"satisficing and stochastic choice"
with victor aguiar and Mark Dean - JET(2016)
Satisficing is a hugely influential model of boundedly rational choice, yet it cannot be easily tested using standard choice data. We develop necessary and sufficient conditions for stochastic choice data to be consistent with satisficing, assuming that preferences are fixed, but search order may change randomly. The model predicts that stochastic choice can only occur amongst elements that are always chosen, while all other choices must be consistent with standard utility maximization. Adding the assumption that the probability distribution over search orders is the same for all choice sets makes the satisficing model a subset of the class of random utility models.
"predictive ability and the fit-power trade-off in theories of consumer behavior"
This paper proposes a predictive ability approach to assess the performance of consumption theories, establishing an implicit trade-off between the fit of the model and the amount of revealed preference information that can be learned from choices. This approach contributes to the literature on revealed preference testing by providing a measure that reflects: (i) the severity of the violations (if any); (ii) the sensitivity of the test to detect any violations; and also accounts for (iii) the effect of the pattern of observed choices on the probability of finding violations. In doing so, it provides finer information than current approaches and its power assessment does not vanish in moderate sample sizes.
"predictive ability for discrete axiomatic models"
Revealed preference restrictions provide testable implications for many theories of consumption behavior. Often, empirical evidence finds violations to the model which raises the question of how severe these are. The severity of the violations does not only depend on the extent of the observed deviations, but also on the sensitivity of the test to detect them if any. This paper provides a joint treatment for the severity of the violations and the sensitivity of the test in discrete choice environments by assessing the amount of information about underlying preferences that can be inferred from data while allowing for errors. The proposed approach allows to compare across (limited) data sets and different models of behavior.
"the impact of confidence on risk taking behavior and performance"
with nicholas coleman