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  5. Complexity and Transparency in Market Design
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Complexity and Transparency in Market Design

Paper Session

Sunday, Jan. 4, 2026 2:30 PM - 4:30 PM (EST)

Philadelphia Marriott Downtown, Room 308
Hosted By: Econometric Society
  • Chair: Lawrence M. Ausubel, University of Maryland

As-If Dominant Strategy Mechanisms

Lea Nagel
,
Stanford University
Roberto Saitto
,
Stanford University

Abstract

We show that achieving dominant strategy incentive compatibility often re-quires to choose a mechanism which severely limits what agents can observe about others’ previous moves.
However, experiments and theoretical arguments suggest increasing the trans-parency of a mechanism’s extensive form can improve reliability of its predictions—even if it breaks the dominant strategy property.
To help resolve this dilemma, we define as-if dominant strategy mechanisms: (i) Each agent has at least one strategy that becomes dominant if the others were restricted to behave as if the mechanism was static, and (ii) all combinations of such strategies are ex-post equilibria. These mechanisms look like a dominant strategy one to cognitively limited agents who neglect others may condition their behavior in sophisticated ways, and can help them avoid dominated behaviors. Moreover, they ensure sophisticated agents never have an incentive to deviate.
Our framework rationalizes the auction format chosen by prominent online platforms, such as eBay. It also provides a unified explanation for experimental evidence in various settings. Further, we provide sufficient conditions for as-if dominant strategy mechanisms to also be weak dominance solvable.

Auction Design for Artificial-Intelligence-Based Sponsored Search

Lawrence M. Ausubel
,
University of Maryland

Abstract

Sponsored search and keyword auctions—by which advertisers place bids on keywords and winning bidders are awarded sponsored links at the top of search pages—have been enduring features of internet search for the past quarter century. However, emerging artificial intelligence technologies such as ChatGPT pose significant challenges to the existing model for monetizing internet search, as there is a growing mismatch. Specifically, the current “sponsored” search engine output consists of an ordered list of advertisers’ URLs at the top of the search page, whereas the “organic” search engine output is evolving from ordered lists of links to paragraphs of free-form text directly addressing the consumer’s query. As a result, it is plausible that consumers will largely disregard the sponsored links.

This paper explores a possible transformation to the auction design of sponsored internet search that may ensue. Specifically, an advertiser would bid to “influence” the output of the search provider in a direction favorable to the advertiser—for example, a higher bid could correspond to a more positive description of the advertiser’s product in the AI-written text response.

We conceptualize the mechanism design problem of a search provider using a trading model among stakeholders. The search provider allows stakeholders to “buy” increased purchase probability by paying into the trading mechanism or to “sell” by ceding purchase probability to other stakeholders. The mechanism designer maximizes revenues subject to a constraint that the increases and decreases in purchase probability can be no greater than specified amounts, capturing that there is a limit on how much the assessments of stakeholders can be allowed to change while maintaining the search provider’s credibility with consumers. In the solution of one formulation, purchase probability is shifted entirely toward the single stakeholder bidding the most and is shifted entirely away from the stakeholder bidding the least.

A Measure of Complexity for Strategy-Proof Mechanisms

Lea Nagel
,
Stanford University
Roberto Saitto
,
Stanford University

Abstract

We propose a complete ranking of strategy-proof mechanisms in terms of the contingent reasoning they require agents to engage in to recognize their dom-inant strategy. Our rankings are consistent with the coarser ones implied by the solution concepts of (strong) obvious strategy-proofness (Li, 2017b; Pycia and Troyan, 2023b). The added flexibility of our approach allows a designer to bal-ance a mechanism’s simplicity with other objectives. Our measure characterizes the Ausubel (2004) auction as the simplest way to implement the VCG outcome in multi-unit allocation problems with transfers, and provides novel rankings of mechanisms that implement stable outcomes in matching problems. Finally, we characterize minimally complex mechanisms for a range of settings, and formal-ize the intuition that some mechanisms are as simple as if they were (strongly) obviously strategy-proof. We explain how this extension can be valuable for high-stakes applications such as the FCC incentive auction.

Search and Information in Centralized School Choice Systems

Juan Escobar
,
University of Chile
Alfonso Montes
,
Universidad Diego Portales

Abstract

Centralized school assignment mechanisms play an important role in educational policy worldwide. In these systems, families face the non-trivial task of discovering and ranking schools. We evaluate the impact of information protocols on equilibrium search behavior and social welfare. We study a large market model in which students are assigned to schools using the deferred acceptance algorithm. We show that full transparency about the number of seats in the market is suboptimal. We also examine the effects of disclosing information about schools that are likely to be attractive to students, showing that transparency regarding top choices reduces congestion and increases welfare. Our analysis provides new insights for market designers as information interventions may subtly affect behavior and welfare.

Discussant(s)
Alfonso Montes
,
Universidad Diego Portales
Lea Nagel
,
Stanford University
Lawrence M. Ausubel
,
University of Maryland
Roberto Saitto
,
Stanford University
JEL Classifications
  • D44 - Auctions
  • D47 - Market Design