An experimental analysis of information aggregation in decision markets : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Economics at Massey University, Albany, New Zealand
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2023
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Massey University
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Abstract
Knowledge in a society is often distributed amongst different individuals, each holding different pieces of information. By aggregating these dispersed, different pieces of information, accurate forecasts can be generated, adding potential to improve decision-making processes. Decision markets are economic mechanisms to concurrently predict the future and decide on it. They incentivize “expert” individuals to predict the consequences of each of a set of possible actions and then select an action based on these predictions. Decision markets rely on scoring rules (payment schemes) to guarantee that experts are properly incentivized to truthfully reveal their beliefs whilst using decision rules to translate aggregated forecasts into decisions.
In this thesis, we present an experimental study of information aggregation in decision markets. Objective of the study is to provide a proof-of-principle for the functioning of decision markets. Market prices are dependent on the private signals given to participants, signifying that signal constellations are the primary determinant of final market prices. We find that decision markets work in aggregating private information and that the incentive compatibility of the decision rule matters for information aggregation. Upon exploring behavioural attributes that might be linked to individual trading performance, we discover that the decision rules also have an impact on participants’ behaviour in the market but we find no evidence that individual behavioural attributes has any influence on market efficiency. Our findings can inform future experiments about decision making processes and real world decision market applications.
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Business forecasting, Economic forecasting, Information theory in economics