Rosenstock Sarita, O'Connor Cailin
Logic and Philosophy of Science, University of California Irvine, Irvine, CA, United States.
Front Robot AI. 2018 Mar 2;5:9. doi: 10.3389/frobt.2018.00009. eCollection 2018.
We use techniques from evolutionary game theory to analyze the conditions under which guilt can provide individual fitness benefits, and so evolve. In particular, we focus on the benefits of guilty apology. We consider models where actors err in an iterated prisoner's dilemma and have the option to apologize. Guilt either improves the trustworthiness of apology or imposes a cost on actors who apologize. We analyze the stability and likelihood of evolution of such a "guilt-prone" strategy against cooperators, defectors, grim triggers, and individuals who offer fake apologies, but continue to defect. We find that in evolutionary models guilty apology is more likely to evolve in cases where actors interact repeatedly over long periods of time, where the costs of apology are low or moderate, and where guilt is hard to fake. Researchers interested in naturalized ethics, and emotion researchers, can employ these results to assess the plausibility of fuller accounts of the evolution of guilt.
我们运用进化博弈论的技术来分析内疚感能够为个体适应性带来益处并因此得以进化的条件。特别地，我们关注内疚式道歉的益处。我们考虑这样的模型：参与者在重复囚徒困境中犯错且有道歉的选项。内疚感要么提高道歉的可信度，要么给道歉的参与者带来成本。我们分析这种“易产生内疚感”的策略相对于合作者、背叛者、冷酷触发者以及那些假意道歉却继续背叛的个体的稳定性和进化可能性。我们发现，在进化模型中，内疚式道歉更有可能在以下情形中进化：参与者长时间反复互动、道歉成本低或适中，以及内疚感难以伪装。对自然化伦理学感兴趣的研究者以及情感研究者，可以利用这些结果来评估关于内疚感进化的更全面解释的合理性。
