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

这项工作旨在为人类和机器智能共同做出稳健决策的系统中的信念共享开发数学定律和基本原则。以前在统计信号处理和心理学方面的工作只独立地考虑了技术限制或人类限制，但联合考虑人和机器的信息限制在设计未来的社会技术系统中是至关重要的，特别是当人们被太多的信息淹没时。关于这类系统的基本极限和最优设计的理论是缺乏的。与代理共享原始数据或局部决策的系统不同，我们开发了基于共享信念的中间设计。与原始数据的中央分析相比，信念共享增加了联网单位之间的模块化，但与分散的地方决策相比，也加强了协调。我们建立了人的先验有限理性模型和人工智能的随机模型，以确定最优的人机混合体系结构。首先，我们发现了在贝叶斯风险和离散选择模型下信念共享的基本信息论极限，这是一类新的CEO问题。作为一个关键子步骤，这涉及到在非二次准则下确定贝叶斯估计的基本ZIV-Zakai界。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优体系结构，包括考虑人类行为的信念的最优分类和判断汇集规则。最后，我们考虑了在复杂的网络结构中交流信念以实现集体智能所需的语言，研究了平衡焦点和共享关切的纳什均衡。

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.