在代的囚犯困境中识别和评估敲诈勒索的有效性
Vincent Knight1, Marc Harper2, Nikoleta E Glynatsi3
1School of Mathematics, Cardiff University, Cardiff, United Kingdom.
PloS one
|July 26, 2024
概括
这项研究引入了一种新方法,用于检测代理相互作用中的勒索行为,超越理论分析,转向经验观察. 它揭示了这些严格的策略,即使被广泛定义,在代理对代理合作中缺乏适应性.
科学领域:
- 人工智能的人工智能
- 游戏理论 游戏理论
- 计算社会科学 计算社会科学
背景情况:
- 在代理与代理之间的互动中,合作往往是使用代囚犯困境来建模的,在这种困境中,代理人平衡短期成本与未来收益.
- 零决定因素 (ZD) 策略,其中的一个子类是被证明是敲诈的,主要通过在特定场景中的理论评估来研究.
- 对ZD策略的现有研究通常需要对策略参数 (内存-一合作概率) 的完整知识,并假定代数刚性.
研究的目的:
- 开发一种新的方法,直接从任意策略的历史交互数据中检测勒索行为.
- 将研究范式从评估已知的勒索策略转变为在广泛的策略中实证量化勒索强度.
- 通过使用更广泛的,经验性推导的定义,研究勒索策略的适应性.
主要方法:
- 分析来自大量代理策略的历史交互数据 (游戏历史).
- 基于观察到的相互作用,开发一种方法来经验性地量化勒索行为的强度.
- 经验研究结果与理论预测的战略刚性和适应性的比较.
主要成果:
- 成功开发并应用了一种方法,通过交互历史记录来检测和量化勒索行为.
- 经验证明,广泛的策略表现出不同程度的敲诈勒索.
- 证实了勒索策略,即使在这个更广泛的定义下,也表现出根本缺乏适应能力.
结论:
- 开发的方法为研究多代理系统中的互惠性和利用提供了一个新的经验镜头.
- 这些发现挑战了代数刚性的假设,通过展示敲诈勒索可以在不同的策略中经验性地检测到.
- 勒索策略的固有不灵活性是一个强大的特征,即使通过历史游戏数据进行分析时也会持续存在.
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