Departamento de Informática and Centro de Inteligência Artificial, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, 2829-516 Caparica, Portugal.
Proc Natl Acad Sci U S A. 2011 Jun 28;108(26):10421-5. doi: 10.1073/pnas.1015648108. Epub 2011 Jun 9.
From group hunting to global warming, how to deal with collective action may be formulated in terms of a public goods game of cooperation. In most cases, contributions depend on the risk of future losses. Here, we introduce an evolutionary dynamics approach to a broad class of cooperation problems in which attempting to minimize future losses turns the risk of failure into a central issue in individual decisions. We find that decisions within small groups under high risk and stringent requirements to success significantly raise the chances of coordinating actions and escaping the tragedy of the commons. We also offer insights on the scale at which public goods problems of cooperation are best solved. Instead of large-scale endeavors involving most of the population, which as we argue, may be counterproductive to achieve cooperation, the joint combination of local agreements within groups that are small compared with the population at risk is prone to significantly raise the probability of success. In addition, our model predicts that, if one takes into consideration that groups of different sizes are interwoven in complex networks of contacts, the chances for global coordination in an overall cooperating state are further enhanced.
从群体狩猎到全球变暖,如何应对集体行动可以用合作的公共物品博弈来描述。在大多数情况下,贡献取决于未来损失的风险。在这里,我们引入了一种进化动力学方法来解决广泛的合作问题,在这些问题中,试图将未来的损失最小化会将失败的风险变成个体决策中的一个核心问题。我们发现,在高风险和严格要求成功的小群体中做出的决策,大大提高了协调行动和避免公地悲剧的可能性。我们还提供了关于合作的公共物品问题最佳解决规模的见解。我们认为,与涉及大多数人口的大规模努力相反,合作的公共物品问题最好通过与风险人口相比规模较小的群体内的局部协议联合来解决,而不是通过大规模努力来解决,这可能会适得其反,难以实现合作。此外,我们的模型预测,如果考虑到不同大小的群体交织在复杂的联系网络中,那么在整体合作状态下实现全球协调的机会将进一步增加。