Strategic Social Learning and the Population Dynamics of Human Behavior: The Game of Go

Abstract

Human culture is widely believed to undergo evolution, via mechanisms rooted in the nature of human cognition. A number of theories predict the kinds of human learning strategies, as well as the population dynamics that result from their action. There is little work, however, that quantitatively examines the evidence for these strategies and resulting cultural evolution within human populations. One of the obstacles is the lack of individual-level data with which to link transmission events to larger cultural dynamics. Here, we address this problem with a rich quantitative database from the East Asian board game known as Go. We draw from a large archive of Go games spanning the last six decades of professional play, and find evidence that the evolutionary dynamics of particular cultural variants are driven by a mix of individual and social learning processes. Particular players vary dramatically in their sensitivity to population knowledge, which also varies by age and nationality. The dynamic patterns of opening Go moves are consistent with an ancient, ongoing arms race within the game itself.

Cultural evolution of football tactics: Strategic social learning in managers' choice of formation

“…The second concern was the lack of controls related to the opponent team in a match. Contrary to the more specific prediction (Hypothesis H2) that managers should rely more on social than personal information, given the difficulty of personally trialling different formations in the high stakes world of football management and previous findings of greater social information use by Beheim et al (2014), there was if anything more reliance on personal information. This is puzzling not only for the aforementioned reasons (the difficulty of individual learning should favour reliance on social learning, plus the previous findings of Beheim et al), but also the fact that the population provides much more information overall in the same time period.…”

“…The strategic combination of individual and social learning is adaptive when decisions or problems are challenging, such as when environments change over time such that social information may become outdated (Boyd & Richerson, 1995;Enquist et al, 2007;Rogers, 1988), or when solutions are causally opaque or multidimensional, such that they cannot be acquired by individual learning alone and require the social learning of accumulated past solutions (Boyd & Richerson, 1985, 1995. People show this strategic mix of individual and social learning in the lab (Kameda & Nakanishi, 2003;McElreath et al, 2005;Mesoudi, 2008;Morgan, Rendell, Ehn, Hoppitt, & Laland, 2011;Toelch, Bruce, Newson, Richerson, & Reader, 2014;Toelch et al, 2009) and the real world (Beheim, Thigpen, & McElreath, 2014;Miu, Gulley, Laland, & Rendell, 2018) (although sometimes imperfectly (Mesoudi, 2011)). When combined appropriately, individual and social learning can generate cumulative cultural evolution at the population level, where innovations generated via individual learning are preserved and accumulated over generations via social learning (Mesoudi & Thornton, 2018).…”

“…When combined appropriately, individual and social learning can generate cumulative cultural evolution at the population level, where innovations generated via individual learning are preserved and accumulated over generations via social learning (Mesoudi & Thornton, 2018). Beheim, Thigpen & McElreath (2014) provided an innovative demonstration of the strategic use of social and individual learning in the real world. They analysed decades of professional matches of the board game Go to understand the spread of an opening move, the "Fourfour".…”

“…Here I use a 5-year dataset of all games (n=9127, 2012-2017) in the five top European leagues (English Premier League, German Bundesliga, Spanish La Liga, French Ligue 1 and Italian Serie A) to test the following hypotheses, derived from the above theory and the results of Beheim et al (2014). All hypotheses and analyses were preregistered before running any analyses on the original data (https://osf.io/er4dx), and all data and code are available at https://github.com/amesoudi/ football.…”

A Distributed Learning Dynamics in Social Groups

“…Instances of such two-stage distributed learning dynamics in social settings have been widely proposed and validated with data in the literature on human choice behavior (e.g., [7,10,29,32,34]) and animal behavior (e.g., [40,43]). They are cognitively simple because individuals need not maintain any history of previous observations; rather they only use the most recent quality signal of one option.…”