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Can we use RUM and don get DRUNK Jorge E. Ara a University of Las Palmas de Gran Canaria

RUM and DC experimentsSources of mistakes in Citizens choicesAn Extended Frame: Bayesian ModellingExample: Heuristics and DCE 4.1. STUDY 1: Is it really a practical problem? A Verbal Protocol Analysis. 4.2. STUDY 2: A Bayesian Finite Mixture Model in the WTP space. The effects of Complexity and Emotional Load on the use of Heuristics. 4.3. STUDY 3: Heuristics Heterogeneity 30560

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Can we use RUM and don get DRUNK Jorge E. Ara a University of Las Palmas de Gran Canaria

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    1. Can we use RUM and don’ get DRUNK? Jorge E. Arańa University of Las Palmas de Gran Canaria

    2. RUM and DC experiments Sources of mistakes in Citizens choices An Extended Frame: Bayesian Modelling Example: Heuristics and DCE 4.1. STUDY 1: Is it really a practical problem? A Verbal Protocol Analysis. 4.2. STUDY 2: A Bayesian Finite Mixture Model in the WTP space. The effects of Complexity and Emotional Load on the use of Heuristics. 4.3. STUDY 3: Heuristics Heterogeneity and Preference Reversals in Choice-Ranking: An Alternative Explanation. 4.4. STUDY 4: Can we use RUM and don’t get DRUNK?. A Monte Carlo Study Discussion and Further Research Outline

    7. However… Strong and large evidence that citizens don’t choose what make them happy? Why? Failing Predicting Future Experiences - Projection bias, Distinction bias, Memory bias, Belief bias, Impact bias Failing Following Predictions - Procrastination , Self-control bias, Overconfidence, Anchoring Effects, Simplifyng Decision Rules,…

    9. Solutions NEED to be … Multidisciplinary - Economic Theory - Social Psychology - Statistics - Cognitive Psychology - Neurology - Political Science,… We need an Extended Frame that integrate contributions from these different areas.

    10. Why not Bayesian? One Elegant and Robust way of integrating Multidisciplinary contributions to DC Theory and Data Analysis: Bayesian Econometrics

    11. Potential Bayesian Contributions to DCE Can use prior information (there is a lot of prior info available!. previous research, experts, Benefit Transfer, Optimal Designs,…). Able to tackle more complex/sophisticated models More accurate results (e.g. Exact theory in finite samples) More informational results (reports full posterior distributions instead of just one or two moments) Sample means are inefficient and sensitive to outliers (this is especially important when studying heterogeneity in behaviour. The role of tails have been long ignored) Bayesian methods can quantify and account for several kinds of components of uncertainty. More interpretable inferences (probabilities, confidence?,…)

    12. EXAMPLE: Heterogeneous Decision Rules and DC

    46. Thanks !!!!!

    47. STUDY 3: Testing the Validity of the Model to screen out Heuristics

    48. STUDY 3: Testing the Validity of the Model to screen out Heuristics

    49. STUDY 3: Testing the Validity of the Model to screen out Heuristics

    50. STUDY 4: Monte Carlo Study. People follow alternative heuristics…. So what are the consequences?

    51. STUDY 4: Monte Carlo Study. People follow alternative heuristics…. So what are the consequences?

    52. STUDY 4: Monte Carlo Study. People follow alternative heuristics…. So what are the consequences?

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