Human societies are collective brains. People within every society have cultural brains—brains that have evolved to selectively seek out adaptive knowledge and socially transmit solutions. Innovations emerge at a population level through the transmission of serendipitous mistakes, incremental improvements and novel recombinations. The rate of innovation through these mechanisms is a function of (1) a society's size and interconnectedness (sociality), which affects the number of models available for learning; (2) fidelity of information transmission, which affects how much information is lost during social learning; and (3) cultural trait diversity, which affects the range of possible solutions available for recombination. In general, and perhaps surprisingly, all three levers can increase and harm innovation by creating challenges around coordination, conformity and communication. Here, we focus on the ‘paradox of diversity’—that cultural trait diversity offers the largest potential for empowering innovation, but also poses difficult challenges at both an organizational and societal level. We introduce ‘cultural evolvability’ as a framework for tackling these challenges, with implications for entrepreneurship, polarization and a nuanced understanding of the effects of diversity. This framework can guide researchers and practitioners in how to reap the benefits of diversity by reducing costs. This article is part of a discussion meeting issue ‘The emergence of collective knowledge and cumulative culture in animals, humans and machines’.

Interventions are to the social sciences what inventions are to the physical sciences – an application of science as technology. Behavioural science has emerged as a powerful toolkit for developing public policy interventions for changing behaviour. However, the translation from principles to practice is often moderated by contextual factors – such as culture – that thwart attempts to generalize past successes. Here, we discuss cultural evolution as a framework for addressing this contextual gap. We describe the history of behavioural science and the role that cultural evolution plays as a natural next step. We review research that may be considered cultural evolutionary behavioural science in public policy, and the promise and challenges to designing cultural evolution informed interventions. Finally, we discuss the value of applied research as a crucial test of basic science: if theories, laboratory and field experiments do not work in the real world, they do not work at all.

No abstract

In the last 12,000 years, human societies have scaled up from small bands to large states of millions and even billions. Many modern societies and even groups of societies cooperate on large-scale projects with relatively low levels of conflict, but the scale and intensity of cooperation varies dramatically between societies. Here we attempt to formalize dynamics that may be driving this rapid increase in cooperation and the differences we see between societies. Our model extends an N-person stag hunt to include population growth dynamics, "stags" with different sized payoffs, and competition for these stags. An increasing number of cooperators is required to access larger stags. The payoff from these stags in turn increases carrying capacity, which increases competition for the stag. As population size increases, new cooperative thresholds are attainable, and as population size shrinks, previously attainable thresholds fall out of reach. Among other predictions, we show that when a new threshold is accessible to a population, the level of cooperation will increase to reach this threshold. However, when the next threshold is out of reach, cooperation decreases as individuals refrain from costly cooperation, preferring a smaller stag. This model offers a framework for understanding the rapid increase in the scale of human cooperation and decline of violence, differences between societies, and challenges to future cooperation.

The multi-site replication study, Many Labs 2 (ML2), attempted to test whether population, site and setting variability moderates the likelihood of replication and effect size. The analysis concluded that sample location and setting did not substantially affect the replicability of findings. In this paper, we raise several issues with the ML2 approach to adjudicating the effect of culture that cast doubt on this conclusion. These theoretical and methodological problems (pre-registered at https://osf.io/6exr4) involve the: (1) selection of studies and sample sites for replication that are not theory-driven, (2) sampling of mostly WEIRD people around the world, (3) conflation of participants’ cultural backgrounds with the country where the samples came from, (4) use of the WEIRD backronym by decomposing it into a scale, and (5) application of a mean split of that WEIRD variable. Moreover, simulations reveal strikingly low statistical power for detecting cultural influences in a multi-side study designed like ML2. We propose methodologies to address problems (3) to( 5) by re-analyzing the ML2 dataset using an alternative approach. These results suggest that tackling only some of the design problems is insufficient to overcome the underlying theoretical and methodological deficiencies. We conclude with specific recommendations for assessing the role of population variability in future multi-site studies that address evidentiary value and effect size.

No abstract

No abstract

No abstract