously produced the
same innovations. Subgroups 1 and 2 independently reached A
4
following these first contacts. Subgroup 3 eventually got out of the local optimum after
second contacts with subgroups 1 and 2. Note that after contacts, subgroups were still progressing along different trajectories as subgroups 1 and 2 exploited
A
5
and subgroup 3 exploited B
4
. Vertical full and hatched bars illustrate incoming and outgoing visits, respectively, by a single individual. Bar color illustrates
subgroups with whom contacts took place. The horizontal black dotted line illustrates the best score that can be reached when exploiting a single evolu-
tionary trajectory.
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events revealed alternative, more rewarding solutions to less effi-
cient subgroups. As a result, these whole groups converged to a single
local optimum and failed to reach both third-level innovations.
Cultural homogenization may also depend on other factors,
such as the structure of cultural learning. For instance, individ-
uals are much more likely to learn skills from kin and members
of their communities than from others. Contemporary data from
Fijian villages show that being from the same village doubles an
individuals likelihood of being selected as a model (31). Such
preferences constrain information flow and, according to recent
experimental results, may facilitate the emergence of different
cultural norms (32). More generally, conformism (the tendency to
acquire the most common behavior exhibited in a group) can also
help maintain significant differences among groups despite factors
such as migration and intermarriage (33). In addition, large and
geographically widespread interconnected populations may experi-
ence and take advantage of diverse local resources, which could
make them more likely to explore different solutions. Heterogeneity
in stone-flaking systems observed in southern Africa during Marine
Isotope stage 5 (13080 ka), for example, is thought to have resulted
from a low rate of information transfer between groups whose
technological systems were strongly locally adapted (27).
Further research will be needed to clarify the extent to which
patterns of between-group interactions or population structure
may have affected cultural complexity in our evolutionary past.
In particular, theoretical work could investigate the effect of
variation in the properties of the fitness landscape; population
structure; and structure of cultural learning, such as payoff-
biased learning, conformism, and accessibility of cultural models.
Recent research indicates that contemporary hunter-gatherer
societies display a unique social structure involving extensive
interactions between people living in different residential groups
(34, 35). This population structure may constrain information
dissemination and promote exploration of the design space.
However, high interaction rates probably prevent bands within
an ethnolin guistic group from progressing along ra dically dif-
ferent technological trajectories. Instead, the effect we report
is more likely to be a result of contacts between different eth-
nolinguistic groups. Archeologists are just starting to infer an-
cestral population structure from the archeological record (27,
28, 36). Our results suggest that it is worth pursuing this effort.
Taking into account levels of population fragmentation and
between-group contact may shed new light on ancestral pat-
terns of cultural accumulation.
Methods
Participants. A total of 144 University of Montpellier students (72 women and
72 men) were randomly selected from a database managed by the Laboratory
of Experimental Economics of Montpellier (LEEM) and recruited by email
from various universities in Montpellier, France. Informed consent was
obtained from all subjects before starting the experiment (ethical approval
was given by the Arizona State University Institutional Review Board, code:
STUDY00002815). The subjects rang ed in age from 18 to 44 y (mean of 24 y, SD
of 4.32 y). Participants received V5 for participating and an additional
amount ranging from V5toV30 depending on their own performance.
Procedure. The experiment took place in a computer room at the LEEM at the
University of Montpellier. For each session, a maximum of 18 participants
(exclusively male or female) were recru ited and randomly assigned to one
condition of the experiment. Participants sat at physically separated and
networked computers, and were randomly assigned to a group. Players did
not know who belonged to their group and were instructed that commu-
nication and note taking were not allowed. Before starting the experiment,
participants were requested to enter their age and sex, and could read in-
structions on their screens. At the end of the game, each subject received a
reward according to his/her performance (V15 on average).
Game Principle. The participants played a computer game (programmed in
Object Pascal with Delphi 6) in which they were asked to develop a remedy to
fight a virtual virus. Players were initially provided with six basic active
ingredients that could be used without any limit and could be associated in
groups of three to create a remedy. All triads were allowed, including those
trials involving the repeated use of the same ingredient. The order of the
ingredients had no effect on the result, so that 56 unique triads could be
produced from the six initial ingredients. Whereas all triads provided players
with a score (as a measure of remedy efficiency), two of them allowed players to
benefit from new active ingredients. New ingredients arose when players
produced a triad that belonged to a list of predetermined successful triads.
When discovered, new ingredients could, in turn, be associated with other
ingredients. Triads using new ingredients allowed players to produce more
rewarding triads and created opportunities to find other new ingredients. A
total of 16 new ingredients could be produced. Players were given 25 s to
generate a triad and were asked to maximize their cumulative score across 72
trials. No information associated with triads (score or resulting ingredient) was
displayed before the end of 25 s.
Fitness Landscape. The fitness landscape associated with our task was designed to
allow players to progress along two symmetrical path-dependent trajectories. To
do so, we randomly divided the initial set of ingredients into two types unknown
to players. Within each of the types, ingredients were randomly assigned one of
three possible values (6, 8, or 10). Triad scores based on the initial set of ingredients
were calculated as follows:
Score =
ð1 + 0.5αÞ. ðS
1
+ S
2
+ S
3
Þ
β
, [1]
with α taking the value 0, 1, or 2 depen ding on whether triads involved one,
two, or three different ingredients and β taking the value 1 or 2 depending
on whether triads involved ingredients from one or two types. S
1
, S
2
, and S
3
are the scores of ingredients 1, 2, and 3, respectively. As a result, two dif-
ferent triads based on initial ingredients provided players with the highest
payoff (Fig. 1). The discovery of these triads provided players with a new
ingredient that allowed them to produce new and more rewarding triads
(Fig. S1).
New ingredients were given a score that was equal to the score of the best
initial ingredients (10) plus 5 × i, with i equal to the innovation rank (A
1
= 15,
A
2
= 20, etc.). Then, ingredients were randomly divided into two types, so
that ingredients previously positively interacting with each other did not
necessarily do so when they were associated with a new ingredient. Triad
scores were then calculated according to Eq. 1, except that 90% of the score
of the best possible triad from the lower level was added to the result. For
example, B
2
-based triad scores were calculated according to Eq. 1, and 90%
of the best B
1
-based triad was then added to the score. This bonus ensured
that scores of lower level innovation-based triads were only slightly over-
lapping with triads involving higher innovations (Fig. S1). Each time a player
generated the most rewarding triad, given his/her set of ingredients, he/she
was provided with a new ingredient.
To simulate innovations that result from the combinations of traits arising
from different evolutionary trajectories, we assigned two pairs of ingredients
specific interacting properties (A
3
/B
3
and A
6
/B
6
) that led to higher payoffs. The
score of triads containing one of these pairs was calculated according to Eq. 1,
and 150% of the score of the best possible triad from the lower level was
added to the result. As a result, fourth- and seventh-level innovations could
only be produced by combining A
3
with B
3
and A
6
with B
6
, respectively. Again,
this bonus ensured that this form of innovation (combining traits coming from
different pathways) provided players with scores that did not overlap with
triads involving single trajectory-based triads (Fig. S1).
Treatments. We compared two different treatments that involved same-sized
groups of players but provided them with different social learning oppor-
tunities. In the fully connected condition, players were part of a group of six
and were provided with information from their other group members after
each trial (the same five sources of social information). In the partially
connected condition, players were part of a network of three subgroups of
two players, and were subsequently connected by the movement of indi-
viduals between groups. During the first 36 trials, players could only observe
the other member of their own subgroup. At the end of trial 36, one player
was randomly removed from his/her own subgroup and joined another
subgroup for three trials, after which he/she returned to his/her initial sub-
group for three trials. Then, another contact event took place. Each individual
was moved from his/her initial subgroup to another subgroup once. In total,
each subgroup experienced four contact events, two incoming and two
outgoing, with two different subgroups (Fig. 4). All treatments involved 72
participants in single-sex populations (12 replicates each).
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EVOLUTIONANTHROPOLOGY
Social Information. After each trial, players were provided with information
about triads that they produced and triads produced by other individuals
with whom they were connected [ingredients that had been used, triad score,
and resulting new ingredient (if any)]. This information was displayed for 5 s,
after which players were provided with an opportunity to produce a new
triad. Although the social information panel was removed from the screen,
players could obtain access to this information through the use of a record
panel. The record panel provided players with their last triad score, the in-
gredients involved, and a record of their new ingredients (if any). Players
could click onto new ingredients to get a reminder about how to produce
them. By clicking onto an anonymized name (e.g., player 3) and associated
last triad score, players could switch between their own record and the re-
cord of players they were connected with. The record panel for other players
displayed the players last triad score, ingredients involved, and a record of
their new ingredients (if any). Players could learn how to produce these
ingredients by clicking onto them.
Tutorial and Pregame Information. Before starting, the players had to com-
plete a tutorial during which basic actions, such as dragging and dropping
ingredients, had to be completed. The tutorial also guided players actions
until they could access (nonrelevant) social information to make sure that all
players mastered the game interface before starting the experiment. Players
were informed that the ultimate aim of the game was to maximize their
cumulative score and that new ingredients were generally more efficient
than initial ones. Players were also informed that their monetary reward
depended on their cumulative score. The fitness function that determined
the value of a triad was unknown to players.
ACKNOWLEDGMENTS. We thank a ll the people who helped test the game,
Dimitri Dubo is for his h elp during experimental sessions, and the Labora-
tory of Experimental Economic s of Montpellier (LEEM, University of
Montpellier) for hostin g the experime nt. We also thank Kim Hill, Cu rtis
Marean, Sarah Mathew, Charles Perreault, Joan Silk, Deborah Strumsky,
Jose Lobo, and people from the Evolutionary Foundations of Human
Uniqueness projec t for helpful discussions. Last, we thank three anonymous
reviewers for their constructive comments that helped to impro ve this
paper. This research was made possible through the support of Grant
48952 from the John Templeton Foundation to the Institute of Human
Origins at Arizona State University. The opinio ns expressed in this publica-
tion are those of the authors and do not necessarily reflect the views of the
John Templeton Foundation.
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www.pnas.org/cgi/doi/10.1073/pnas.1518798113 Derex and Boyd
... Unlike herds or swarms, human social networks solve different tasks with different communication network topologies (i.e. who communicates with whom) (Momennejad, 2022). Human studies show that while some network topologies (e.g., with high connectivity) are better suited for convergence among networks (Coman et al., 2016;Momennejad et al., 2019), others (e.g., with dynamic and partial connectivity) are best suited for innovation (Derex and Boyd, 2016). However, machine learning approaches to multi-agent learning rarely consider communication topologies that are not fully connected (Mnih et al., 2016;Horgan et al., 2018;Espeholt et al., 2018;Nair et al., 2015;Christianos et al., [n.d.]; Schmitt et al., 2019). ...
... The solitary genius narrative can be misleading: innovation is often a result of recombining and modifying solutions provided by a continuous collective exploration of vast hierarchical search spaces (Solé et al., 2013), e.g., Gutenberg repurposed the screw press to invent the print press. This process is called cumulative cultural evolution (Derex and Boyd, 2016;Mesoudi and Thornton, [n.d.]), in which previous innovations act as a curriculum for future ones. Notably, finding optimal solutions to a task by cumulative culture is highly dependent on the topology of communication networks that share knowledge, with some topologies leading to sub-optimal solutions. ...
... In fact laboratory and field studies of human groups have shown that collective innovation is highly contingent on the social network structure, which dictates who shares experiences with whom (Momennejad, 2022;Migliano and Vinicius, 2022;Derex and Boyd, 2016). This body of human research points to a trade-off: full connectivity (e.g., when everyone talks to everyone) increases the spread and conformity of shared knowledge within the group, but simultaneously reduces the diversity of solutions (Momennejad et al., 2019). ...
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The human cultural repertoire relies on innovation: our ability to continuously and hierarchically explore how existing elements can be combined to create new ones. Innovation is not solitary, it relies on collective accumulation and merging of previous solutions. Machine learning approaches commonly assume that fully connected multi-agent networks are best suited for innovation. However, human laboratory and field studies have shown that hierarchical innovation is more robustly achieved by dynamic communication topologies. In dynamic topologies, humans oscillate between innovating individually or in small clusters, and then sharing outcomes with others. To our knowledge, the role of multi-agent topology on innovation has not been systematically studied in machine learning. It remains unclear a) which communication topologies are optimal for which innovation tasks, and b) which properties of experience sharing improve multi-level innovation. Here we use a multi-level hierarchical problem setting (WordCraft), with three different innovation tasks. We systematically design networks of DQNs sharing experiences from their replay buffers in varying topologies (fully connected, small world, dynamic, ring). Comparing the level of innovation achieved by different experience-sharing topologies across different tasks shows that, first, consistent with human findings, experience sharing within a dynamic topology achieves the highest level of innovation across tasks. Second, experience sharing is not as helpful when there is a single clear path to innovation. Third, two metrics we propose, conformity and diversity of shared experience, can explain the success of different topologies on different tasks. These contributions can advance our understanding of optimal AI-AI, human-human, and human-AI collaborative networks, inspiring future tools for fostering collective innovation in large organizations.
... 4 See also models by Lazer & Friedman, 2007, Derex et al., 2018 developed in organizational sciences and the model by Angere & Olsson, 2017 in social epistemology of science, which gave similar results as Zollman's model while being structurally different. Empirical studies in the form of computer-based experiments by Mason et al., 2008, Derex & Boyd, 2016 also came to similar conclusions, while an experiment run by Mason & Watts, 2012 indicated that a higher degree of connectivity is rewarding once the exploration/exploitation trade-off is relaxed. Just before the publication of this article, Douven & Hegselmann, 2022 published a study of network effects in a bounded confidence model, which did not replicate the Zollman effect. ...
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The paper examines recent developments in agent‐based modeling of scientific inquiry with a special focus on network epistemology. It provides a survey of different types of ABMs studying network effects in scientific inquiry: ABMs based on bandit problems, ABMs based on epistemic landscapes and ABMs based on argumentative dynamics. It further presents models that study the impact of biased and deceptive researchers on the success of collective inquiry. The paper concludes with a discussion on the contribution of ABMs to the broader field of philosophy of science given their highly idealized nature.
... Individuals or organisations connecting different social groups are believed to have higher potential of innovation because they can leverage knowledge from different groups. Empirical studies have provided evidence on the correlation between social network structure and creativity [21,25,28,29]. Correlation studies are however unable to explain the mechanisms connecting the network structure to the potential of innovation creation, and the interplay of social network and innovation dynamics at various structural scales. ...
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Innovation is fundamental for development and provides a competitive advantage for societies. It is the process of creating more complex technologies, ideas, or protocols from existing ones. While innovation may be created by single agents (i.e. individuals or organisations), it is often a result of social interactions between agents exchanging and combining complementary expertise and perspectives. The structure of social networks impacts this knowledge exchange process. To study the role of social network structures on the creation of new technologies, we design an evolutionary mechanistic model combining self-creation and social learning. We find that social heterogeneity allows agents to leverage the benefits of diversity and to develop technologies of higher complexity. Social heterogeneity, however, reduces the group ability to innovate. Not only the social structure but also the openness of agents to collaborate affect innovation. We find that interdisciplinary interactions lead to more complex technologies benefiting the entire group but also increase the inequality in the innovation output. Lower openness to interdisciplinary collaborations may be compensated by a higher ability to collaborate with multiple peers, but low openness also neutralises the intrinsic benefits of network heterogeneity. Our findings indicate that social network heterogeneity has contrasting effects on microscopic (local) and macroscopic (group) levels, suggesting that the emergence of innovation leaders may suppress the overall group performance.
... The ubiquity of this exploration-exploitation tradeoff has prompted a wide range of research from animal foraging to computer science [1][2][3] . Previous research has also shown that decision makers interacting with others can increase their decision performances collectively, through decentralized information pooling affected by various social learning strategies [4][5][6][7][8][9] . ...
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Social learning is beneficial for efficient information search in unfamiliar environments (“within-task” learning). In the real world, however, possible search spaces are often so large that decision makers are incapable of covering all options, even if they pool their information collectively. One strategy to handle such overload is developing generalizable knowledge that extends to multiple related environments (“across-task” learning). However, it is unknown whether and how social information may facilitate such across-task learning. Here, we investigated participants’ social learning processes across multiple laboratory foraging sessions in spatially correlated reward landscapes that were generated according to a common rule. The results showed that paired participants were able to improve efficiency in information search across sessions more than solo participants. Computational analysis of participants’ choice-behaviors revealed that such improvement across sessions was related to better understanding of the common generative rule. Rule understanding was correlated within a pair, suggesting that social interaction is a key to the improvement of across-task learning.
... In particular, scientific communities are homophilic, especially when it comes to co-authorship patterns (Ferber & Teiman, 1980;McDowell & Smith, 1992;Boschini & Sjögren, 2007;del Carmen & Bing, 2000;West, Jacquet, King, Correll, & Bergstrom, 2013;Wang, Lee, West, Bergstrom, & Erosheva, 2019) and citation patterns (Wardle, 1995;Paris, De Leo, Menozzi, & Gatto, 1998;Ghiasi, Mongeon, Sugimoto, & Larivière, 2018). Previous works provide reasons to think that homophily might be beneficial to scientific inquiry: When dealing with difficult or complex tasks, limiting information flow can prevent scientists from erroneously converging on a belief that a less effective treatment is best (Zollman, 2007(Zollman, , 2010 or it can spur a greater number of independent innovations upon which to ultimately build (Derex & Boyd, 2016). And, homophily has been shown to slow the spread of ideas (Golub & Jackson, 2012b), thus hindering information flow in ways that can be beneficial to inquiry. ...
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Diversity of social identities can improve the performance of groups through varied cognitive and communicative pathways. Recently, research efforts have focused on identifying when we should expect to see these potential benefits in real-world settings. While most research to date has studied this topic at individual and interpersonal levels, in this paper, we develop an agent-based model to explore how various aspects of homophily, the tendency of individuals to associate with similar others, affects performance at a larger scale. Study 1 examines how two types of homophily---identity-driven and opinion-driven---impact collective performance on a sequential decision-making task via modulating network formation and trust relations. Study 2 considers how the presence of identity-based conformity pressure can affect the findings from the first study. Overall, we find that the effect of homophily on performance is complex, depending on the operative dimensions of similarity, mediating pathways, and the specific outcome of interest. Finally, we discuss the implications of our results for policy interventions aiming to improve group performance.
... See alsoFazelpour & Steel (in press). 4 Other papers have found similar results using NK-landscape models(March, 1991;Lazer & Friedman, 2007;Fang et al., 2010), and have confirmed these empirically(Mason et al., 2008;Derex & Boyd, 2016). 5 SeeWu & O'Connor (2022) for an overview of network models considering how mechanisms that slow learning, and thus promote transient diversity of practice, improve group outcomes. ...
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... This ecological perspective can also be readily applied to fields of innovation as well, and ideas have indeed been translated from genetic to cultural evolution, showing how local diversity benefits the entire collective (Creanza, Kolodny, and Feldman 2017). Recent experimental studies of cultural evolution have shown that collectives that feature a diverse set of separate traditions within innovate more, compared to a more uniform community (Barkoczi and Galesic 2016;Derex and Boyd 2016). ...
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