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 $\Lambda (s_{i},{ \sf f}_{q})$ 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.

翻译：我们研究一种竞标博弈模型，其中针对$m$个提案的评审工作由$n$个策略型代理玩家以众包方式完成。玩家$i$对评审提案$\ell$具有技能参数$s_{i\ell}$；其在评审过程中策略性地选择质量等级$q \in \{ 1, 2, \ldots, Q \}$并支付严格随$q$递增的努力成本${\sf f}_{q} \geq 0$。对于其付出的努力，玩家将获得由支付函数确定的严格正向报酬——该支付函数既可采用玩家无关形式（例如流行的比例分配函数），也可采用玩家特定形式。对于特定提案，各玩家的支付额度与其努力程度成正比，且竞赛组织者提供的总支付额为1。玩家$i$为其每项评审所承担的总成本，等于技能-努力函数$\Lambda (s_{i},{ \sf f}_{q})$与其实际获得报酬的差额。不同玩家与不同提案的技能参数可任意变化。提案无偏型玩家$i$具有相同的技能参数，即对所有$\ell$均有$s_{i\ell} = s_{i}$；匿名玩家意味着对所有玩家$i$均有$s_{i} = 1$。在纯纳什均衡状态下，任何玩家均无法通过单方面改变质量选择来降低其成本。本文提出了计算纯纳什均衡的算法结果。