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on Microeconomics |
| By: | Anna D’Annunzio (Tor Vergata University of Rome, CSEF and Toulouse School of Economics.); Antonio Russo (Institut Mines-Telecom Business School.) |
| Abstract: | We study the effects of taxes and fees in markets where sellers practice second-degree price discrimination, offering multiple versions of their product. Sellers distort the quantity (or quality) intended for all types of consumers, except for those with the highest marginal willingness to pay. We show that ad valorem taxes/fees can alleviate this distortion, thereby generating revenue while increasing consumer surplus and welfare, provided the tax rate increases with the size or quality of the version it applies to. We explore the implications of this result for important issues in fiscal policy (taxation of sin goods and of goods affecting labor supply). We also consider applications to the analysis of vertical relations between firms, as well as the strategy of platforms when setting prices for access and when competing with sellers. |
| Keywords: | Commodity taxation, tax incidence, price discrimination, sin goods. |
| JEL: | D4 H21 H22 L1 |
| Date: | 2025–09–26 |
| URL: | https://d.repec.org/n?u=RePEc:sef:csefwp:761 |
| By: | Ori Livson; Mikhail Prokopenko |
| Abstract: | Arrow's Impossibility Theorem is a seminal result of Social Choice Theory that demonstrates the impossibility of ranked-choice decision-making processes to jointly satisfy a number of intuitive and seemingly desirable constraints. The theorem is often described as a generalisation of Condorcet's Paradox, wherein pairwise majority voting may fail to jointly satisfy the same constraints due to the occurrence of elections that result in contradictory preference cycles. However, a formal proof of this relationship has been limited to D'Antoni's work, which applies only to the strict preference case, i.e., where indifference between alternatives is not allowed. In this paper, we generalise D'Antoni's methodology to prove in full (i.e., accounting for weak preferences) that Arrow's Impossibility Theorem can be equivalently stated in terms of contradictory preference cycles. This methodology involves explicitly constructing profiles that lead to preference cycles. Using this framework, we also prove a number of additional facts regarding social welfare functions. As a result, this methodology may yield further insights into the nature of preference cycles in other domains e.g., Money Pumps, Dutch Books, Intransitive Games, etc. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.09076 |
| By: | Felicia Nguyen |
| Abstract: | We study how a platform should design early exposure and rewards when creators strategically choose quality before release. A short testing window with a pass/fail bar induces a pass probability, the slope of which is the key sufficient statistic for incentives. We derive three main results. First, a closed-form ``implementability bounty'' can perfectly align creator and platform objectives, correcting for incomplete revenue sharing. Second, front-loading guaranteed impressions is the most effective way to strengthen incentives for a given attention budget. Third, when impression and cash budgets are constrained, the optimal policy follows an equal-marginal-value rule based on the prize spread and certain exposure. We map realistic ranking engines (e.g., Thompson sampling) into the model's parameters and provide telemetry-based estimators. The framework is simple to operationalize and offers a direct, managerially interpretable solution for platforms to solve the creator cold-start problem and cultivate high-quality supply. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.14102 |
| By: | Bó, Inácio Guerberoff Lanari; Caspari, Gian; Khanna, Manshu |
| Abstract: | Priority-based allocation often requires eliminating justified envy, making serial dictatorship (SD) the only non-wasteful direct mechanism with that property. However, SD's outcomes can conflict with the policymaker's objectives. We introduce visible fairness, a framework where fairness is evaluated using coarser information. This is achieved by designing message spaces that strategically conceal information that could render desired allocations unfair. We characterize these mechanisms as generalizations of SD, establish conditions for strategy-proofness, and show how to implement distributional constraints. This creates a new trade-off: achieving distributional goals may require limiting preference elicitation, forgoing efficiency gains even when compatible with the constraints. |
| Keywords: | Matching Theory, Market Design, Indirect Mechanisms |
| JEL: | C78 D47 D78 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:zewdip:328245 |
| By: | Maxwell Rosenthal |
| Abstract: | This paper proposes a fully prior-free model of experimentation in which the decision maker observes the entire distribution of signals generated by a known experiment under an unknown distribution of the state of the world. One experiment is robustly more informative than another if the decision maker's maxmin expected utility after observing the output of the former is always at least her maxmin expected utility after observing the latter. We show that this ranking holds if and only if the less informative experiment is a linear transformation of the more informative experiment; equivalently, the null space of the more informative experiment is a subset of the null space of the less informative experiment. Our criterion is implied by Blackwell's order but does not imply it, and we show by example that our ranking admits strictly more comparable pairs of experiments than the classical ranking. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.08709 |
| By: | Marcin Dziubi\'nski; Sanjeev Goyal; Junjie Zhou |
| Abstract: | We study a two-player model of conflict with multiple battlefields -- the novel element is that each of the players has their own network of spillovers so that resources allocated to one battle can be utilized in winning neighboring battles. There exists a unique equilibrium in which the relative probability of a player winning a battle is the product of the ratio of the centrality of the battlefield in the two respective competing networks and the ratio of the relative cost of efforts of the two players. We study the design of networks and characterize networks that maximize total efforts and maximize total utility. Finally, we characterize the equilibrium of a game in which players choose both networks and efforts in the battles. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.11452 |
| By: | Hang Jiang |
| Abstract: | The first-order approach (FOA) is the standard tool for solving principal-agent problems, replacing the incentive compatibility (IC) constraint with its first-order condition to obtain a relaxed problem. We show that FOA is not a valid relaxation when the support of the outcome distribution shifts with the agent's effort, as in well-studied additive-noise models. In such cases, the optimal effort may occur at a kink point that the first-order condition cannot capture, causing FOA to miss optimal contracts, including widely adopted bonus schemes. Motivated by this limitation, we introduce the Implementation Relaxation Approach (IRA), which relaxes the set of agent actions and payoffs that feasible contracts can induce, rather than directly relaxing IC. IRA accommodates non-differentiable optima and is straightforward to apply across settings, particularly for deriving optimality conditions for simple contracts. Using IRA, we derive an optimality condition for quota-bonus contracts that is more general, encompassing a broader range of scenarios than FOA-based conditions, including those established in the literature under fixed-support assumptions. This also fills a gap where the optimality of quota-bonus contracts in shifting-support settings has been examined only under endogenous assumptions, and it highlights the broader applicability of IRA as a methodological tool. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.14766 |
| By: | Kevin He; Jonathan Libgober |
| Abstract: | We extend the indirect evolutionary approach to the selection of (possibly misspecified) models. Agents with different models match in pairs to play a stage game, where models define feasible beliefs about game parameters and about others' strategies. In equilibrium, each agent adopts the feasible belief that best fits their data and plays optimally given their beliefs. We define the stability of the resident model by comparing its equilibrium payoff with that of the entrant model, and provide conditions under which the correctly specified resident model can only be destabilized by misspecified entrant models that contain multiple feasible beliefs (that is, entrant models that permit inference). We also show that entrants may do well in their matches against the residents only when the entrant population is large, due to the endogeneity of misspecified beliefs. Applications include the selection of demand-elasticity misperception in Cournot duopoly and the emergence of analogy-based reasoning in centipede games. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.16067 |
| By: | Olivier Bochet (Division of Social Science, New York University Abu Dhabi; Center for Behavioral Institutional Design (C-BID), New York University Abu Dhabi); Mathieu Faure (Aix-Marseille Univ., CNRS, AMSE, Marseille, France); Yan Long (Huazhong University of Science and Technology, China); Yves Zenou (Monash University, Australia, and CEPR) |
| Abstract: | In contrast to standard economic models, recent empirical evidence suggests that agents often operate based on subjective and divergent views of the competitive landscape. We develop a novel framework in which such imperfections are explicitly modeled through subjective perception networks, and introduce the concept of perception-consistent equilibrium (PCE), in which agents' actions and conjectures respond to the feedback generated by perceived competition. We establish the existence of equilibrium in broad classes of aggregative games. The model typically yields multiple equilibria, including outcomes that feature patterns of localized exclusion. Remarkably, heterogeneity in beliefs induces perceived competition rents-payoff differentials that arise purely from subjective misperceptions. We further show that PCE actions correspond to ordinal centrality measures, with eigenvector centrality emerging as a behavioral benchmark in separable payoff environments. Finally, a graph-theoretic taxonomy of PCEs reveals a hierarchical structure that ranks perceived competition rents. We also give conditions under which a unique stable equilibrium exists. |
| Keywords: | competition, perception-consistent equilibrium, exclusionary equilibria, bounded rationality, ordinal centrality, eigenvector centrality, perceived competition rent |
| JEL: | C72 D43 Z13 |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:aim:wpaimx:2515 |
| By: | Natalie Collina; Surbhi Goel; Aaron Roth; Emily Ryu; Mirah Shi |
| Abstract: | Aligning AI systems with human values remains a fundamental challenge, but does our inability to create perfectly aligned models preclude obtaining the benefits of alignment? We study a strategic setting where a human user interacts with multiple differently misaligned AI agents, none of which are individually well-aligned. Our key insight is that when the users utility lies approximately within the convex hull of the agents utilities, a condition that becomes easier to satisfy as model diversity increases, strategic competition can yield outcomes comparable to interacting with a perfectly aligned model. We model this as a multi-leader Stackelberg game, extending Bayesian persuasion to multi-round conversations between differently informed parties, and prove three results: (1) when perfect alignment would allow the user to learn her Bayes-optimal action, she can also do so in all equilibria under the convex hull condition (2) under weaker assumptions requiring only approximate utility learning, a non-strategic user employing quantal response achieves near-optimal utility in all equilibria and (3) when the user selects the best single AI after an evaluation period, equilibrium guarantees remain near-optimal without further distributional assumptions. We complement the theory with two sets of experiments. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.15090 |
| By: | Shuige Liu |
| Abstract: | We examine how misinformation spreads in social networks composed of individuals with long-term offline relationships. Especially, we focus on why misinformation persists and diffuses despite being recognized by most as false. In our psychological game theoretical model, each agent who receives a piece of (mis)information must first decide how to react -- by openly endorsing it, remaining silent, or openly challenging it. After observing the reactions of her neighbors who also received the message, the agent then chooses whether to forward it to others in her own chatroom who have not yet received it. By distinguishing these two roles, our framework addresses puzzling real-world phenomena, such as the gap between what individuals privately believe and what they publicly transmit. A key assumption in our model is that, while perceived veracity influences decisions, the dominant factor is the alignment between an agent's beliefs and those of her social network -- a feature characteristic of communities formed through long-term offline relationships. This dynamic can lead agents to tacitly accept and even propagate information they privately judge to be of low credibility. Our results challenge the view that improving information literacy alone can curb the spread of misinformation. We show that when agents are highly sensitive to peer pressure and the network exhibits structural polarization, even if the majority does not genuinely believe in it, the message still can spread widely without encountering open resistance. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.08658 |
| By: | Tejas Pagare; Agniv Bandyopadhyay; Sandeep Juneja |
| Abstract: | We study the problem of pure exploration in matching markets under uncertain preferences, where the goal is to identify a stable matching with confidence parameter $\delta$ and minimal sample complexity. Agents learn preferences via stochastic rewards, with expected values indicating preferences. This finds use in labor market platforms like Upwork, where firms and freelancers must be matched quickly despite noisy observations and no prior knowledge, in a stable manner that prevents dissatisfaction. We consider markets with unique stable matching and establish information-theoretic lower bounds on sample complexity for (1) one-sided learning, where one side of the market knows its true preferences, and (2) two-sided learning, where both sides are uncertain. We propose a computationally efficient algorithm and prove that it asymptotically ($\delta\to 0$) matches the lower bound to a constant for one-sided learning. Using the insights from the lower bound, we extend our algorithm to the two-sided learning setting and provide experimental results showing that it closely matches the lower bound on sample complexity. Finally, using a system of ODEs, we characterize the idealized fluid path that our algorithm chases. |
| Date: | 2025–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2509.14466 |