Mariann Ollár

I am an Assistant Professor of Economics at NYU Shanghai. I received my PhD from the University of Wisconsin, Madison.

My research is in Mechanism Design and Market Design. I like to think and talk about externalities, moment conditions in economic design, information aggregation, information design in exchanges, uniform price market clearing, and game theoretic solution concepts such as rationalizability under various assumptions on beliefs.

I graduated from the mathematical economics program at the Corvinus University of Budapest. I grew up in Gyula, Hungary.

Contact: mo2639 at nyu dot edu

CV

Publications:

A Network Solution to Robust Implementation: the Case of Identical but Unknown Distributions, with Antonio Penta, Review of Economic Studies 2023

Efficient Full Implementation via Transfers: Uniqueness and Sensitivity in Symmetric Environments, with Antonio Penta, AEA Papers and Proceedings 2022

Full Implementation and Belief Restrictions, with Antonio Penta, American Economic Review 2017

FROM BEFORE PHD: Monotonicity and Robustness of Majority Rule, Economics Letters 2010

Working Papers:

[1] Incentive Compatibility and Belief Restrictions, with Antonio Penta

pdf & slides

We study a framework for robust mechanism design that can accommodate various degrees of robustness with respect to agents’ beliefs with the belief-free and Bayesian settings as special cases. For general belief restrictions, we characterize incentive compatible direct transfer mechanisms in general environments with interdependent values. Our main result provides a first order approach to incentive design under belief restrictions. It informs the design of transfers via belief-based terms to attain incentive compatibility. Using the resulting design principle, we provide possibility results in environments that violate standard single-crossing and monotonicity conditions. We discuss a robust version of the revenue equivalence theorem that holds under a notion of independence generalized to non-Bayesian settings. Further, to contrast implications with the well-known anything goes results from Bayesian mechanism design, within our framework, we study a fairly general model of comovement of payoff types and beliefs. We show that while under comovement implementation possibilities are rich, full rent extraction typically can not be attained.

[2] Incentive Compatibility and Belief-Restrictions: Multidimensional Types, with Antonio Penta

This paper highlights possibilities of implementation with multidimensional types in environments with transfers, for various degrees of robustness with respect to agents' beliefs. We allow for multidimensional private information - possibly, correlated across agents - in environments with smooth valuation functions and allocation rules. It is known that multidimensional ex-post incentive compatibility is impossible unless the environment satisfies a notion of alignment between the agents' valuations and the designer's allocation rule. This is expressed as the "integral condition" in Wilson (1993) and, more generally in Jehiel et al. (2006), who also show that this condition is non-generic. We generalize these conditions to various degrees of robustness and show, that robust interim incentive compatibility can be substantially less demanding. The possibilities provide benchmark results that are important for evaluations of applications in auctions, bundling, as well as, taxation. Our proofs are constructive and, for various strengths of robustness requirements, demonstrate how to design the transfers that ensure the desired outcomes.

[3] Robust Unique Implementation, with Antonio Penta

We study implementation via transfers with a focus on uniqueness and successively stronger knowledge assumptions on beliefs. Agents' private information is single-dimensional we study the design of transfers that implement the desired allocation as a unique outcome while eliciting only agents' payoff relevant information. We present the parallel results for the three assumptions on beliefs: the belief-free case; common belief that others' types are identically distributed without the distribution being commonly known among the agents nor known to the designer (common belief in identicality); and the case of commonly believed priors. We show that the qualitative insights from the literature under linearity assumptions extend to larger classes of environments with general valuations and allocations. We are motivated by allocation problems with non-linear preference interdependence, for example, due to congestion effects or peer-dependent usage value.

Old Working Papers:

[++] Full Implementation under Identical but Unknown Distributions, with Antonio Penta, long version, pdf

[++] Shared Information Sources in Exchanges, pdf

[++] Privacy-Preserving Market Design, with Marzena Rostek and Ji Hee Yoon, pdf

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