Scientific theories such as cultural group selection (CGS) must be assessed in two ways. First, the basic underlying assumptions on which an idea rests should be shown to be coherent and realistic. Second, the theory should generate hypotheses with testable predictions about phenomena in the real world that we should observe if the theory is correct. In the target article, Richerson et al. do an excellent job of demonstrating that, indeed, the assumptions of CGS theory are often met, and it is therefore a useful framework for generating plausible hypotheses. In particular, the properties of cultural inheritance, processes such as conformity and frequency dependence, and the ubiquity and importance of institutions enable the maintenance of variation between groups upon which selection can act even in the face of physical migration between groups (a large hurdle facing genetic group selection models). The importance of this should not be underestimated, given the somewhat controversial and divisive history of this subject.
However, we feel there is more that can be said about how we can test the predictions of CGS hypotheses as explanations of human cooperation, specifically with respect to testing them against competing explanations. CGS is an overarching framework that can generate more specific hypotheses that can be tested against alternatives. Let's consider the following: over the last 10,000 years, the scale of human cooperation has increased by several orders of magnitude: from small-scale groups of some hundreds of foragers to large modern states with populations of hundreds of millions. Social scientists have advanced a multitude of theories explaining this “major evolutionary transition” (sensu Maynard Smith & Szathmáry Reference Maynard Smith and Szathmáry1995). Such theories tend to come in several flavors (Carballo et al. Reference Carballo, Roscoe and Feinman2014). “Functionalist” (or “voluntaristic”) explanations emphasize benefits of cooperation to all: buffering environmental risk, managing competition and efficient allocation of resources, producing public goods such as an irrigation system, and capturing returns to scale in, for example, economic production (Johnson & Earle Reference Johnson and Earle2000). In contrast, “conflict” explanations focus on the dark side of large-scale sociality: class struggle and exploitation, warfare and conquest (e.g., Carneiro Reference Carneiro1970). CGS theory can combine these functionalist and conflict elements, but in a highly specific way: Cooperation within societies evolves as a result of conflict and competition between societies.
It is possible (indeed likely) that the best explanatory model will combine more than one mechanism, with different factors, perhaps, interacting in nonlinear, synergistic ways. Evaluation of such complex quantitative explanations is not a problem for modern methods of analysis, especially when combined with a program of building mathematical models that explicitly incorporate such interactions. In our own research we have made a number of steps in this direction. In a recent paper (Turchin et al. Reference Turchin, Currie, Turner and Gavrilets2013) we examined whether increased competition between groups due to the development of horse-based forms of warfare (i.e., involving chariots, cavalry, etc.) was an important force in the historical emergence of very large-scale human societies (“empires”). Following the logic of CGS (or multi-level selection more generally), we constructed an agent-based computer simulation in which “cooperative” cultural traits were only selected for due to the beneficial effects they had in competition between groups (without between-group competition, there was a heavy bias against developing such traits). We were able to test the predictions of this model against historical data about the spatial distribution of empires over a 3,000-year period. Encouragingly, the predictions of the model showed a good match to the real data. Furthermore, turning off some of the important parameters in the models produced a large drop-off in the match between simulations and data. This indicates that our hypothesis is at least a plausible explanation for the evolution of socio-political complexity. This model is admittedly a gross simplification of the actual historical process, and these results are still somewhat preliminary; however, this work does demonstrate the ability to quantitatively test the predictions of hypotheses informed by CGS, using the empirical record of past human societies.
The next step is to test this hypothesis more explicitly against other alternative explanations, including those not motivated by CGS. An important point here is that different theories make very different predictions as to where, when, and under what circumstances we should see the rise of large-scale societies in the archaeological and historical record, and such things as the order in which different aspects of societies emerge. So far the progress in testing such theories has been slow. Yet the huge corpus of historical and archaeological information provides us with a remarkable empirical resource for testing theories and rejecting empirically inadequate explanations. The key is transforming the wealth of information into a systematic form that facilitates the kinds of analyses we described above. Currently, we are collaborating with colleagues from across multiple disciplines and around the world to develop a databank of coded and quantitative historical and archaeological information about past societies (Seshat: Global History Databank: http://seshatdatabank.info/), with which hypotheses about cultural evolution and human history can be tested, including those informed by CGS theory (Turchin et al. Reference Turchin, Brennan, Currie, Feeney, Francois, Hoyer, Manning, Marciniak, Mullins, Palmisano, Peregrine, Turner and Whitehouse2015). For example, in one project we are assessing the idea that competition between groups led to increased egalitarianism in human groups, particularly beginning with developments of several “axial-age” religions (Bellah Reference Bellah2011). Importantly, this idea will be rigorously tested against other competing explanations, for example, the idea that religion is the “opiate of the masses,” by which elites keep the majority of the population subservient. Rather than the common approach of the social sciences or humanities where the world is interpreted according to a particular theoretical perspective, we follow the approach in which it is ultimately the data that decide which hypothesis provides the best explanation.
In summary, we contend that CGS has been shown to be a very productive generator of testable hypotheses. This theoretical framework is capable of producing novel, even unexpected predictions that can be then contrasted with predictions made by alternative theories using historical and archaeological data. Whether hypotheses derived from CGS theory are “true” or not, this framework has already demonstrated its value as a productive research program in the sense of the philosopher of science Imre Lakatos (Reference Lakatos1978).
Scientific theories such as cultural group selection (CGS) must be assessed in two ways. First, the basic underlying assumptions on which an idea rests should be shown to be coherent and realistic. Second, the theory should generate hypotheses with testable predictions about phenomena in the real world that we should observe if the theory is correct. In the target article, Richerson et al. do an excellent job of demonstrating that, indeed, the assumptions of CGS theory are often met, and it is therefore a useful framework for generating plausible hypotheses. In particular, the properties of cultural inheritance, processes such as conformity and frequency dependence, and the ubiquity and importance of institutions enable the maintenance of variation between groups upon which selection can act even in the face of physical migration between groups (a large hurdle facing genetic group selection models). The importance of this should not be underestimated, given the somewhat controversial and divisive history of this subject.
However, we feel there is more that can be said about how we can test the predictions of CGS hypotheses as explanations of human cooperation, specifically with respect to testing them against competing explanations. CGS is an overarching framework that can generate more specific hypotheses that can be tested against alternatives. Let's consider the following: over the last 10,000 years, the scale of human cooperation has increased by several orders of magnitude: from small-scale groups of some hundreds of foragers to large modern states with populations of hundreds of millions. Social scientists have advanced a multitude of theories explaining this “major evolutionary transition” (sensu Maynard Smith & Szathmáry Reference Maynard Smith and Szathmáry1995). Such theories tend to come in several flavors (Carballo et al. Reference Carballo, Roscoe and Feinman2014). “Functionalist” (or “voluntaristic”) explanations emphasize benefits of cooperation to all: buffering environmental risk, managing competition and efficient allocation of resources, producing public goods such as an irrigation system, and capturing returns to scale in, for example, economic production (Johnson & Earle Reference Johnson and Earle2000). In contrast, “conflict” explanations focus on the dark side of large-scale sociality: class struggle and exploitation, warfare and conquest (e.g., Carneiro Reference Carneiro1970). CGS theory can combine these functionalist and conflict elements, but in a highly specific way: Cooperation within societies evolves as a result of conflict and competition between societies.
It is possible (indeed likely) that the best explanatory model will combine more than one mechanism, with different factors, perhaps, interacting in nonlinear, synergistic ways. Evaluation of such complex quantitative explanations is not a problem for modern methods of analysis, especially when combined with a program of building mathematical models that explicitly incorporate such interactions. In our own research we have made a number of steps in this direction. In a recent paper (Turchin et al. Reference Turchin, Currie, Turner and Gavrilets2013) we examined whether increased competition between groups due to the development of horse-based forms of warfare (i.e., involving chariots, cavalry, etc.) was an important force in the historical emergence of very large-scale human societies (“empires”). Following the logic of CGS (or multi-level selection more generally), we constructed an agent-based computer simulation in which “cooperative” cultural traits were only selected for due to the beneficial effects they had in competition between groups (without between-group competition, there was a heavy bias against developing such traits). We were able to test the predictions of this model against historical data about the spatial distribution of empires over a 3,000-year period. Encouragingly, the predictions of the model showed a good match to the real data. Furthermore, turning off some of the important parameters in the models produced a large drop-off in the match between simulations and data. This indicates that our hypothesis is at least a plausible explanation for the evolution of socio-political complexity. This model is admittedly a gross simplification of the actual historical process, and these results are still somewhat preliminary; however, this work does demonstrate the ability to quantitatively test the predictions of hypotheses informed by CGS, using the empirical record of past human societies.
The next step is to test this hypothesis more explicitly against other alternative explanations, including those not motivated by CGS. An important point here is that different theories make very different predictions as to where, when, and under what circumstances we should see the rise of large-scale societies in the archaeological and historical record, and such things as the order in which different aspects of societies emerge. So far the progress in testing such theories has been slow. Yet the huge corpus of historical and archaeological information provides us with a remarkable empirical resource for testing theories and rejecting empirically inadequate explanations. The key is transforming the wealth of information into a systematic form that facilitates the kinds of analyses we described above. Currently, we are collaborating with colleagues from across multiple disciplines and around the world to develop a databank of coded and quantitative historical and archaeological information about past societies (Seshat: Global History Databank: http://seshatdatabank.info/), with which hypotheses about cultural evolution and human history can be tested, including those informed by CGS theory (Turchin et al. Reference Turchin, Brennan, Currie, Feeney, Francois, Hoyer, Manning, Marciniak, Mullins, Palmisano, Peregrine, Turner and Whitehouse2015). For example, in one project we are assessing the idea that competition between groups led to increased egalitarianism in human groups, particularly beginning with developments of several “axial-age” religions (Bellah Reference Bellah2011). Importantly, this idea will be rigorously tested against other competing explanations, for example, the idea that religion is the “opiate of the masses,” by which elites keep the majority of the population subservient. Rather than the common approach of the social sciences or humanities where the world is interpreted according to a particular theoretical perspective, we follow the approach in which it is ultimately the data that decide which hypothesis provides the best explanation.
In summary, we contend that CGS has been shown to be a very productive generator of testable hypotheses. This theoretical framework is capable of producing novel, even unexpected predictions that can be then contrasted with predictions made by alternative theories using historical and archaeological data. Whether hypotheses derived from CGS theory are “true” or not, this framework has already demonstrated its value as a productive research program in the sense of the philosopher of science Imre Lakatos (Reference Lakatos1978).