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Is the time men use on childcare and household work the result of preferences or cultural, institutional and economic constraints? Can such constraints be measured when we only observe men's choices (functionings) but not their capabilities? Using a random utility model together with stochastic specifications of the probability of having different capabilities, this paper shows that it is possible to distinguish between preferences and capabilities. Utilising time use data for Spain, we find that even though men do relatively little childcare, it is important to them. So, men do care to care. Our estimates show that, given our model, only about 9% of men with children have the full capability set, while 58% of them are constrained to a low level of care and housework. According to our model, many of these would not change behaviour if they had the full capability set, but about 20% of fathers would choose to provide more childcare and housework. [ABSTRACT FROM AUTHOR]

This work deals with consumers' preferences about coffee. Firstly, a choice experiment is performed on a sample of potential consumers. Following this, a sensory test involving the tasting of two varieties of coffee is carried out with the respondents, after which the same choice experiment is supplied to them again. An innovative approach for building heterogeneous choice designs is specifically developed for the case‐study, based on approximate design theory and compound design criterion. Panel Mixed Logit models are used, thereby allowing for the inclusion of correlation among consumers' responses; choice‐sets are supplied to a proportion of respondents according to optimal weights. The estimation results of the Panel Mixed Logit model are satisfactory, confirming the validity of the proposed approach. [ABSTRACT FROM AUTHOR]

Urban delivering is facing some significant changes that are heading towards unsustainable scenarios. At the same time, local administrations as well as city planners are involved in promoting new solutions that can help to improve city sustainability and livability. In this context, electric micromobility could offer a valuable contribution. In fact, electric micromobility systems such as e-bikes and e-scooters, both at an individual level or as a shared service, could represent sustainable mobility options for city logistics, especially for specific classes of parcel delivery, users' characteristics and travelled distances. Considering both the growth of e-commerce and the spreading of new options for delivering parcels (e.g., crowdshipping), electric micromobility (e-bikes and e-scooters) could support the penetration and acceptability of such new options, limiting the impacts of delivery operations. After analysis of the current e-commerce background and a review of the current delivery options to satisfy delivery demand, crowdshipping stands out. Thus, the potential shift from private transport to e-micromobility for crowdshipping is investigated, assuming that potential crowdshippers may, mainly, be commuters. The methodology is based on using probabilistic-behavioral models developed within random utility theory, which allow the potential shift towards e-micromobility for commuting to be forecasted. The models were calibrated in Rome, where more than 200 interviews with commuters were available. [ABSTRACT FROM AUTHOR]

In the absence of a complete voting record, decision records are an important data source to analyze committee decision-making in various institutions. Despite the ubiquity of decision records, we know surprisingly little about how to analyze them. This paper highlights the costs in terms of bias, inefficiency, or inestimable effects when using decision instead of voting records and introduces a Bayesian structural model for the analysis of decision-record data. I construct an exact likelihood function that can be tailored to many institutional contexts, discuss identification, and present a Gibbs sampler on the data-augmented posterior density. I illustrate the application of the model using data from US state supreme court abortion decisions and UN Security Council deployment decisions. [ABSTRACT FROM AUTHOR]

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Modeling Choices with Different Variabilities In "Heteroscedastic Exponomial Choice" (HEC), Aydın Alptekinoğlu and John Semple investigate a discrete choice model that can handle choices with utilities having different variances. This new model, the HEC model, nests the exponomial choice (EC) model as a special case. Like EC, HEC has excellent structure and permits the choice probabilities, demand elasticities, and consumer surplus to be expressed in an analytically convenient (closed) form. Determining optimal monopoly prices for products in an assortment as well as determining equilibrium prices for an oligopoly of single-product firms are both tractable problems. Moreover, fitting the choice model to real data is straightforward, given the shape (log-concavity) of the likelihood function for a given variance structure. The HEC model performed well against MNL (multinomial logit) and EC models in empirical tests on household panel data for 30 categories of grocery products. In particular, brand variance was statistically significant in virtually every product category tested. We investigate analytical and empirical properties of the Heteroscedastic Exponomial Choice (HEC) model to lay the groundwork for its use in theoretical and empirical studies that build demand models on a discrete choice foundation. The HEC model generalizes the Exponomial Choice (EC) model by including choice-specific variances for the random components of utility (the error terms). We show that the HEC model inherits some of the properties found in the EC model: closed-form choice probabilities, demand elasticities, and consumer surplus; optimal monopoly prices that are increasing with ideal utilities in a hockey-stick pattern; and unique equilibrium oligopoly prices that are easily computed using a series of single-variable equations. However, the HEC model has several key differences with the EC model, which show that variances matter: the choice probabilities (market shares) as well as equilibrium oligopoly prices are not necessarily increasing with ideal utilities; and the new model can include choices with deterministic utility or choices with zero probability. However, because the HEC model uses more parameters, it is harder to estimate. To justify its use, we apply HEC to grocery purchase data for 30 product categories and find that it significantly improves model fit and generally improves out-of-sample prediction compared with EC. We go on to investigate the more nuanced impact of the variance parameters on oligopoly pricing. We find that the individual and collective incentives differ in equilibrium: firms individually want lower error variability for their own product but collectively prefer higher error variability for all products—including their own—because higher error variability softens the price competition. [ABSTRACT FROM AUTHOR]

The success of machine-learning methods is spreading their use to many different fields. This paper analyses one of these methods, the Kernel Logistic Regression (KLR), from the point of view of Random Utility Model (RUM) and proposes the use of the KLR to specify the utilities in RUM, freeing the modeler from the need to postulate a functional relation between the features. A Monte Carlo simulation study is conducted to empirically compare KLR with the Multinomial Logit (MNL) method, the Support Vector Machine (SVM) and the Random Forests (RF). We have shown that, using simulated data, KLR is the only method that achieves maximum accuracy and leads to an unbiased willingness-to-pay estimator for non-linear phenomena. In a real travel mode choice problem, RF achieved the highest predictive accuracy, followed by KLR. However, KLR allows for the calculation of indicators such as the value of time, which is of great importance in the context of transportation. [ABSTRACT FROM AUTHOR]

We empirically investigate the welfare effects of providing product quality information to consumers in the form of expert opinion scores in a setting of asymmetric information. Identification of the effects of the provision of information comes from a field experiment in the retail wine market. We use a monthly‐product‐store panel scanner data set, collected before and during the field experiment, which involves treating a random subset of wine products by displaying expert scores in one store and comparing sales in similar non‐treated stores. Using a structural random utility model of demand, we show that, on average, consumers significantly value one score point increase due to the treatment by about 0.5 to 0.7 cents. As a consequence, for a bottle featuring an average score of 83, consumers would be willing to pay additionally between twenty and sixty cents more due to the treatment. Using counterfactual scenarios, we find that adding expert opinion shelf labels increases consumer surplus. In addition, allowing for strategic price reactions by retailers would lead to an overall significant welfare improvement given that (a) consumers significantly value the score information albeit facing higher prices, and (b) the profits increase with the market power of firms. [ABSTRACT FROM AUTHOR]

A more general family of random utility models is developed to model a cognitive heuristic, known as consideration sets. These new models, denoted as Multinomial Logit Cardinality Effect models (MNL-CE), define perceived representative utility of items by assigning a penalty as a function of assortment cardinality to the representative utility of each item beyond a threshold value (except for the no-choice option). This definition of perceived representative utility of an item is context-dependent and thus a function of assortment attributes (cardinality), in addition to item and user attributes. The user's net benefit is therefore a trade-off between the benefits and the costs of considering a certain number of items. A developed algorithm efficiently solves the subscription platform assortment optimization problem with equal profit when user selection is modeled via variants of the MNL-CE. The sensitivity of model parameters on the optimal assortment cardinality and no-choice probability is analyzed with the MovieLens dataset. [ABSTRACT FROM AUTHOR]

This paper builds upon the work of Professor Marley, who, since the beginning of his long research career, has proposed rigorous axiomatics in the area of probabilistic choice models. Our study concentrates on models that can be applied to best and worst choice scaling experiments. We focus on those among these models that are based on strong assumptions about the underlying ranking of the alternatives with which the individual is assumed to be endowed when making the choice. Taking advantage of an inclusion–exclusion identity that we showed a few years ago, we propose a variety of best–worst choice probability models that could be implemented in software packages that are flourishing in this field. [ABSTRACT FROM AUTHOR]

The emergence of a variety of Machine Learning (ML) approaches for travel mode choice prediction poses an interesting question to transport modellers: which models should be used for which applications? The answer to this question goes beyond simple predictive performance, and is instead a balance of many factors, including behavioural interpretability and explainability, computational complexity, and data efficiency. There is a growing body of research which attempts to compare the predictive performance of different ML classifiers with classical Random Utility Models (RUMs). However, existing studies typically analyse only the disaggregate predictive performance, ignoring other aspects affecting model choice. Furthermore, many existing studies are affected by technical limitations, such as the use of inappropriate validation schemes, incorrect sampling for hierarchical data, a lack of external validation, and the exclusive use of discrete metrics. In this paper, we address these limitations by conducting a systematic comparison of different modelling approaches, across multiple modelling problems, in terms of the key factors likely to affect model choice (out-of-sample predictive performance, accuracy of predicted market shares, extraction of behavioural indicators, feature importance analysis, and computational efficiency). The modelling problems combine several real world datasets with synthetic datasets, where the data generation function is known. The results indicate that the models with the highest disaggregate predictive performance (namely Extreme Gradient Boosting (XGBoost) and Random Forests (RF)) provide poorer estimates of behavioural indicators and aggregate mode shares, and are more expensive to estimate, than other models, including Deep Neural Networks (DNNs) and Multinomial Logit (MNL). It is further observed that the MNL model performs robustly in a variety of situations, though ML techniques can improve the estimates of behavioural indices such as Willingness To Pay (WTP). [Display omitted] • Systematic evaluation of ML and discrete choice models for travel mode choice. • Addressing common methodological failures found in the literature. • Assessing the quality of model outputs extracted from ML methods. • Evaluating the models on a wide variety of real-world and synthetic datasets. [ABSTRACT FROM AUTHOR]

Chronic wasting disease (CWD) is a prion disease that affects deer, elk and other cervid wildlife species. Although there is no known link between the consumption of CWD affected meat and human health, hunters are advised to have animals from CWD affected areas tested and are advised against consuming meat from CWD infected animals (Government of Alberta 2010). We model hunter response to the knowledge that deer in a wildlife management unit have been found to have CWD in Alberta, Canada. We examine hunter site choice over two hunting seasons using revealed and stated preference data in models that incorporate preferences, choice set formation, and scale. We compare a fully endogenous choice set model using the independent availability logit model (Swait in Probabilistic choice set formation in transportation demand models. Dissertation, MIT, 1984) with the availability function approach (Cascetta and Papola in Transp Res C 9(4):249-263, 2001) that approximates choice set formation. We find that CWD incidence affects choice set formation and preferences and that ignoring choice set formation would result in biased estimates of impact and welfare measures. This study contributes to the broader recreation demand literature by incorporating choice set formation, scale and temporal impacts into a random utility model of recreation demand. [ABSTRACT FROM AUTHOR]

We propose a novel model of stochastic choice: the single-crossing random utility model (SCRUM). This is a random utility model in which the collection of preferences satisfies the single-crossing property. We offer a characterization of SCRUMs based on two easy-to-check properties: the classic Monotonicity property and a novel condition, Centrality. The identified collection of preferences and associated probabilities is unique. We show that SCRUMs nest both single-peaked and single-dipped random utility models and establish a stochastic monotone comparative result for the case of SCRUMs. [ABSTRACT FROM AUTHOR]

We compare two common ways of incorporating service frequency into models of airline competition. One is based on the so called s-curve, in which, all else equal, market shares are determined by frequency shares. The other is based on schedule delay—the time difference between when travelers wish to travel and when flights are available. We develop competition models that differ only with regard to which of the above approaches is used to capture the effect of frequency. The demand side of both models is an approximation of a nested logit model which yields endogenous travel demand by including not traveling in the choice set. We find symmetric competitive equilibrium for both models analytically, and compare their predictions concerning market frequency with empirical evidence. In contrast to the s-curve model, the schedule delay model depicts a more plausible relationship between market share and frequency share and accurately predicts observed patterns of supply side behavior. Moreover, the predictions from both models are largely the same if we employ numerical versions of the model that capture real-world aspects of competition. We also find that, for either model, the relationship between airline frequency and market traffic is the same whether frequency is determined by competitive equilibrium, social optimality, or social optimality with a break-even constraint. [ABSTRACT FROM AUTHOR]

We propose an approach for estimating customer preferences for a set of substitutable products using only sales transactions and product availability data. The underlying demand framework combines a general, nonparametric discrete choice model with a Bernoulli process of arrivals over time. The choice model is defined by a discrete probability mass function (pmf) on a set of possible preference rankings of alternatives, and it is compatible with any random utility model. An arriving customer is assumed to purchase the available option that ranks highest in her preference list. The problem we address is how to jointly estimate the arrival rate and the pmf of the rank-based choice model under a maximum likelihood criterion. Since the potential number of customer types is factorial, we propose a market discovery algorithm that starts with a parsimonious set of types and enlarge it by automatically generating new types that increase the likelihood value. Numerical experiments confirm the potential of our proposal. For a realistic data set in the hospitality industry, our approach improves the root mean square errors between predicted and observed purchases computed under independent demand model estimates by 67%-93%. [ABSTRACT FROM AUTHOR]

Cognitive models of decision making aim to explain the process underlying observed ehoices. Here, we test a sequential sampling model of decision making, multialternative decision field theory (MDFT; Roe, Busemeyer, & Townsend, 2001), on empirical grounds and compare it against 2 established random utility models of choice: the probit and the logit model. Using a within-subject experimental design, participants in 2 studies repeatedly choose among sets of options (consumer products) described on several attributes. The results of Study 1 showed that ali models predicted participants' choices equally well. In Study 2, in which the choice sets were explicitly designed to distinguish the models, MDFT had an advantage in predicting the observed choices. Study 2 further revealed the occurrence of multiple context effects within single participants, indicating an interdependent evaluation of choice options and correlations between different context effects. In sum, the results indicate that sequential sampling models can provide relevant insights into the cognitive process underlying preferential choices and thus can lead to better choice predictions. [ABSTRACT FROM AUTHOR]

Models of labor supply derived from stochastic utility representations and discretized sets of feasible hours of work have gained popularity because they are more practical than the standard approaches based on marginal calculus. In this paper we argue that practicality is not the only feature that can be addressed by means of stochastic choice theory. This theory also offers a powerful framework for developing a more realistic model for labor supply choices, founded on individuals having preferences over jobs and facing restrictions on the choice of jobs and hours of work. We discuss and clarify how this modeling framework deviates from both the conventional discrete approach [Van Soest, A. () Structural models of family labor supply. A discrete choice approach. Journal of Human Resources 30: 63-88), as well as the standard textbook approach based on marginal calculus (Hausman, J.A. () The econometrics of nonlinear budget sets. Econometrica 53: 1255-1282]. We furthermore discuss how the model based on job choice can be applied to simulate effects of alternative restrictions on hours of work. [ABSTRACT FROM AUTHOR]

Highlights: [•] We model joint before and after choices with imperfectly correlated errors. [•] We model impacts of before–after correlation on transition probabilities. [•] We model impacts of before–after correlation on welfare. [•] Unconditional welfare mean is independent of correlation if no income effects. [•] Conditional welfare means are close to unconditional mean if independent errors. [ABSTRACT FROM AUTHOR]

Estimation of consistent parameter estimates for recreational demand models faces challenges arising from the choice-based nature of the data collected primarily for resource management purposes. As an alternative to randomized respondent-based sampling, choice-based onsite sampling can provide information on actual choices made by a subset of the population where participation has a low incidence. While the literature has shown that under specific restrictions the estimation of choice models from onsite sampling data yields unbiased fixed parameter estimates for the conditional logit model, this result does not carry over to estimation of the random parameter logit model. We propose an estimator for the unbiased estimation of the random parameter model using choice-based data; our estimator uses weights based on information about the level of sampling effort. An empirical application of the standard and weighted discrete choice RUM models to onsite sample data on recreational fishing illustrates the advantages of the proposed estimator. The estimation results indicate the compensating variation associated with an decrease, or increase, of 50 % in expected catch rates for a recreational shoreline sportfishing trip to a man-made structure in southern California is $$-{\$}2.80$$ or $${\$}3.54$$ per trip, respectively. [ABSTRACT FROM AUTHOR]