Anthropologist and Professor of Psychology and Economics. Canada Research Chair in Culture, Cognition and Coevolution. University of British Columbia.
Apprehending the place of “group selection” in evolutionary thinking requires understanding the use of formal mathematical models. Many scientific disciplines, ranging from engineering to ecology, develop mathematical models to study, analyze and understand complex dynamic processes. This is an important component of research in most scientific disciplines because—unaided and untutored—our minds are ill-equipped to think clearly about such processes. For many purposes, these models act as mental prostheses that make us rigorously formulate our ideas, help us understand key features of even simple systems, and develop predictions and insights, often including non-intuitive predictions one would not have made before studying the model. Building a useful model hinges on the specific modeling approach, assumptions and choices made during construction.
What Pinker wants banished from our science is a modeling tool that has proved useful for breaking down and analyzing different components of a selective process. Natural selection is, at the same time, blindingly simple and incredibly subtle. Studying genetic evolutionary processes formally often involves specifying and integrating several different contributors to understand the total effect of natural selection, not to mention a whole host of other evolutionary forces such as drift and mutation. When the situation under investigation involves something like group extinctions, due to warfare or environmental shocks, or biased migration due to economic success, multi-level selection accounting can help isolate and analyze the impacts of different components of selection.
As with other kinds of dynamic processes, models of genetic evolution can be built many different ways. The issue of “group selection” revolves around the choice of an accounting system: how does one wish to track fitness or changes in gene frequencies. In many cases (though not all), the exact same process can be represented and developed using quite different evolutionary accounting systems [1-3]. These accounting systems include (1) individual fitness, (2) inclusive fitness, and (3) multi-level or “group” selection.
This last accounting system involves first tallying up all the effects of natural selection within groups on genes (“within group selection”), and then putting that together with the relative contributions of each group to the overall gene pool (“between-group selection”). In such an accounting system, we sometimes find that the average effect of everything going on within groups is opposed by the differences in the relative contributions of groups with different compositions. Or, in a situation that Pinker skips, the net effect of what’s going on within groups is zero (a “stable equilibria”), and all the action comes from the relative contribution of each group to the evolutionary change. In verbal descriptions, when the between-group component of natural selection influences the evolutionary process, this is “group selection”. This is what “group selection” has meant since 1972 .
It’s certainly true that often one can figure out how to use any of these three accounting systems to solve a simple problem, and they give the same answer (about equilibrium states). However, it is NOT true that all three are equally easy to apply to any given problem. It is also not true that all methods generate the same kinds of insights or understandings about the evolutionary dynamics or equilibrium states. Which accounting system is best entirely depends on the problem and the assumptions one is willing to make in obtaining an answer.
A useful analogy might be the problem that an aerospace engineer faces when trying to model the trajectory of a satellite. A critical first step in solving such a problem is to select a coordinate system and a place to anchor that coordinate system in space (the origin). Among others, one can pick a spherical coordinate system (two angles and a distance) and anchor it to, say, the center of the earth; or, one can pick a Cartesian coordinate system (x, y, z orthogonal dimensions) and anchor it to a passing meteor. It is completely possible to calculate the orbit of a satellite with any number of different coordinate systems including these two, but picking the first system will allow you to easily solve the problem (analytically, using some solid assumptions) while building your intuitions about the movements of earth’s satellites. The second approach will be really hard, and provide you with no new intuitions. So, these are “equivalent” in some sense, but they are not equally useful for any particular problem. And, so it is with evolutionary accounting systems.
Pinker writes, “There’s no need to complicate the theory of natural selection with a new ‘level of selection’…” This makes little sense. Why would we remove an analytical tool from our toolbox? Following from his argument, Pinker presumably doesn’t think we should study and label “sexual selection,” since it’s really just another component of natural selection. Why break natural selection down into the sexual and non-sexual components? The answer is, of course, because breaking down more complicated processes into components has proved analytically useful, and driven much empirical work. Below, I will show that explicitly considering and modeling inter-group processes has done the same.
Rejecting group selection models is like banning spherical coordinates because you prefer to do your verbal reasoning in Cartesian coordinates.
Now, you’ll notice I’ve not mentioned “replicators” or “vehicles.” There are several reasons for this, but I’ll mention just one here that proceeds from the above argument. Theorists working at the forefront of improving evolutionary theory neither routinely use, nor need, these metaphors. In fact, many genetic modeling approaches do not even assume discrete traits (replicators), such as adaptive dynamics or quantitative genetics. Quantitative genetic models, which track continuous phenotypes (not genes), are some of the most predictive models we have, and consequently are used in practical applications (e.g., animal breeding). Since it’s phenotypes, and not genes, that are the true target of selection, then for some purposes it makes good sense to track phenotype. Just like group selection models, these approaches break the total effect of natural selection down into parts linked to different variance components. Steve would presumably argue that—in the interest of theoretical purity—we should toss “quantitative genetic” models onto the junk heap with “group selection” models because such models unnecessarily complicate matters by ignoring the digital nature of DNA code.
This brings us to cultural evolution, which Pinker dismisses as a bad metaphor. Let’s first consider what cultural evolutionists actually do, keeping in mind my points about the purposes of mathematical modeling. From its beginning, cultural evolutionists have sought to formalize human learning processes, and then ask what happens in the long-run if we have a population of human learners who are interacting with each other. The psychological ingredients of these learning models come from (1) theoretical genetic evolutionary models focused on the kinds of psychological processes that will be favored by natural selection under different conditions, and (2) empirical evidence on how people actually learn. By combining psychology with social interaction, these models are designed to improve our understanding of otherwise complex historical processes.
To be clear, these models do NOT require any assumptions about replicators, discrete traits, longevity, fidelity or fecundity. In fact, going back to Boyd and Richerson’s (1985) Culture and the Evolutionary Process, we find that 19 of the 38 different models presented involve continuous traits with arbitrary amounts of error—nothing ever replicates exactly in these models . Much work since then has underlined this point [6, 7]. Now, for a gene-like system, fidelity, fecundity and longevity are required. But, it turns out that there are other ways to turn the trick of creating a stable inheritance system without those characteristics. Perhaps Pinker disagrees with this work, but he does not appear to be aware of it.
To underline this point, consider Pinker’s suggestion that if somehow the variation on which a selective process acts is non-random, it is not an “evolutionary” process. It turns out that some of the earliest cultural evolutionary models included precisely this, individual (insight-driven) learning with an arbitrary amount of random noise along with social learning [5, 8-12]. Rather than just asserting it, these models allow theorists to study how varying amounts of random variation vs. individual insights influence the cultural evolutionary process. Furthermore, such models allow us to consider what happens when our intuitions or judgments from experience tend to be wrong, and when this can be overcome by our instinct to just copy more successful people [6, 13]. Supporting this, empirical work suggests that humans possess complex cultural repertoires that exist because of our tendency to learn from more successful or prestigious people, and despite our individual learning abilities, not because of them [13, 14].
So, in what way is cultural evolution a metaphorical extension of genetic evolution? It’s not. It’s standard-issue science that involves the construction of a class of simple models as a means to glean insights into complex processes. This is why so many mathematical evolutionary biologists are now building cultural evolutionary and culture-gene coevolutionary models [15-20].
Above I pointed out that the proper use of mathematical models, whether they are aimed at genetic or cultural evolutionary processes, is often to make verbal theorizing more rigorous, generate novel insights, formulate non-intuitive predictions, and drive empirical research. On these counts, formal models of cultural evolution, and gene-culture coevolution more broadly, have been highly successful [21-23]. These models are driving thriving research programs in primatology [24, 25], evolutionary biology [19, 26], animal social learning , genetics [27, 28], anthropology [29-31], archeology [32, 33], paleoanthropology , religion [35, 36] and psychology [37, 38].
Within a large class of models designed to illuminate various aspects of cultural evolution ranging from technological change to ethnicity [10, 39], a small subset have examined the impact of inter-group competition, as one element in the evolutionary process. Here are three examples that illustrate how the explicit consideration of inter-group competition and interaction has already driven important empirical work.
Example 1: Sam Bowles has used multi-level selection (MLS) to examine how social norms that level fitness differences within groups can create the conditions for the spread of altruism via inter-group competition . To be clear, the model tracks an allele frequency, but the MLS formulation naturally exposes the inter-group dynamics in a convenient and intuitive manner. Bowles’s formulation tells you exactly what kind of empirical data you need to test the model: (1) what the minimum ratio of genetic variances needs to be (between vs. within groups), (2) how much fitness differences within groups have to be leveled by social norms, and (3) how much lethal inter-group violence has to (or had to) occur. This model directed Bowles to the key pieces of data he needed to obtain and analyze. Of course, Bowles might be wrong about the importance of inter-group competition, but now we know just how to show it.
Pinker might point out that it should be possible to entirely reformulate Bowles’s model using another fitness accounting system. Of course, it may be possible! It’s also possible to calculate the position of that earth orbiting satellite using the coordinate system attached to the passing meteor. Good luck.
Example 2: For decades anthropologists have been trying to explain cooperation among foragers. They find evidence for both kinship and reciprocity-based mechanisms, but there still seems to be a fair amount of unexplained cooperation. The problem with much of this work is that it was based on single-group theorizing. When one studies a single population model, one is only studying within-group processes. Working among Hadza hunter-gatherers in Tanzania, Coren Apicella and her collaborators mapped the social networks and willingness to cooperate of people from 17 foraging bands/camps . Unlike their predecessors, these researchers had studied the cultural evolutionary models of cooperation, and understood the potential importance of population structure and networks. Consequently, they calculated the within-band and between-band variation in cooperativeness. According to the models of group-selection from the 1970s, on which Pinker is leaning for his dismissal of group selection, this team found something that should not exist. As Figure 1 shows, the variation between-camps was much larger than the variation within camps. That can’t be right? According to those old models, the between-group variation is supposed to be MUCH smaller than the within-group variation. Not larger. This suggests there’s something wrong with those old models when applied to humans (well, at least to foragers like the Hadza). This also means that the summary dismissal of the importance of between-group processes (cultural and genetic) in the 1970’s caused most evolutionary researchers to focus tightly on only what happens within one-band—because they believed, the between group differences couldn’t matter .
Example 3: Peter Turchin is combining cultural evolutionary theory and tools from ecology to better model historical processes. His formal models, which include and consider inter-group competition, provide general theories that (1) can be applied to different times and places in history, (2) tell him precisely what kind of data will test his theories, and (3) make predictions, about—for example, when and where empires will emerge [43-46]. Empirical efforts to test these predictions have already borne fruit. These efforts are going far beyond what standard history supplies. Moreover, this body of theory is currently driving the assembly of a vast historical database, which will allow even greater quantitative testing of cultural evolutionary theories. Thus, Pinker’s claim that carefully specifying and analyzing the causal processes underlying historical change (which include inter-group competition) and testing these ideas across times and places, “nothing to conventional history,” is like saying genetic evolutionary theory adds nothing to butterfly collecting.
The Mismatch Hypothesis
Pinker argues in favor of an explanation for large-scale human cooperation known as the “Big Mistake”  or “Mismatch” Hypothesis. This hypothesis has been one of a set of hypotheses about human sociality that have been pursued intensely for many years. While a consistent favorite among those who eschew any role for a dynamic interrelationship between genes and culture, the Mismatch Hypothesis has not fared well in empirical tests against alternative hypotheses [47-54]. Moreover, it emerges from dubious theoretical foundations [52, 55, 56]. The problems with this idea are too numerous to present here, so I will summarize three categories of problems.
The first problem is that the Mismatch Hypothesis does not meet any of the five major challenges of human cooperation [56, 57]. It does not explain why (1) the scale and intensity of human cooperation and sociality have expanded in the last 10,000 years, (2) humans are different from other primates, who also live in kin-groups of foragers with lots of repeated interaction, (3) cooperation and sociality vary so dramatically across modern human societies, right down to the behavior in controlled psychological tasks, (4) cooperation and sociality vary across domains within a society, independent of their costs and benefits, and (5) the same incentive mechanisms related to reputation and punishment also operate on non-cooperative behavior, such as ritual performance or food taboos (this is impossible in reciprocity models).
Second, the Mismatch Hypothesis was tested by performing Ultimatum Games across a diverse set of human societies, as well as with American participants. Unlike the Westerners so commonly used in behavioral experiments, many of the participants from these societies really do live in small, face-to-face groups with limited anonymity. If the prosociality in both fairness and willingness to punish in such games was the result of a “misfiring” reciprocity psychology, as Pinker proposes, then we’d expect either (1) no variation among societies, or (2) variation such that those populations who actually experienced a non-anonymous, face-to-face, life would be more prosocial and more willing to build their reputations by punishing low offers. Instead, what this collaboration of evolutionary psychologists, economists, and evolutionary anthropologists actually found contradicted this prediction. People from societies with larger populations, more market integration and more anonymous roles were more prosocial and ready to punish unfairness, not less. In fact, people from the smallest-scale societies sometimes showed no willingness to punish. These results have been replicated and extended, both with a new sample and to include two additional experiments [48, 49, 58, 59].
This large body of ethnographic and experimental work stands in stark contrast to the empirical work Pinker offers to support his view, being limited to American undergraduates who are known not only to be psychologically unusual in many important dimensions, but also to be particularly unusual in behavioral games, in stark contrast to the several foraging groups we tested [48, 49, 59]. Theories of human nature cannot seriously be tested using only WEIRD people .
Finally, reciprocity theory does not say what Pinker thinks it says. For example, the finding that successful strategies are generally “nice,” meaning they always cooperate on round 1, was shown to be an artifact of the standard formulation of reciprocity models . If, instead, individuals can only maintain a limited number of relationships, the result disappears.
Pinker is also under the mistaken impression that “reputation” is a solution to cooperative dilemmas and an alternative to cultural group selection. Models that examine how reputational systems might solve the problem of large-scale cooperation show that, in fact reputation, can sustain a wide range of behaviors, which may or may not be cooperative [56, 61]. This fits with the fact that human societies vary immensely in what goes into a “good reputation.” Honorable or required behaviors in one place can be horrible in another. Moreover, reputations specify how much and when one should cooperate, but this varies from place to place. For example, tipping at least 10% at a restaurant is nearly obligatory in the U.S., while it would be considered strange in Australia and Japan (no tipping). The ability of reputation to sustain almost anything creates what economists have long recognized as an “equilibrium selection problem.” Thus, even if we agree that much human cooperation is in response to reputational concerns, this merely opens up the question of why people, and populations, vary so much regarding what goes into a reputation and how it is weighted. Cultural evolutionary hypotheses, which include inter-group competition, provide theories regarding how reputational systems might evolve (culturally) in ways that create group benefits . Cultural group selection and reputation systems are in fact complementary in addressing the problem of cooperation, not alternatives.
The False Allure of One-Group Thinking
Pinker may be right that for non-specialists an evolutionary approach that includes “group selection” may have an allure that arises from its emotional impact. However, there is also a false allure and intuitive appeal that comes from the “individual as strategist in a single group” heuristic used by many evolutionary psychologists (made explicit by Daly and Wilson ). Thinking only about one group means you are considering only one component of natural selection, the within group component. To see how this thinking can steer one wrong, consider this situation: an organism faces a single binary choice once in her lifetime, she can either (a) consume half of a valuable resource herself (eating her fill) and allow a randomly selected member of her social group (size N) to consume the other half, or (b) consume half the resource and leave the rest to rot. In a world with only one social group, natural selection will favor only picking choice b (half the resource gets wasted every time). This occurs because an animal that picks choice a will, on-average, benefit all others in his group, leaving herself at a relative fitness disadvantage to those who pick b (and only benefit themselves). This favored strategy is pretty selfish, since even the classical selfish rational actor would be merely indifferent between a and b, perhaps picking a or b at random (this is because she gets the same payoff herself either way). So, a group of selfish agents will be better off than a group of agents built by within-group natural selection. Now, if you create a world with multiple social groups, natural selection will often favor some picking of choice a, and everyone will do better. This can be true even when all the groups are randomly remixed every generation such that genealogical relatedness is irrelevant.
The consequence of this overemphasis on single-groups is that zero-sum thinking has come to pervade much of the verbal reasoning in evolutionary psychology. You can see this in the way Pinker prefers hypotheses based on manipulation, even suggesting that kinship systems and forms of social organization are primarily products of individuals’ efforts at a grand deception or conspiracy. Single-group thinking also causes researchers to theorize in terms of strategically navigating a particular reputational system, rather than stepping back and asking why different groups have different reputational systems with different aggregate consequences. Deception and manipulation are obviously part of the human condition, but there is much more to the story, including inter-group competition and much cultural evolution. I recommend that Pinker and those he cites try modeling their ideas. I predict that they will learn that deception and manipulation, as evolutionary strategies, have serious limitations (they are frequency dependent), especially in a world with multiple social groups, since they will cause natural selection to favor avoidance, group dissolution, and social disengagement.
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