Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
Abstract Many theoretical models of stochastic choice are characterized by availability variation. Instead, most stochastic choice datasets have information on attribute values that vary across decision problems. This paper uses attribute variation to characterize a framework that encompasses existing interpretations of stochastic choice including consideration sets and nested choice behavior. The model has utility indices that depend on attribute values, and is characterized by a monotonicity condition relating probabilities and utility indices. Linear utility indices can be estimated for the model using existing methods without taking a stand on a single reason why choice is stochastic.