Agents, Intelligence, and Social Atoms

Bentley, Alex; Ormerod, Paul

doi:10.1093/acprof:oso/9780199794393.003.0012

Cited by 13 publications

(9 citation statements)

References 103 publications

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“…Furthermore, Henrich (2001) demonstrated that biased social learning that excludes individual trial-and-error is required to account for the 'S-shape' curves that describe the uptake of the vast majority of well-studied innovations. There has been significant interest lately in the explanatory power of models based on random social copying (Bentley and Shennan 2005;Bentley and Ormerod 2012) and we include this in a variant of our model, but our principal interest is forms of social learning in which the selection of a cultural trait from a set of alternatives in a sample pool is biased by direct or indirect (Boyd and Richerson 1985) estimation of its 'worth'.…”

Section: Social Learning and Payoff Signalsmentioning

confidence: 99%

Cultural Incubators and Spread of Innovation

Crema¹,

Lake²

2015

Human Biology

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Several forms of social learning rely on the direct or indirect evaluation of the fitness of cultural traits. Here we argue, via a simple agent-based model, that payoff uncertainty, that is the correlation between a trait and the signal used to evaluate its fitness, plays a pivotal role in the spread of beneficial innovation. More specifically, we examine how this correlation affects the evolutionary dynamics of different forms of social learning and how each can generate divergent historical trajectories depending on the size of the sample pool.In particular, we demonstrate that social learning by copying the best model is particularly susceptible to a sampling effect caused by the interaction of payoff uncertainty, the number of models sampled (the sample pool) and the frequency with which a trait is present in the population. As a result, we identify circumstances in which smaller sample pools can act as 'cultural incubators' that promote the spread of innovations, while more widespread sampling of the population actually retards the rate of cultural evolution. 3 the explicit use of replicator dynamics to explore optimal strategies for copying with payoff uncertainty (Schlag 1998). In this paper we cross-cut some of these established themes by exploring the impact of population size/sampling and payoff uncertainty on the early stages in the spread of innovations. Our finding augments the results of some existing studies (e.g., Baldini 2012Baldini , 2013 Lake and Crema 2012), but also identifies circumstances in which the positive correlation between population size and the rate of cultural evolution (Henrich 2004;Powell et al. 2009Powell et al. , 2010Shennan 2001) is significantly modified by the joint effect of payoff uncertainty and sample pool size. We begin with a brief review of findings from the most immediately relevant recent literature on the value of social learning, the nature of payoff and success-biased social learning and how it is mediated by payoff uncertainty and population size and/or the way in which the population is sampled.

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Section: Social Learning and Payoff Signalsmentioning

confidence: 99%

Cultural Incubators and Spread of Innovation

Crema¹,

Lake²

2015

Human Biology

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“…Human dynamics is not limited to the study of behaviour in communication networks, and has a broader remit similar to the aims of sociophysics [Gal08, SC14,Sta14], which uses concepts and methods from statistical physics to investigate social phenomena, opinion formation and political behaviour. A central idea here is that, in the context of statistical physics, individual humans can be thought of as "social atoms", each exhibiting simple individual behaviour and possessing very limited intelligence, but nevertheless collectively yielding complex social patterns [BO11]. In sociophysics, critical phenomena are important in demonstrating how the transition to global behaviour can emerge from the interactions of many individual social atoms, for example, as described in [PS10], where the popularity of movies emerges as collective choice behaviour.…”

Section: Introductionmentioning

confidence: 99%

A multiplicative process for generating a beta-like survival function with application to the UK 2016 EU referendum results

Fenner

Kaufmann

Levene

et al. 2017

Int. J. Mod. Phys. C

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Human dynamics and sociophysics suggest statistical models that may explain and provide us with better insight into social phenomena. Contextual and selection effects tend to produce extreme values in the tails of rank-ordered distributions of both census data and district-level election outcomes. Models that account for this nonlinearity generally outperform linear models. Fitting nonlinear functions based on rank-ordering census and election data therefore improves the fit of aggregate voting models. This may help improve ecological inference, as well as election forecasting in majoritarian systems.We propose a generative multiplicative decrease model that gives rise to a rank-order distribution, and facilitates the analysis of the recent UK EU referendum results. We supply empirical evidence that the beta-like survival function, which can be generated directly from our model, is a close fit to the referendum results, and also may have predictive value when covariate data are available.

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“…If human activity is established as an important driver of ecosystem change, then understanding the implications of their behavior for systems dynamics becomes central. As an aside, an interesting issue in this context is whether the appropriate null for human decision-making is the "zero intelligence agent" or the entirely rational and informed "Homo oeconomicus" of classical economics (Bentley and Ormerod, 2012); most neutral models of human-environment interaction developed by non-economists have favored the former. Soon after human arrival in NZ in the late Thirteenth century CE (Wilmshurst et al, 2008) widespread deforestation took place as a result of anthropic fire.…”

Section: "Behaviourally Neutral" Nullsmentioning

confidence: 99%