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      Evolution of All-or-None Strategies in Repeated Public Goods Dilemmas

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          Abstract

          Many problems of cooperation involve repeated interactions among the same groups of individuals. When collective action is at stake, groups often engage in Public Goods Games ( PGG), where individuals contribute (or not) to a common pool, subsequently sharing the resources. Such scenarios of repeated group interactions materialize situations in which direct reciprocation to groups may be at work. Here we study direct group reciprocity considering the complete set of reactive strategies, where individuals behave conditionally on what they observed in the previous round. We study both analytically and by computer simulations the evolutionary dynamics encompassing this extensive strategy space, witnessing the emergence of a surprisingly simple strategy that we call All-Or-None ( AoN). AoN consists in cooperating only after a round of unanimous group behavior (cooperation or defection), and proves robust in the presence of errors, thus fostering cooperation in a wide range of group sizes. The principles encapsulated in this strategy share a level of complexity reminiscent of that found already in 2-person games under direct and indirect reciprocity, reducing, in fact, to the well-known Win-Stay-Lose-Shift strategy in the limit of the repeated 2-person Prisoner's Dilemma.

          Author Summary

          The problem of cooperation has been a target of many studies, and some of the most complex dilemmas arise when we deal with groups repeatedly interacting by means of a Public Goods Game ( PGG), where individuals may contribute to a common pool, subsequently sharing the resources. Here we study generalized direct group reciprocity by incorporating the complete set of reactive strategies, where action is dictated by what happened in the previous round. We compute the pervasiveness in time of each possible reactive strategy, and find a ubiquitous strategy profile that prevails throughout evolution, independently of group size and specific PGG parameters, proving also robust in the presence of errors. This strategy, that we call All-Or-None ( AoN), consists in cooperating only after a round of unanimous group behavior (cooperation or defection); not only is it conceptually very simple, it also ensures that cooperation can self-sustain in a population. AoN contains core principles found, e.g., in the repeated 2-person Prisoner's Dilemma, in which case it reduces to the famous Win-Stay-Lose-Shift strategy.

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          Social diversity promotes the emergence of cooperation in public goods games.

          Humans often cooperate in public goods games and situations ranging from family issues to global warming. However, evolutionary game theory predicts that the temptation to forgo the public good mostly wins over collective cooperative action, and this is often also seen in economic experiments. Here we show how social diversity provides an escape from this apparent paradox. Up to now, individuals have been treated as equivalent in all respects, in sharp contrast with real-life situations, where diversity is ubiquitous. We introduce social diversity by means of heterogeneous graphs and show that cooperation is promoted by the diversity associated with the number and size of the public goods game in which each individual participates and with the individual contribution to each such game. When social ties follow a scale-free distribution, cooperation is enhanced whenever all individuals are expected to contribute a fixed amount irrespective of the plethora of public goods games in which they engage. Our results may help to explain the emergence of cooperation in the absence of mechanisms based on individual reputation and punishment. Combining social diversity with reputation and punishment will provide instrumental clues on the self-organization of social communities and their economical implications.
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            Emergence of cooperation and evolutionary stability in finite populations.

            To explain the evolution of cooperation by natural selection has been a major goal of biologists since Darwin. Cooperators help others at a cost to themselves, while defectors receive the benefits of altruism without providing any help in return. The standard game dynamical formulation is the 'Prisoner's Dilemma', in which two players have a choice between cooperation and defection. In the repeated game, cooperators using direct reciprocity cannot be exploited by defectors, but it is unclear how such cooperators can arise in the first place. In general, defectors are stable against invasion by cooperators. This understanding is based on traditional concepts of evolutionary stability and dynamics in infinite populations. Here we study evolutionary game dynamics in finite populations. We show that a single cooperator using a strategy like 'tit-for-tat' can invade a population of defectors with a probability that corresponds to a net selective advantage. We specify the conditions required for natural selection to favour the emergence of cooperation and define evolutionary stability in finite populations.
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              A strategy of win-stay, lose-shift that outperforms tit-for-tat in the Prisoner's Dilemma game.

              The Prisoner's Dilemma is the leading metaphor for the evolution of cooperative behaviour in populations of selfish agents, especially since the well-known computer tournaments of Axelrod and their application to biological communities. In Axelrod's simulations, the simple strategy tit-for-tat did outstandingly well and subsequently became the major paradigm for reciprocal altruism. Here we present extended evolutionary simulations of heterogeneous ensembles of probabilistic strategies including mutation and selection, and report the unexpected success of another protagonist: Pavlov. This strategy is as simple as tit-for-tat and embodies the fundamental behavioural mechanism win-stay, lose-shift, which seems to be a widespread rule. Pavlov's success is based on two important advantages over tit-for-tat: it can correct occasional mistakes and exploit unconditional cooperators. This second feature prevents Pavlov populations from being undermined by unconditional cooperators, which in turn invite defectors. Pavlov seems to be more robust than tit-for-tat, suggesting that cooperative behaviour in natural situations may often be based on win-stay, lose-shift.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Comput Biol
                PLoS Comput. Biol
                plos
                ploscomp
                PLoS Computational Biology
                Public Library of Science (San Francisco, USA )
                1553-734X
                1553-7358
                November 2014
                13 November 2014
                : 10
                : 11
                : e1003945
                Affiliations
                [1 ]Centro de Biologia Molecular e Ambiental da Universidade do Minho, Braga, Portugal
                [2 ]INESC-ID & Instituto Superior Técnico, Universidade de Lisboa, Taguspark, Porto Salvo, Portugal
                [3 ]Centro de Física da Universidade do Minho, Braga, Portugal
                [4 ]ATP-group, CMAF, Instituto para a Investigação Interdisciplinar, Lisboa, Portugal
                [5 ]Departamento de Matemática e Aplicações da Universidade do Minho, Braga, Portugal
                Brain and Spine Institute (ICM), France
                Author notes

                The authors have declared that no competing interests exist.

                Conceived and designed the experiments: FLP VVV FCS JMP. Performed the experiments: FLP VVV FCS JMP. Analyzed the data: FLP VVV FCS JMP. Contributed reagents/materials/analysis tools: FLP VVV FCS JMP. Wrote the paper: FLP VVV FCS JMP.

                Article
                PCOMPBIOL-D-14-00933
                10.1371/journal.pcbi.1003945
                4230726
                25393661
                feff8d64-e090-4813-8758-cb7d9684d9c9
                Copyright @ 2014

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 27 May 2014
                : 27 September 2014
                Page count
                Pages: 5
                Funding
                This research was supported by FEDER through POFC – COMPETE and by FCT-Portugal through fellowships SFRH/BD/77389/2011 and SFRH/BD/86465/2012, by grants PTDC/MAT/122897/2010 and EXPL/EEI-SII/2556/2013, by multi-annual funding of CMAF-UL, CBMA-UM and INESC-ID (under the projects PEst-OE/MAT/UI0209/2013, PEst-OE/BIA/UI4050/2014 and PEst-OE/EEI/LA0021/2013) provided by FCT-Portugal, and by Fundação Calouste Gulbenkian through the “Stimulus to Research” program for young researchers. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Computational Biology
                Population Modeling
                Ecology
                Behavioral Ecology
                Population Ecology
                Evolutionary Biology
                Evolutionary Processes
                Evolutionary Emergence
                Psychology
                Behavior
                Altruistic Behavior
                Collective Human Behavior
                Computer and Information Sciences
                Computer Modeling
                Systems Science
                Adaptive Systems
                Agent-Based Modeling
                Complex Systems
                Dynamical Systems
                Nonlinear Dynamics
                Ecology and Environmental Sciences
                Physical Sciences
                Mathematics
                Applied Mathematics
                Game Theory
                Probability Theory
                Stochastic Processes
                Physics
                Interdisciplinary Physics
                Statistical Mechanics
                Research and Analysis Methods
                Simulation and Modeling
                Custom metadata
                The authors confirm that all data underlying the findings are fully available without restriction. All relevant data are within the paper and its Supporting Information files.

                Quantitative & Systems biology
                Quantitative & Systems biology

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