Estimating subjective probabilities
By Steffen Andersen, John Fountain, Glenn W. Harrison, E Elisabet Rutström, John Duffy, Peter P. Wakker, R. Walsh, Nathaniel T. Wilcox. Journal of Risk and Uncertainty, 2014
130 citations recorded by the source corpus.
Subjective probabilities play a central role in many economic decisions and act as an immediate confound of inferences about behavior, unless controlled for. Several procedures to recover subjective probabilities have been proposed, but in order to recover the correct latent probability one must either construct elicitation mechanisms that control for risk aversion, or construct elicitation mechanisms which undertake “calibrating adjustments” to elicited reports. We illustrate how the joint estimation of risk attitudes and subjective probabilities can provide the calibration adjustments that theory calls for. We illustrate this approach using data from a controlled experiment with real monetary consequences to the subjects. This allows the observer to make inferences about the latent subjective probability, under virtually any well-specified model of choice under subjective risk, while still employing relatively simple elicitation mechanisms.
Subfields: Artificial Intelligence, General Decision Sciences, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/ead7cc7982b42490e9c5de4bd1ca87f8accd0fed
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Citations in the corpus
- The Binarized Scoring Rule (2013, background): “This also holds true when we include only risk-averse subjects in column (2).”
- A penny for your thoughts: a survey of methods for eliciting beliefs (2014, unknown): “Andersen et al. (2013) present subjects with a range of bets and use maximum likelihood to jointly estimate the risk preferences and subjective beliefs, assuming a structural form for the subject’s utility function and the decision making model.”
- Risk aversion relates to cognitive ability: Preferences or Noise? (2016, methodology): “Using a similar design, Andersen et al. (2006) report somewhat mixed support; in the case where skewing the table has a significant effect, the direction is consistent with a bias towards the middle of the table. Harrison, List and Towe (2008) present structural estimations on an experimental data s”