CIF: Small: Foundations of Belief Sharing in Human-Machine Systems

这项工作旨在开发数学定律和基本原则，用于在人类和机器智能共同工作的系统中进行信念共享，以做出稳健的决策。以前在统计信号处理和心理学方面的工作只独立考虑了技术限制或人类限制，但联合考虑人类和机器的信息限制对于设计未来的社会技术系统至关重要，特别是当人们被太多信息淹没时。这样的系统的基本限制和最优设计的理论是缺乏的。而不是与代理共享原始数据或本地决策的系统，我们开发基于共享信念的中间设计。与集中分析原始数据相比，信念共享增加了网络单元之间的模块化，但与分散的地方决策相比，也加强了协调。我们建立在我们以前的有限理性模型的人和人工智能的随机模型，以确定最佳的混合人机架构。首先，我们发现贝叶斯风险和离散选择模型下的信念共享的基本信息理论限制，新类型的CEO问题。作为一个关键的子步骤，这涉及到确定基本的Ziv-Zakai界限贝叶斯估计下的非二次准则。然后，我们使用量化和决策理论来开发具有认知和算法限制的代理的最佳架构，包括考虑到人类行为的信念和判断池规则的最佳分类。最后，我们认为，在复杂的网络结构，以实现集体智慧，研究纳什均衡平衡焦点和共同关注的信念沟通所需的语言。

This work aims to develop mathematical laws and foundational principles for belief sharing in systems with human and machine intelligence working together to make robust decisions. Prior work in statistical signal processing and in psychology has only considered technological limitations or human limitations independently, but jointly considering informational limitations of both humans and machines is critical in engineering future sociotechnical systems, especially when people are overwhelmed by too much information. A theory for fundamental limits and optimal designs for such systems is lacking.Rather than systems with agents sharing either raw data or local decisions, we develop intermediate designs based on sharing beliefs. Belief sharing increases modularity among networked units compared to central analysis of raw data, yet also strengthens coordination compared to decentralized local decisions. We build on our prior bounded rationality models of people and stochastic models of artificial intelligence to determine optimal mixed human-machine architectures. First, we find fundamental information-theoretic limits of belief-sharing under Bayes risk and discrete choice models, new kinds of CEO problems. As a key substep, this involves determining fundamental Ziv-Zakai bounds on Bayesian estimation under non-quadratic criteria. We then use quantization and decision theory to develop optimal architectures that have cognitively- and algorithmically-limited agents, including optimal categorization of beliefs and judgment pooling rules taking human behavior into account. Finally, we consider the language needed to communicate beliefs in complicated network structures to achieve collective intelligence, studying Nash equilibria balancing focal and shared concerns.