Impact of decision theoretic models in information elicitation

预测市场和评分规则每天都被用来得出个人对未来事件结果的概率估计。例如，“项目B将于2018年6月1日完成吗？”是一种可以在预测市场中交易的证券，或者让代理人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“群众的智慧”。在最初提出时，预测市场和评分规则都被证明适用于风险中性（在概率为0.5的情况下获得50美元或100美元之间无所谓，否则什么都没有），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是不成立的，人们提出了更现实的人类决策模型，并在各种实验室和实地研究中得到了验证。在拟议的研究计划中，我们将描述预测市场和评分规则的有效性时，人类，代理，使用这些更现实和现代的决策模型建模。特别是，我们将描述如何以及预测市场和评分规则汇总信息时，参与者是累积前景理论代理，当外部激励存在时，当参与者是模糊厌恶代理，当代理有双曲线折扣。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，有些在评分规则中也没有被考虑。这项研究计划的结果将是新的预测市场和评分规则机制，提高这些概率启发方法的准确性时，面对代理建模使用上述决策模型。拟议的研究计划的结果将是更准确的预测，从预测市场和评分规则的设置更经常发生在实践中比设置时考虑这些机制被提出。这项研究将进一步突出预测市场和评分规则的局限性和好处。此外，可以为所考虑的环境开发新的信息获取机制。加拿大将从拟议的研究中受益，因为我们将努力使所开发的方法可以在加拿大各组织中部署的软件中使用，从而为他们提供一个明显的竞争优势，即对未来事件的可能性进行更准确的估计，从而做出更快、更准确的决定。根据已开发的计划培训的HQP可以部署已开发的机制或使用他们的分析和技术技能在整个加拿大的工业，教育或公共部门。

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.