In this paper, we report the results of experiments where subjects participate in a prediction market utilizing the Hanson automated market maker. The key distinction of our environment when compared with previous studies in the prediction market space is in the introduction of a conditional market that allows subjects to make conscious decisions to infuence market outcomes, and thus it investigates the quality of the decisions made by the subjects as opposed to the accuracy of the market prices. Utilizing dispersed “not-state” information, we evaluate whether subjects can accurately infer true states and maximize organizational welfare. Our initial baseline results with fully informed subjects demonstrate that while the mechanism can achieve perfect coordination and optimal decision making in certain sessions, the strategic environment is cognitively demanding and remains susceptible to coordination failures. Furthermore, we outline our experimental design for future treatment sessions that will introduce uninformed subjects to test the robustness of these decision markets against noise trader risk.
","abstract_html":"<p>In this paper, we report the results of experiments where subjects participate in a prediction market utilizing the Hanson automated market maker. The key distinction of our environment when compared with previous studies in the prediction market space is in the introduction of a conditional market that allows subjects to make conscious decisions to infuence market outcomes, and thus it investigates the quality of the decisions made by the subjects as opposed to the accuracy of the market prices. Utilizing dispersed “not-state” information, we evaluate whether subjects can accurately infer true states and maximize organizational welfare. Our initial baseline results with fully informed subjects demonstrate that while the mechanism can achieve perfect coordination and optimal decision making in certain sessions, the strategic environment is cognitively demanding and remains susceptible to coordination failures. Furthermore, we outline our experimental design for future treatment sessions that will introduce uninformed subjects to test the robustness of these decision markets against noise trader risk. </p>","abstract_has_math":false,"creators":["Vershel, Leo"],"institution":null,"degree_name":"Master of Science (MS)","degree_level":"Thesis","degree_discipline":"Behavioral and Computational Economics","degree_department":null,"school":null,"contributors":["David Porter","Stephen Rassenti","Ryan French"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026-05-01T07:00:00Z","date_published":"2026-05-01T07:00:00Z","updated_at":"2026-07-24T01:38:47Z","subjects":["Decision Markets","Hanson Market Maker","Conditional Markets","Market-based Governance","Information Aggregation","Not-states","Behavioral Economics"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.chapman.edu/behavioral_and_computational_economics_theses/8","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["David Porter","Stephen Rassenti","Ryan French"]},{"key":"dc:creator","label":"Author","values":["Vershel, Leo"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Behavioral and Computational Economics"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Decision Markets","Hanson Market Maker","Conditional Markets","Market-based Governance","Information Aggregation","Not-states","Behavioral Economics"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.chapman.edu/behavioral_and_computational_economics_theses/8"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["In this paper, we report the results of experiments where subjects participate in a prediction market utilizing the Hanson automated market maker. The key distinction of our environment when compared with previous studies in the prediction market space is in the introduction of a conditional market that allows subjects to make conscious decisions to infuence market outcomes, and thus it investigates the quality of the decisions made by the subjects as opposed to the accuracy of the market prices. Utilizing dispersed “not-state” information, we evaluate whether subjects can accurately infer true states and maximize organizational welfare. Our initial baseline results with fully informed subjects demonstrate that while the mechanism can achieve perfect coordination and optimal decision making in certain sessions, the strategic environment is cognitively demanding and remains susceptible to coordination failures. Furthermore, we outline our experimental design for future treatment sessions that will introduce uninformed subjects to test the robustness of these decision markets against noise trader risk.
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