Robust Institutional Design in Expert-Decision Maker Systems

Across multiple sectors, including health, environmental management, and economics, decision makers are faced with making choices that affect society writ large. These choices require the decision makers to synthesize information from varying sources, handle uncertainty, and weigh competing benefits and costs, often within complex environments. To handle this challenge, many decision makers delegate information processing to experts. These experts and decision makers are often embedded in institutions, with institutional structures varying in terms of factors including hierarchy, size, and adaptability. From a design standpoint, while expert-decision maker dynamics may be difficult to intervene upon, institutions offer a promising means to structurally direct multiagent systems towards desired social objectives.

This meeting brings together researchers to understand how institutional designs affect the robustness of desired social objectives in expert-decision maker systems. We will develop a theoretical model using dynamical systems, game theory, and network science, which will be motivated by qualitative knowledge and empirically validated using one or more case studies. We will develop (i) a theoretical framework for conceptualizing expert-decision maker systems dynamically, (ii) a typology of policy problems that rely on expert-decision maker systems, (iii) test the model with data from public goods problems to operationalize the framework, and (iv) provide insights on tradeoffs for varying institutional structures across scales. This research will offer insights to the following questions: how can differences in institutional structures among experts lead to vastly different outcomes in decision making? and how could institutional structures be optimized to benefit the public good?