| Summary: | A decision maker’s preferences are revealed through their choices, hence a desirable method for recovering preferences should take into account as much preference information as is available. In the context of choice from convex budget sets, we introduce the Money Metric Index (MMI) which recovers parameters that minimize the inconsistency between a decision maker's revealed preferences and the rankings implied by a given parametric family of utility functions. This approach differs from statistical methods which discard much of this preference information and select parameters based on a comparison between observed and predicted choices alone. Additionally, the MMI has many practical advantages: it is simple to compute, it can accommodate non-convex preferences, and it can be decomposed into separate measures of inconsistency and mis-specification.
In Chapter 2, we compare these methods for recovering parameters using a two-stage experimental design. We use the data from the first part of the experiment to construct choices in the second part that can be used to evaluate the predictive success of the two methods. We find that, in all cases, the MMI outperforms the statistical method in terms of its ability to accurately predict subject choices. Additionally, we find substantial evidence of First-Order Risk Aversion, both with respect to the recovered parameters and through direct inspection of subject choices.
The final chapter approaches this problem from a different perspective by considering to what extent decision makers are capable of making consistent choices in a laboratory context. We implement a simple experiment in order to assess the effect of computational difficulty on a decision maker's propensity to randomize their choices. We find substantial evidence of stochastic choice when subjects have limited time and computational resources available. An implication of our findings is that observed anomalous behavior in laboratory experiments involving choice under risk may be the result of ambiguity attitudes arising from the computational difficulty of the task rather than risk attitudes. === Arts, Faculty of === Vancouver School of Economics === Graduate
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