“保守主义者过度适应，自由主义者不足适应”：情感和不确定性的社会心理控制
"Conservatives Overfit, Liberals Underfit": The Social-Psychological Control of Affect and Uncertainty
摘要：
人类社交网络中人工代理的存在正在增长。从聊天机器人到机器人，发达国家的人类经验正在走向一个社会技术系统，在这个系统中，代理人可以是技术的，也可以是生物的，两者之间的区别越来越模糊。鉴于情感是人类互动的关键要素，使人工智能体能够推理情感是走向技术智能体和人类可以共同工作的未来的关键垫脚石。本文介绍了构建智能计算代理，整合情感和认知的工作。这些代理人是建立在社会心理学贝叶斯情感控制理论（贝叶斯法）。贝叶斯法的核心思想是，人类在社会交往中受到情感一致性的激励：他们努力使自己的社会经验在深层的情感层面上与他们的身份感和通过文化共享符号构建的一般世界观保持一致。这种情感上的一致性创造了群体成员之间的凝聚力，并有助于合作巩固为关系群体的承诺。贝叶斯行为代理的动机，在他们的社会互动的情感对齐和决策理论推理的组合，权衡两者作为一个功能的不确定性或不可预测性的情况。本文提供了一个高层次的双过程理论和先进的贝叶斯行为作为一个合理的，计算上听话的模型，基于社会心理学理论。我们引入了一个修订后的贝叶斯法模型，更深入地整合了社会心理理论，我们证明了该模型的一个组成部分，足以解释公平，不和谐和一致性的认知偏见。我们展示了该模型如何在强化学习中统一不同的探索策略。
英文摘要：
The presence of artificial agents in human social networks is growing. From chatbots to robots, human experience in the developed world is moving towards a socio-technical system in which agents can be technological or biological, with increasingly blurred distinctions between. Given that emotion is a key element of human interaction, enabling artificial agents with the ability to reason about affect is a key stepping stone towards a future in which technological agents and humans can work together. This paper presents work on building intelligent computational agents that integrate both emotion and cognition. These agents are grounded in the well-established social-psychological Bayesian Affect Control Theory (BayesAct). The core idea of BayesAct is that humans are motivated in their social interactions by affective alignment: they strive for their social experiences to be coherent at a deep, emotional level with their sense of identity and general world views as constructed through culturally shared symbols. This affective alignment creates cohesive bonds between group members, and is instrumental for collaborations to solidify as relational group commitments. BayesAct agents are motivated in their social interactions by a combination of affective alignment and decision theoretic reasoning, trading the two off as a function of the uncertainty or unpredictability of the situation. This paper provides a high-level view of dual process theories and advances BayesAct as a plausible, computationally tractable model based in social-psychological theory. We introduce a revised BayesAct model that more deeply integrates social-psychological theorising, and we demonstrate a component of the model as being sufficient to account for cognitive biases about fairness, dissonance and conformity. We show how the model can unify different exploration strategies in reinforcement learning.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
