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Revisiting Random Utility Models
(2014-06-06)
This thesis explores extensions of Random Utility Models (RUMs), providing more flexible models and adopting a computational perspective. This includes building new models and understanding their properties such as ...
Toward Generality: Building Better Counterfactual Regret Minimization for Imperfect Information Games
(2022-02-24)
The imperfect information game is a mathematical model that is very useful for modeling interactions in a wide variety of domains, and algorithms that compute the solutions of such games are extremely valuable. Counterfactual ...
Information Markets for Multi-Robot Navigation Under Uncertainty
(2021-11-19)
We investigate the role of an information market in improving navigation outcomes for risk-averse multi-agent systems with incomplete state information. Given a partially observed graph representation of an environment map ...
AI Pricing Collusion: Multi-Agent Reinforcement Learning Algorithms in Bertrand Competition
(2021-06-03)
As e-commerce and online shopping become more widespread, firms are starting to maximize profit by using artificial intelligence, or more specifically reinforcement learning, to price goods. Calvano et al. showed that in ...
The Impact of Tech Demand During COVID-19: A Local Optimization Utility-Based Human Migration Model
(2022-06-03)
Over the past two years, COVID-19 has dramatically disrupted the movement of hu- mans across the United States but its impact on society long-term is still unclear. In this paper, I present a locally optimized utility model ...
Zoom or Bust?: An Exploration into Remote Work Productivity and Best Practices
(2022-05-23)
COVID-19 pushed millions of workers from the office to Zoom. With remote work becoming the
new normal, firms and employees alike continue to wonder about the benefits or drawbacks to remote work on overall productivity. ...
Agent-Based Modeling for Optimal Economic Policy with Exogenous Shocks
(2021-06-17)
My thesis explores the application of reinforcement learning and agent-based computational economics to the problem of optimal policy in an environment with exogenous shocks. I develop an agent-based general equilibrium ...
Reinforcement Learning for Modeling Platform Economies Under Shock
(2022-05-23)
Large-scale platform markets such as Amazon and Uber are playing an increasingly vital and pervasive role in today's economy, especially during periods of economic shock such as during the COVID-19 pandemic. Across many ...
Incentive-Aware Machine Learning for Decision Making
(2022-08-12)
Machine Learning algorithms are increasingly being deployed in consequential decision-making for people's lives. These decisions affect widely different aspects of our lives; e.g., Machine Learning algorithms decide what ...