Research has shown that when two individuals meet repeatedly they are more likely to cooperate with one another. Flávio Pinheiro and colleagues from the Universities of Minho and Lisbon show that the most successful strategy for cooperation occurs only after an experience of group unanimous behaviour.
In this week's PLOS Computational Biology, the authors explore the core principles of the 'prisoner's dilemma' as applied to decision-making within a group environment. The prisoner's dilemma of cooperation is a useful metaphor employed in situations where personal interests impel individuals to make decisions that oppose the interests of the group.
Using an Evolutionary Game Theory model, the authors investigate whether cooperation might emerge in individuals who assemble into groups that interact through repeated 'Public Goods Games', where individuals may contribute to a common pool, subsequently sharing the resources.
The authors explore a large set of possible responses that depend on previous levels of group cooperation. The most successful strategy for cooperation, which they call 'All-or-None', only occurs following a round of unanimous group behaviour and is both cooperative and punitive.
The results of the study may find applications to biological, technological, social and economic studies that incorporate some form of cooperation or coordination within groups, from group hunting and social welfare to climate change negotiations.
All works published in PLOS Computational Biology are Open Access, which means that all content is immediately and freely available. Use this URL in your coverage to provide readers access to the paper upon publication: http://www.
Contact: Jorge M. Pacheco
Address: Universidade do Minho,
Departamento de Matemática e Aplicações,
Braga, 1649-003, PORTUGAL
Phone: +351 253 604 364
For more information on the authors see http://www.
Citation: Pinheiro FL, Vasconcelos VV, Santos FC, Pacheco JM (2014) Evolution of All-or-None Strategies in Repeated Public Goods Dilemmas. PLoS Comput Biol 10(11): e1003945. doi:10.1371/journal.pcbi.1003945
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 Fundacao 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.
Competing Interests: The authors have declared that no competing interests exist.
About PLOS Computational Biology
PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales through the application of computational methods. All works published in PLOS Computational Biology are Open Access. All content is immediately available and subject only to the condition that the original authorship and source are properly attributed. Copyright is retained. For more information follow @PLOSCompBiol on Twitter or contact firstname.lastname@example.org.
PLOS is a nonprofit publisher and advocacy organization founded to accelerate progress in science and medicine by leading a transformation in research communication. For more information, visit http://www.