Evolutionary Establishment of Moral and Double Moral Standards through Spatial Interactions

By Dirk Helbing, Attila Szolnoki, Matjaz Perc, György Szabó. PLoS Comput. Biol., 2010

374 citations recorded by the source corpus.

Situations where individuals have to contribute to joint efforts or share scarce resources are ubiquitous. Yet, without proper mechanisms to ensure cooperation, the evolutionary pressure to maximize individual success tends to create a tragedy of the commons (such as over-fishing or the destruction of our environment). This contribution addresses a number of related puzzles of human behavior with an evolutionary game theoretical approach as it has been successfully used to explain the behavior of other biological species many times, from bacteria to vertebrates. Our agent-based model distinguishes individuals applying four different behavioral strategies: non-cooperative individuals (“defectors”), cooperative individuals abstaining from punishment efforts (called “cooperators” or “second-order free-riders”), cooperators who punish non-cooperative behavior (“moralists”), and defectors, who punish other defectors despite being non-cooperative themselves (“immoralists”). By considering spatial interactions with neighboring individuals, our model reveals several interesting effects: First, moralists can fully eliminate cooperators. This spreading of punishing behavior requires a segregation of behavioral strategies and solves the “second-order free-rider problem”. Second, the system behavior changes its character significantly even after very long times (“who laughs last laughs best effect”). Third, the presence of a number of defectors can largely accelerate the victory of moralists over non-punishing cooperators. Fourth, in order to succeed, moralists may profit from immoralists in a way that appears like an “unholy collaboration”. Our findings suggest that the consideration of punishment strategies allows one to understand the establishment and spreading of “moral behavior” by means of game-theoretical concepts. This demonstrates that quantitative biological modeling approaches are powerful even in domains that have been addressed with non-mathematical concepts so far. The complex dynamics of certain social behaviors become understandable as the result of an evolutionary competition between different behavioral strategies.

Subfields: Genetics, Safety Research, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/c2f36e4b88444a132d5057deb64389600fc788f4

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Citations in the corpus

Evolutionary dynamics of group interactions on structured populations: a review (2013, background): “[59] for further details).”
Second-order freeriding on antisocial punishment restores the effectiveness of prosocial punishment (2017, background): “In the absence of a limited interaction range the behavior is largely trivial and resembles that reported before for the traditional two-strategy public goods game [29, 55].”
Statistical physics of human cooperation (2017, background): “In peer-based strategies, individual players take it upon themselves to either punish defectors [75, 110, 228], or to reward cooperators [60], or, as in the case of correlated negative and positive reciprocity [50], to do both.”
Punish, but not too hard: how costly punishment spreads in the spatial public goods game (2010, neutral): “Accordingly, the system turns from D-only to a PC-only phase, similar to what is found in the public goods game with all four strategies (C, D, PC, and PD) [29].”
Determinants of corporate social disclosure in Spanish local governments (2013, background): “Several authors (e.g. Bithas and Christofakis, 2006; Helbing et al., 2010) also emphasize the need to consider municipalities’ sustainability status.”
Cyclic dominance in evolutionary games: a review (2014, background): “Examples include the stabilization of reward [70] and punishment [66] in structured populations, the evolution o f empathy and fairness in human bargaining [154], as well as an oscillatory coexistence of defection and cooperation with the “tit-for-tat” strategy [156, 157].”
Networks beyond pairwise interactions: structure and dynamics (2020, background): “[561] Dirk Helbing, Attila Szolnoki, Matjaž Perc, and György Szabó.”