This paper investigates an evolutionary iterated prisoner’s dilemma (IPD) model of multiple agents, in which agents interact in terms of the pair-wise IPD game while adapting their attitudes towards income stream risk. Specifically, agents will become more risk averse (or more risk seeking) if their game payoffs exceed (or fall below) their expectations. In particular, agents use their peers’ average payoffs as expectations (social comparison) when their payoffs are lower than their peers’ averages, but use their own historical payoffs as expectations (historical comparison) when their payoffs are higher than their peers’ averages. Such selective attention to social comparison or historical comparison manifests a desire for continuous improvement of agents. Simulations are conducted to investigate the evolution of cooperation under the selective attention mechanism. Results indicate that agents can sustain a highly cooperative equilibrium when they consider selective attention in adjusting their risk attitudes. This holds true for both the well-mixed and the network-based games, even in the presence of uncertain game payoffs. The reason is that, selective attention can significantly induce agents to adhere to conditional cooperation as well as to identify uncertainty in payoffs, which enhances the risk-averse behavior of agents in the IPD game. As a result, high levels of cooperation can be attained.

中文翻译：

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进化迭代囚徒困境博弈中对历史比较或社会比较的选择性关注

本文研究了多个代理的进化迭代囚徒困境 (IPD) 模型，其中代理根据成对 IPD 博弈进行交互，同时调整他们对收入流风险的态度。具体来说，如果他们的游戏收益超过（或低于）他们的预期，代理将变得更加规避风险（或更多的风险寻求）。特别是，当他们的回报低于同行的平均水平时，代理人将他们的同行的平均回报作为预期（社会比较），而当他们的回报高于同行的平均水平时，他们将自己的历史回报作为预期（历史比较）。这种对社会比较或历史比较的选择性关注表明了对代理不断改进的渴望。进行模拟以研究选择性注意机制下合作的演变。结果表明，当代理人在调整风险态度时考虑选择性注意时，他们可以维持高度合作的平衡。这对于混合游戏和基于网络的游戏都适用，即使存在不确定的游戏收益也是如此。原因在于，选择性注意可以显着诱导智能体遵守条件合作以及识别收益的不确定性，从而增强了智能体在 IPD 博弈中的风险规避行为。因此，可以实现高水平的合作。这对于混合游戏和基于网络的游戏都适用，即使存在不确定的游戏收益也是如此。原因在于，选择性注意可以显着诱导智能体遵守条件合作以及识别收益的不确定性，从而增强了智能体在 IPD 博弈中的风险规避行为。因此，可以实现高水平的合作。这对于混合游戏和基于网络的游戏都适用，即使存在不确定的游戏收益也是如此。原因在于，选择性注意可以显着诱导智能体遵守条件合作以及识别收益的不确定性，从而增强了智能体在 IPD 博弈中的风险规避行为。因此，可以实现高水平的合作。