Limited memory can be beneficial for the evolution of cooperation

Horváth, Gergely; Kovářík, Jaromír; Mengel, Friederike

doi:10.1016/j.jtbi.2012.01.034

Cited by 29 publications

(16 citation statements)

References 43 publications

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“…For instance in Axelrod's tournament, the dominating strategy (Tit-fortat) was not the most complex of all the proposed strategies (Axelrod, 1980). Further, research in economics also indicate that repeated games and social interactions do not always select for more complex strategies, and these results were obtained either by taking into account explicitly the costs of cognitive complexity (Binmore & Samuelson, 1992) or, more recently, in the absence of such costs (Duersch, Oechssler, & Schipper, 2014;Horv ath, Kov a rík, & Mengel, 2012;Mohlin, 2012). All this suggests that it is relevant to try to characterize the type of games species are playing in nature (or the social problems they face), in order to characterize the real demands on social cognition.…”

Section: Evolutionary Paths To Increased Cognitionmentioning

confidence: 99%

A model for the evolution of reinforcement learning in fluctuating games

Dridi

Lehmann

2015

Animal Behaviour

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Keywords: evolution of cognition evolutionarily stable learning rules explorationeexploitation trade-off repeated games social interactions trial-and-error learning Many species are able to learn to associate behaviours with rewards as this gives fitness advantages in changing environments. Social interactions between population members may, however, require more cognitive abilities than simple trial-and-error learning, in particular the capacity to make accurate hypotheses about the material payoff consequences of alternative action combinations. It is unclear in this context whether natural selection necessarily favours individuals to use information about payoffs associated with nontried actions (hypothetical payoffs), as opposed to simple reinforcement of realized payoff. Here, we develop an evolutionary model in which individuals are genetically determined to use either trial-and-error learning or learning based on hypothetical reinforcements, and ask what is the evolutionarily stable learning rule under pairwise symmetric two-action stochastic repeated games played over the individual's lifetime. We analyse through stochastic approximation theory and simulations the learning dynamics on the behavioural timescale, and derive conditions where trial-and-error learning outcompetes hypothetical reinforcement learning on the evolutionary timescale. This occurs in particular under repeated cooperative interactions with the same partner. By contrast, we find that hypothetical reinforcement learners tend to be favoured under random interactions, but stable polymorphisms can also obtain where trial-and-error learners are maintained at a low frequency. We conclude that specific game structures can select for trial-and-error learning even in the absence of costs of cognition, which illustrates that cost-free increased cognition can be counterselected under social interactions.

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Section: Evolutionary Paths To Increased Cognitionmentioning

confidence: 99%

A model for the evolution of reinforcement learning in fluctuating games

Dridi

Lehmann

2015

Animal Behaviour

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“…Initialization of the network with randomly assigned node properties For each iteration round, Immigration of one agent, Occupy an empty network node and accept its properties End For each agent, Interact with connected neighbors and memorize encounters End For each agent, Calculate payoffs with memory-based modifications and update the reproductive potential End For each agent, Reproduce (with newborns inheriting parental traits subject to mutation) End For each agent, Chance to die End End the interaction-related information from h last time steps (Horváth et al, 2012), which further affected the outcome of each interaction, i.e. the cost c and benefit b are multiplied by the number of cooperations from this co-player during the last h time steps, divided by h. More specifically, if an agent A interacts with agent B according to the rules and strategies specified above, then let k be the number of events during the last h time steps where B has helped A.…”

Section: Methodsmentioning

confidence: 99%

Randomness in the evolution of cooperation

Hadzibeganovic

Stauffer

Xiao

2015

Behavioural Processes

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“…It also includes, although I will not discuss this in the present paper, the properties that enable groups to form and function in the first place. Here, there has been some intriguing work regarding the role of individual mnemonic shortcomings, such as forgetting and distortion, in enhancing sociality (Brown et al 2012;Fagin et al 2013) and supporting the emergence of cooperative behaviors (Horváth et al 2012).…”

Section: Mandevillian Intelligencementioning

confidence: 99%

Mandevillian Intelligence

Smart¹

2018

Oxford Scholarship Online

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Mandevillian intelligence is a specific form of collective intelligence in which individual cognitive vices (i.e., shortcomings, limitations, constraints and biases) are seen to play a positive functional role in yielding collective forms of cognitive success. The present paper introduces the concept of mandevillian intelligence and reviews a number of strands of empirical research that help to shed light on the phenomenon. The paper also attempts to highlight the value of the concept of mandevillian intelligence from a philosophical, scientific and engineering perspective. Inasmuch as we accept the notion of mandevillian intelligence, then it seems that the cognitive and epistemic value of a specific social or technological intervention will vary according to whether our attention is focused at the individual or collective level of analysis. This has a number of important implications for how we think about the design and evaluation of collective cognitive systems. For example, the notion of mandevillian intelligence forces us to take seriously the idea that the exploitation (or even the accentuation) of individual cognitive shortcomings could, in some situations, provide a productive route to collective forms of cognitive and epistemic success.

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Limited memory can be beneficial for the evolution of cooperation

Cited by 29 publications

References 43 publications

A model for the evolution of reinforcement learning in fluctuating games

A model for the evolution of reinforcement learning in fluctuating games

Randomness in the evolution of cooperation

Mandevillian Intelligence

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