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Understanding Norm Change: An Evolutionary Game-Theoretic Approach

Authors:

Michele J. GelfandAuthors Info & Claims

AAMAS '17: Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems

Pages 1433 - 1441

Published: 08 May 2017 Publication History

Abstract

Human societies around the world interact with each other by developing and maintaining social norms, and it is critically important to understand how such norms emerge and change. In this work, we define an evolutionary game-theoretic model to study how norms change in a society, based on the idea that different strength of norms in societies translate to different game-theoretic interaction structures and incentives. We use this model to study, both analytically and with extensive agent-based simulations, the evolutionary relationships of the need for coordination in a society (which is related to its norm strength) with two key aspects of norm change: cultural inertia (whether or how quickly the population responds when faced with conditions that make a norm change desirable), and exploration rate (the willingness of agents to try out new strategies). Our results show that a high need for coordination leads to both high cultural inertia and a low exploration rate, while a low need for coordination leads to low cultural inertia and high exploration rate. This is the first work, to our knowledge, on understanding the evolutionary causal relationships among these factors.

References

[1]

D. Balliet and P. A. Van Lange. Trust, punishment, and cooperation across 18 societies a meta-analysis. Perspectives on Psychological Science, 8(4):363--379, 2013.

[2]

A.-L. Barabási and R. Albert. Emergence of scaling in random networks. Science, 286(5439):509--512, 1999.

[3]

L. E. Blume. The statistical mechanics of strategic interaction. Games and Economic Behavior, 5(3):387--424, 1993.

[4]

S. Bowles and H. Gintis. The evolution of strong reciprocity: cooperation in heterogeneous populations. Theoretical Population Biology, 65(1):17--28, 2004.

[5]

R. Boyd, H. Gintis, S. Bowles, and P. J. Richerson. The evolution of altruistic punishment. Proceedings of the National Academy of Sciences, 100(6):3531--3535, 2003.

[6]

H. Brandt, C. Hauert, and K. Sigmund. Punishment and reputation in spatial public goods games. Proceedings of the Royal Society of London B: Biological Sciences, 270(1519):1099--1104, 2003.

[7]

H. Brandt, C. Hauert, and K. Sigmund. Punishing and abstaining for public goods. Proceedings of the National Academy of Sciences, 103(2):495--497, 2006.

[8]

S. De, M. J. Gelfand, D. Nau, and P. Roos. The inevitability of ethnocentrism revisited: Ethnocentrism diminishes as mobility increases. Scientific Reports, 5, 2015.

[9]

S. De, D. Nau, and M. Gelfand. Using game theory to study the evolution of cultural norms. arXiv preprint arXiv:1606.02570, 2016.

[10]

S. De, D. Nau, and M. Gelfand. Understanding norm change: An evolutionary game-theoretic approach (extended version). arXiv preprint, 2017.

[11]

J. Ensminger and J. Henrich. Experimenting with social norms: Fairness and punishment in cross-cultural perspective. Russell Sage Foundation, 2014.

[12]

M. J. Gelfand, J. L. Raver, L. Nishii, L. M. Leslie, J. Lun, B. C. Lim, L. Duan, A. Almaliach, S. Ang, J. Arnadottir, et al. Differences between tight and loose cultures: A 33-nation study. Science, 332(6033):1100--1104, 2011.

[13]

W. D. Hamilton and R. Axelrod. The evolution of cooperation. Science, 211(27):1390--1396, 1981.

[14]

R. A. Hammond and R. Axelrod. The evolution of ethnocentrism. Journal of Conflict Resolution, 50(6):926--936, 2006.

[15]

J. R. Harrington and M. J. Gelfand. Tightness--looseness across the 50 united states. Proceedings of the National Academy of Sciences, 111(22):7990--7995, 2014.

[16]

M. Hartshorn, A. Kaznatcheev, and T. Shultz. The evolutionary dominance of ethnocentric cooperation. Journal of Artificial Societies and Social Simulation, 16(3):7, 2013.

[17]

J. Henrich, J. Ensminger, R. McElreath, A. Barr, C. Barrett, A. Bolyanatz, J. C. Cardenas, M. Gurven, E. Gwako, N. Henrich, et al. Markets, religion, community size, and the evolution of fairness and punishment. Science, 327(5972):1480--1484, 2010.

[18]

J. Henrich, R. McElreath, A. Barr, J. Ensminger, C. Barrett, A. Bolyanatz, J. C. Cardenas, M. Gurven, E. Gwako, N. Henrich, et al. Costly punishment across human societies. Science, 312(5781):1767--1770, 2006.

[19]

B. Herrmann, C. Thöni, and S. G\"achter. Antisocial punishment across societies. Science, 319(5868):1362--1367, 2008.

[20]

J. Hofbauer and K. Sigmund. Evolutionary games and population dynamics. Cambridge University Press, 1998.

[21]

S. Levin. Complex adaptive systems: exploring the known, the unknown and the unknowable. Bulletin of the American Mathematical Society, 40(1):3--19, 2003.

[22]

M. A. Nowak. Five rules for the evolution of cooperation. Science, 314(5805):1560--1563, 2006.

[23]

M. A. Nowak and K. Sigmund. Tit for tat in heterogeneous populations. Nature, 355(6357):250--253, 1992.

[24]

D. G. Rand and M. A. Nowak. The evolution of antisocial punishment in optional public goods games. Nature Communications, 2:434, 2011.

[25]

R. L. Riolo, M. D. Cohen, and R. Axelrod. Evolution of cooperation without reciprocity. Nature, 414(6862):441--443, 2001.

[26]

P. Roos, M. Gelfand, D. Nau, and R. Carr. High strength-of-ties and low mobility enable the evolution of third-party punishment. Proceedings of the Royal Society of London B: Biological Sciences, 281(1776):20132661, 2014.

[27]

P. Roos, M. Gelfand, D. Nau, and J. Lun. Societal threat and cultural variation in the strength of social norms: An evolutionary basis. Organizational Behavior and Human Decision Processes, 129:14--23, 2015.

[28]

P. Shakarian, P. Roos, and A. Johnson. A review of evolutionary graph theory with applications to game theory. Biosystems, 107(2):66--80, 2012.

[29]

J. M. Smith. Evolution and the Theory of Games. Cambridge University Press, 1982.

[30]

P. D. Taylor and L. B. Jonker. Evolutionary stable strategies and game dynamics. Mathematical Biosciences, 40(1--2):145--156, 1978.

[31]

A. Traulsen, C. Hauert, H. De Silva, M. A. Nowak, and K. Sigmund. Exploration dynamics in evolutionary games. Proceedings of the National Academy of Sciences, 106(3):709--712, 2009.

[32]

D. J. Watts and S. H. Strogatz. Collective dynamics of 'small-world' networks. Nature, 393(6684):440--442, 1998.

[33]

J. W. Weibull. Evolutionary Game Theory. MIT press, 1997.

Cited By

Santos FMascarenhas SSantos FCorreia FGomes SPaiva AElkind EVeloso MAgmon NTaylor M(2019)Outcome-based Partner Selection in Collective Risk DilemmasProceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3306127.3331873(1556-1564)Online publication date: 8-May-2019
https://dl.acm.org/doi/10.5555/3306127.3331873
Picascia STermos AYorke-Smith NAndre EKoenig SDastani MSukthankar G(2018)Initial Results from an Agent-Based Simulation of Housing in Urban BeirutProceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3237383.3238066(2045-2047)Online publication date: 9-Jul-2018
https://dl.acm.org/doi/10.5555/3237383.3238066
Srour FYorke-Smith NAndre EKoenig SDastani MSukthankar G(2018)On Collusion and CoercionProceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3237383.3237940(1622-1630)Online publication date: 9-Jul-2018
https://dl.acm.org/doi/10.5555/3237383.3237940
Show More Cited By

Index Terms

Understanding Norm Change: An Evolutionary Game-Theoretic Approach
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing design and evaluation methods
    2. Collaborative and social computing theory, concepts and paradigms

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Information

Published In

cover image ACM Other conferences

AAMAS '17: Proceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems

May 2017

1914 pages

General Chairs:
Kate Larson
University of Waterloo, Canada
,
Michael Winikoff
University of Otago, New Zealand
,
Program Chairs:
Sanmay Das
Washington University in St. Louis, USA
,
Edmund Durfee
University of Michigan, USA

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IFAAMAS

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ACM: Association for Computing Machinery

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International Foundation for Autonomous Agents and Multiagent Systems

Richland, SC

Publication History

Published: 08 May 2017

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U.S. Air Force

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AAMAS '17 Paper Acceptance Rate 127 of 457 submissions, 28%;

Overall Acceptance Rate 1,155 of 5,036 submissions, 23%

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Cited By

Santos FMascarenhas SSantos FCorreia FGomes SPaiva AElkind EVeloso MAgmon NTaylor M(2019)Outcome-based Partner Selection in Collective Risk DilemmasProceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3306127.3331873(1556-1564)Online publication date: 8-May-2019
https://dl.acm.org/doi/10.5555/3306127.3331873
Picascia STermos AYorke-Smith NAndre EKoenig SDastani MSukthankar G(2018)Initial Results from an Agent-Based Simulation of Housing in Urban BeirutProceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3237383.3238066(2045-2047)Online publication date: 9-Jul-2018
https://dl.acm.org/doi/10.5555/3237383.3238066
Srour FYorke-Smith NAndre EKoenig SDastani MSukthankar G(2018)On Collusion and CoercionProceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3237383.3237940(1622-1630)Online publication date: 9-Jul-2018
https://dl.acm.org/doi/10.5555/3237383.3237940
Morales JWooldridge MRodríguez-Aguilar JLópez-Sánchez M(2018)Off-line synthesis of evolutionarily stable normative systemsAutonomous Agents and Multi-Agent Systems10.1007/s10458-018-9390-332:5(635-671)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1007/s10458-018-9390-3

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