Cursed yet Satisfied Agents

• URL: http://arxiv.org/abs/2104.00835v1
• Date: Fri, 2 Apr 2021 01:15:53 GMT
• Title: Cursed yet Satisfied Agents
• Authors: Yiling Chen, Alon Eden, Juntao Wang
• Abstract summary: Winner's high bid implies that the winner often over-estimates the value of the good for sale, resulting in an incurred negative utility. We propose mechanisms that incentivize agents to bid their true signal even though they are cursed.
• Score: 15.104201344012344
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In real life auctions, a widely observed phenomenon is the winner's curse -- the winner's high bid implies that the winner often over-estimates the value of the good for sale, resulting in an incurred negative utility. The seminal work of Eyster and Rabin [Econometrica'05] introduced a behavioral model aimed to explain this observed anomaly. We term agents who display this bias "cursed agents". We adopt their model in the interdependent value setting, and aim to devise mechanisms that prevent the cursed agents from obtaining negative utility. We design mechanisms that are cursed ex-post IC, that is, incentivize agents to bid their true signal even though they are cursed, while ensuring that the outcome is individually rational -- the price the agents pay is no more than the agents' true value. Since the agents might over-estimate the good's value, such mechanisms might require the seller to make positive transfers to the agents to prevent agents from over-paying. For revenue maximization, we give the optimal deterministic and anonymous mechanism. For welfare maximization, we require ex-post budget balance (EPBB), as positive transfers might lead to negative revenue. We propose a masking operation that takes any deterministic mechanism, and imposes that the seller would not make positive transfers, enforcing EPBB. We show that in typical settings, EPBB implies that the mechanism cannot make any positive transfers, implying that applying the masking operation on the fully efficient mechanism results in a socially optimal EPBB mechanism. This further implies that if the valuation function is the maximum of agents' signals, the optimal EPBB mechanism obtains zero welfare. In contrast, we show that for sum-concave valuations, which include weighted-sum valuations and l_p-norms, the welfare optimal EPBB mechanism obtains half of the optimal welfare as the number of agents grows large.

Related papers

• Robust and Performance Incentivizing Algorithms for Multi-Armed Bandits with Strategic Agents [57.627352949446625]
  We consider a variant of the multi-armed bandit problem. Specifically, the arms are strategic agents who can improve their rewards or absorb them. We identify a class of MAB algorithms which satisfy a collection of properties and show that they lead to mechanisms that incentivize top level performance at equilibrium.
  arXiv  Detail & Related papers  (2023-12-13T06:54:49Z)
• Estimating and Incentivizing Imperfect-Knowledge Agents with Hidden Rewards [4.742123770879715]
  In practice, incentive providers often cannot observe the reward realizations of incentivized agents. This paper explores a repeated adverse selection game between a self-interested learning agent and a learning principal. We introduce an estimator whose only input is the history of principal's incentives and agent's choices.
  arXiv  Detail & Related papers  (2023-08-13T08:12:01Z)
• Do the Rewards Justify the Means? Measuring Trade-Offs Between Rewards and Ethical Behavior in the MACHIAVELLI Benchmark [61.43264961005614]
  We develop a benchmark of 134 Choose-Your-Own-Adventure games containing over half a million rich, diverse scenarios. We evaluate agents' tendencies to be power-seeking, cause disutility, and commit ethical violations. Our results show that agents can both act competently and morally, so concrete progress can be made in machine ethics.
  arXiv  Detail & Related papers  (2023-04-06T17:59:03Z)
• Benefits of Permutation-Equivariance in Auction Mechanisms [90.42990121652956]
  An auction mechanism that maximizes the auctioneer's revenue while minimizes bidders' ex-post regret is an important yet intricate problem in economics. Remarkable progress has been achieved through learning the optimal auction mechanism by neural networks.
  arXiv  Detail & Related papers  (2022-10-11T16:13:25Z)
• Robust Allocations with Diversity Constraints [65.3799850959513]
  We show that the Nash Welfare rule that maximizes product of agent values is uniquely positioned to be robust when diversity constraints are introduced. We also show that the guarantees achieved by Nash Welfare are nearly optimal within a widely studied class of allocation rules.
  arXiv  Detail & Related papers  (2021-09-30T11:09:31Z)
• Maximizing Information Gain in Partially Observable Environments via Prediction Reward [64.24528565312463]
  This paper tackles the challenge of using belief-based rewards for a deep RL agent. We derive the exact error between negative entropy and the expected prediction reward. This insight provides theoretical motivation for several fields using prediction rewards.
  arXiv  Detail & Related papers  (2020-05-11T08:13:49Z)
• VCG Mechanism Design with Unknown Agent Values under Stochastic Bandit Feedback [104.06766271716774]
  We study a multi-round welfare-maximising mechanism design problem in instances where agents do not know their values. We first define three notions of regret for the welfare, the individual utilities of each agent and that of the mechanism. Our framework also provides flexibility to control the pricing scheme so as to trade-off between the agent and seller regrets.
  arXiv  Detail & Related papers  (2020-04-19T18:00:58Z)
• Bounded Incentives in Manipulating the Probabilistic Serial Rule [8.309903898123526]
  Probabilistic Serial is not incentive-compatible. A substantial utility gain through strategic behaviors would trigger self-interested agents to manipulate the mechanism. We show that the incentive ratio of the mechanism is $frac32$.
  arXiv  Detail & Related papers  (2020-01-28T23:53:37Z)

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.