Archive of European Projects

Bayesian Truth Serum and its applications to conjoint analysis: a reliable way to assess user preferences for new products, services and policies (BayInno)
Start date: 01 Apr 2015, End date: 31 Mar 2018 PROJECT  FINISHED 

Many areas of economics use subjective data. In use of subjective data the crucial question is how much confidence one can have in truthfulness of respondents' answers. Resolving this problem would improve reliability of current uses of subjective data and also open up new possibilities of use. One solution to this problem is presented by Bayesian Truth serum (BTS). BTS is a game-theoretic scoring system that takes as inputs respondents’ answers to questions, as well as their probabilistic predictions about the distribution of answers in the sample. It induces truthful answers from a sample of rational, i.e. Bayesian, expected value maximizing respondents. The method allows for collecting data on issues that are not common knowledge of neither respondents nor experimenters. In particular, BTS can be applied to judgments involving counter-factual or conditional scenarios, such as arise naturally in policy discussions. BTS is a very new methodology that is currently being experimentally validated through a large scale project IARPA RFI-10-01 at MIT, starting from 2011. The BTS method offers various opportunities for application to economic and social sciences. This project proposes to apply the BTS method to innovation research by making adaptations to the BTS so it can be embedded in the conjoint analysis. Conjoint analysis is a methodology widely used across industries and in public sector for preference elicitation related to new products, services or policies. Conjoint analysis allows for designing products/services/policies with the ideal combination of features thus increasing probability of successful introduction. As all methods that use primary data collected through surveys, conjoint analysis crucially depends on the truthfulness of the subjective data. BTS in this setting can improve the ability to accurately assess user preferences, and in this way improve decision making of innovator developers in private and public sector.
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