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Social norms and cooperation in a collective-risk social dilemma : Comparing reinforcing learning and norm-based approaches

Payette, N. (author)
University of Oxford, School of Geography and the Environment, United Kingdom
Szekely, A. (author)
Collegio Carlo Alberto, Department of Social and Political Sciences, Italy
Andrighetto, Giulia (author)
Mälardalens högskola,Utbildningsvetenskap och Matematik,MAM
 (creator_code:org_t)
Institute of Electrical and Electronics Engineers Inc. 2020
2020
English.
In: 29th IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2020. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728160757 ; , s. 1403-1406
  • Conference paper (peer-reviewed)
Abstract Subject headings
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  • Human cooperation is both powerful and puzzling. Large-scale cooperation among genetically unrelated individuals makes humans unique with respect to all other animal species. Therefore, learning how cooperation emerges and persists is a key question for social scientists. Recently, scholars have recognized the importance of social norms as solutions to major local and large-scale collective action problems, from the management of water resources to the reduction of smoking in public places to the change in fertility practices. Yet a well-founded model of the effect of social norms on human cooperation is still lacking.We present here a version of the Experience-Weighted Attraction (EWA) reinforcement learning model that integrates norm-based considerations into its utility function that we call EWA+Norms. We compare the behaviour of this hybrid model to the standard EWA when applied to a collective risk social dilemma in which groups of individuals must reach a threshold level of cooperation to avoid the risk of catastrophe. We find that standard EWA is not sufficient for generating cooperation, but that EWA+Norms is. Next step is to compare simulation results with human behaviour in large-scale experiments.

Subject headings

SAMHÄLLSVETENSKAP  -- Annan samhällsvetenskap (hsv//swe)
SOCIAL SCIENCES  -- Other Social Sciences (hsv//eng)

Keyword

Agricultural robots
Behavioral research
Disaster prevention
Reinforcement learning
Water management
Collective action
Human behaviours
Large scale experiments
Reinforcement learning models
Social scientists
Threshold levels
Utility functions
Well-founded models
Social robots

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ref (subject category)
kon (subject category)

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Szekely, A.
Andrighetto, Giu ...
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