Information that is of relevance for decision-making is often distributed, and held by self-interested agents. Decision markets are well-suited mechanisms to elicit such information and aggregate it into conditional forecasts that can be used for decision-making. However, for incentive-compatible elicitation, decision markets rely on stochastic decision rules which entails that sometimes actions have to be taken that have been predicted to be sub-optimal. In this work, we propose three closely related mechanisms that elicit and aggregate information similar to a decision market, but are incentive compatible despite using a deterministic decision rule. Following ideas from peer prediction mechanisms, proxies rather than observed future outcomes are used to score predictions. The first mechanism requires the principal to have her own signal, which is then used as a proxy to elicit information from a group of self-interested agents. The principal then deterministically maps the aggregated forecasts and the proxy to the best possible decision. The second and third mechanisms expand the first to cover a scenario where the principal does not have access to her own signal. The principal offers a partial profit to align the interest of one agent and retrieve its signal as a proxy; or alternatively uses a proper peer prediction mechanism to elicit signals from two agents. Aggregation and decision-making then follow the first mechanism. We evaluate our first mechanism using a multi-agent bandit learning system. The result suggests that the mechanism can train agents to achieve a performance similar to a Bayesian inference model with access to all information held by the agents.

翻译：与决策相关的信息通常是分布式的，并由自利的参与者持有。决策市场是能够有效获取此类信息并将其整合为可用于决策的条件预测的机制。然而，为了实现激励兼容的信息获取，决策市场依赖于随机决策规则，这意味着有时必须采取已被预测为次优的行动。在本文中，我们提出了三种密切相关的机制，这些机制能够像决策市场一样获取和聚合信息，但在使用确定性决策规则的情况下仍能保持激励兼容性。遵循同行预测机制的思想，我们采用代理变量而非观察到的未来结果来对预测进行评分。第一个机制要求委托方拥有自己的信号，该信号随后被用作代理变量，以从一组自利参与者那里获取信息。然后，委托方根据聚合的预测和代理变量确定性地做出最佳决策。第二个和第三个机制将第一个机制扩展到委托方无法获取自身信号的情景。委托方提供部分利润以对齐某个参与者的利益，并获取其信号作为代理变量；或者，委托方使用适当的同行预测机制从两个参与者那里获取信号。聚合和决策过程随后遵循第一个机制。我们使用多智能体老虎机学习系统对第一个机制进行了评估。结果表明，该机制能够训练智能体达到与能够访问所有参与者信息的贝叶斯推理模型相似的性能。