Parity in Markets -- Methods, Costs, and Consequences

• URL: http://arxiv.org/abs/2210.02586v1
• Date: Wed, 5 Oct 2022 22:27:44 GMT
• Title: Parity in Markets -- Methods, Costs, and Consequences
• Authors: Alexander Peysakhovich, Christian Kroer, Nicolas Usunier
• Abstract summary: We show how market designers can use taxes or subsidies in Fisher markets to ensure that market equilibrium outcomes fall within certain constraints. We adapt various types of fairness constraints proposed in existing literature to the market case and show who benefits and who loses from these constraints.
• Score: 109.5267969644294
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fisher markets are those where buyers with budgets compete for scarce items, a natural model for many real world markets including online advertising. We show how market designers can use taxes or subsidies in Fisher markets to ensure that market equilibrium outcomes fall within certain constraints. We adapt various types of fairness constraints proposed in existing literature to the market case and show who benefits and who loses from these constraints, as well as the extent to which properties of markets including Pareto optimality, envy-freeness, and incentive compatibility are preserved. We find that several prior proposed constraints applied to markets can hurt the groups they are intended to help.

Related papers

• Market Concentration Implications of Foundation Models [0.2913760942403036]
  We analyze the structure of the market for foundation AI models such as those that power ChatGPT. We observe that the most capable models will have a tendency towards natural monopoly and may have potentially vast markets.
  arXiv  Detail & Related papers  (2023-11-02T19:00:42Z)
• Finding Regularized Competitive Equilibria of Heterogeneous Agent Macroeconomic Models with Reinforcement Learning [151.03738099494765]
  We study a heterogeneous agent macroeconomic model with an infinite number of households and firms competing in a labor market. We propose a data-driven reinforcement learning framework that finds the regularized competitive equilibrium of the model.
  arXiv  Detail & Related papers  (2023-02-24T17:16:27Z)
• Quantum computational finance: martingale asset pricing for incomplete markets [69.73491758935712]
  We show that a variety of quantum techniques can be applied to the pricing problem in finance. We discuss three different methods that are distinct from previous works.
  arXiv  Detail & Related papers  (2022-09-19T09:22:01Z)
• Competition, Alignment, and Equilibria in Digital Marketplaces [97.03797129675951]
  We study a duopoly market where platform actions are bandit algorithms and the two platforms compete for user participation. Our main finding is that competition in this market does not perfectly align market outcomes with user utility.
  arXiv  Detail & Related papers  (2022-08-30T17:43:58Z)
• Price Interpretability of Prediction Markets: A Convergence Analysis [6.623653707600985]
  We show that the limiting price converges to the geometric mean of agent beliefs in exponential utility-based markets. We also show that the limiting price can be characterized by systems of equations that encapsulate agent beliefs, risk parameters, and wealth.
  arXiv  Detail & Related papers  (2022-05-18T13:15:11Z)
• Deep Q-Learning Market Makers in a Multi-Agent Simulated Stock Market [58.720142291102135]
  This paper focuses precisely on the study of these markets makers strategies from an agent-based perspective. We propose the application of Reinforcement Learning (RL) for the creation of intelligent market markers in simulated stock markets.
  arXiv  Detail & Related papers  (2021-12-08T14:55:21Z)
• Roles of Retailers in the Peer-to-Peer Electricity Market: A Single Retailer Perspective [60.293347226071795]
  This perspective takes the first step to discuss the feasibility of retailers' involvement in the peer-to-peer market. We identify key characteristics of retail-based and peer-to-peer electricity markets and discuss our viewpoint on how to incorporate a single retailer in a peer-to-peer market.
  arXiv  Detail & Related papers  (2021-10-12T00:02:08Z)
• Achieving Diverse Objectives with AI-driven Prices in Deep Reinforcement Learning Multi-agent Markets [35.02265584959417]
  We propose a practical approach to computing market prices and allocations via a deep reinforcement learning policymaker agent. Our policymaker is much more flexible, allowing us to tune the prices with regard to diverse objectives. As a highlight of our findings, our policymaker is significantly more successful in maintaining resource sustainability.
  arXiv  Detail & Related papers  (2021-06-10T21:26:17Z)
• Subgroup Fairness in Two-Sided Markets [7.854010769859225]
  We suggest a novel market-clearing mechanism for two-sided markets. We show that a certain non-linear problem can be approximated to any subgroup in time. On the example of driver-ride assignment in an Uber-like system, we demonstrate the efficacy and scalability of the approach.
  arXiv  Detail & Related papers  (2021-06-04T20:26:16Z)
• Beating the market with a bad predictive model [0.0]
  We prove that it is generally possible to make systematic profits with a completely inferior price-predicting model. The key idea is to alter the training objective of the predictive models to explicitly decorrelate them from the market.
  arXiv  Detail & Related papers  (2020-10-23T16:20:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.