Impact of decision theoretic models in information elicitation

决策理论模型对信息获取的影响

• 批准号: RGPIN-2018-04005
• 负责人: Dimitrov, Stanko
• 金额: $ 3.79万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=758139
• 关键词: Impact; decision; theoretic; models; information

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.

每天都会使用预测市场和评分规则来得出对未来事件结果的个人概率估计。例如，“项目 B 会在 2018 年 6 月 1 日之前完成吗？”是一种可以在预测市场上交易的证券，或者让代理人报告他们对评分规则的信念。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“群体的智慧”。最初提出时，预测市场和评分规则都适用于风险中性的代理（以 0.5 的概率获得 50 美元或 100 美元之间无关紧要，什么也没有） 否则），短视（不考虑未来的回报），理性（最大化他们的总回报）。众所周知，风险中性、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在各种实验室和现场研究中被提出和验证。在拟议的研究计划中，我们将描述当使用这些更现实和现代的决策模型对人类（代理人）进行建模时，预测市场和评分规则的功效。特别是，我们将描述当参与者是累积前景理论代理人、当存在外部激励、当参与者是厌恶模糊性代理人以及当代理人具有双曲线贴现时，预测市场和评分规则聚合信息的效果如何。所有这些模型都已被证明在实践中存在于人类中，但它们尚未在预测市场中被考虑，有些还没有在评分规则中被考虑。该研究计划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，这些机制可以提高这些概率引发方法的准确性。拟议的研究计划的结果将是来自预测市场的更准确的预测，以及在实践中比提出这些机制时考虑的设置更频繁发生的设置中的评分规则。这项研究将进一步强调预测市场和评分规则的局限性和好处。此外，可以针对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，因为我们将努力在可在加拿大组织中部署的软件中提供所开发的方法，从而为他们提供明显的竞争优势，可以更准确地估计未来事件的可能性，从而做出更快、更准确的决策。根据已开发计划接受培训的总部可以在加拿大各地的工业、教育或公共部门部署已开发的机制或使用其分析和技术技能。