What Can Social Complexity Teach Us about ?

Alex Schaefer1, Dries Daems2, Ignacio Garnham3, and Kazuya Horibe4 1University of Arizona 2University of Leuven 3Parsons School of Design 4Osaka University

November 1, 2019

1 Introduction

The relevance of genetically inherited traits for explaining human culture per- sists as a controversial topic among evolutionary theorists. One group, roughly defined, has organized around what might be called. . .

The Sociobiological Position: Culture is a product of, or is strongly constrained by, biological evolution. Purely biological mod- els of evolution therefore provide the greatest insight into social evo- lution.

Although this view attracts many ([1], [38], [18], [64], [80]), quite a few scientists gravitate towards an alternative view ([28], [29], [27], [58], [80], [36]). . .

The Autonomous Culture Position: Biology imposes, at most, weak constraints on the development of culture. Explicit cultural models therefore provide essential insight into the evolution of soci- ety.

In reality, very few adhere to either position strictly. Instead, theorists fall along a spectrum, approaching one position or the other to different degrees. Those falling closest to the sociobiological position include E.O. Wilson (in both his classic work [79] and his more recent Genesis [80]) as well as those thinkers associated with “high church ” ([4], [51]). Theorists falling closest to the center constitute the so-called “California School,” which tends to emphasize a bidirectional process of gene-culture and

1 supposes the existence of a large, substantive set of cognitive instincts, such as learning biases. These instincts are inherited genetically, but drive a process of cumulative cultural evolution ([7], [33], [69]). Finally, the theorists that most closely approximate the autonomous culture position include (i) cultural psychologists, who assert that (nearly) all mental and behavioral characteris- tics depend upon cultural input, (ii) the so-called “Paris School,” which denies that genetic evolution illuminates the process of cultural development [68], and (iii) the “Campbellian” theorists who model and analyze culture itself as an autonomous evolutionary process, involving variation, selection, and heredity along non-genetic lines([10], [52], [46], [47], [36]). Although the truth likely lies somewhere in between the two extreme positions, a lively debate continues as to where exactly the correct theory lies along this spectrum. Interestingly, both of the extreme positions can make a compelling case for their respective views. The Sociobiological Position could point out that cul- tural traits undermining biological fitness are unstable; if the cultural capacities of individuals undermine their ability to spread their genetic material, would quickly remove such capacities. The Autonomous Culture Po- sition, on the other hand, can retort that group-level selection may overpower individual-level selection [65]. Moreover, in cases where cultural traits do not significantly impact genetic fitness, sociobiological approaches are at a loss to explain or predict social formations. Given that both sides present plausible a priori arguments for their preferred approaches, adjudicating between them requires empirical investigation. In the present paper, we draw upon a recently compiled data set that measures and compares the social complexity of various societies. Each of the positions out- lined above offers predictions as to the pace and structure of cultural evolution. If social complexity is a product of evolution, then the social complexity data set may contain information capable of falsifying one or both of the main positions. Although this paper does not undertake the task of rigorous hypothesis testing, it does draw on the social complexity data set, as well as evolutionary theory, to propose various hypotheses worth examining in a further stage. The evolution of social complexity is commonly considered to pertain to the rise and development of social/administrative/religious/military/political hier- archies and inequality associated with societies consisting of large, dense pop- ulations, formal information systems, economic development, socio-economic specialization and urbanism ([21], [43], [75]). It is often used to describe a tra- jectory of social evolution where societies are ranked in a number of discrete categories depending on a certain degree of complexity ([62]). This categoriza- tion of complexity implied an inescapable, teleological evolution from egalitar- ian and small-scale societies, such as ‘Bands’ or ‘Tribes’ to increasingly socially stratified and large-scale societies such as ‘Chiefdoms’ and ‘States’. Evolution of social complexity in this sense is said to occur through adap- tation to external circumstances and stimuli within a competitive struggle for survival in an environment of scarce resources ([70]). However, such reductionist evolutionary and teleological perspectives have been severely criticized ([53]) and are no longer accepted in modern scholarly discourse. Instead, a rich and varied

2 literature has emerged on the evolution of complex societies and the drivers of cultural change. A small selection of commonly mentioned factors include: population growth, environmental risk management, warfare and competition, agricultural production, information management, infrastructure management, economic specialization, and long-distance trade ([59]. In practice, most au- thors these days argue for a set of relevant factors in the evolution of social complexity. Approaches such as Cultural Multilevel Selection (CMLS) for ex- ample, stress the integration of various drivers on multiple scales across polities, such as cooperation among lower-level units such as villages, and competition between higher-level units such as states ([56]). The following analysis assumes that social complexity arises from evolution- ary processes. Several studies on the nature of complexity justify this assump- tion. Evolution provides the best comprehensive theory for explaining the emer- gence of complexity without intentional planning or engineering ([3], [14]). This holds for a wide range of definitions and measures of complexity ([71]); it also holds for nearly any complex phenomenon one considers, including biological and social systems ([44]). With this assumption, the complexity data set provides a basis for adjudicat- ing between the Sociobiological Position and the Autonomous Culture Position. Although a definite conclusion would require a more in-depth analysis than the one offered here, we believe that the social complexity data set lends more sup- port to the Autonomous Culture Position for three main reasons: (1) the pace of increased social complexity outstrips the feasible pace of biological evolution, (2) the structure of changes in social complexity is often oblique or horizontal, rather than vertical, as it would be if it followed a particular germline, and (3) the transfer of complexity, conceived of as a phenotypic trait, violates basic Mendelian principles of biological transmission. In defending this thesis, we begin by reviewing important background litara- ture (section 2). In section 3, we present and describe the data set on which we base our subsequent arguments. As mentioned above, the Sociobiological Pos- tition yields several predictions that likely contradict evidence provided by the complexity data set. We present these predictions and show how they might run contrary to the evidence on social complexity in section 4. Finally, we conclude in section 5 by considering objections to our analysis and pointing to future work that remains to be done.

2 Literature review

The field of cultural evolutionary theory began as an exercise of using new- Darwinian principles and mathematical approaches used in the study of genetics to draw explicit parallels between cultural patterns and genetic processes [11],[6]. Although these early models proved to be useful in the study of culture [24], [2], it is essential to highlight that the models from which cultural evolution emerged in the ’70s are not exact representations of reality [9]. Over time, the mathematical techniques applied to examine culture through

3 an evolutionary lens have been adapted to acknowledge the processes in which culture and genes interact with one another [11], [6], [22], [12], [55], [42], [61] and to account for the differences between genetic and cultural transmission in the dynamics that emerge from the entanglement of gene, culture, and environment [41], [49]. A relevant example of how transmission dynamics differ between genes and culture is that cultural transmission cannot be expected to follow all three Mendelian laws [48] because cultural traits - beliefs, norms, ideas, behaviors, attitudes, and artifacts - can be copied, learned and transmitted between indi- viduals and can change over time (1). More-so, cultural traits can be transmit- ted vertically just as regularly as through oblique and horizontal exchanges [11], [6]; therefore, in the Autonomous Culture position, the prevalence of a cultural trait in the population is not directly proportional to the probability that an individual’s parents had that trait. Furthermore, oblique transmission allows for some traits to spread and be adopted despite potentially reducing the fitness of those who adopt them [50], [54]. If cultural traits can function outside of Mendelian laws of inheritance, a case for the predominance of Autonomous Culture over Sociobiological driven complex societies needs to critically revise traditional Darwinian models of evo- lution as applied to social culture. In the context of cultural evolution in complex societies, the degree to which humans place attention in certain cultural traits affects the degree to which these traits circulate the population; cultural traits, like genes, are sensitive to the environment and spread according to its pressures. But unlike germlines, traits can behave stochastically as replicators or passively fall into oblivion [8] because cultural innovation does not depend only on mutations and its subject to multiple selective pressures at different scales [11],[26],[39]. Changes in selective pressures allow for variations in frequency and trans- mission patterns to change drastically over short periods [57], [31] because novel cultural traits can be the result of recombination and aggregation of know traits [20], and can be transmitted either via trial-and-error learning or individual in- teractions with the environment. In addition, patterns of cultural diffusion can be informed by frequency- dependent biases because social complexity enables individuals to choose their cultural models [23], giving room for cultural transmission biases to interfere in the frequency of spread and adoption of a trait in a population. Some examples of such behaviors are conformity bias [19], [30] novelty bias [35] and prestige bias [34]. There is no consensus on how determining is the environmental context, and the population size that operates within, for the adoption and transmission of cultural traits [32], [13], but models predict that environments with higher varying characteristics will favor cultural transmission. In contrast, only highly stable environments, which decrease as social complexity arises, would offer a favorable scenario for genetic constraints to undermine culture in explaining behavior [39], [35]. Lastly, the dynamics of niche construction that are driven by gradually

4 Figure 1: Spatial distribution of polities selected in Seshat dataset (http:// seshatdatabank.info/methods/world-sample-30/) changing cultural practices play a prominent role in [40], [15] as the rates to which contextual pressures interact with individual pressures can accelerate major cultural shifts [37], [50], [25].

3 Measuring Social Complexity

The data set that will be used to test the two positions outlined earlier is compiled by the Seshat Project (http://seshatdatabank.info/). This project was initiated by a team of social scientists and humanities scholars with the goal of systematically compiling an encompassing data set to test theories about the evolution of complex societies ([74]. The ultimate goal of the Seshat project is to document all human societies through time and space. For practical reasons, Seshat currently focuses on the period between the Neolithic and Industrial Revolutions. The first data sampling was initiated for 414 societies from 30 geographical areas divided over 10 macro-regions across the globe (Figure 1). For each macro-region, three polities were selected, one where social complexity emerged early, one late, and in-between. For each polity, a team of researchers systematically coded quantitative and qualitative information on a large set of variables (for example polity population) or multiple proxy variables capturing aspects of an aggregate characteristic (for example well-being) to reduce biases and maximize comparative potential of the code book. The data set incorporates 51 variables reflecting nine complexity characteris- tics (Figure 2): polity population, hierarchy, government, money, infrastructure, information system, texts, capital population, and polity territory ([75]). A first analysis showed that most of these characteristics are strongly correlated, mean- ing that variation in social organization across space and time could be captured by a single measure of social complexity reflected by the first principal compo-

5 Figure 2: Nine complexity characteristics aggregating 51 variables from the Seshat dataset ([75]) nent of a PCA analysis. This component reflects "a composite measure of the various roles, institutions, and technologies that enable the coordination of large numbers of people to act in a politically unified manner" [75]). When plotting the values of this first principal component over time, it indicates a dramatic increase in scale and complexity of societies over the last 10,000 years (Figure 3). However, using the values of the first principal component of a PCA as a composite measure of complexity and the plotting of these PC1 values as a quantitative comparison of complexity trajectories has been criticized ([72]). The close correlations between the complexity characteristics do suggest that these are relevant variables to study social complexity, in the sense that changes in one are highly likely to result in developments in the others. However, the plotted trends do not necessarily have the statistical significance that appears at face value. Indeed, what is measured is rather that large societies tend to require increasingly sophisticated structures of government and infrastructure compared to small ones. The works of the Seshat Project has sparked a lively debate in the fields of archaeology, history and cultural evolution about the advantages and perils

6 Figure 3: Evolutionary trajectories of first PC for each macro-region ([75])

7 of "big data" quantitative analysis with historical data. Publications by the Seshat Project ([75], [73], [78]) have been met with a series of critical responses ([5],[72], [63]), in turn sparking a round of counterarguments by Seshat ([60], [17],[76], [77]). Whatever one’s stance, the value of this debate cannot be denied and indeed must be encouraged if this type of research is to make an essential step forward. The value of quantitative analysis lies exactly in making claims and statements comparable and open for critique by uncovering any underlying black boxes.The historical are still coming to grips with this relatively new form of argument, but the early signs appear promising for the future of the debate. Still, clearly several issues exist that need to be dealt with for the debate to move forward. One of the main issues is the treatment of missing data in the data set, which the Seshat Project has dealt with by either imputing val- ues through stochastic linear regression ([75]) or by interpreting it as a negative value ([78]). However,both approaches result in some problematic assumptions (as discussed by [72] and [5]). Most prominently, the logical fallacy "absence of evidence is not evidence of absence" comes into play here. Dealing with missing data is of course a wider issue related to the inherent patchiness of archaeologi- cal and historical data due to uneven biases of preservation and research focus. Its ramifications, however, are most plainly visible when conducting large-scale quantitative analysis such as attempted by the Seshat project and its implica- tions for their results must be carefully considered. It is against the background of this wider debate that we will position our- selves in the current paper. Let us start by putting the critical assessments of the graphs in figure 3 discussed earlier between brackets for the time being. We will discuss them in more detail again later on. When looking at these trajec- tories, some interesting features can be observed. Not only do we see generally upward trajectories towards increasing complexity, we also observe intermit- tent decreases as well as long periods of stasis or gradual change interspersed with sudden, large upsweeps. The former clearly indicates that, indeed, social complexity is not necessarily a teleological progression towards ever-increasing complexity, but that, at certain times, complex societies met with obstacles se- vere enough to induce a rapid and widespread loss of complexity. The latter is reminiscent of the so-called "punctual" model of social evolution, where the evolution of complex polities required rapid changes in socio-political organiza- tion to allow for their whole-ended transformation, but which also resulted in subsequent stable states ([66]). Both observations raise questions regarding the underlying mechanisms be- hind the development of social complexity. Evolutionary biology suggests two general mechanisms can be observed: passive and driven processes ([16], [45], [67]). The Seshat group claims that their analysis suggest a driven process, with group competition particularly highlighted as an important driving force in the emergence of large, complex societies [75]). However, they immediately add that more in-depth analysis is needed to properly support this claim. In the following discussion, we will trace the conceptual outlines of such an in-depth follow-up discussion. We will focus particularly on three elements of discussion: (1) the rate of change in complexity trajectories; (2) transmission patterns; (3)

8 variance.

4 Comparing Hypotheses 4.1 The Pace of Increasing Complexity If cultural evolution is brought about only by biological evolution, it logically follows that its rate of evolution may not exceed that of biological evolution. To confirm this, we first describe the common rate indicators of biological and cultural evolution. We begin with an overview of the rate of biological evolu- tion, which has been measuring evolution for many years, and then introduce indicators of the rate of evolution that can be compared with the rate of cultural evolution.

4.1.1 An overview of the rate of biological evolution The rate at which a particular molecule evolves is called the rate of molecular evolution, and is usually defined as the number of amino acid or base substi- tutions that occur in a given time per locus. This amount is usually derived from actual data: the sequence of a gene or protein is compared between two different organisms, and the differences in bases or amino acids between the sequences are counted. The number of substitutions is estimated by correcting the number. The observed number of base or amino acid substitutions divided by the total number of loci in the compared sequence, that is, the number of substitutions per locus, K, is obtained. If the two compared organisms diverged from a common ancestor T years ago, then the rate of molecular evolution, k , was defined as k = K/2T. (1) The reason for dividing by two here is that the number of substitutions, K , is the sum of the substitutions that have occurred in the two lineages between the two organisms that have branched off from a common ancestor. T is usually given from fossil data. When comparing the rates of evolution of two molecules, it is possible to estimate the magnitude of the rate of evolution by comparing the number of substitutions, K , per locus of the two molecules, because the two molecules share a common time of divergence, T , if they can be compared between the same species. In this case, K can be regarded as the relative rate of evolution. From the definition of evolutionary rate described above, an increase in the number of functionally important loci that do not allow amino acids or bases to change reduces the number of loci that can change relative to one another, thus reducing K and slowing the rate of evolution. An amino acid of the position which is important for the function of the molecule tends to be kept unchanged in the long evolution process. In addition to the active site of the enzyme, some critical sites cannot be replaced by other amino acids to maintain the structure of the protein. Other sites of contact with

9 other molecules are often immutable. The number of invariant sites that are functionally and structurally important varies from protein to protein. Because mutations in these functionally and structurally important sites often impair protein function, they are detrimental to the survival of individuals carrying the mutation. Mutations unfavorable to an individual’s survival are removed from the population by natural selection, so that amino acids at functionally and structurally important sites remain unchanged over the course of long evolution. This was called functional constraint. The rate of evolution of molecules such as DNA, RNA, and proteins depends directly on the strength of functional constraints. According to the neutral theory of molecular evolution, the rate of molecular evolution, k , is determined by the neutral mutation rate. That is, k = fµ where µ is the total mutation rate and f is the rate of neutral mutations. Mutations can be broadly divided into favorable, unfavorable, and neutral mutations, but because the number of favorable mutations is so small that ignoring them, 1 − f is the proportion of unfavorable mutations that is proportional to the magnitude of the functional constraint. Thus, as the functional constraint increases, f decreases and the rate of evolution k decreases. The rate of molecular evolution can be understood in terms of the number of functionally important loci, the degree of functional constraint. At the extreme, if almost all regions of a protein are functionally important, there are few loci at which amino acids can change, and evolution proceeds slowly. Conversely, pro- teins with few functionally important sites can change amino acids at any locus, increasing the rate of evolution. The nature of each molecule determines the rate of evolution. Such constraints determined by the properties of individual molecules are referred to herein as local constraints.

4.1.2 How to calculate the rate of cultural evolution? On the other hand, when considering cultural evolution, archaeological data corresponding to phenotypes in the evolution of organisms, such as the length and diameter of tools, are used, so it is not appropriate to use the index of the rate of evolution of genotypes introduced earlier. The measure of evolutionary speed with respect to phenotypes of organisms has been mainly used in fossilism, where it is difficult to extract genotypes. One of the representative indicators is darwins (d) defined by J.B.S Haldane.

d = (lnx2 − lnx1)/∆t (2) where x 1 and x 2 are the mean trait value at time 1 and time 2, respectively, and D t is the time interval between x 1 and x 2 , measured in millions of years. Using this indicator, we compared the evolution data of the morphology of organisms with the evolution data of the morphology of tools and found that the speed of tool evolution was faster than that of the morphology of organisms. This strongly suggests that cultural evolution is independent of biological evolution.

10 4.2 The Structure of Complexity Transmission Perhaps the most obvious predictive disparity between the sociobiological posi- tion and the autonomous culture position concerns the pattern of transmission. If the sociobiological position is correct, then cultural transmission should ex- hibit similar inheritance patterns as other biological traits. In particular, social complexity should flow vertically, from one generation to the next. The use of complicated processes or tools, for example, should pass between generations via the germline. The Seshat data set, as well as other seminal studies of cultural complexity, supports a different picture ([11]). Although cultural features can be passed down vertically, they are just as often passed horizontally between peers and between societies, absent any sharing of gametes. Horizontal transmission of cultural traits would suggest that, pace the Sociobiological position, culture evolves more or less autonomously. Moreover, if the items that spread from peer to peer, or society to society, are basic ways of thinking, rather than particular pieces of knowledge, this would further support the autonomous culture position. The Seshat data set sheds some light on this issue by measuring the complexity of “texts” as well as information systems. If the abilities to compose philosophy or to develop spread horizontally, then biological evolution is of little relevance to understanding short or medium-term changes in culture. Moreover, some studies show that cultural adaptations exhibit alarming fragility. Random shocks can cause populations to lose cultural adaptations, such as the ability to craft certain tools or even processes as basic as starting fires [33, 218-222]. By focusing on episodes of complexity retrogression, the data set could reveal further ways in which the development of culture operates autonomously from that of genetic changes. When information is genetically en- coded, it is more robust to external shocks. The Seshat data set could thus be used to check how obliquely social complexity spreads and how fragile it might be to external shocks. Based on intuitions and past studies, the sociobiological position would not fare well if put to such tests.

4.3 Mendelian Predictions In addition to the vertical structure of transmission, the sociobiological thesis predicts that transmission will occur in accordance with certain Mendelian prin- ciples. Mendel showed that “biological inheritance involves the all-or-nothing transmission of discrete units (genes), with evolutionary change occurring when one of these discrete units mutates into a different version.”1 If the substrates of culture — the information stored in individual minds in the form of values, skills, knowledge, beliefs, etc. — transmit themselves biologically, then the pat- tern of transmission should accord with the nature of biological transmission. In particular, it should accord with Mendel’s principles of inheritance: dominance, segregation, and independent assortment.

1[46, p.48].

11 Dominance implies that the presence of certain alleles, the dominant ones, will overpower recessive alleles that regulate the same trait. The recessive al- leles will determine the phenotypic expression only when dominant alleles are absent. This principle yields testable implications for social complexity as an evolved process. To understand these implications, consider a simplified model in which the level of social complexity is the expression of a single gene, high complexity is dominant, and simplicity is recessive. First, certain simplicity substrates should be overpowered by complexity ones. For example, we should observe that the blending of populations from complex societies and simple so- cieties to yield a societies that are sometimes complex and sometimes simple. Second, the blending of populations from two simple societies should yield a society of roughly equal simplicity compared to the parent societies. Thirdly, the blending of populations from two complex societies should sometimes yield a simple society. A deeper analysis of the data set may allow us to falsify one or more of these predictions. Of course, social complexity is itself a complex phenomenon made up of many cultural traits. It is therefore likely unrealistic to treat it as an inherited trait, regulated by a single pair of alleles. However, the same falsifiable pre- diction would apply to more narrow and specific cultural traits associated with complexity. For example, complexity as measured by texts. Or perhaps, even more narrowly, complexity as measured by the use of calendars. Whichever trait one identifies as the genetic product, the same principle applies: if the So- ciobiological thesis is correct, then we should be able to identify some cultural features that are regulated by certain genes, and they should abide by Mendel’s principle of dominance. Since the data set divides complexity into various, sim- pler measures, an analysis could test to see whether any of these features adhere to Mendel’s principles of inheritance. Mendel’s second two principles of inheritance, segregation and indepenent assortment, together imply that the alleles regulating some aspect of social complexity, e.g. the ability to construct and utilize calendars, separate during meiosis. If the development of culture were a biological process, then we would expect that the information underlying specific cultural phenomena to divide and recombine when new generations emerge or when populations blend. This process manifests itself on the phenotypic level. Not only should populations that combine sometimes absorb the each others’ complexity adaptations, they should also occasionally lose those adaptations as well. This would occur if a cer- tain trait requires two alleles of a given type, but upon separation, assortment, and recombination with other gametes, one of the necessary alleles is missing. For example, if using calendars were a genetically regulated trait requiring two alleles for its facilitation, then we should predict certain outcomes of population mixing. Sometimes, two calendar-using populations should combine to produce a population with individuals who are incapable of using calendars. Such a scenario, of course, seems absurd. Surely, it is the more basic skills of information processing, memory, and learning that are required for complex cultural adaptations like calendar use. This is correct, but again, the question is how thick or how thin this set of genetic adaptations is. The Seshat data set

12 suggests that cultural complexity can exhibit extreme variation with little or no genetic changes. Basic Mendelian principles are unlikely to be discerned in the patterns of cultural change revealed by the data. Checking for those patterns would provide better support for our intuitions that genes have little to do with cultural change. If cultural complexity fluctuates as a response to changes in the thought patterns and information processing of individuals, then the data set may support the more extreme theorists who assert that even basic cognitive functions arise as products of cumulative cultural evolution [36].

5 Conclusion

The wide scope of the Seshat Project has resulted in a data set that contains a wealth of information on the evolution of cultural complexity through time and space. In particular, it illuminates the pace and structure of the evolution of an important cultural characteristic: social complexity. Those studying cul- tural evolution might therefore gain valuable insight by examining this data. We have proposed three potential tests that would allow scientists to adjudicate between competing visions of cultural evolution. The Sociobiological Position predicts complexity to increase at a far slower rate than the Autonomous Cul- ture Position; it predicts a more vertical transmission of complexity than the Autonomous Culture Position; and it predicts that changes in complexity should abide by the Mendelian principles of segregation, independent assortment, and dominance. Although a closer examination is warranted, a preliminary look at the data suggests that the Autonomous Culture Position provides a better fit. Those who propound a biological theory of cultural evolution will likely object that our analysis has misconstrued their position. Only an extreme, and perhaps confused, fringe believes that cultural traits are passed directly through the germline. Culture is built atop a host of biological traits that allow humans to accumulate cultural knowledge and dispositions. The Seshat data set does not threaten to disprove this more sophisticated view of Sociobiology. It is wrong, however, to claim that we have presented a strawman of current Sociobiological views. First, some of today’s most respected theorists adhere to views that suggest that most, if not all, of the development of culture is due to biological features. , for example, does not even accept what he calls the "Veneer Theory," i.e. that culture represents a thin, but autonomous, realm of cultural evolution. We cannot support such a theory, he claims, because our emotions, which drive our behavior and interactions, result from biological evolution [81]. The Seshat data set suggests an alternative view: culture is not explicable in terms of biological evolution, nor is it merely a thin veneer on top of a substantive set of biological traits. It is, rather, a realm of its own, connected to, but autonomous from, a thin set of biological enabling conditions. Second, the Seshat data set provides information that may adjudicate be- tween theories that fall along the spectrum between the Sociobiological Position and the Autonomous Culture Position. It may guide us in determining just how

13 substantive or thin the set of biological enabling conditions must be to explain cultural evolution. For example, the California School suggests that cultural evolution depends on a large set of biological features, cognitive instincts, such as learning biases and special, innate modes of information processing. If this is the case, the Seshat data set should reveal patterns of social complexity that cor- respond to the appearance of these biological adaptations. We might compare populations with and without these adaptations to see if one becomes complex while the other does not, or we might compare two populations under similar conditions, each of which possesses the crucial biological features, and compare the growth in social complexity between these two groups. If cultural evolu- tion really does depend upon a rich set of biological features, then data on the development of complexity should bear this out. A new data set, such as that provided by Seshat, will often contain new clues. It will sometimes spur new hypotheses, but even more often it will aid scientists in adjudicating between old views. We have argued that the Seshat data set has great potential for adjudicating between extant views on the process of cultural evolution. For instance, it can help us to determine how active a role biology plays in the process. Although it may not support one view in particular, it can certainly narrow the range of plausibility within which our theories of cultural evolution must reside.

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