Contact Information
Department of Economics
Northwestern University
2211 Campus Drive
Evanston, IL 60208
Phone: 202-415-2751
Email: mcamara@u.northwestern.edu
Personal Website: mkcamara.github.io
Twitter: @modibokhane
Education
Ph.D., Economics, Northwestern University, 2022 (expected)
MA, Economics, Northwestern University, 2018
BA, Mathematics and Economics, University of Pennsylvania, 2016
Primary Fields of Specialization
Microeconomic Theory, Economics & Computation
Secondary Fields of Specialization
Econometrics
Curriculum Vitae
Job Market Paper
“Computationally Tractable Choice”
I incorporate computational constraints into decision theory in order to capture how cognitive limitations affect behavior. I impose an axiom of computational tractability that rules out behaviors that are thought to be fundamentally hard. I use this framework to better understand common behavioral heuristics: if choices are tractable and consistent with the expected utility axioms, then they are observationally equivalent to forms of choice bracketing. Then I show that a computationally-constrained decisionmaker can be objectively better off if she is willing to use heuristics that would not appear rational to an outside observer.
Working Papers
“Mechanisms for a No-Regret Agent: Beyond the Common Prior” (with Jason Hartline and Aleck Johnsen)
Proceedings of FOCS 2020
We study repeated games of incomplete information between a policymaker with commitment power and a single agent. We propose policies that adapt to historical data over time, assuming the agent does the same, without making any assumptions about the data-generating process. They are competitive with optimal static policies that rely on much stronger assumptions, like common prior beliefs. We conclude that robust solution concepts in mechanism design may be too pessimistic if they do not account for the possibility of learning over time.
“Mechanism Design with a Common Dataset”
I propose a new approach to mechanism design: rather than assume a common prior belief, assume access to a common dataset. I restrict attention to incomplete information games where a designer commits to a policy and a single agent responds. I proposed a penalized policy that performs well under weak assumptions on how the agent learns from data. Policies that are too complex, in a precise sense, are penalized because they lead to unpredictable responses by the agent. This approach leads to new insights in models of vaccine distribution, prescription drug approval, performance pay, and product bundling.
Work in Progress
Incentives for Informed Voting (with Nicole Immorlica and Brendan Lucier)
Signaling through Non-Disclosure (with Ian Ball, Sid Banerjee, and Nicole Immorlica)
Fair Mechanism Design (with Hedyeh Beyhaghi, Jason Hartline, Aleck Johnsen, and Sheng Long)
Research Statement
Download Research Statement (PDF)
References
Prof. Eddie Dekel (Committee Co-Chair)
Prof. Jason Hartline (Committee Co-Chair)
Prof. Marciano Siniscalchi
Prof. Jeffrey Ely