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arXiv 2302.14701cs.GT
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众包评审的竞赛博弈

The Contest Game for Crowdsourcing Reviews

Marios Mavronicolas, Paul G. Spirakis

AI总结:

针对众包评审竞赛,建模战略玩家选择质量与努力,提出计算纯纳什均衡的算法。

摘要:

我们考虑一个竞赛博弈,该博弈模拟了从 $n$ 个战略智能体(玩家)处众包评审 $m$ 个提案的场景。玩家 $i$ 对提案 $\ell$ 的评审具有技能 $s_{i\ell}$;对于其评审,她策略性地选择质量 $q \in \{ 1, 2, \ldots, Q \}$,并付出努力 ${\sf f}_{q} \geq 0$,该努力随 $q$ 严格递增。对于她的努力,她获得由支付函数决定的严格为正的报酬,该支付函数可以是玩家不变的(例如流行的比例分配函数),也可以是玩家特定的;对于给定的提案,报酬与相应的努力成正比,且竞赛组织者提供的总支付为 1。玩家 $i$ 为其每次评审所承担的成本是技能-努力函数 $\Lambda(s_{i},{ \sf f}_{q})$ 减去其报酬的差值。技能可能因任意玩家和任意提案而异。一个提案无差异的玩家 $i$ 具有相同技能:对所有 $\ell$ 有 $s_{i\ell} = s_{i}$;匿名玩家意味着对所有玩家 $i$ 有 $s_{i} = 1$。在纯纳什均衡中,没有玩家能通过切换到不同质量来单方面降低成本。我们提出了计算纯纳什均衡的算法结果。

英文摘要:

We consider a contest game modelling a contest where reviews for $m$ proposals are crowdsourced from $n$ strategic agents} players. Player $i$ has a skill $s_{i\ell}$ for reviewing proposal $\ell$; for her review, she strategically chooses a quality $q \in \{ 1, 2, \ldots, Q \}$ and pays an effort ${\sf f}_{q} \geq 0$, strictly increasing with $q$. For her effort, she is given a strictly positive payment determined by a payment function, which is either player-invariant, like, e.g., the popular proportional allocation function, or player-specific; for a given proposal, payments are proportional to the corresponding efforts and the total payment provided by the contest organizer is 1. The cost incurred to player $i$ for each of her reviews is the difference of a skill-effort function $Λ(s_{i},{ \sf f}_{q})$ minus her payment. Skills may vary for arbitrary players and arbitrary proposals. A proposal-indifferent player $i$ has identical skills: $s_{i\ell} = s_{i}$ for all $\ell$; anonymous players means $s_{i} = 1$ for all players $i$. In a pure Nash equilibrium, no player could unilaterally reduce her cost by switching to a different quality. We present algorithmic results for computing pure Nash equilibria.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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