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on Economic Design |
| By: | Hideo Konishi (Boston College); Michel Le Breton (Toulouse School of Economics); Shlomo Weber (Southern Methodist University) |
| Abstract: | In this paper, we define additive dyadic social interactions games (ADG), in which each player cares not only about the selected action, but also about interactions with other players, especially those who choose the same action. This class of games includes alliance formation games, network games, and dis- crete choice problems with network externalities. While it is known that games in the ADG class admit a pure strategy Nash equilibrium that is a maximizer of the game's potential, the potential approach does not always apply if all coalitional deviations are allowed. We then introduce a novel notion of a strong landscape equilibrium, which relies on a limited scope of coalitional deviations. We show the existence of a strong landscape equilibrium for a class of basic additive dyadic social interactions games (BADG), even though a strong Nash equilibrium may fail to exist. Somewhat surprisingly, a potential-maximizing strong landscape equilibrium is not always a strong Nash equilibrium even if the set of the latter is nonempty. We also provide applications and extensions of our results. |
| Keywords: | social interactions games, coalition, landscape equilibrium |
| Date: | 2025–10–25 |
| URL: | https://d.repec.org/n?u=RePEc:boc:bocoec:1098 |
| By: | Sarah Kühn (Paderborn University); Nadja Stroh-Maraun (Paderborn University) |
| Abstract: | We define neighborhood help problems where agents may seek and/or provide various kinds of help as matching markets with an explicitly modeled outside option. In most matching markets a short supply of compatible helpers may result in many agents being unmatched, forcing them to rely on costly outside options. These unmatched agents leave the market without helping and substantial potential is lost. To overcome this issue we introduce the pool option which incentivizes agents to provide help while receiving help outside the market. By explicitly modeling the outside option our model becomes applicable to a broad range of applications. The top trading cycles (TTC) mechanism (Shapley & Scarf, 1974) no longer provides a Pareto efficient allocation in this setting. Thus, we propose the neighborhood top trading cycles and chains (NTTCC) mechanism which incorporates the pool option and is based on the TTCC by Roth et al. (2004). The NTTCC is individual rational, Pareto efficient, and strategy-proof. The NTTCC (weakly) reduces overall costs compared to the TTC. More generally, the NTTCC is cost-minimal in the class of individual rational and Pareto-efficient mechanisms. |
| Keywords: | Matching, School Choice, Kidney Exchange, Top Trading Cycles, Pareto Efficiency, Strategy-Proofness |
| JEL: | C78 D47 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:pdn:ciepap:167 |
| By: | Sarah Kühn (Paderborn University) |
| Abstract: | Child care allocation markets in Germany widely employ variants of the Gale-Shapley Deferred Acceptance (DA) mechanism (Gale & Shapley, 1962) to match children to child care centers. However, implementers often make seemingly minor adjustments to the original mechanism without thoroughly evaluating their implications. We show that these adjustments can significantly affect a mechanism’s desirable properties. Adjustments are necessary on these markets as care durations, capturing the contractual terms agreed upon by children and child care centers, are key to finding an allocation. Thus, we model a child care allocation problem with care durations. We demonstrate how the cumulative offer process, as developed for matching with contracts (Hatfield & Milgrom, 2005), can be effectively adapted to our context. However, a key practical disadvantage is that this mechanism requires full preference disclosure from participants, which is often unrealizable in real-world settings. We analyze how existing practical implementations of the DA deal with incomplete preference elicitation and examine the implications of these approaches. Our comparative analysis reveals that one mechanism from practice offers a distinct advantage over the others when considering incomplete preference elicitation. |
| Keywords: | Deferred Acceptance, Stability, Strategy-Proofness, Preference elicitation, Matching with contracts |
| JEL: | C78 D47 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:pdn:ciepap:166 |
| By: | Klajdi Hoxha |
| Abstract: | E-commerce platforms are rolling out ambitious targeted advertising initiatives that rely on merchants sharing customer data with each other via the platform. Yet current platform designs fail to address participating merchants' concerns about customer poaching. This paper proposes a model of designing targeted advertising platforms that incentivizes merchants to voluntarily share customer data despite poaching concerns. I characterize the optimal mechanism that maximizes a weighted sum of platform's revenues, customer engagement and merchants' surplus. In sufficiently large platforms, the optimal mechanism can be implemented through the design of three markets: $i)$ selling market, where merchants can sell all their data at a posted price $p$, $ii)$ exchange market, where merchants share all their data in exchange for high click-through rate (CTR) ads, and $iii)$ buying market, where high-value merchants buy high CTR ads at the full price. The model is broad in scope with applications in other market design settings like the greenhouse gas credit markets and reallocating public resources, and points toward new directions in combinatorial market exchange designs. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.27112 |
| By: | Kaibalyapati Mishra |
| Abstract: | In this paper, I develop a refinement of stability for matching markets with incomplete information. I introduce Information-Credible Pairwise Stability (ICPS), a solution concept in which deviating pairs can use credible, costly tests to reveal match-relevant information before deciding whether to block. By leveraging the option value of information, ICPS strictly refines Bayesian stability, rules out fear-driven matchings, and connects belief-based and information-based notions of stability. ICPS collapses to Bayesian stability when testing is uninformative or infeasible and coincides with complete-information stability when testing is perfect and free. I show that any ICPS-blocking deviation strictly increases total expected surplus, ensuring welfare improvement. I also prove that ICPS-stable allocations always exist, promote positive assortative matching, and are unique when the test power is sufficiently strong. The framework extends to settings with non-transferable utility, correlated types, and endogenous or sequential testing. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.22750 |
| By: | Ce Li; Qianfan Zhang; Weiqiang Zheng |
| Abstract: | We study the welfare of a mechanism in a dynamic environment where a learning investor can make a costly investment to change her value. In many real-world problems, the common assumption that the investor always makes the best responses, i.e., choosing her utility-maximizing investment option, is unrealistic due to incomplete information in a dynamically evolving environment. To address this, we consider an investor who uses a no-regret online learning algorithm to adaptively select investments through repeated interactions with the environment. We analyze how the welfare guarantees of approximation allocation algorithms extend from static to dynamic settings when the investor learns rather than best-responds, by studying the approximation ratio for optimal welfare as a measurement of an algorithm's performance against different benchmarks in the dynamic learning environment. First, we show that the approximation ratio in the static environment remains unchanged in the dynamic environment against the best-in-hindsight benchmark. Second, we provide tight characterizations of the approximation upper and lower bounds relative to a stronger time-varying benchmark. Bridging mechanism design with online learning theory, our work shows how robust welfare guarantees can be maintained even when an agent cannot make best responses but learns their investment strategies in complex, uncertain environments. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.01157 |
| By: | Zhang, Qiaoxi (Institute of Economics, Corvinus University of Budapest, Budapest, Hungary); Azacis, Helmuts; Ray, Indrajit (Cardiff Business School, Cardiff University) |
| Abstract: | We analyse how privacy regulations affect information transmission when a platform observes a sensitive and a nonsensitive signal. If privacy is sufficiently important, restrictions that prohibit conditioning on the sensitive signal or require sufficient statistics can eliminate information transmission. When the platform must elicit the sensitive signal from a privacy-concerned sender, the optimal policy endogenously satisfies one or the other requirement depending on parameters. |
| Keywords: | privacy; information design; mechanism design |
| JEL: | D82 D83 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:cdf:wpaper:2025/20 |
| By: | Sarah Kühn (Paderborn University); Papatya Duman (Bielefeld University); Britta Hoyer (University of Tübingen); Thomas Streck (Paderborn University); Nadja Stroh-Maraun (Paderborn University) |
| Abstract: | Preferences are central to matching markets, yet experiments typically rely on induced preferences that may not reflect real-world decisionmaking. We examine how induced versus non-induced preferences shape behavior in matching experiments, extending Chen & Sönmez (2006). Using the most frequently used school choice mechanisms (Boston, Deferred Acceptance, and Top Trading Cycles), we supplement monetary incentives with participants’ own preferences. Our results show that preference induction systematically affects truthful reporting and comprehension of mechanisms. These findings underscore that experimental design choices matter for the validity of behavioral insights and have direct implications for policy evaluation. |
| Keywords: | Non-induced Preferences, Experiments, Matching, School Choice |
| JEL: | C78 D47 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:pdn:ciepap:165 |
| By: | Amit Kumar; Nisheeth K. Vishnoi |
| Abstract: | We study a two-institution stable matching model in which candidates from two distinct groups are evaluated using partially correlated signals that are group-biased. This extends prior work (which assumes institutions evaluate candidates in an identical manner) to a more realistic setting in which institutions rely on overlapping, but independently processed, criteria. These evaluations could consist of a variety of informative tools such as standardized tests, shared recommendation systems, or AI-based assessments with local noise. Two key parameters govern evaluations: the bias parameter $\beta \in (0, 1]$, which models systematic disadvantage faced by one group, and the correlation parameter $\gamma \in [0, 1]$, which captures the alignment between institutional rankings. We study the representation ratio, i.e., the ratio of disadvantaged to advantaged candidates selected by the matching process in this setting. Focusing on a regime in which all candidates prefer the same institution, we characterize the large-market equilibrium and derive a closed-form expression for the resulting representation ratio. Prior work shows that when $\gamma = 1$, this ratio scales linearly with $\beta$. In contrast, we show that the representation ratio increases nonlinearly with $\gamma$ and even modest losses in correlation can cause sharp drops in the representation ratio. Our analysis identifies critical $\gamma$-thresholds where institutional selection behavior undergoes discrete transitions, and reveals structural conditions under which evaluator alignment or bias mitigation are most effective. Finally, we show how this framework and results enable interventions for fairness-aware design in decentralized selection systems. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.23628 |