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Can the Prediction Market Improve Predictions of COVID-19?

2020年5月30日 更新者:Ho Teck Hua、National University of Singapore

The goal of this study is to better understand how people predict the future risks of the novel Coronavirus (COVID-19).

Specifically, the investigators will ask the following research questions:

  • How well do participants predict the future risks of COVID-19?
  • Can the predictions be improved by using a prediction market mechanism?
  • Does the prediction market reduce people's fear of COVID-19?

研究概览

地位

完全的

详细说明

The proposed study is an online experiment. Students enrolled at National University of Singapore are recruited to participate in the study.

Participants will first complete a pre-experiment survey, which contains basic demographic questions. Then, participants will be randomly assigned to one of two conditions: "Survey" and "Prediction Market".

"SURVEY" CONDITION:

Participants in the "Survey" condition are asked 16 prediction questions in a survey format. The questions are of the following format:

"What do you think will be the total cumulative number of cases in Singapore on 8th of June, at 12pm?"

Each question has 5 answer options. Each answer option is a range of outcomes, e.g. "< 28,900", "between 28,900 and 33,899", "between 33,900 and 38,899", "between 38,900 and 43,899", and "> 43,899". Participants are required to enter their perceived likelihood of each answer option in %.

The 16 prediction questions come from the following variations: 4 countries (Mexico, Singapore, Turkey, USA) x 2 outcome measures (cases, deaths) x 2 time periods (8th of June, 6th of July).

Participants have 24 hours to submit their predictions.

After the 24-hour period, participants are requested to fill out a post-experiment survey, which includes questions about their subjective attitudes and fears towards COVID-19.

"PREDICTION MARKET" CONDITION:

For participants in the "Prediction Market" condition, the same 16 prediction questions are presented in the form of prediction markets. The prediction market is a well-established method of eliciting people's predictions. The method is briefly described below.

There are 16 prediction markets, one for each question. Participants are given 100 tokens per market, which can be used to buy "stocks" on possible outcomes. There are 5 possible outcomes per market (identical to the 5 answer options per question in the "Survey" condition).

Each stock (i.e., possible outcome) will have a price that is dynamically determined by the central marketplace, which is a function of real-time demand and supply of the option. If the option is popular, its price will become higher, and vice versa.

Participants can trade at any time, and as many times as they want, during a 24-hour period. Upon closure of the prediction market, participants will be rewarded proportional to the number of shares that they hold on options that later turn out to be true.

The final prices of stocks correspond to the group's predictions of COVID-19.

After the 24-hour period, participants are requested to fill out a post-experiment survey, which includes questions about their subjective attitudes and fears towards COVID-19.

=====

HYPOTHESES

The prediction market leads to better predictions about COVID-19. The investigators will compare the survey predictions and the prediction-market predictions with the actual realized outcome. The investigators hypothesize that the prediction-market predictions are more accurate than the survey predictions through information aggregation.

The prediction market reduces fear. Fear is measured by participants' responses to subjective attitude questions in the post-experiment survey.

研究类型

介入性

注册 (实际的)

560

阶段

  • 不适用

联系人和位置

本节提供了进行研究的人员的详细联系信息,以及有关进行该研究的地点的信息。

学习地点

      • Singapore、新加坡
        • National University of Singapore

参与标准

研究人员寻找符合特定描述的人,称为资格标准。这些标准的一些例子是一个人的一般健康状况或先前的治疗。

资格标准

适合学习的年龄

18年 及以上 (成人、年长者)

接受健康志愿者

是的

有资格学习的性别

全部

描述

Inclusion Criteria:

  • National University of Singapore students

Exclusion Criteria:

  • N/A

学习计划

本节提供研究计划的详细信息,包括研究的设计方式和研究的衡量标准。

研究是如何设计的?

设计细节

  • 主要用途:其他
  • 分配:随机化
  • 介入模型:并行分配
  • 屏蔽:无(打开标签)

武器和干预

参与者组/臂
干预/治疗
无干预:Control
Participants' COVID-19 predictions are elicited via a survey
实验性的:Treatment
Participants' COVID-19 predictions are elicited via a prediction market
Participants "bet" on likely future outcomes using a prediction market

研究衡量的是什么?

主要结果指标

结果测量
措施说明
大体时间
Predictions of COVID-19 Cases and Deaths
大体时间:24 hours

Participants are asked 16 questions of the following format:

"What do you think will be the total cumulative number of cases in Singapore on 8th of June, at 12pm?"

Each question has 5 answer options. Each answer option is a range of possible outcomes. The primary outcome measure is participants' perceived likelihood of each answer option.

The 16 questions come from the following variations: 4 countries (Mexico, Singapore, Turkey, USA) x 2 outcome measures (cases, deaths) x 2 time periods (8th of June, 6th of July).

24 hours

次要结果测量

结果测量
措施说明
大体时间
Fear
大体时间:24 hours (participants are required to submit post-experiment survey within 24 hours of completion of the main experiment)
Fear is measured by participants' responses to subjective attitude questions in the post-experiment survey. The questions are on a 5-point Likert scale.
24 hours (participants are required to submit post-experiment survey within 24 hours of completion of the main experiment)

合作者和调查者

在这里您可以找到参与这项研究的人员和组织。

调查人员

  • 首席研究员:Teck Ho, PhD、National University of Singapore

出版物和有用的链接

负责输入研究信息的人员自愿提供这些出版物。这些可能与研究有关。

研究记录日期

这些日期跟踪向 ClinicalTrials.gov 提交研究记录和摘要结果的进度。研究记录和报告的结果由国家医学图书馆 (NLM) 审查,以确保它们在发布到公共网站之前符合特定的质量控制标准。

研究主要日期

学习开始 (实际的)

2020年5月15日

初级完成 (实际的)

2020年5月16日

研究完成 (实际的)

2020年5月17日

研究注册日期

首次提交

2020年5月27日

首先提交符合 QC 标准的

2020年5月29日

首次发布 (实际的)

2020年6月1日

研究记录更新

最后更新发布 (实际的)

2020年6月2日

上次提交的符合 QC 标准的更新

2020年5月30日

最后验证

2020年5月1日

更多信息

与本研究相关的术语

其他研究编号

  • SG-COVID

计划个人参与者数据 (IPD)

计划共享个人参与者数据 (IPD)?

是的

IPD 计划说明

Investigators will not be storing or sharing any personal identifiers. All individual level data will be anonymized, and only anonymized data will be shared with other researchers, upon request.

IPD 共享时间框架

After completion of all analysis. It will be made available in the supporting documentation.

IPD 共享访问标准

It will be made available in the supporting documentation.

IPD 共享支持信息类型

  • 研究方案
  • 国际碳纤维联合会
  • 分析代码

药物和器械信息、研究文件

研究美国 FDA 监管的药品

不

研究美国 FDA 监管的设备产品

不

此信息直接从 clinicaltrials.gov 网站检索,没有任何更改。如果您有任何更改、删除或更新研究详细信息的请求,请联系 register@clinicaltrials.gov. clinicaltrials.gov 上实施更改,我们的网站上也会自动更新.

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