Integrating Models of Trust, Gossip, and Emotion for Artifical Agents

该奖项支持使用基于有限理性代理的模型，通过将信任和情感模型整合到这些代理中，来研究在基本的社会群体环境中出现的多代理“可信信息结构”。由于这些代理只交换信息，这些可信结构的出现代表了组织学习和适应的一种原始(但基本)形式。然而，社会环境使问题复杂化，因为这些结构的调整也纯粹是信息性的--通过关于其他代理人的八卦，选择是否信任其他代理人，以及对特定类别事件的情绪型反应。该模型的计算公式综合了心理学、社会学和人类学的理论。这项研究推进了社会科学和计算科学领域的知识，提供了对互联网上出现的建议网络的新兴形式的洞察，探索了基本形式的情感如何改变主体(和群体)行为，并有助于将主体属性与新兴群体和组织学习和行为联系起来。

This award supports the use of boundedly-rational agent-based models to study the emergence of multi-agent "trusted information structures" in a rudimentary societal group setting by incorporating models of trust and emotion in these agents. As these agents exchange only information, the emergence of these trusted structures represent a primitive (but fundamental) form of organizational learning and adaptation. However, the social setting complicates matters, as the adjustment of these structures is also purely informational - through gossip about the other agents, choices to trust other agents or not, and emotional-type responses to particular categories of events. The computational formulation of the model integrates theories from psychology, sociology, and anthropology. This research advances knowledge at the interstices of social and computational sciences, provides insight into the emergent forms of advice networks occurring on the Internet, explores how elementary forms of emotion alter agent (and group) behaviors, and contributes to the linking of agent properties to emergent group and organizational learning and behaviors.