Impact of decision theoretic models in information elicitation

预测市场和评分规则每天用于引出个人对未来事件结果的概率估计。例如，“项目B将在2018年6月1日前完成吗？”是一种可以在预测市场中交易的证券，或者让代理在评分规则中报告他们的信念。预测市场和评分规则用于没有历史数据的问题和实例，预测未来事件结果的唯一方法是询问专家、工人或任何有意见的人，从而利用“人群的智慧”。******最初提出时，预测市场和评分规则都被证明适用于风险中性（在获得50美元或100美元的概率为0.5的情况下，对其他情况不感兴趣）、短视（不考虑未来的回报）和理性（最大化他们的总回报）的代理人。风险中立、理性和短视行为在实践中并不成立，更现实的人类决策模型已经被提出并在各种实验室和实地研究中得到验证。在提出的研究计划中，我们将描述当人类，代理人，使用这些更现实和现代的决策模型建模时，预测市场和评分规则的功效。特别是，我们将描述预测市场和评分规则在参与者是累积前景理论代理人、存在外部激励、参与者是模糊性厌恶代理人以及代理人具有双曲贴现时如何很好地汇总信息。所有这些模型在实践中都被证明存在于人类身上，但它们在预测市场中没有被考虑，有些在评分规则中没有被考虑。这个研究项目的结果将是新的预测市场和评分规则机制，当面对使用上述决策模型建模的代理时，这些概率启发方法的准确性将得到提高。******所提出的研究计划的结果将是更准确的预测市场和评分规则的预测，在设置中更经常发生在实践中，而不是当这些机制被提出时所考虑的设置。本研究将进一步强调预测市场和评分规则的局限性和优点。此外，可以为所考虑的环境发展新的信息获取机制。加拿大将从拟议的研究中受益，因为我们将努力使开发的方法可用在加拿大组织中部署的软件中，从而为他们提供对未来事件可能性进行更准确估计的独特竞争优势，从而导致更快，更准确的决策。HQP可以在加拿大的工业、教育或公共部门部署开发的机制或使用他们的分析和技术技能。

Prediction markets and scoring rules are used daily to elicit personal probability estimates on the outcome of future events. For example, “Will project B be complete by June 1, 2018?” is one security that may be traded in a prediction market, or have agents report their beliefs in a scoring rule. Prediction markets and scoring rules are used for questions and instances in which there is no historical data, and the only way to forecast the outcome of a future event is to ask experts, workers, or anyone having an opinion, thereby leveraging the “wisdom of the crowd.”******When initially proposed, both prediction markets and scoring rules were shown to work for agents that are risk-neutral (are indifferent between getting $50 or $100 with probability 0.5, and nothing otherwise), myopic (do not take future payoffs into account), and rational (are maximizing their total reward). Risk-neutrality, rationality, and myopic behavior are known not to hold in practice, and more realistic models of human decision-making have been proposed and verified in various laboratory and field studies. In the proposed research program, we will characterize the efficacy of prediction markets and scoring rules when humans, agents, are modeled using these more realistic and modern decision models. In particular, we will characterize how well prediction markets and scoring rules aggregate information when participants are cumulative prospect theory agents, when external incentives exist, when participants are ambiguity averse agents, and when agents have hyperbolic discounting. All of these models have been shown to exist in humans in practice, but they have not been considered in prediction markets, and some have not been considered in scoring rules. The results of this research program will be new prediction market and scoring rule mechanisms that improve the accuracy of these probability elicitation methods when faced with agents modeled using the decision models discussed above. ******The outcomes of the proposed research program will be more accurate forecasts from prediction markets and scoring rules in settings that occur more often in practice than the settings considered when these mechanisms were proposed. This research will further highlight the limitations and benefits of prediction markets and scoring rules. In addition, new information elicitation mechanisms may be developed for the settings considered. Canada stands to benefit from the proposed research as we will work to make the developed methods available in software that may be deployed in Canadian organizations, thereby providing them with a distinct competitive advantage of having more accurate estimates on the likelihood of a future event, leading to faster and more accurate decisions. HQP trained under the developed program may deploy the developed mechanisms or use their analysis and technical skills in industry, education, or public sector throughout Canada.