Attitude towards information in multi-agent settings: Understanding and mitigating Avoidance and Over-Evaluation

传感、数据通信和处理方面的新技术允许对建筑环境进行广泛的测量，传感器收集的大量信息流可以改变城市系统的运营和功能。然而，这一发展还取决于公民和利益相关者对信息的态度。这个项目调查了相互作用的代理人如何就收集信息做出决定，重点是与公共政策互动的城市系统的用户和管理者。对于理性的、孤立的、不受外部约束的行为主体而言，“信息不伤人”和对主体信念影响小的数据具有较小的价值。例如，这意味着这些代理总是愿意安装免费(或廉价)传感器，而只有在提供高影响力信息的情况下才愿意安装昂贵的传感器。然而，当代理之间相互竞争时，这些直观特性在多代理设置中并不成立，也不适用于在法规强加的外部约束下行事的代理。该项目将结合社会科学、工程学和计算机科学的分析，根据代理人的偏好和外部法规，开发一个框架来模拟在这些背景下对信息的态度。该项目的目标是：1)开发一个在多主体环境下评估信息价值的框架，对决策者和决策者之间遵循外部法规的交互进行建模；2)收集和分析关于信息态度的经验数据，使用用户的调查和访谈，并校准(1)、3)中建立的模型，以设计缓解信息回避和过度评价的机制，并评估其有效性。该项目集成了待测量和传感器性能的概率模型、代理的效用函数和外部约束、优化方法和行为建模，通过贝叶斯预后验分析来评估价值或信息。这种方法将使人们能够理解信息回避和过度评估是如何产生的，以及适当的激励和监管机制如何能够减轻它们。该项目的结果将是更好地实证理解对信息的态度、开发对建筑环境的有效大规模监测以及促进有效信息收集的公共政策、整合社会和代理人的效用的关键。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，认为值得支持。

New technologies in sensing, data communication and processing allow for extensive instrumentation of the built environment, and the massive flow of information collectable by sensors can transform the operation and the functionality of urban systems. However, this development depends also on the attitude of citizens and stakeholders toward information. This project investigates how interacting agents take decisions about collecting information, with focus on users and managers of urban systems interacting with public policies. For rational and isolated agents acting without external constraints, "information never hurts" and data with low impact on the agents' belief have a small value. This implies, for example, that these agents are always willing to install free (or cheap) sensors, and to install expensive ones only if they provide high-impact information. However, these intuitive properties do not hold true in multi-agent settings, when agents compete one against each other, nor for agents acting under external constraints as those imposed by regulations. Integrating analysis in social science, engineering and computer science, the project will develop a framework for modeling the attitude towards information in these contexts, depending on the agents' preference and the external regulations.The goals of the project are: 1) To develop a framework for assessing the Value of Information in multi-agent settings, modeling the interaction between policy makers and decision makers following external regulations, 2) to gather and analyze empirical data about the attitude toward information, using surveys and interviews among users, and calibrate the models developed in (1), 3) to design mechanisms alleviating Information Avoidance and Over Evaluation, and assess their effectiveness. The project integrates probabilistic models of quantities to be measured and of sensor performance, agents' utility functions and external constraints, optimization methods and behavior modeling, to assess the Value or Information via Bayesian pre-posterior analysis. Such approach will allow understanding how Information Avoidance and Over Evaluation arise, and how appropriate mechanisms of incentives and regulations can mitigate them. The project's outcomes will be key for a better empirical understanding of the attitude towards information, for developing effective large-scale monitor of the built environment and public policies promoting effective information collection, integrating societal and agents' utilities.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.