Microeconomics http://lists.repec.org/mailman/listinfo/nep-mic Microeconomics 2026-05-18 What Do You Want Me To Say? https://d.repec.org/n?u=RePEc:boc:bocoec:1111&r=&r=mic We analyze societies where people express their opinions with respect to a single issue. These opinions also affect social connections. People enjoy being connected to others, but only with those whose opinions they deem acceptable. In such environments people behave strategically to optimize their social connections and therefore their expressed opinions do not necessarily represent their true ones. Dis- tributions of expressed opinions thus depend on the social structure. Changes in the views of some people or changes in the relative size of different groups may trigger changes in the map of social connections and in the distribution of expressed opinions. Chaim Fershtman Uzi Segal Strategic opinion expression, social connections 2026-05-05 Truthful Communication and Exclusive Information Clubs https://d.repec.org/n?u=RePEc:arx:papers:2605.02776&r=&r=mic This paper studies how the possibility of strategic misreporting shapes endogenous communication networks. Agents observe noisy private signals about a common state, form costly communication links, exchange private messages with their neighbors, and then choose actions. Payoffs reward both accuracy and coordination with linked agents. A link is valuable because it gives access to information, but it is useful only if the induced local information structure makes truthful transmission incentive compatible. We show that clique components support truthful communication: within a clique, all members observe the same profile of local signals, choose the same posterior action, and therefore have no incentive to distort reports. With heterogeneous signal precisions and convex linking costs, the core selects assortative information clubs ordered by signal precision. These stable truthful networks need not be socially efficient. Because the informational value of precision is decreasing, concentrating high-precision agents in the same club may be privately stable but socially dominated by more mixed partitions. Paolo Pin 2026-05 Misspecified beliefs and the evolution of peer pressure https://d.repec.org/n?u=RePEc:arx:papers:2605.02756&r=&r=mic We study the emergence of conformity preferences in an environment in which agents choose effort under heterogeneous, possibly misspecified returns, and social interactions do not directly affect material payoffs. Some agents choose effort by trading off performance and conformity to expected peer behavior. We characterize subjective best responses. For any given beliefs, an optimal and unique level of peer pressure exists and is evolutionarily stable within groups of agents sharing the same misspecification. Such a level is zero for correctly specified agents and may be positive for misspecified ones. When the efficient level of peer pressure is interior, misspecified agents choose effort equal to their true return, resulting in an equilibrium behavior that is both self-confirming and Nash, allowing the persistence of misspecifications. Peer pressure need not generate long-run allocative distortions, but it creates a perceived value of social information. In equilibrium, this value depends only on misspecification, generating scope for informational rents. Paolo Pin Roberto Rozzi 2026-05 Principal-agent problems with adverse selection: A stochastic target problem formulation https://d.repec.org/n?u=RePEc:arx:papers:2605.01080&r=&r=mic We study a principal-agent problem with adverse selection, where the principal does not know the agent's true cost but must design a contract to optimize a specific criterion. Unlike standard screening frameworks that allow for self-selection, we assume the principal can only offer a unique contract. We show that the agent's optimization problem can be reformulated as a stochastic target problem. After characterizing the credible domain of this target problem, we show that the principal's objective can be solved as a stochastic optimal control problem with partial information and state constraints. The description of the credible domain also allows us to obtain the value of screening contracts. Guillermo Alonso Alvarez Ibrahim Ekren Liwei Huang 2026-05 Going Public: Communication in Collective Decisions https://d.repec.org/n?u=RePEc:arx:papers:2605.03621&r=&r=mic A principal and $n\ge 2$ agents can launch a project if the principal proposes it and at least $k$ agents accept. Their individual payoffs from the project depend on an ex ante unknown state. The principal can conduct a test to learn about the state and then communicate her findings to the agents via cheap talk. This paper focuses on comparing two communication regimes: public and private messaging. We show that public messaging is weakly dominant: any outcome implementable under private messaging can also be implemented under public messaging. Moreover, in a canonical environment with linear payoffs, we characterize the principal's optimal test in each regime and show that public messaging can be strictly dominant if and only if there exist two agents who are the principal's conflicting allies. Zhicheng Du Yingkai Li Boli Xu 2026-05 Regulating Physicians’ Prices in the Presence of Health Platforms https://d.repec.org/n?u=RePEc:ces:ceswps:_12646&r=&r=mic Online platforms connecting physicians and patients are increasingly common and often operate in heavily regulated contexts. We consider a platform that provides cost-reducing services for physicians and quality-enhancing services for patients. The platform also improves the matching between patients and physicians, thereby increasing competition among the latter. When prices are unregulated, physicians charge different prices online and offline, yet not all join the platform, which is suboptimal in terms of social welfare. The platform may also under- or over-invest in the quality level offered to patients, making their participation suboptimal as well. We then analyze price regulation. Under a single regulated price for medical visits, regardless of the booking channel, all physicians join the platform. However, the first-best allocation cannot be implemented: patient participation remains inefficiently low because patients do not internalize the platform’s cost-reducing effect. In contrast, allowing two regulated prices, one for offline visits and one for platform bookings, restores the first best. Overall, our findings suggest that an optimal pricing or reimbursement mechanism should differentiate across booking channels. Chiara Canta Leonardo Madio Andrea Mantovani Carlo Reggiani healthcare online platforms, price regulation, patient-physician matching 2026 Strategy-proof and Efficient Job Matching with Participation Constraints https://d.repec.org/n?u=RePEc:arx:papers:2605.01715&r=&r=mic We study the design of strategy-proof and efficient mechanisms satisfying participation constraints in the job-matching problem. Each firm can hire multiple workers and each worker can be employed at only one firm. While firm utilities over subsets of workers are common knowledge, worker disutilities for working at each firm are private information. The VCG mechanism is the unique mechanism that is strategy-proof, efficient, and individually rational for workers; however, it may not be individual rational for firms. We show that the VCG mechanism is individually rational for firms if and only if firm utilities satisfy a condition called weak substitutes. We then strengthen participation constraints of firms to {\sl strong individual rationality}, which requires that each firm has no incentive to fire some of the workers assigned to it. The VCG mechanism is strongly individual rational if and only if firm utilities satisfy submodularity. Sushil Bikhchandani Debasis Mishra 2026-05 Nash Equilibria as Limits of Equilibria of Nearby Finite Games https://d.repec.org/n?u=RePEc:bon:boncrc:crctr224_2025_744&r=&r=mic We study finite-player normal-form games with compact metric ac on spaces and bounded measurable payoffs. Our main theorem shows that every Nash equilibrium of such a game can be recovered as the limit, in the product weak topology, of Nash equilibria of finite games obtained by discre zing the ac on spaces and perturbing payoffs by a uniformly vanishing amount. The proof samples from the target equilibrium, uses concentra on inequali es to control weak convergence and incen ve constraints on a growing finite set, and then applies a payoff perturba on to convert the resul ng approximate equilibrium into an exact one. We also provide an example of a con nuous game with a Nash equilibrium that cannot be approximated through Nash equilibria of finite games without perturbing payoffs. Francesc Dilmé Infinite games, Nash equilibria, finite approxima ons 2026-05 Costs and benefits of discretion in performance evaluation and patterns of bias https://d.repec.org/n?u=RePEc:mag:wpaper:25003&r=&r=mic This paper investigates incentive effects from subjective performance evaluation (SPE) in an agency setting. An employee (agent) is evaluated by his superior (principal) via a subjective, potentially biased, performance report. We assume that this subjectiveness in evaluation affects the utility of both players, causing costs from biasing the report to the principal and benefits (costs) from over- (under-) evaluation to the agent. If the superior chooses the reporting bias sequentially optimal, we find that benefits from subjective, as opposed to objective performance measurement, do not outweigh its costs. If, in contrast, the supervisor is able to commit to an ex ante optimal bias choice, SPE can be beneficial if the agent’s preference for over-evaluation is sufficiently strong. While a centrality bias arises independent from the supervisor’s ability to commit, a leniency bias results only along with an ex ante optimal bias. Max-Frederik Neubert Barbara Schoendube-Pirchegger agency, subjective performance evaluation, behavioral accounting, accuracy, leniency, centrality 2025 The Endogeneity of Miscalibration: Impossibility and Escape in Scored Reporting https://d.repec.org/n?u=RePEc:arx:papers:2605.07671&r=&r=mic Eliciting truthful reports from autonomous agents is a core problem in scalable AI oversight: a principal scores the agent's report using a strictly proper scoring rule, but the agent also benefits from the report through a non-accuracy channel (approval for autonomous action, allocation share, downstream control). The same structure appears in classical mechanism-design settings such as marketplace operation. Our main result is an endogeneity: the principal's optimal oversight necessarily uses a non-affine approval function to screen types, yet any non-affine approval makes truthful reporting suboptimal under the combined objective whenever deviation is undetectable. The principal cannot avoid the perturbation that undermines calibration. This impossibility holds for all strictly proper scoring rules, with a closed-form perturbation formula. A constructive escape exists: a step-function approval threshold achieves first-best screening for every strictly proper scoring rule, because the agent's binary inflate-or-not choice creates a type-space threshold regardless of the generator's curvature. Under the Brier score specifically, the type-independent inflation cost yields a welfare equivalence between second-best and first-best; we prove this equivalence is unique to Brier (the welfare gap under smooth $C^1$ oversight is bounded below by $\Omega(\text{Var}(1/G'') (\gamma/\beta)^2)$ for every non-Brier rule). Two instances develop the framework: AI agent oversight (the lead motivating setting) and marketplace operation (a parallel mechanism-design domain). The message for AI alignment is direct: smooth scoring-based oversight cannot elicit truthful reports from a strategic agent; sharp thresholds are the calibration-preserving design. Lauri Lov\'en Sasu Tarkoma 2026-05 Coordination Mechanisms with Partially Specified Probabilities https://d.repec.org/n?u=RePEc:arx:papers:2605.07469&r=&r=mic We study which outcomes are implementable by disclosing coarse statistics of a data-generating process rather than its full distribution. Players observe data whose joint distribution is only partially known: they know the expectations of finitely many random variables and form beliefs by maximum-entropy inference. We obtain two characterizations. When message spaces are unrestricted, implementable outcomes coincide with jointly coherent outcomes, expanding the set of correlated equilibria. With canonical mechanisms, implementability reduces to a single cross-entropy condition: the target outcome must lie on the cross-entropy level set of some correlated equilibrium that passes through that equilibrium itself. Examples and several classes of games illustrate the reach of the framework. Francesco Giordano 2026-05 Nash without Numbers: A Social Choice Approach to Mixed Equilibria in Context-Ordinal Games https://d.repec.org/n?u=RePEc:arx:papers:2605.07996&r=&r=mic Nash equilibrium serves as a fundamental mathematical tool in economics and game theory. However, it classically assumes knowledge of player utilities, whereas economics generally regards preferences as more fundamental. To leverage equilibrium analysis in strategic scenarios, one must first elicit numerical utilities consistent with player preferences, a delicate and time-consuming process. In this work, we forgo precise utilities and generalize the Nash equilibrium to a setting where we only assume a player is capable of providing an ordinal ranking of their actions within the context of other players' joint actions. The key technical challenge is to rethink the definition of a best-response. While the classical definition identifies actions maximizing expected payoff, we naturally look towards social choice theory for how to aggregate preferences to identify the most preferred actions. We define this generalized notion of a context-ordinal Nash equilibrium, establish its existence under mild conditions on aggregation methods, introduce notions of regularization, approximation, and regret, explore complexity for simple settings, and develop learning rules for computing such equilibria. In doing so, we provide a generalization of Nash equilibrium and demonstrate its direct applicability to elicited preferences in human experiments. Ian Gemp Crystal Qian Marc Lanctot Kate Larson 2026-05 Partition function form games with probabilistic beliefs https://d.repec.org/n?u=RePEc:arx:papers:2605.01521&r=&r=mic We revisit games in partition function form, i.e. cooperative games where the payoff of a coalition depends on the partition of the entire set of players. We assume that each coalition computes its worth having probabilistic beliefs over the coalitional behavior of the outsiders, i.e., it assigns various probability distributions over the set of partitions that the outsiders can form. These beliefs are not necessarily consistent with respect to the actual choices of the outsiders. We apply this framework to symmetric partition function form games characterized by either positive or negative externalities and we derive conditions on coalitional beliefs that guarantee the non-emptiness of the core of the induced games. Paraskevas V. Lekeas Giorgos Stamatopoulos 2026-05 Uncountably many conditionally inaccessible decisions exist in every finite probability space https://d.repec.org/n?u=RePEc:arx:papers:2605.02865&r=&r=mic In a recent paper \cite{Redei-Jing2026} the notion of conditional $p$-inaccessibility of a decision based on utility maximization was defined and examples of conditionally $p$-inaccessible decisions were given. The conditional inaccessibility of a decision based on maximizing utility calculated by a probability measure $p^*$ expresses that the decision cannot be obtained if the expectation values of the utility functions are calculated using the (Jeffrey) conditional probability measure obtained by conditioning $p$ on partial evidence about the probability $p^*$ that determines the decision. The paper \cite{Redei-Jing2026} conjectured that conditionally $p$-inaccessible decisions exist in some probability spaces having arbitrary large finite number of elementary events. In this paper we prove that for any $p$ in any finite probability space there exist an uncountable number of probability measures $p^*$ for each of which there exist an uncountable number of pairs of utility functions that represent conditionally $p$-inaccessible decisions. If $p^*$ is an objective probability determining objectively good decisions and $p$ is the subjective probability determining a rational decision of a decision making Agent, the result says that there is an enormous number of decision situations in which the Agent's subjective probability prohibits the Agent's informed rational decision to be objectively good. Zal\'an Gyenis Mikl\'os R\'edei Leszek Wro\'nski 2026-05 Availability of AI tools and their effect on the auditing process https://d.repec.org/n?u=RePEc:mag:wpaper:25004&r=&r=mic In this paper we model the interaction between an auditor and a client firm. The client firm’s manager can either report truthfully or commit fraud. The auditor needs to plan a two stage audit that allows to detect fraud. In the first stage an AI tool is employed that provides a signal about the quality of the client’s internal control system (ICS). Classifying the ICS as weak or strong, the signal alters the auditor’s expectations regarding the client’s fraud probability. In the second stage, the auditor decides about her audit effort conditional on the information provided by the AI. Comparing the AI setting to a benchmark setting without AI use, we find that employing the AI tool reduces the manager’s incentives to commit fraud. At the same time it reduces the equilibrium effort provided by the auditor. As a consequence, the probability that actual fraud is detected remains unchanged. We extend our model and allow the AI tool to be customized such that it can either focus on detection of the weak ICS, the strong ICS, or on both equally. We find that the AI specification that minimizes ex ante probability for fraud not necessarily coincides with the specification that minimizes auditing costs. It follows that the auditor in charge of customizing the AI cannot necessarily be expected to do so in a fraud minimizing way. Jens Robert Schoendube Barbara Schoendube-Pirchegger Artificial Intelligence, Auditing, Game Theory, Fraud detection 2025 Compound Attrition Games: A Unified Model for Inter- and Intra-Coalition Rivalry https://d.repec.org/n?u=RePEc:arx:papers:2605.02354&r=&r=mic Strategic competitions in the real world, from wars to geopolitical rivalries, often involve coalitions competing against rival groups. These contests are not simple interactions between unified entities, but multilayered processes in which coalitions face external competition while dealing with internal conflicts over resources and strategy. Existing game-theoretic models typically treat inter-coalition rivalry and intra-coalition competition separately. This paper introduces the Compound Coalition-Attrition Game (CCAG), a unified framework that integrates a war of attrition between coalitions with a simultaneous war of attrition within each coalition. In this model, the endurance of a coalition in external competition is determined by the strategic choices of its members, who compete internally for shares of the outcome. We prove the nonexistence of pure-strategy equilibria and characterize the unique mixed-strategy Nash equilibrium. The analysis reveals feedback effects: external competition intensifies internal conflict, while internal discord weakens external performance. A case study compares traditional commodity markets, including gold, copper, and silver, with cryptocurrency markets, including Bitcoin, Ethereum, and Solana, using data from 2018 to 2023 in a simulation framework. The results demonstrate applicability in industrial strategy, corporate decision-making, and geopolitical competition. The CCAG framework provides a tool for analysing complex strategic environments. Madjid Eshaghi Gordji Mohamad Ali Berahman 2026-05 The Adversarial Discount - AI, Signal Correlation, and the Cybersecurity Arms Race https://d.repec.org/n?u=RePEc:arx:papers:2605.04336&r=&r=mic We study a contest-theoretic model of adversarial investment in which an attacker and a defender allocate resources to AI-augmented capabilities across multiple attack surfaces. The attacker's investment operates through two channels: it amplifies offensive potency unconditionally and erodes defensive effectiveness conditionally, generating an adversarial discount that deepens endogenously with the defender's own investment. We derive a closed-form arms race ratio decomposing the relative marginal effectiveness of offensive and defensive investment into six structural primitives and establish equilibrium uniqueness and global convergence under a continuous best-response dynamic. The central result concerns signal cross-correlation, the degree to which threat intelligence on one surface informs detection on another. With full cross-correlation, the arms race ratio is independent of the number of attack surfaces: the attacker's structural advantage from surface proliferation is completely neutralized. Under the benchmark full-dilution case, without cross-correlation, per-surface defense effectiveness vanishes as the attack surface grows. Extending the analysis to heterogeneous defenders facing an attacker who targets by expected value, we argue that the model points to a dual inefficiency: overinvestment in private defense (a zero-sum redirective externality) and underinvestment in shared signal correlation (a public good). These formal results, together with public-good reasoning outside the base model, characterize when collective information aggregation can dominate private capability investment as the decisive margin in adversarial contests. James W. Bono 2026-05