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

点击翻译按钮获取中文摘要

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.