Parameter Recovery for Decision Modeling Using Choice Data


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Abstract

We introduce a general framework to predict how decision sets used in decision-making experiments impact the quality of parameter estimates. We applied our framework to cumulative prospect theory (CPT) to investigate the expected parameter discrimination achieved by current research practices. Our approach revealed several regularities in the ability of recent decision sets to recover CPT parameters. We analyzed 3 decision sets and we found that (a) the randomly generated stimuli performed just as well as the researcher designed stimuli, (b) outcome magnitude impacted the recoverability of parameters, and (c) even under ideal circumstances, the parameters representing loss aversion and choice sensitivity were associated with large amounts of error in their estimates. Our analysis is the first to accurately predict the relative estimation precision of each parameter of CPT. We additionally applied our framework to analyze the decision sets that were used to produce the empirical evidence for the description–experience gap. Specifically, we found that choices based on few experienced draws from a gamble provided little information for estimating decision weights when compared to equivalent description based choices. Therefore, choices between experienced gambles can be explained by a wider range of decision weights than choices between equivalent described gambles, providing an alternative explanation for the empirical evidence surrounding the description–experience gap. We conclude with implications for future experiments designed to estimate parameters from choice data.

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