Research agenda
I am an economic theorist with interests in Matching Markets, Mechanism Design, Economics of Information, Bounded Rationality, Evolutionary Game Theory, and Experimental Economics.
My research aims at both a descriptive value (improving our understanding of the world) and a normative value (making policy design recommendations to achieve a number of social objectives such as efficiency or fairness). It is mostly applied. I use game theory - both non-cooperative game theory (eg.: computing Bayes-Nash equilibria in WWA and PMIA) and cooperative game theory (eg.: characterizing pairwise stability of a matching in my VVS) - as a tool to analyze strategic interactions.When the standard tools fail, I propose methodological contributions by defining new solution concepts (eg.: in VVS, steady state of a dynamic blocking pair process with memory, or stable mixed matching), and identifying novel equilibrium structures (eg.: in WWA, a block structure arising at Bayes-Nash equilibrium).
1) Frictional matching markets
My Ph.D. research has mainly analyzed frictional matching markets. Matching refers to the formation of productive partnerships, with applications to civil servants' job markets (allocation of teachers to schools, of doctors to hospitals), admissions of students to universities, social housing, organ donations, marriage or dating market, and many more.The economic literature on matching has mostly abstracted from the many frictions that burden real-life operating of matching markets, implicitly assuming that agents have common knowledge of the whole market structure and that any action on the market is costless and focusing on the pure allocation problem. I model frictional matching markets featuring limited information prior to matching and boundedly rational participants. I revise predictions about market outcome and policy recommandations accordingly.Convinced about its (still growing) empirical relevance, I would like to continue developing this research in the future.
2) Other research interests
I am keen to open up my research agenda, in particular, to study:
- information in general games: information acquisition, information disclosure, information aggregation
- cognition in general imperfect information games: using tools from evolutionary game theory, or experimental economics
Working Papers
IMDPS - Whether and where to apply? Information and Discrimination in Matching with Priority Scores
Solo paper - Submitted for publication
Latest version: here
Slides: here
This paper considers a matching market where agents have private information on their priority scores and must choose an object to which they apply.
The analysis derives the Bayes-Nash equilibria, computes welfare ex ante and interim, and discusses implications for market design.
Three main findings emerge. One, there is no symmetric equilibrium in pure strategies. Second, the symmetric equilibrium exhibits a block structure: agents sort into a finite number of classes of neighboring scores where they use the same application strategy. Third, the inefficiencies proceeding from the frictional market design prove interim asymmetric: low-score agents are better off under private information than under public information. In total, private information mitigates the discriminatory power of the priority system.
HAMS - How can I know how much I like you? A Heuristic Approach to Matching and Stability
Solo paper
Lastest version: here
Slides: here
On a marriage market with unknown preferences (agents only observe the current matching and realized match utilities), we define a novel and natural heuristic of belief formation (valuation), which incorporates a famous and documented cognitive bias (the projection bias). Under this heuristic, an agent estimates a counterfactual match utility by extrapolating from realized match utilities: his own utility and the weighted average utility of all current partners of the targeted partner's type. We study how this reshuffles the market outcome, as given by pairwise stable matchings when agents have valuation beliefs (v-stability).
When restricting our attention to pure matchings, we find that v-stability is equivalent to any two partners holding the same rank according to current utilities (happiness sorting). The predictions under specific preference structures are then straightforward. The alignment of interests across the market governs the size of the v-stable set from empty to maximal. The correlation of preferences by agent or target stabilizes the positive assortative matching. For a generic market, though, we get neither the existence of a pure v-stable matching nor the convergence of a dynamic blocking pair process (predicting persistent moves on the market).
The most general version of the model defines a notion of mixed matching, characterizing the proportions of each productive type matched with each partner type.
Our main result is a general existence theorem for v-stable matchings in the mixed extension.
RIIS - Robust Incomplete-Information Stability for matching markets without monetary transfers
Solo paper
Latest version: here
We consider a matching market with no transfers and incomplete asymmetric information - on one side, agents do not observe types of potential partners; they just observe the type of their current partner. The model can represent civil servants' job markets where wages are regulated and where employers have trouble learning about workers' productivity prior to hiring.
We apply the definition of incomplete-information stable matchings by Liu, Mailath, Postlewaite, and Samuelson (2014) - a pair is blocking if both partners strictly want to block under any reasonable beliefs they may have using their private information and common knowledge of stability.
Even under monotonic payoffs, the incomplete-information stable set may be large - it depends finely on the market structure and the prior belief support. If the unknown workers' type function is a bijection, the stable sets with complete and incomplete information perfectly coincide (to include only positive assortative matchings). We show, using examples, that the robust approach can reach precise predictions even beyond the monotonic case.
PMIA - Campus visits, or Pre-Matching Information Acquisition in school choice
Joint with Francis BLOCH
This paper studies a college admission problem gathering heterogeneous students and colleges where students can endogenously acquire information on their own preferences.
Students' preferences over colleges include a common component, which is common knowledge, and a private component, which is unknown ex-ante. Students can learn about the private components, before matching occurs through a standard Deferred Acceptance mechanism with common priorities.
The question is: What information do students acquire, as a function of their priority rank? With unit constraint on learning and unit capacities at colleges, we find that the best student learns about one of the best colleges. Students with lower-priority learn about the best college among the ones where they are admitted for sure. The proof uncovers a novel additive property of the values of information.
We discuss matching and welfare implications and ongoing generalizations.
EA - Forming expectations by analogies: an evolutionary perspective
Joint with Giacomo WEBER
The Analogy-Based Expectation Equilibrium (Jehiel (2005)) models coarse agents that form expectations on the behaviors of other players by averaging the actual equilibrium behaviors of the players in several states bundled in analogy classes.
The standard evolutionary approach sets an inter-generational model where higher payoffs in a game map to higher fitness and a growing population to get intuition on what kind of agents should survive in the long run - in terms of behaviors (Maynard Smith (1982)), preferences (Robson and Samuelson (2011)) or cognition (Fudenberg and Lanzani (2020)).
We apply the evolutionary framework to answer the following: Can an agent benefit from being coarse?
Answering this question amounts to endogenizing the analogy partition in the ABEE with a payoff reinforcement approach that contrasts with the action clustering approach in Jehiel and Weber (2023).