School of Computing, Media and the Arts, Teesside University, Borough Road, Middlesbrough, TS1 3BA, UK.
School of Electronics and Computer Science, University of Southampton, University Road, Southampton, SO17 1BJ, UK.
Sci Rep. 2018 Oct 30;8(1):15997. doi: 10.1038/s41598-018-34435-2.
The problem of promoting the evolution of cooperative behaviour within populations of self-regarding individuals has been intensively investigated across diverse fields of behavioural, social and computational sciences. In most studies, cooperation is assumed to emerge from the combined actions of participating individuals within the populations, without taking into account the possibility of external interference and how it can be performed in a cost-efficient way. Here, we bridge this gap by studying a cost-efficient interference model based on evolutionary game theory, where an exogenous decision-maker aims to ensure high levels of cooperation from a population of individuals playing the one-shot Prisoner's Dilemma, at a minimal cost. We derive analytical conditions for which an interference scheme or strategy can guarantee a given level of cooperation while at the same time minimising the total cost of investment (for rewarding cooperative behaviours), and show that the results are highly sensitive to the intensity of selection by interference. Interestingly, we show that a simple class of interference that makes investment decisions based on the population composition can lead to significantly more cost-efficient outcomes than standard institutional incentive strategies, especially in the case of weak selection.
在行为学、社会科学和计算科学等多个领域，人们深入研究了如何在以自我为中心的个体的群体中促进合作行为的进化。在大多数研究中，合作被假设是由群体中参与的个体的共同行为产生的，而没有考虑到外部干扰的可能性以及如何以高效的方式进行干扰。在这里，我们通过研究基于进化博弈论的高效干扰模型来弥合这一差距，其中一个外部决策者旨在以最小的成本确保在玩一次性囚徒困境的个体群体中实现高水平的合作。我们推导出了分析条件，即在保证给定合作水平的同时，通过干扰策略或方案将投资总成本最小化，结果表明，干扰的选择强度对结果高度敏感。有趣的是，我们表明，一种简单的基于群体构成做出投资决策的干扰方式可以比标准的制度激励策略产生更高效的结果，特别是在选择较弱的情况下。
