车对车通信模型中的理性与行为反馈
Rationality and Behavior Feedback in a Model of Vehicle-to-Vehicle Communication
AI总结：
本文研究V2V通信中人类智能体的行为建模，提出非贝叶斯行为模型和外生均衡模型，证明其与原始贝叶斯内生模型在均衡行为上等价，但信息悖论仅在内生模型中出现，揭示了固定类型分布贝叶斯博弈的局限性。
摘要：
车对车（V2V）通信旨在通过分布式信息共享来改善道路安全；然而，这类系统面临一个设计挑战：难以预测和优化人类智能体在引入这些信息后将如何响应。贝叶斯博弈是建模此类场景的标准方法；在贝叶斯博弈中，智能体基于一个固定的、已知的分布以概率方式采取各种类型。此类模型中的智能体表面上执行贝叶斯推断，这对大多数人类而言可能不是一种合理的认知要求。使问题更加复杂的是，提供给智能体的信息往往隐含地依赖于智能体的行为，这意味着智能体类型的分布是智能体行为的函数（即类型分布是内生的）。在本文中，我们研究了一个现有的V2V通信模型，但沿两个维度对其进行了松弛：首先，我们提出一个不要求人类智能体执行贝叶斯推断的行为模型；其次，我们提出一个避免困难的内生递归的均衡模型。令人惊讶的是，我们表明简化的非贝叶斯行为模型产生了与原始贝叶斯模型完全相同的均衡行为，这可能为贝叶斯模型赋予可信度。然而，我们也表明，原始的内生均衡模型对于获得某些信息悖论是严格必要的；这些悖论不会出现在更简单的外生模型中。这表明具有固定类型分布的标准贝叶斯博弈模型不足以表达某些重要现象。
英文摘要：
Vehicle-to-Vehicle (V2V) communication is intended to improve road safety through distributed information sharing; however, this type of system faces a design challenge: it is difficult to predict and optimize how human agents will respond to the introduction of this information. Bayesian games are a standard approach for modeling such scenarios; in a Bayesian game, agents probabilistically adopt various types on the basis of a fixed, known distribution. Agents in such models ostensibly perform Bayesian inference, which may not be a reasonable cognitive demand for most humans. To complicate matters, the information provided to agents is often implicitly dependent on agent behavior, meaning that the distribution of agent types is a function of the behavior of agents (i.e., the type distribution is endogenous). In this paper, we study an existing model of V2V communication, but relax it along two dimensions: first, we pose a behavior model which does not require human agents to perform Bayesian inference; second, we pose an equilibrium model which avoids the challenging endogenous recursion. Surprisingly, we show that the simplified non-Bayesian behavior model yields the exact same equilibrium behavior as the original Bayesian model, which may lend credibility to Bayesian models. However, we also show that the original endogenous equilibrium model is strictly necessary to obtain certain informational paradoxes; these paradoxes do not appear in the simpler exogenous model. This suggests that standard Bayesian game models with fixed type distributions are not sufficient to express certain important phenomena.
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