nep-mic New Economics Papers
on Microeconomics
Issue of 2023‒03‒27
eight papers chosen by
Jing-Yuan Chiou
National Taipei University

  1. Coordination via Selling Information By Alessandro Bonatti; Munther Dahleh; Thibaut Horel; Amir Nouripour
  2. Information Favoritism and Scoring Bias in Contests By Shanglyu Deng; Hanming Fang; Qiang Fu; Zenan Wu
  3. Sorting Versus Screening in Decentralized Markets With Adverse Selection By Sarah Auster; Piero Gottardi
  4. On time-consistent equilibrium stopping under aggregation of diverse discount rates By Shuoqing Deng; Xiang Yu; Jiacheng Zhang
  5. Reciprocal Preferences in Matching Markets By Timm Opitz; Christoph Schwaiger
  6. Welfare Distribution in Two-sided Random Matching Markets By Itai Ashlagi; Mark Braverman; Geng Zhao
  7. Content Moderation and Advertising in Social Media Platforms By Leonardo Madio
  8. Competitive Model Selection in Algorithmic Targeting By Ganesh Iyer; T. Tony Ke

  1. By: Alessandro Bonatti; Munther Dahleh; Thibaut Horel; Amir Nouripour
    Abstract: We consider games of incomplete information in which the players' payoffs depend both on a privately observed type and an unknown but common "state of nature". External to the game, a data provider knows the state of nature and sells information to the players, thus solving a joint information and mechanism design problem: deciding which information to sell while eliciting the player' types and collecting payments. We restrict ourselves to a general class of symmetric games with quadratic payoffs that includes games of both strategic substitutes (e.g. Cournot competition) and strategic complements (e.g. Bertrand competition, Keynesian beauty contest). By to the Revelation Principle, the sellers' problem reduces to designing a mechanism that truthfully elicits the player' types and sends action recommendations that constitute a Bayes Correlated Equilibrium of the game. We fully characterize the class of all such Gaussian mechanisms (where the joint distribution of actions and private signals is a multivariate normal distribution) as well as the welfare- and revenue- optimal mechanisms within this class. For games of strategic complements, the optimal mechanisms maximally correlate the players' actions, and conversely maximally anticorrelate them for games of strategic substitutes. In both cases, for sufficiently large uncertainty over the players' types, the recommendations are deterministic (and linear) conditional on the state and the type reports, but they are not fully revealing.
    Date: 2023–02
  2. By: Shanglyu Deng (University of Maryland); Hanming Fang (University of Pennsylvania); Qiang Fu (National University of Singapore); Zenan Wu (Peking University)
    Abstract: Two potentially asymmetric players compete for a prize of common value, which is initially unknown, by exerting efforts. A designer has two instruments for contest design. First, she decides whether and how to disclose an informative signal of the prize value to players. Second, she sets the scoring rule of the contest, which varies the relative competitiveness of the players. We show that the optimum depends on the designer's objective. A bilateral symmetric contest - in which information is symmetrically distributed and the scoring bias is set to offset the initial asymmetry between players - always maximizes the expected total effort. However, the optimal contest may deliberately create bilateral asymmetry - which discloses the signal privately to one player, while favoring the other in terms of the scoring rule - when the designer is concerned about the expected winner's effort. The two instruments thus exhibit complementarity, in that the optimum can be made asymmetric in both dimensions even if the players are ex ante symmetric. Our results are qualitatively robust to (i) affiliated signals and (ii) endogenous information structure. We show that information favoritism can play a useful role in addressing affirmative action objectives.
    Keywords: All-pay Auction; Contest Design; Information Favoritism; Scoring Bias
    JEL: C72 D44 D82
    Date: 2023–03–09
  3. By: Sarah Auster; Piero Gottardi
    Abstract: We study the role of traders' meeting capacities in decentralized markets with adverse selection. Uninformed customers choose trading mechanisms in order to find a provider for a service. Providers are privately informed about their quality and aim to match with one of the customers. We consider a rich set of meeting technologies and characterize the properties of the equilibrium allocations for each of them. In equilibrium, different provider types can be separated either via sorting---they self-select into different submarkets---or screening within the trading mechanism, or a combination of the two. We show that, as the meeting technology improves, the equilibrium features more screening and less sorting. Interestingly, this reduces both the average quality of trade as well as the total level of trade in the economy. The trading losses are, however, compensated by savings in entry costs, so that welfare increases.
    Keywords: Competitive Search, Adverse Selection, Market Segmentation
    JEL: C78 D44 D83
    Date: 2022–08
  4. By: Shuoqing Deng; Xiang Yu; Jiacheng Zhang
    Abstract: This paper studies the central planner's decision making on behalf of a group of members with diverse discount rates. In the context of optimal stopping, we work with a smooth aggregation preference to incorporate all heterogeneous discount rates with an attitude function that reflects the aggregation rule in the same spirit of ambiguity aversion in the smooth ambiguity preference proposed in Klibanoff et al.(2005). The optimal stopping problem renders to be time inconsistent, for which we develop an iterative approach using consistent planning and characterize all time-consistent equilibria as fixed points of an operator in the setting of one-dimensional diffusion processes. We provide some sufficient conditions on both the underlying models and the attitude function such that the smallest equilibrium attains the optimal equilibrium in which the attitude function becomes equivalent to the linear aggregation rule as of diversity neutral. When the sufficient condition of the attitude function is violated, we can illustrate by various examples that the characterization of the optimal equilibrium may differ significantly from some existing results for an individual agent, which now sensitively depends on the attitude function and the diversity distribution of discount rates.
    Date: 2023–02
  5. By: Timm Opitz (Max Planck Institute for Innovation and Competition, LMU Munich); Christoph Schwaiger (LMU Munich)
    Abstract: Agents with reciprocal preferences prefer to be matched to a partner who also likes to collaborate with them. In this paper, we introduce and formalize reciprocal preferences, apply them to matching markets, and analyze the implications for mechanism design. Formally, the preferences of an agent can depend on the preferences of potential partners and there is incomplete information about the partners’ preferences. We find that there is no stable mechanism in standard two-sided markets. Observing the final allocation of the mechanism enables agents to learn about each other's preferences, leading to instability. However, in a school choice setting with one side of the market being non-strategic, modified versions of the deferred acceptance mechanism can achieve stability. These results provide insights into non-standard preferences in matching markets, and their implications for efficient information and mechanism design.
    Keywords: market design; matching; reciprocal preferences; non-standard preferences; gale-shapley deferred acceptance mechanism; incomplete information;
    JEL: C78 D47 D82 D83 D91
    Date: 2023–02–20
  6. By: Itai Ashlagi; Mark Braverman; Geng Zhao
    Abstract: We study the welfare structure in two-sided large random matching markets. In the model, each agent has a latent personal score for every agent on the other side of the market and her preferences follow a logit model based on these scores. Under a contiguity condition, we provide a tight description of stable outcomes. First, we identify an intrinsic fitness for each agent that represents her relative competitiveness in the market, independent of the realized stable outcome. The intrinsic fitness values correspond to scaling coefficients needed to make a latent mutual matrix bi-stochastic, where the latent scores can be interpreted as a-priori probabilities of a pair being matched. Second, in every stable (or even approximately stable) matching, the welfare or the ranks of the agents on each side of the market, when scaled by their intrinsic fitness, have an approximately exponential empirical distribution. Moreover, the average welfare of agents on one side of the market is sufficient to determine the average on the other side. Overall, each agent's welfare is determined by a global parameter, her intrinsic fitness, and an extrinsic factor with exponential distribution across the population.
    Date: 2023–02
  7. By: Leonardo Madio (University of Padova Author-Name: Martin Quinn; Rotterdam School of Management)
    Abstract: On social media platforms, advertisers can be exposed to brand safety issues if they are associated with unsafe content. In this paper, we study the incentive of an ad-funded platform to curb the presence of unsafe content. Moderating unsafe content reduces the risk of advertiser presence on social media platforms, but it can change users’ participation on the platform and, in turn, affect advertisers’ monetization. This indirect “eyeball effect†can be either positive or negative and is key for the platform’s design of its content moderation policy. We identify conditions for the platform not to moderate unsafe content and demonstrate how the optimal moderation policy depends on the risk the advertisers face. We also study the intended and unintended effects of a policy that mandates social media platforms to moderate (more) unsafe content. We show that although it can benefit advertisers, users may be worse off because of the greater number of ads they are exposed to. Finally, we study how social media platform competition and the introduction of taxes on social media activity can distort the platform’s moderation strategies.
    Keywords: Advertising; Content moderation; Social media platforms; Platforms.
    Date: 2023–03
  8. By: Ganesh Iyer; T. Tony Ke
    Abstract: This paper studies how market competition influences the algorithmic design choices of firms in the context of targeting. Firms face the general trade-off between bias and variance when choosing the design of a supervised learning algorithm in terms of model complexity or the number of predictors to accommodate. Each firm then appoints a data analyst that uses the chosen algorithm to estimate demand for multiple consumer segments, based on which, it devises a targeting policy to maximize estimated profit. We show that competition may induce firms to strategically choose simpler algorithms which involve more bias. This implies that more complex/flexible algorithms tend to have higher value for firms with greater monopoly power.
    JEL: D43 L13 M37
    Date: 2023–03

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