Adversity magnifies the importance of social information in decision-making

Pérez‐Escudero, Alfonso; Polavieja, Gonzalo G. de

doi:10.1101/061630

Cited by 4 publications

(4 citation statements)

References 50 publications

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“…More recent work has attempted to build a fully descriptive model of such collective decision making by considering the purportedly rational beliefs and decisions of agents exposed to the social information provided by choices of others (19,20), and the studies have been successful in reproducing the observed response functions in a variety of taxa including insects (20), fish (19)(20)(21), and birds (5,6). Recent extensions of these models have also considered how social responses might vary as a result of changes in environmental context (22). However, while these models have had success in reproducing the observed features of collective decisions, this has been at the cost of internal consistency as theories of rational behavior.…”

Section: As Well As Showing That Rational Decision Making Is Consistementioning

confidence: 99%

Collective decision making by rational individuals

Mann

2018

Proc. Natl. Acad. Sci. U.S.A.

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The patterns and mechanisms of collective decision making in humans and animals have attracted both empirical and theoretical attention. Of particular interest has been the variety of social feedback rules and the extent to which these behavioral rules can be explained and predicted from theories of rational estimation and decision making. However, models that aim to model the full range of social information use have incorporated ad hoc departures from rational decision-making theory to explain the apparent stochasticity and variability of behavior. In this paper I develop a model of social information use and collective decision making by fully rational agents that reveals how a wide range of apparently stochastic social decision rules emerge from fundamental information asymmetries both between individuals and between the decision makers and the observer of those decisions. As well as showing that rational decision making is consistent with empirical observations of collective behavior, this model makes several testable predictions about how individuals make decisions in groups and offers a valuable perspective on how we view sources of variability in animal, and human, behavior.agent-based model | collective behavior | rational choice | social information | utility C ollective decision making is a ubiquitous task for social animal species, including humans (1). Whether deciding where to forage, which nest site to choose, or when to move, individual decisions are greatly informed by observing the choices that others make. As recently as 2008, Ward et al. (ref. 2, p. 1) were able to state that "little is known about the mechanisms underlying decision-making in vertebrate animal groups." Since then, however, a large literature has explored the rules governing social information use in collective decisions across various taxa, for example in insects (3), fish (2, 4), birds (5, 6), and mammals (7, 8), including primates (9) and humans (10, 11). What links decision making in all of these groups is the presence of social reinforcement, with individuals demonstrating a strong preference for an option chosen by others, which increases with the number of others who have selected it. This reinforcement can be expressed as a social response function-the probability of selecting a given option conditioned on the number of other individuals that have previously chosen it. A large degree of variation has been observed in these social response functions, ranging from linear relationships (e.g., refs. 3 and 12) to strongly nonlinear "quorum" rules (13), where the apparent attractiveness of an option appears to increase exponentially with the number of individuals choosing it, before saturating as this number passes a quorum level. In addition to variation between taxa, studies have also highlighted how the same species can exhibit different patterns of collective behavior under different laboratory or field conditions (14-17), highlighting the potential importance of context-dependent social responses.Complementing these em...

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Section: As Well As Showing That Rational Decision Making Is Consistementioning

confidence: 99%

Collective decision making by rational individuals

Mann

2018

Proc. Natl. Acad. Sci. U.S.A.

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“…with social information (observations of others' behaviors) in estimating probabilities; and a probability matching rule for making choices (Pérez-Escudero and de Polavieja, 2011;Arganda et al, 2012;Pérez-Escudero et al, 2013). Such work naturally has a focus on the individual, including for fish and humans (Pérez-Escudero and De Polavieja, 2017;Eguíluz et al, 2015;Mann, 2018), because individuals 'selfishly' optimise for their own advantage; this contrasts with highly related ant colonies, for example, which can properly be described as a form of 'superorganism' (Hölldobler and Wilson, 2009). In the superorganism, one can expect sophisticated collective strategies to emerge that are optimised for group-level performance.…”

Section: Bayes and The Superorganismmentioning

confidence: 99%

The Bayesian Superorganism: Collective Probability Estimation in Swarm Systems

Hunt¹,

Franks²,

Baddeley³

2020

The 2020 Conference on Artificial Life

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Superorganisms such as social insect colonies are very successful relative to their non-social counterparts. Powerful emergent information processing capabilities would seem to contribute to the abundance of such 'swarm' systems, as they effectively explore and exploit their environment collectively. We develop a Bayesian model of collective information processing in a decision-making task: choosing a nest site (a 'multi-armed bandit' problem). House-hunting Temnothorax ants are adept at discovering and choosing the best available nest site for their colony: we propose that this is possible via rapid, decentralized estimation of the probability that each choice is best. Viewed this way, their behavioral algorithm can be understood as a statistical method that anticipates recent advances in mathematics. Our nest finding model incorporates insights from approximate Bayesian computation as a model of colony-level behavior; and particle filtering as a model of Temnothorax 'tandem running'. Our framework suggests that the mechanisms of complex collective behavior can sometimes be explained as a spatial enactment of Bayesian inference. It facilitates the generation of quantitative hypotheses regarding individual and collective movement behaviors when collective decisions must be made. It also points to the potential for bioinspired statistical techniques. Finally, it suggests simple mechanisms for collective decision-making in engineered systems, such as robot swarms.

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“…with social information (observations of others' behaviours) in estimating probabilities; and a probability matching rule for making choices (Pérez-Escudero and de Polavieja, 2011;Arganda et al, 2012;Pérez-Escudero et al, 2013). Such work naturally has a focus on the individual, including for fish and humans (Pérez-Escudero and De Polavieja, 2017;Eguíluz et al, 2015;Mann, 2018), because individuals 'selfishly' optimise for their own advantage; this contrasts with highly related ant colonies, for example, which can properly be described as a form of 'superorganism' (Hölldobler and Wilson, 2009). In the superorganism, one can expect sophisticated collective strategies to emerge that are optimised for group-level performance.…”

Section: Bayes and The Superorganismmentioning

confidence: 99%

The Bayesian Superorganism: Collective Probability Estimation in Swarm Systems

Hunt

Franks

Baddeley

2018

Preprint

View full text Add to dashboard Cite

Superorganisms such as social insect colonies are very successful relative to their non-social counterparts. Powerful emergent information processing capabilities would seem to contribute to the abundance of such 'swarm' systems, as they effectively explore and exploit their environment collectively. We develop a Bayesian model of collective information processing in a decision-making task: choosing a nest site (a 'multi-armed bandit' problem). House-hunting Temnothorax ants are adept at discovering and choosing the best available nest site for their colony: we propose that this is possible via rapid, decentralised estimation of the probability that each choice is best. Viewed this way, their behavioural algorithm can be understood as a statistical method that anticipates recent advances in mathematics. Our model of their nest finding incorporates insights from approximate Bayesian computation as a model of colony-level behaviour; and particle filtering as a model of Temnothorax 'tandem running'. Our Bayesian framework suggests that the mechanisms of complex collective behaviour can sometimes be explained with reference to Bayesian inference. It facilitates the generation of quantitative hypotheses regarding individual and collective movement behaviours when collective decisions must be made. It also points to the potential for bio-inspired statistical techniques. Finally, it suggests simple mechanisms for collective decision-making in engineered swarm systems, such as robot swarms.

show abstract

Adversity magnifies the importance of social information in decision-making

Cited by 4 publications

References 50 publications

Collective decision making by rational individuals

Collective decision making by rational individuals

The Bayesian Superorganism: Collective Probability Estimation in Swarm Systems

The Bayesian Superorganism: Collective Probability Estimation in Swarm Systems

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