<clinical_study>
  <!-- This xml conforms to an XML Schema at:
    https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
  <required_header>
    <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
    <link_text>Link to the current ClinicalTrials.gov record.</link_text>
    <url>https://clinicaltrials.gov/ct2/show/NCT04410692</url>
  </required_header>
  <id_info>
    <org_study_id>SG-COVID</org_study_id>
    <nct_id>NCT04410692</nct_id>
  </id_info>
  <brief_title>Can the Prediction Market Improve Predictions of COVID-19?</brief_title>
  <official_title>Can the Prediction Market Improve Predictions of COVID-19?</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>National University of Singapore</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
  </sponsors>
  <source>National University of Singapore</source>
  <oversight_info>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      The goal of this study is to better understand how people predict the future risks of the&#xD;
      novel Coronavirus (COVID-19).&#xD;
&#xD;
      Specifically, the investigators will ask the following research questions:&#xD;
&#xD;
        -  How well do participants predict the future risks of COVID-19?&#xD;
&#xD;
        -  Can the predictions be improved by using a prediction market mechanism?&#xD;
&#xD;
        -  Does the prediction market reduce people's fear of COVID-19?&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      The proposed study is an online experiment. Students enrolled at National University of&#xD;
      Singapore are recruited to participate in the study.&#xD;
&#xD;
      Participants will first complete a pre-experiment survey, which contains basic demographic&#xD;
      questions. Then, participants will be randomly assigned to one of two conditions: &quot;Survey&quot;&#xD;
      and &quot;Prediction Market&quot;.&#xD;
&#xD;
      &quot;SURVEY&quot; CONDITION:&#xD;
&#xD;
      Participants in the &quot;Survey&quot; condition are asked 16 prediction questions in a survey format.&#xD;
      The questions are of the following format:&#xD;
&#xD;
      &quot;What do you think will be the total cumulative number of cases in Singapore on 8th of June,&#xD;
      at 12pm?&quot;&#xD;
&#xD;
      Each question has 5 answer options. Each answer option is a range of outcomes, e.g. &quot;&lt;&#xD;
      28,900&quot;, &quot;between 28,900 and 33,899&quot;, &quot;between 33,900 and 38,899&quot;, &quot;between 38,900 and&#xD;
      43,899&quot;, and &quot;&gt; 43,899&quot;. Participants are required to enter their perceived likelihood of&#xD;
      each answer option in %.&#xD;
&#xD;
      The 16 prediction questions come from the following variations: 4 countries (Mexico,&#xD;
      Singapore, Turkey, USA) x 2 outcome measures (cases, deaths) x 2 time periods (8th of June,&#xD;
      6th of July).&#xD;
&#xD;
      Participants have 24 hours to submit their predictions.&#xD;
&#xD;
      After the 24-hour period, participants are requested to fill out a post-experiment survey,&#xD;
      which includes questions about their subjective attitudes and fears towards COVID-19.&#xD;
&#xD;
      &quot;PREDICTION MARKET&quot; CONDITION:&#xD;
&#xD;
      For participants in the &quot;Prediction Market&quot; condition, the same 16 prediction questions are&#xD;
      presented in the form of prediction markets. The prediction market is a well-established&#xD;
      method of eliciting people's predictions. The method is briefly described below.&#xD;
&#xD;
      There are 16 prediction markets, one for each question. Participants are given 100 tokens per&#xD;
      market, which can be used to buy &quot;stocks&quot; on possible outcomes. There are 5 possible outcomes&#xD;
      per market (identical to the 5 answer options per question in the &quot;Survey&quot; condition).&#xD;
&#xD;
      Each stock (i.e., possible outcome) will have a price that is dynamically determined by the&#xD;
      central marketplace, which is a function of real-time demand and supply of the option. If the&#xD;
      option is popular, its price will become higher, and vice versa.&#xD;
&#xD;
      Participants can trade at any time, and as many times as they want, during a 24-hour period.&#xD;
      Upon closure of the prediction market, participants will be rewarded proportional to the&#xD;
      number of shares that they hold on options that later turn out to be true.&#xD;
&#xD;
      The final prices of stocks correspond to the group's predictions of COVID-19.&#xD;
&#xD;
      After the 24-hour period, participants are requested to fill out a post-experiment survey,&#xD;
      which includes questions about their subjective attitudes and fears towards COVID-19.&#xD;
&#xD;
      =====&#xD;
&#xD;
      HYPOTHESES&#xD;
&#xD;
      The prediction market leads to better predictions about COVID-19. The investigators will&#xD;
      compare the survey predictions and the prediction-market predictions with the actual realized&#xD;
      outcome. The investigators hypothesize that the prediction-market predictions are more&#xD;
      accurate than the survey predictions through information aggregation.&#xD;
&#xD;
      The prediction market reduces fear. Fear is measured by participants' responses to subjective&#xD;
      attitude questions in the post-experiment survey.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">May 15, 2020</start_date>
  <completion_date type="Actual">May 17, 2020</completion_date>
  <primary_completion_date type="Actual">May 16, 2020</primary_completion_date>
  <phase>N/A</phase>
  <study_type>Interventional</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <allocation>Randomized</allocation>
    <intervention_model>Parallel Assignment</intervention_model>
    <primary_purpose>Other</primary_purpose>
    <masking>None (Open Label)</masking>
  </study_design_info>
  <primary_outcome>
    <measure>Predictions of COVID-19 Cases and Deaths</measure>
    <time_frame>24 hours</time_frame>
    <description>Participants are asked 16 questions of the following format:&#xD;
&quot;What do you think will be the total cumulative number of cases in Singapore on 8th of June, at 12pm?&quot;&#xD;
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.&#xD;
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).</description>
  </primary_outcome>
  <secondary_outcome>
    <measure>Fear</measure>
    <time_frame>24 hours (participants are required to submit post-experiment survey within 24 hours of completion of the main experiment)</time_frame>
    <description>Fear is measured by participants' responses to subjective attitude questions in the post-experiment survey. The questions are on a 5-point Likert scale.</description>
  </secondary_outcome>
  <number_of_arms>2</number_of_arms>
  <enrollment type="Actual">560</enrollment>
  <condition>Health Knowledge, Attitudes, Practice</condition>
  <arm_group>
    <arm_group_label>Control</arm_group_label>
    <arm_group_type>No Intervention</arm_group_type>
    <description>Participants' COVID-19 predictions are elicited via a survey</description>
  </arm_group>
  <arm_group>
    <arm_group_label>Treatment</arm_group_label>
    <arm_group_type>Experimental</arm_group_type>
    <description>Participants' COVID-19 predictions are elicited via a prediction market</description>
  </arm_group>
  <intervention>
    <intervention_type>Other</intervention_type>
    <intervention_name>Prediction Market</intervention_name>
    <description>Participants &quot;bet&quot; on likely future outcomes using a prediction market</description>
    <arm_group_label>Treatment</arm_group_label>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  National University of Singapore students&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  N/A&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>18 Years</minimum_age>
    <maximum_age>N/A</maximum_age>
    <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Teck Ho, PhD</last_name>
    <role>Principal Investigator</role>
    <affiliation>National University of Singapore</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>National University of Singapore</name>
      <address>
        <city>Singapore</city>
        <country>Singapore</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>Singapore</country>
  </location_countries>
  <reference>
    <citation>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.</citation>
    <PMID>31346273</PMID>
  </reference>
  <verification_date>May 2020</verification_date>
  <study_first_submitted>May 27, 2020</study_first_submitted>
  <study_first_submitted_qc>May 29, 2020</study_first_submitted_qc>
  <study_first_posted type="Actual">June 1, 2020</study_first_posted>
  <last_update_submitted>May 30, 2020</last_update_submitted>
  <last_update_submitted_qc>May 30, 2020</last_update_submitted_qc>
  <last_update_posted type="Actual">June 2, 2020</last_update_posted>
  <responsible_party>
    <responsible_party_type>Principal Investigator</responsible_party_type>
    <investigator_affiliation>National University of Singapore</investigator_affiliation>
    <investigator_full_name>Ho Teck Hua</investigator_full_name>
    <investigator_title>Senior Deputy President &amp; Provost</investigator_title>
  </responsible_party>
  <patient_data>
    <sharing_ipd>Yes</sharing_ipd>
    <ipd_description>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_description>
    <ipd_info_type>Study Protocol</ipd_info_type>
    <ipd_info_type>Informed Consent Form (ICF)</ipd_info_type>
    <ipd_info_type>Analytic Code</ipd_info_type>
    <ipd_time_frame>After completion of all analysis. It will be made available in the supporting documentation.</ipd_time_frame>
    <ipd_access_criteria>It will be made available in the supporting documentation.</ipd_access_criteria>
  </patient_data>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


download date: ClinicalTrials.gov processed this data on September 20, 2023
link text: Link to the current ClinicalTrials.gov record.
url: https://clinicaltrials.gov/ct2/show/NCT04410692
org study id: SG-COVID
nct id: NCT04410692
lead sponsor: 
is fda regulated drug: No
is fda regulated device: No
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?
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.
allocation: Randomized
intervention model: Parallel Assignment
primary purpose: Other
masking: None (Open Label)
measure: Predictions of COVID-19 Cases and Deaths
time frame: 24 hours
description: 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).
measure: Fear
time frame: 24 hours (participants are required to submit post-experiment survey within 24 hours of completion of the main experiment)
description: Fear is measured by participants' responses to subjective attitude questions in the post-experiment survey. The questions are on a 5-point Likert scale.
arm group label: Control
arm group type: No Intervention
description: Participants' COVID-19 predictions are elicited via a survey
arm group label: Treatment
arm group type: Experimental
description: Participants' COVID-19 predictions are elicited via a prediction market
intervention type: Other
intervention name: Prediction Market
description: Participants "bet" on likely future outcomes using a prediction market
arm group label: Treatment
criteria: 
gender: All
minimum age: 18 Years
maximum age: N/A
healthy volunteers: Accepts Healthy Volunteers
last name: Teck Ho, PhD
role: Principal Investigator
affiliation: National University of Singapore
facility: 
country: Singapore
citation: 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.
PMID: 31346273
responsible party type: Principal Investigator
investigator affiliation: National University of Singapore
investigator full name: Ho Teck Hua
investigator title: Senior Deputy President & Provost
sharing ipd: Yes
ipd description: 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 info type: Study Protocol
ipd info type: Informed Consent Form (ICF)
ipd info type: Analytic Code
ipd time frame: After completion of all analysis. It will be made available in the supporting documentation.
ipd access criteria: It will be made available in the supporting documentation.