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Can the Prediction Market Improve Predictions of COVID-19?

30 maj 2020 uppdaterad av: 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?

Studieöversikt

Status

Avslutad

Intervention / Behandling

Detaljerad beskrivning

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.

Studietyp

Interventionell

Inskrivning (Faktisk)

560

Fas

  • Inte tillämpbar

Kontakter och platser

Det här avsnittet innehåller kontaktuppgifter för dem som genomför studien och information om var denna studie genomförs.

Studieorter

      • Singapore, Singapore
        • National University of Singapore

Deltagandekriterier

Forskare letar efter personer som passar en viss beskrivning, så kallade behörighetskriterier. Några exempel på dessa kriterier är en persons allmänna hälsotillstånd eller tidigare behandlingar.

Urvalskriterier

Åldrar som är berättigade till studier

18 år och äldre (Vuxen, Äldre vuxen)

Tar emot friska volontärer

Ja

Kön som är behöriga för studier

Allt

Beskrivning

Inclusion Criteria:

  • National University of Singapore students

Exclusion Criteria:

  • N/A

Studieplan

Det här avsnittet ger detaljer om studieplanen, inklusive hur studien är utformad och vad studien mäter.

Hur är studien utformad?

Designdetaljer

  • Primärt syfte: Övrig
  • Tilldelning: Randomiserad
  • Interventionsmodell: Parallellt uppdrag
  • Maskning: Ingen (Open Label)

Vapen och interventioner

Deltagargrupp / Arm
Intervention / Behandling
Inget ingripande: Control
Participants' COVID-19 predictions are elicited via a survey
Experimentell: Treatment
Participants' COVID-19 predictions are elicited via a prediction market
Participants "bet" on likely future outcomes using a prediction market

Vad mäter studien?

Primära resultatmått

Resultatmått
Åtgärdsbeskrivning
Tidsram
Predictions of COVID-19 Cases and Deaths
Tidsram: 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

Sekundära resultatmått

Resultatmått
Åtgärdsbeskrivning
Tidsram
Fear
Tidsram: 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)

Samarbetspartners och utredare

Det är här du hittar personer och organisationer som är involverade i denna studie.

Utredare

  • Huvudutredare: Teck Ho, PhD, National University of Singapore

Publikationer och användbara länkar

Den som ansvarar för att lägga in information om studien tillhandahåller frivilligt dessa publikationer. Dessa kan handla om allt som har med studien att göra.

Studieavstämningsdatum

Dessa datum spårar framstegen för inlämningar av studieposter och sammanfattande resultat till ClinicalTrials.gov. Studieposter och rapporterade resultat granskas av National Library of Medicine (NLM) för att säkerställa att de uppfyller specifika kvalitetskontrollstandarder innan de publiceras på den offentliga webbplatsen.

Studera stora datum

Studiestart (Faktisk)

15 maj 2020

Primärt slutförande (Faktisk)

16 maj 2020

Avslutad studie (Faktisk)

17 maj 2020

Studieregistreringsdatum

Först inskickad

27 maj 2020

Först inskickad som uppfyllde QC-kriterierna

29 maj 2020

Första postat (Faktisk)

1 juni 2020

Uppdateringar av studier

Senaste uppdatering publicerad (Faktisk)

2 juni 2020

Senaste inskickade uppdateringen som uppfyllde QC-kriterierna

30 maj 2020

Senast verifierad

1 maj 2020

Mer information

Termer relaterade till denna studie

Andra studie-ID-nummer

  • SG-COVID

Plan för individuella deltagardata (IPD)

Planerar du att dela individuella deltagardata (IPD)?

JA

IPD-planbeskrivning

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.

Tidsram för IPD-delning

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

Kriterier för IPD Sharing Access

It will be made available in the supporting documentation.

IPD-delning som stöder informationstyp

  • STUDY_PROTOCOL
  • ICF
  • ANALYTIC_CODE

Läkemedels- och apparatinformation, studiedokument

Studerar en amerikansk FDA-reglerad läkemedelsprodukt

Nej

Studerar en amerikansk FDA-reglerad produktprodukt

Nej

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