Random utility models with binary tree decomposable rank orders satisfy Tversky's elimination-by-aspects model

Stochastic orderings in random utility models

Multivariate Extreme Value Distributions and Coverage of Ranking Probabilities

We divide a data set on lake recreation in Wisconsin into two nonoverlappmg samples, Milwaukee residents and non‐Milwaukee residents. We then consider several hypothetical benefit transfers from a non‐Milwaukee‐based random utility model to Milwaukee residents. All transfers are for measuring water quality improvements. We consider transfers in which we assume no information on Milwaukee residents, limited (no behavioral) information, and some behavioral information. We consider simple transfers, model transfers, and updated transfers. In all cases we test the viability of the transfer by comparing it with benefits estimated from a random utility model estimated over the Milwaukee sample, which in effect is our “true” model. The values from the model and updated transfers typically deviated less than 10% from the true values. Confidence intervals are estimated by using the Krinsky‐Robb procedure for all of the benefit measures computed from the random utility model.

Branching-independent random utility model

Probabilistic versus random-utility models of state dependence: an empirical comparison

A historical and contemporary perspective on random scale representations of choice probabilities and reaction times in the context of , Journal of Mathematical Psychology, 34) results

A conceptual model of location decisions of small office firms is presented. Most firm location models consider all firms in a region; this process usually enables market segmentation only according to the industry classification of a firm. This approach introduces a high level of heterogeneity into the modeling system, since firms' attributes vary greatly in multiple dimensions. Moreover, most firm location models assume utility maximization behavior and use random utility models. In this conceptual model, an effort is made to reduce heterogeneity through focusing on the relatively homogeneous population of small office firms. Small office firms are also theorized to be satisficers rather than maximizers or optimizers; consequently, more adequate models for the phenomenon may incorporate rule-based approaches rather than random utility techniques. The conceptual model depicted constitutes a first step toward the validation of the proposition that firm location models should be more sensitive to market se...

Generalized Extreme Value models for count data: Application to worker telecommuting frequency choices

This article investigates the implications of nonlinear income effects in Random Utility Models (RUM) for measuring general equilibrium welfare impacts. A popular approach in applied welfare analysis is to approximate the expected compensating variation (cv) for an amenity change as the cv of a representative consumer whose indirect utility is given by the expected maximum utility. However, this approach can be misleading in the case of nonmarginal changes as it implies that changes in income do not affect the consumer's choice. In this case the true expected cv can be obtained via simulation. Empirical applications to recreational demand find that the bias from the representative approach is small. This article re-evaluates the accuracy of the representative consumer approximation in the context of measuring the general equilibrium welfare impacts of large environmental changes. Our findings suggest that, though the representative consumer approximation could lead to biased point estimates of the expected cv, this bias is overwhelmed by the size of the confidence intervals that result from the empirical estimation of household preferences.

Random utility models (RUMs) are used in the literature to model consumer choices from among a discrete set of alternatives, and they typically impose a constant marginal utility of income on individual preferences. This assumption is driven partially by the difficulty of constructing welfare estimates in models with nonlinear income effects. Recently, McFadden (1995) developed an algorithm for computing these welfare impacts using a Monte Carlo Markov chain simulator for generalized extreme-value variates. This paper investigates the empirical consequences of nonlinear RUMs in the case of sportfishing modal choice, while refining and contrasting the available methods for welfare estimation.

Inequalities on orderings of independent random variables are derived in the context of random utility models for ranking and subset choice data. The inequalities can be used to assess whether ranking or subset choice data are consistent with an independent random utility model. The main technique used for the inequalities is “association”, with conditions for the sharpness for the inequalities coming from identifying when the “association” inequality is an equality. Applications to real data sets are given.

Drivers' route choice model is essential in transportation software such as navigation, fleet management, and simulation, where the random utility models (RUM) have dominated for years. The authors investigate here whether machine learning (ML) models could be applied into this field, and whether these approaches outperform the traditional models in goodness‐of‐fit and prediction. The application framework and data structure are proposed, where the challenging problems lie in: (i) to pool data from multiple origin–destination pairs; and (ii) to interpret results for behaviour analysis. All RUM and ML models are applied in a real network. Results suggest that the random forest, one of the ML models, has satisfying performances with acceptable computation time, making it suitable for large network and real‐time analysis. This study shows that the ML models can be adopted for behaviour analysis, such as to prioritise the importance of variables, compute the elasticity, and forecast for scenarios. Future directions on combining the RUM and ML models are discussed.

Consumer Surplus from Discrete Choice Models

Theoretical and practical problems arise when Random Utility Models (RUM) of location choice developed for recreational fisheries are applied to model spatial choice in ocean commercial fisheries. To be relevant to actual open-ocean fisheries, discrete-choice models of ocean fisheries should avoid imposing unrealistic assumptions about spatial decision-making while incorporating detailed and flexible geographic scales. An alternative approach based on poisson approximation can be theoretically consistent with RUM while permitting specification of a choice set of almost unlimited spatial detail. Extension to nested and mixed logit models is straightforward. An application of the approach to spatial valuation of the North Pacific groundfish fishery shows that it can produce nearly identical results to those achieved by a conventional logit model. Although harvests are spread over a wide geographic extent, estimated short-run fishery value densities are highly concentrated in a few relatively small regions.

Investigation of the Stochastic Choice under Risk using Experimental Data