A shift in strategy or “error”? Strategy classification over multiple stochastic specifications
Year: 2011
Type: article
Abstract: We present a classification methodology that jointly assigns to a decision maker a best-fitting decision strategy for a set of choice data as well as a best-fitting stochastic specification of that decision strategy. Our methodology utilizes normalized maximum likelihood as a model selection criterion to compare multiple, possibly non-nested, stochastic specifications of candidate strategies. In addition to single strategy with “error” stochastic specifications, we considermixturespecifications, i.e., strategies comprised of a probability distribution over multiple strategies. In this way, our approach generalizes the classification framework of Bröder and Schiffer (2003a). We apply our methodology to an existing dataset and find that some decision makers are best fit by a single strategy with varying levels of error, while others are best described as using a mixture specification over multiple strategies.
Source: Judgment and Decision Making
Authors: Clintin P. Davis-Stober, Nicholas Brown
Institution: University of Missouri
Language: English
Subfield: General Decision Sciences
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $3,550
OpenAlex ID: W1524034944