Optimal mechanisms with budget for user generated contents

Chen, Mengjing; Tang, Pingzhong; Wang, Zihe; Xiao, Shenke; Yang, Xiwang

doi:10.48550/arxiv.1907.04740

Cited by 2 publications

(3 citation statements)

References 19 publications

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“…After this adjustment, some agent T b i i,j that takes action variable b i i before the adjustment may turn out to take action clause j,k for some k. This is the only possible cause of payoff loss of the principal. Suppose the payments for variable b i i (before the adjustment) and clause j,k are t 1 , t 2 respectively, since agent T b i i,j chooses to take action variable b i i before the adjustment, we have t (3). This means the adjustment does not reduce the payoff of the principal, hence we can assume the payment for action variable b i i is exactly δ.…”

Section: Hardnessmentioning

confidence: 99%

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Optimal Common Contract with Heterogeneous Agents

Xiao

Wang

Chen

et al. 2019

Preprint

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We consider the principal-agent problem with heterogeneous agents. Previous works assume that the principal signs independent incentive contracts with every agent to make them invest more efforts on the tasks. However, in many circumstances, these contracts need to be identical for the sake of fairness. We investigate the optimal common contract problem. To our knowledge, this is the first attempt to consider this natural and important generalization. We first show this problem is NP-complete. Then we provide a dynamic programming algorithm to compute the optimal contract in O(n 2 m) time, where n, m are the number of agents and actions, under the assumption that the agents' cost functions obey increasing difference property. At last, we generalize the setting such that each agent can choose to directly produce a reward in [0, 1]. We provide an O(log n)-approximate algorithm for this generalization.

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Section: Hardnessmentioning

confidence: 99%

“…The central question in this research field asks: What is the principal's optimal contract? Due to the wide application, principal-agent model has been extensively studied [1,2,3,4,5]. The agent takes a hidden action like effort which cannot be observed by the principal directly.…”

Section: Introductionmentioning

confidence: 99%

Optimal Common Contract with Heterogeneous Agents

Xiao

Wang

Chen

et al. 2019

Preprint

Self Cite

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“…The previous version considered the linear cost setting[Chen et al, 2019].Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence …”

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confidence: 99%

Optimal Anonymous Independent Reward Scheme Design

Chen

Tang

Wang

et al. 2022

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence

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We consider designing reward schemes that incentivize agents to create high-quality content (e.g., videos, images, text, ideas). The problem is at the center of a real-world application where the goal is to optimize the overall quality of generated content on user-generated content platforms. We focus on anonymous independent reward schemes (AIRS) that only take the quality of an agent's content as input. We prove the general problem is NP-hard. If the cost function is convex, we show the optimal AIRS can be formulated as a convex optimization problem and propose an efficient algorithm to solve it. Next, we explore the optimal linear reward scheme and prove it has a 1/2-approximation ratio, and the ratio is tight. Lastly, we show the proportional scheme can be arbitrarily bad compared to AIRS.

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Optimal mechanisms with budget for user generated contents

Cited by 2 publications

References 19 publications

Optimal Common Contract with Heterogeneous Agents

Optimal Common Contract with Heterogeneous Agents

Optimal Anonymous Independent Reward Scheme Design

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