Before revision:  This value captures the degree of complexity /  decision difficulty at the relevant menuand allows for the choice probability of the outside option to either increase or decrease when the menu is expanded, depending on *how many* as well as *how attractive* options are added to it. The *multicriteria logit* is a special case of this model and introduces multiple utility functions that jointly predict behaviour in a multiplicative-logit way. Every multicriteria logit admits a simple discrete-choice formulation  .
After revision:  This value captures the degree of complexity / decision difficulty at each menu. We focus on two subclasses where such difficulty is endogenous and either:(i) caused by resampling, as captured by the quadratic and dual logit models; (ii) decreasing in the utility differences between alternatives. In the latter approach we focus on reciprocal utility differences. This special case is inspired by the influential drift diffusion neuroeconomic model and predicts several observed choice-deferral phenomena. On the applications side, we show how the quadratic logit admits a simple discrete-choice formulation .