Suchergebnisse

After more then 50 years of probabilistic choice modeling in Economics, Marketing, Political Science, Psychology, and related disciplines, theoretical and computational advances give scholars access to a sophisticated array of modeling and inference resources. We review some important, but perhaps often overlooked, properties of major classes of probabilistic choice models. For within-respondent applications, we discuss which models require repeated choices by an individual to be independent and response probabilities to be stationary. We show how some model classes, but not others, are invariant over variable preferences, variable utilities, or variable choice probabilities. These models, but not others, accommodate pooling of responses or averaging of choice proportions within participant when underlying parameters vary across observations. These, but not others, permit pooling/averaging across respondents in the presence of individual differences. We also review the role of independence and stationarity in statistical inference, including for probabilistic choice models that, themselves, do not require those properties.

As has been known for over a century, aggregated preferences of a group may bear little or no similarity to the preference of any single individual, regardless of the aggregation method. Yet, it remains routine to fit or test theories of individual decision making on pooled data, and it remains routine to cast theories of individual decision making at the aggregate level. This mindset may have disastrous policy and business implications. A population of individuals who all satisfy one theory may behave collectively as though they satisfied a competing theory. A collection of individuals satisfying a given theory may collectively satisfy a version of the same theory with qualitatively different scientific or decision analytic implications. Because the resulting artifacts apply at the population level, replications, large samples, and high-quality data can do nothing to detect or repair them.

We investigate optimal group member configurations for producing a maximally accurate group forecast. Our approach accounts for group members that may be biased in their forecasts and/or have errors that correlate with the criterion values being forecast. We show that for large forecasting groups, the diversity of individual forecasts linearly trades off with forecaster accuracy when determining optimal group composition.