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Can the Prediction Market Improve Predictions of COVID-19?

30. mai 2020 oppdatert 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?

Studieoversikt

Status

Fullført

Intervensjon / Behandling

Detaljert beskrivelse

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.

Studietype

Intervensjonell

Registrering (Faktiske)

560

Fase

  • Ikke aktuelt

Kontakter og plasseringer

Denne delen inneholder kontaktinformasjon for de som utfører studien, og informasjon om hvor denne studien blir utført.

Studiesteder

      • Singapore, Singapore
        • National University of Singapore

Deltakelseskriterier

Forskere ser etter personer som passer til en bestemt beskrivelse, kalt kvalifikasjonskriterier. Noen eksempler på disse kriteriene er en persons generelle helsetilstand eller tidligere behandlinger.

Kvalifikasjonskriterier

Alder som er kvalifisert for studier

18 år og eldre (Voksen, Eldre voksen)

Tar imot friske frivillige

Ja

Kjønn som er kvalifisert for studier

Alle

Beskrivelse

Inclusion Criteria:

  • National University of Singapore students

Exclusion Criteria:

  • N/A

Studieplan

Denne delen gir detaljer om studieplanen, inkludert hvordan studien er utformet og hva studien måler.

Hvordan er studiet utformet?

Designdetaljer

  • Primært formål: Annen
  • Tildeling: Randomisert
  • Intervensjonsmodell: Parallell tildeling
  • Masking: Ingen (Open Label)

Våpen og intervensjoner

Deltakergruppe / Arm
Intervensjon / Behandling
Ingen inngripen: Control
Participants' COVID-19 predictions are elicited via a survey
Eksperimentell: Treatment
Participants' COVID-19 predictions are elicited via a prediction market
Participants "bet" on likely future outcomes using a prediction market

Hva måler studien?

Primære resultatmål

Resultatmål
Tiltaksbeskrivelse
Tidsramme
Predictions of COVID-19 Cases and Deaths
Tidsramme: 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

Sekundære resultatmål

Resultatmål
Tiltaksbeskrivelse
Tidsramme
Fear
Tidsramme: 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)

Samarbeidspartnere og etterforskere

Det er her du vil finne personer og organisasjoner som er involvert i denne studien.

Etterforskere

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

Publikasjoner og nyttige lenker

Den som er ansvarlig for å legge inn informasjon om studien leverer frivillig disse publikasjonene. Disse kan handle om alt relatert til studiet.

Studierekorddatoer

Disse datoene sporer fremdriften for innsending av studieposter og sammendragsresultater til ClinicalTrials.gov. Studieposter og rapporterte resultater gjennomgås av National Library of Medicine (NLM) for å sikre at de oppfyller spesifikke kvalitetskontrollstandarder før de legges ut på det offentlige nettstedet.

Studer hoveddatoer

Studiestart (Faktiske)

15. mai 2020

Primær fullføring (Faktiske)

16. mai 2020

Studiet fullført (Faktiske)

17. mai 2020

Datoer for studieregistrering

Først innsendt

27. mai 2020

Først innsendt som oppfylte QC-kriteriene

29. mai 2020

Først lagt ut (Faktiske)

1. juni 2020

Oppdateringer av studieposter

Sist oppdatering lagt ut (Faktiske)

2. juni 2020

Siste oppdatering sendt inn som oppfylte QC-kriteriene

30. mai 2020

Sist bekreftet

1. mai 2020

Mer informasjon

Begreper knyttet til denne studien

Andre studie-ID-numre

  • SG-COVID

Plan for individuelle deltakerdata (IPD)

Planlegger du å dele individuelle deltakerdata (IPD)?

JA

IPD-planbeskrivelse

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-delingstidsramme

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

Tilgangskriterier for IPD-deling

It will be made available in the supporting documentation.

IPD-deling Støtteinformasjonstype

  • STUDY_PROTOCOL
  • ICF
  • ANALYTIC_CODE

Legemiddel- og utstyrsinformasjon, studiedokumenter

Studerer et amerikansk FDA-regulert medikamentprodukt

Nei

Studerer et amerikansk FDA-regulert enhetsprodukt

Nei

Denne informasjonen ble hentet direkte fra nettstedet clinicaltrials.gov uten noen endringer. Hvis du har noen forespørsler om å endre, fjerne eller oppdatere studiedetaljene dine, vennligst kontakt register@clinicaltrials.gov. Så snart en endring er implementert på clinicaltrials.gov, vil denne også bli oppdatert automatisk på nettstedet vårt. .

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