Can the Prediction Market Improve Predictions of COVID-19?

Overview

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?

Study Type

  • Study Type: Interventional
  • Study Design
    • Allocation: Randomized
    • Intervention Model: Parallel Assignment
    • Primary Purpose: Other
    • Masking: None (Open Label)
  • Study Primary Completion Date: May 16, 2020

Detailed Description

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.

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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.

Interventions

  • Other: Prediction Market
    • Participants “bet” on likely future outcomes using a prediction market

Arms, Groups and Cohorts

  • No Intervention: Control
    • Participants’ COVID-19 predictions are elicited via a survey
  • Experimental: Treatment
    • Participants’ COVID-19 predictions are elicited via a prediction market

Clinical Trial Outcome Measures

Primary Measures

  • Predictions of COVID-19 Cases and Deaths
    • Time Frame: 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).

Secondary Measures

  • Fear
    • Time Frame: 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.

Participating in This Clinical Trial

Inclusion Criteria

  • National University of Singapore students

Exclusion Criteria

  • N/A

Gender Eligibility: All

Minimum Age: 18 Years

Maximum Age: N/A

Are Healthy Volunteers Accepted: Accepts Healthy Volunteers

Investigator Details

  • Lead Sponsor
    • National University, Singapore
  • Provider of Information About this Clinical Study
    • Principal Investigator: Ho Teck Hua, Senior Deputy President & Provost – National University, Singapore
  • Overall Official(s)
    • Teck Ho, PhD, Principal Investigator, National University, Singapore

References

Camerer CF, Dreber A, Holzmeister F, Ho TH, Huber J, Johannesson M, Kirchler M, Nave G, Nosek BA, Pfeiffer T, Altmejd A, Buttrick N, Chan T, Chen Y, Forsell E, Gampa A, Heikensten E, Hummer L, Imai T, Isaksson S, Manfredi D, Rose J, Wagenmakers EJ, Wu H. Evaluating the replicability of social science experiments in Nature and Science between 2010 and 2015. Nat Hum Behav. 2018 Sep;2(9):637-644. doi: 10.1038/s41562-018-0399-z. Epub 2018 Aug 27.

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