用量子随机漫步建模人机交互中信任的动态变化。
Modeling the quantum-like dynamics of human reliability ratings in Human-AI interactions by interaction dependent Hamiltonians
- 基于交互特性设计不同哈密顿量，模拟信任演化。
- 实证参数驱动的模型能捕捉信任波动的敏感性。
- 适合研究人机信任机制或高风险决策场景的学者。
随着信息环境日益由人工智能驱动，人类在与智能系统互动中的信任问题变得愈发重要。未来，人类与智能机器人将共同应对飓风、地震或核事故等高风险灾难，即使在高度不确定的情况下，仍需协同决策，而信任是互动有效性的核心。建模信任动态的关键挑战在于如何纳入对人类信任判断波动的敏感性。本文探索了量子随机漫步模型在模拟人机交互中信任动态方面的潜力，并基于交互特性引入不同的哈密顿量，以整合对信任判断波动的响应。研究发现，利用实证参数确定哈密顿量可为建模人机交互中的信任演化提供有效途径。
原文摘要 · Abstract (English)
As our information environments become ever more powered by artificial intelligence (AI), the phenomenon of trust in a human's interactions with this intelligence is becoming increasingly pertinent. For example, in the not too distant future, there will be teams of humans and intelligent robots involved in dealing with the repercussions of high-risk disaster situations such as hurricanes, earthquakes, or nuclear accidents. Even in such conditions of high uncertainty, humans and intelligent machines will need to engage in shared decision making, and trust is fundamental to the effectiveness of these interactions. A key challenge in modeling the dynamics of this trust is to provide a means to incorporate sensitivity to fluctuations in human trust judgments. In this article, we explore the ability of Quantum Random Walk models to model the dynamics of trust in human-AI interactions, and to integrate a sensitivity to fluctuations in participant trust judgments based on the nature of the interaction with the AI. We found that using empirical parameters to inform the use of different Hamiltonians can provide a promising means to model the evolution of trust in Human-AI interactions.
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