In Latin America as in the wider world, corruption is rooted in our relationships

Acknowledging that cooperation and corruption are two sides of the same coin can help us to understand why some states succeed and others fail, why some oscillate, and which triggers lead failed states to succeed and successful states to fail, writes Michael Muthukrishna (LSE Psychological and Behavioural Science)

Long read: cultural evolution, Covid-19, and preparing for what’s next

Our behaviour is rarely a function of causal understanding: humans create and thrive in a world too complicated to understand, writes Michael Muthukrishn

Cultural evolutionary public policy

Interventions to reverse harmful traditions, such as female genital cutting, have had mixed success, sometimes backfiring. Policymakers’ intentions collide with cultural traditions and the ethics of tolerance collide with universal human rights. New research introduces a cultural evolutionary modelling framework to explain previous results and guide future campaigns for endogenous change

Modeling cultural change: computational models of interpersonal influence dynamics can yield new insights about how cultures change, which cultures change more rapidly than others, and why

Cultural change can occur as an emergent consequence of social influence dynamics within cultural populations. These influence dynamics are complex, and formal modeling methods-such as agent-based models-are a useful means of predicting implications for cultural change. These models may be especially useful if they not only model the psychological outcomes of interpersonal influence, but also model social network structures within a culture. When combined, these components provide a flexible modeling framework that allows other variables to also be modeled for the purposes of predicting plausible implications for cultural change. The article illustrates this approach by summarizing recent research that used these methods to model cross-cultural differences in the pace of cultural change. The article then identifies additional variables that could potentially be modeled within this conceptual framework, to produce additional insights-and additional new hypotheses-about different circumstances associated with different patterns of cultural change

Innovations are rarely (if ever) the product of a single individual

Instead, they're a product of our collective brains, people sharing thoughts and learning from each other, write Katie Dowbiggin and Michael Muthukrishn

Indirect reciprocity undermines indirect reciprocity destabilizing large-scale cooperation

Previous models suggest that indirect reciprocity (reputation) can stabilize large-scale human cooperation [K. Panchanathan, R. Boyd, Nature 432, 499–502 (2004)]. The logic behind these models and experiments [J. Gross et al., Sci. Adv. 9, eadd8289 (2023) and O. P. Hauser, A. Hendriks, D. G. Rand, M. A. Nowak, Sci. Rep. 6, 36079 (2016)] is that a strategy in which individuals conditionally aid others based on their reputation for engaging in costly cooperative behavior serves as a punishment that incentivizes large-scale cooperation without the second-order free-rider problem. However, these models and experiments fail to account for individuals belonging to multiple groups with reputations that can be in conflict. Here, we extend these models such that individuals belong to a smaller, “local” group embedded within a larger, “global” group. This introduces competing strategies for conditionally aiding others based on their cooperative behavior in the local or global group. Our analyses reveal that the reputation for cooperation in the smaller local group can undermine cooperation in the larger global group, even when the theoretical maximum payoffs are higher in the larger global group. This model reveals that indirect reciprocity alone is insufficient for stabilizing large-scale human cooperation because cooperation at one scale can be considered defection at another. These results deepen the puzzle of large-scale human cooperation

Innovation in the collective brain

Innovation is often assumed to be the work of a talented few, whose products are passed on to the masses. Here, we argue that innovations are instead an emergent property of our species' cultural learning abilities, applied within our societies and social networks. Our societies and social networks act as collective brains. We outline how many human brains, which evolved primarily for the acquisition of culture, together beget a collective brain. Within these collective brains, the three main sources of innovation are serendipity, recombination and incremental improvement. We argue that rates of innovation are heavily influenced by (i) sociality, (ii) transmission fidelity, and (iii) cultural variance. We discuss some of the forces that affect these factors. These factors can also shape each other. For example, we provide preliminary evidence that transmission efficiency is affected by sociality—languages with more speakers are more efficient. We argue that collective brains can make each of their constituent cultural brains more innovative. This perspective sheds light on traits, such as IQ, that have been implicated in innovation. A collective brain perspective can help us understand otherwise puzzling findings in the IQ literature, including group differences, heritability differences and the dramatic increase in IQ test scores over time

Parsimony in model selection: tools for assessing fit propensity

Theories can be represented as statistical models for empirical testing. There is a vast literature on model selection and multimodel inference that focuses on how to assess which statistical model, and therefore which theory, best fits the available data. For example, given some data, one can compare models on various information criterion or other fit statistics. However, what these indices fail to capture is the full range of counterfactuals. That is, some models may fit the given data better not because they represent a more correct theory, but simply because these models have more fit propensity - a tendency to fit a wider range of data, even nonsensical data, better. Current approaches fall short in considering the principle of parsimony (Occam’s Razor), often equating it with the number of model parameters. Here we offer a toolkit for researchers to better study and understand parsimony through the fit propensity of Structural Equation Models. We provide an R package (ockhamSEM) built on the popular lavaan package. To illustrate the importance of evaluating fit propensity, we use ockhamSEM to investigate the factor structure of the Rosenberg Self-Esteem Scale

Integrating cultural evolution and behavioral genetics

The 29 commentaries amplified our key arguments; offered extensions, implications, and applications of the framework; and pushed back and clarified. To help forge the path forward for cultural evolutionary behavioral genetics, we (1) focus on conceptual disagreements and misconceptions about the concepts of heritability and culture; (2) further discuss points raised about the intertwined relationship between culture and genes; and (3) address extensions to the proposed framework, particularly as it relates to cultural clusters, development, and power. These commentaries, and the deep engagement they represent, reinforce the importance of integrating cultural evolution and behavioral genetics