RESEARCH

A quantum information–theoretic primitive is presented for computing a discrete-valued function that depends on multiple parties’ local private inputs. The resulting function values are shared among the parties, but may remain hidden from any external observer, as well as from any adversarial state-preparation or measurement process within the quantum system, in every iteration. The primitive underlies a broad class of secure communication and computation protocols, including quantum key distribution, function evaluation, multi-party coordination and decision schemes, and private signatures, among others.

Quantum information resources enable decentralized sharing and coordination on local private information between interacting agents without classical communication. In non-local measurement settings, this framework permits full information revelation between agents, a capability impossible in classical environments. This paper shows by example, that information revealed to agents conditional on joint measurement outcomes can facilitate decentralized classical mechanisms, which may be used to conduct joint decisions, such as marketplace transactions, without any central authority or cryptographic commitments. The mechanism additionally may be designed so as to obscure information obtained from within the quantum system itself.

This paper studies games of incomplete information in which players’ interactions are mediated by quantum information resources. I establish classical baseline conditions, closely related to Bell non-locality, that characterize outcome distributions implementable under classical correlation, and show by example that quantum implementations can violate these conditions, yielding equilibrium outcomes unattainable classically. Quantum advantage arises not from restricting feasible outcomes, but from allowing greater flexibility in how outcome distributions vary across states of Nature. In particular, quantum interactions enable decentralized sharing and coordination on local private information without classical communication. In non-local measurement settings, this framework permits full information revelation between players, a capability impossible in classical environments. These results highlight a new informational role for quantum resources in strategic interaction, with implications for decentralized coordination, distributed computation, and classical mechanism design.

Previous work in quantum game theory has shown that in environments where players can utilize quantum information resources, they can achieve higher expected payoffs in a Nash equilibrium than in any classical counterpart. This paper studies a class of complete information quantum games, where the chosen basis and initial state may be considered as design choices which determine the set of feasible outcome distributions in a Nash equilibrium in quantum strategies. I derive necessary and sufficient conditions for a Nash equilibrium in quantum strategies to Pareto improve upon classical correlated equilibria in quantum games in local measurement bases with maximally-entangled initial states.

A platform's preferencing mechanism, or rule that assigns the placement of offers on a platform, determines both firm and consumer welfare. First, we theoretically derive a platform's consumer-optimal preferencing rule in a setting in which firms choose prices in a locally envy-free profile, given the preferencing rule. In special cases, locally envy-free outcomes correspond with equilibria in a Generalized Second Price auction. Additionally, we show that Reimers and Waldfogel (2023)'s empirical test to measure platform bias is valid even under prices that are endogenous to the preferencing rule in a locally envy-free profile. On Amazon, we measure the extent of platform bias using a large dataset from the platform during the years 2020–2022.

A previous study of platform preferencing is extended to an environment in which firm prices are determined in a non-myopic, dynamic setting, which depends on the platform's preferencing rule. These dynamic pricing strategies result in pricing patterns that closely resemble Edgeworth cycles. I provide a method to estimate the primitives that govern these pricing cycles, which allows me to assess the counterfactual welfare implications associated with various preferencing rules, using a large dataset from the Amazon platform from 2018–2022. The welfare effects of policy proposals in this environment, in particular those that eliminate self-preferencing, significantly differ from those under the static price competition environment.

Most of auction theory is concerned with studying mechanisms that maximize efficiency in settings in which bidder valuations are independent. However, in many real-world settings, firms engage in strategic behavior ex-post auction, in which their payoffs depend not only on their auction allocations, but also their rivals’. Moreover, auction rules that aim to maximize efficiency may come at the cost of consumer welfare, when auctions enable certain bidders excessive shares in downstream markets.

This paper aims to study the question of how an auctioneer can shape market power and downstream competition via design choices in an auction when bidder valuations are interdependent. First, we study bidder behavior in simultaneous ascending auctions in which bidder valuations arise from a downstream pricing game conditional on auction allocations. We then estimate the bidding restrictions that maximize consumer welfare in the mobile phone market, using data from simultaneous ascending auctions conducted by the FCC.