论文标题
与反复的小组互动的进化信任游戏中的有条件投资策略
Conditional investment strategy in evolutionary trust games with repeated group interactions
论文作者
论文摘要
通过研究信任游戏，研究人类或人造代理商之间的信任行为具有悠久的传统。尽管以前基于进化游戏理论的研究表明，如果考虑网络结构或声誉，可以促进信任和可信赖性，但他们经常认为代理商之间的互动是一声的，并且投资者在做出决策之前不考虑投资环境，这与许多现实情况相撞。在本文中，我们将有条件的投资策略介绍到重复的N-Player Trust游戏中，其中有条件的投资者决定根据对集团的可信度水平的评估进行投资或不投资。通过使用马尔可夫决策过程的方法，我们研究了与有条件投资策略的重复群体相互作用中信任的进化动态。我们发现有条件的投资者可以与值得信赖的受托人建立有效的联盟，因此他们可以扫除不值得信赖的受托人。此外，我们验证了这种联盟可以在广泛的模型参数中存在。这些结果可以解释为什么在智能代理商之间的游戏互动中可以维持对他人的信任并以值得信赖的行动来偿还他们。
It has a long tradition to study trust behavior among humans or artificial agents by investigating the trust game. Although previous studies based on evolutionary game theory have revealed that trust and trustworthiness can be promoted if network structure or reputation is considered, they often assume that interactions among agents are one-shot and investors do not consider the investment environment before making decisions, which collide with many realistic situations. In this paper, we introduce the conditional investment strategy into the repeated N-player trust game, in which conditional investors decide to invest or not depending on their assessment of the trustworthiness level of the group. By using the approach of the Markov decision process, we study the evolutionary dynamics of trust in repeated group interactions with the conditional investment strategy. We find that conditional investors can form an effective alliance with trustworthy trustees, hence they can sweep out untrustworthy trustees. Moreover, we verify that such alliance can exist in a wide range of model parameters. These results may explain why trusting in others and reciprocating them with trustworthy actions can be sustained in game interactions among intelligent agents.
