Nicolas Eschenbaum

I am a researcher and consultant at the intersection of game theory, computation, and market regulation.

I design incentives for startups and Web3 protocols, develop and implement computable economic models, support companies in antitrust investigations, analyze strategic behavior in auctions, and help regulators and policymakers understand the economics of digital markets/AI. More details on my consulting work are here. My latest research funded by the Hasler Foundation integrates a programmatic, modular framework of mathematical games with machine learning libraries (repository). You can find details on my various research activities here.

selected publications

arXiv
Dealing with Uncertainty: The Value of Reputation in the Absence of Legal Institutions

Nicolas Eschenbaum, and Helge Liebert

May 2021

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This paper studies reputation in the online market for illegal drugs in which no legal institutions exist to alleviate uncertainty. Trade takes place on platforms that offer rating systems for sellers, thereby providing an observable measure of reputation. The analysis exploits the fact that one of the two dominant platforms unexpectedly disappeared. Re-entering sellers reset their rating. The results show that on average prices decreased by up to 9% and that a 1% increase in rating causes a price increase of 1%. Ratings and prices recover after about three months. We calculate that identified good types earn 1,650 USD more per week.
@unpublished{eschenbaum-liebert-2021, author = {Eschenbaum, Nicolas and Liebert, Helge}, title = {Dealing with Uncertainty: The Value of Reputation in the Absence of Legal Institutions}, month = may, year = {2021}, keywords = {Reputation, institutions, uncertainty, dark web, drugs}, }
arXiv
Dynamic Monopoly Pricing with Multiple Varieties: Trading Up

Stefan Buehler, and Nicolas Eschenbaum

Dec 2021

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This paper studies dynamic monopoly pricing for a class of settings that includes multiple durable, multiple rental, or a mix of varieties. We show that the driving force behind pricing dynamics is the seller’s incentive to switch consumers - buyers and non-buyers - to higher-valued consumption options by lowering prices ("trading up"). If consumers cannot be traded up from the static optimal allocation, pricing dynamics do not emerge in equilibrium. If consumers can be traded up, pricing dynamics arise until all trading-up opportunities are exhausted. We study the conditions under which pricing dynamics end in finite time and characterize the final prices at which dynamics end.
@unpublished{buehler-eschenbaum-2021, author = {Buehler, Stefan and Eschenbaum, Nicolas}, title = {Dynamic Monopoly Pricing with Multiple Varieties: Trading Up}, month = dec, year = {2021}, }
arXiv
Robust Algorithmic Collusion

Filip Mellgren Nicolas Eschenbaum, and Philipp Zahn

Dec 2021

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This paper develops a formal framework to assess policies of learning algorithms in economic games. We investigate whether reinforcement-learning agents with collusive pricing policies can successfully extrapolate collusive behavior from training to the market. We find that in testing environments collusion consistently breaks down. Instead, we observe static Nash play. We then show that restricting algorithms’ strategy space can make algorithmic collusion robust, because it limits overfitting to rival strategies. Our findings suggest that policy-makers should focus on firm behavior aimed at coordinating algorithm design in order to make collusive policies robust
@unpublished{eschenbaum-zahn-2021, author = {Nicolas Eschenbaum, Filip Mellgren and Zahn, Philipp}, title = {Robust Algorithmic Collusion}, month = dec, year = {2021}, }