Random Utility Maximization Framework Research Articles

Fishery participation and location choice model: the West Coast salmon troll commercial fishery

Random utility models have been widely used to model spatial choice within fisheries, but less attention has been paid to modeling participation and movement between fisheries. Fishers may switch fisheries in response to time closures or changes in profitability potentially creating management implications for those fisheries, as well as the fishery with the closure. We used a random utility maximization framework to model participation, fishery choice, and location choice for a large fleet of West Coast salmon trollers, many of which also participate in other fisheries. We used the model to demonstrate substitution effects across fisheries due to spatial policies implemented in the salmon fishery. Our work suggests that spatial management of a single fishery needs to take into consideration fishers’ full choice set to predict behavioral responses to spatial policies. Our analysis also provides insights into how fishers construct multifishery harvest strategies that enable them to more fully use capital or adjust to closures or changes in relative profitability.

Joint location and cost planning in maximum capture facility location under random utilities

We study a joint facility location and cost planning problem in a competitive market under random utility maximization (RUM) models. The objective is to locate new facilities and make decisions on the costs (or budgets) to spend on the new facilities, aiming to maximize an expected captured customer demand, assuming that customers choose a facility among all available facilities according to a RUM model. We examine two RUM frameworks in the discrete choice literature, namely, the additive and multiplicative RUM. While the former has been widely used in facility location problems, we are the first to explore the latter in the context. We numerically show that the two RUM frameworks can well approximate each other in the context of the cost optimization problem. In addition, we show that, under the additive RUM framework, the resultant cost optimization problem becomes highly non-convex and may have several local optima. In contrast, the use of the multiplicative RUM brings several advantages to the competitive facility location problem. For instance, the cost optimization problem under the multiplicative RUM can be solved efficiently by a general convex optimization solver, or can be reformulated as a conic quadratic program and handled by a conic solver available in some off-the-shelf solvers such as CPLEX or GUROBI. Furthermore, we consider a joint location and cost optimization problem under the multiplicative RUM and propose three approaches to solve the problem, namely, an equivalent conic reformulation, a multi-cut outer-approximation algorithm, and a local search heuristic. We provide numerical experiments based on synthetic instances of various sizes to evaluate the performances of the proposed algorithms in solving the cost optimization and the joint location and cost optimization problems.

Assessment of Metastatic Colorectal Cancer Patients’ Preferences for Biologic Treatments in Germany Using a Discrete Choice Experiment

BackgroundCurrent treatment regimens for metastatic colorectal cancer (mCRC) include biologics such as epidermal growth factor receptor and vascular endothelial growth factor inhibitors, which have specific side-effect profiles. There is a lack of information on mCRC patient preference in Germany regarding biologics in combination with chemotherapy. This study aims to identify German mCRC patients’ preference for these treatments Patients and MethodsThis was a multicenter cross-sectional discrete choice experiment (DCE). Data were collected using electronic case report forms and structured phone interviews. DCE attributes were related to efficacy, side effects, frequency of administration, and distance to treating physicians’ practice. Patients’ characteristics and choices were analyzed descriptively. Choice data was modeled using a random utility maximization framework. ResultsAll attributes, except distance to treating physicians’ practice, had a significant impact on patients’ decision. The most important driver of patients’ treatment decision was overall survival, followed by safety-related attributes and frequency of administration. Overall survival was the main driver in all subgroups analyzed. Risk of severe skin toxicities was more important to women, than men. In patients with prior experience of side effects, the risk of side effects accounted for 45% of a patient's decision, compared to 35% in those without prior experience. ConclusionOverall survival remains the most important driver in mCRC patients’ preferences for biologic treatment in combination with chemotherapy. Attributes related to safety were less important to patients when considering their treatment decision. These results indicate that understanding patient preferences may lead to increased treatment compliance.

Comparing utility functions between risky and riskless choice in rhesus monkeys

Decisions can be risky or riskless, depending on the outcomes of the choice. Expected utility theory describes risky choices as a utility maximization process: we choose the option with the highest subjective value (utility), which we compute considering both the option’s value and its associated risk. According to the random utility maximization framework, riskless choices could also be based on a utility measure. Neuronal mechanisms of utility-based choice may thus be common to both risky and riskless choices. This assumption would require the existence of a utility function that accounts for both risky and riskless decisions. Here, we investigated whether the choice behavior of two macaque monkeys in risky and riskless decisions could be described by a common underlying utility function. We found that the utility functions elicited in the two choice scenarios were different from each other, even after taking into account the contribution of subjective probability weighting. Our results suggest that distinct utility representations exist for risky and riskless choices, which could reflect distinct neuronal representations of the utility quantities, or distinct brain mechanisms for risky and riskless choices. The different utility functions should be taken into account in neuronal investigations of utility-based choice.

Assortment Optimization under the Multi-Purchase Multinomial Logit Choice Model

In this paper, we introduce the Multi-Purchase Multinomial Logit choice model, which extends the random utility maximization framework of the classical Multinomial Logit model to a multiple-purchase setting. In this model, customers sample random utilities for each offered product as in the Multinomial Logit model. However, rather than focusing on a single product, they concurrently sample a ``budget'' parameter M, which indicates the maximum number of products that the customer is willing to purchase. Subsequently, the M highest utility products are purchased, out of those whose utilities exceed that of the no-purchase option. When fewer than M products satisfy the latter condition, only these products will be purchased. First and foremost, we propose the first multi-purchase choice model that can be fully operationalized. Specifically, we first provide a recursive procedure to compute the choice probabilities in this model, which in turn provides a framework to study its resulting assortment problem, where the goal is to select a subset of products to make available for purchase so as to maximize expected revenue. Our main algorithmic results consist of two distinct polynomial time approximations schemes (PTAS); the first, and simpler of the two, caters to a setting where each customer may buy only a constant number of products, whereas the second more nuanced algorithm applies to our multi-purchase model in its general form. Additionally, we study the revenue-potential of making assortment decisions that account for multi-purchase behavior in comparison to those that overlook this phenomenon. In particular, we relate both the structure and revenue performance of the optimal assortment under a traditional single-purchase model to that of the optimal assortment in the multi-purchase setting. Finally, we complement our theoretical work with an extensive set of computational experiments, where the efficacy of our proposed PTAS is tested against natural heuristics. Ultimately, we find that our approximation scheme outperforms these approaches by 1-5% on average.

What Is the Impact of Nonrandomness on Random Choice Models?

Problem definition: This paper examines the impact of nonrandomness on random choice models and studies various operations problems under the new discrete choice models. Academic/practical relevance: The literature often assumes that the random utility components follow some independent and identically distributed distribution. This assumption is too restrictive in some real-world scenarios, because, for example, consumers may have known well about the attribute values for the product that they have repeatedly purchased. Methodology: We adopt the random utility maximization framework and characterize the choice probabilities when the utility of some alternative is deterministic. The log-likelihood function is jointly concave in the attribute coefficients under the linear utility-attribute assumption; an expectation-maximization algorithm is developed to overcome the missing data issue in estimation. Results: Surprisingly, if the utility of a particular product is deterministic, the assortment problem is still polynomial-time solvable, whereas if the utility of the no-purchase option is deterministic, the decision problem corresponding to the assortment optimization is NP-complete. We show that the price minus the reciprocal of price sensitivity is product invariant at optimality, which helps to simplify the multiproduct pricing problems. Managerial implications: Empirical study on real data shows that incorporating nonrandomness into random choice models can increase model fitting and prediction accuracy. Failure of accounting for the impact of nonrandomness may result in substantial losses.

Comparing Utility Functions Between Risky and Riskless Choice in Rhesus Monkeys

Decisions can be risky or riskless, depending on the outcomes of the choice. Expected Utility Theory describes risky choices as a utility maximization process: we choose the option with the highest utility, which we compute considering both the value of the option and its associated risk. According to the random utility maximization framework, riskless choices could also be based on a utility measure. Neuronal mechanisms of utility-based choice may thus be common to both risky and riskless choices. This assumption would require the existence of a utility function that accounts for both risky and riskless decisions. Here, we investigated whether the choice behavior of macaque monkeys in riskless and risky decisions could be described by a common underlying utility function. We found that the utility functions elicited in the two choice scenarios were different from each other, even after taking into account the contribution of subjective probability weighting. Our results suggest that distinct utility representations exist for riskless and risky choices, which could reflect distinct neuronal representations of the utility quantities, or distinct brain mechanisms for risky and riskless choices. The different utility functions should be taken into account in neuronal investigations of utility-based choice.

Linguistic distance, networks and migrants’ regional location choice

This paper analyzes the location choice of migrants at the regional level. We test the hypothesis that networks and the ability to communicate in the host country language, as proxied by linguistic distance, are substitutes in the location decision. Based on individual-level data from a special evaluation of the European Labour Force Survey (EU-LFS) and a random utility maximization framework, we find that networks have a positive effect on the location decisions while the effect of linguistic distance is negative. We also find a strong positive interaction effect between the two factors: networks are more important the larger the linguistic distance between the home country and the host region, and the negative effect of linguistic distance is smaller the larger the network size. In several extensions and robustness checks, we show that this substitutable relationship is extremely robust. Especially, we demonstrate that our results are not biased by multilateral resistance to migration.

What is the Impact of Non-Randomness on Random Choice Models?

Problem definition. This paper examines the impact of non-randomness on random choice models, and then studies various operations problems under the new discrete choice models. Academic/Practical Relevance. The literature often assumes that the random utility components follow some i.i.d. distribution. This assumption may be too restrictive in some real-world scenarios, because for example some consumers may know well about attribute values for some product (which could also be the no-purchase option) that they have repeatedly purchased. Methodology. We adopt the random utility maximization framework, and characterize the choice probabilities when the utility of some alternative is deterministic. The log-likelihood function is still jointly concave; the EM algorithm is developed to overcome the missing data issue. Results. Surprisingly, if the utility of a particular product is deterministic, the assortment problem is polynomial-time solvable; whereas if the utility of the no-purchase option is deterministic, the assortment problem is NP-hard. We show that the prices are product-invariant at optimality and use this result to simplify the multi-product pricing problems. Managerial Implications. Empirical study on real data shows that incorporating non-randomness into random choice models can increase model fitting and prediction accuracy. Failure of accounting for the impact of non-randomness on random choice models may result in substantial losses.

Spillover effects of education on internal migration in Malawi

ABSTRACT The study examines whether education externality effects on migration among household heads in Malawi exists and uses data from the Fourth Malawi Integrated Household Survey. Following the Von Neumann Morgenstern in the random utility maximization framework, the paper aims at contributing to literature in three strands. Firstly, by delving into the possible intra-household and inter-household effects of education on migration decision. Secondly, by assessing the effects of education at different levels of schooling in Malawi and finally by questioning who benefits more from education externalities between the household and the community. Drawing from a discrete choice model and using both the Probit and the random effects Probit models the study finds statistically significant spillover effects measured through the intra-household and inter-household externality effects. Almost 65% of the variations in the propensity to migrate are due to intra-household heterogeneity implying that households benefit more to migration than community. Different levels of education have different levels of impact on migration decision with an increasing positive effect as a person moves up on education ladder. Communities with higher literacy is more unlikely to send its members into migration.

Aflatoxins and Health Considerations in Consumer Food Choices in Ghana

Abstract Food safety enjoys sustained attention among the scientific community, policymakers, and the general public due to health impacts. However, pursuing appropriate regulations for pervasive food contaminants is a challenging policy issue, particularly for naturally-occurring food toxins such as aflatoxins and other mycotoxins. This paper explores consumer preferences for quality aflatoxin-free peanuts, and how food safety concerns may impact willingness to pay more for safer foods. Incorporating ‘risky’ foods into random utility-maximization framework, we analyze contingent valuation survey data on Ghana. Model and survey results show consumers in Ghana approve of food aflatoxin regulations, and are prepared to pay price premiums as incentives to ensure supply of quality peanuts. Findings reveal that consumers prioritize food safety above prices in market decisions. People prefer introduction of aflatoxin regulations that would guarantee good health; useful information for policy makers in Ghana, Africa, and rest of the developing world.

Linguistic Distance, Networks and Migrants' Regional Location Choice

This paper analyzes the interaction between migrant networks and linguistic distance in the location choice of migrants to the EU at the regional level. We test the hypothesis that networks and the ability to communicate in the host country language, proxied by linguistic distance, are substitutes in the location decision. Based on individual level data from a special evaluation of the European Labour Force Survey (EU-LFS) and a random utility maximization framework, we find that networks have a positive effect on the location decisions while the effect of linguistic distance is negative. We also find a strong positive interaction effect between the two factors: networks are more important the larger the linguistic distance between the home country and the host region, and the negative effect of linguistic distance is smaller the larger the network size. In several extensions and robustness checks, we show that this substitutable relationship is extremely robust.

Predicting the evolution of social networks with life cycle events.

This paper presents a model of social network evolution, to predict and simulate changes in social networks induced by lifecycle events. We argue that social networks change with lifecycle events, and we extend a model of friendship selection to incorporate these dynamics of personal social networks. The model uses theories of homophily and reciprocity and is formulated in a random utility maximization framework to predict the formation of social ties between individuals in the population. It is then extended to predict the evolution of social networks in response to life cycle events. The model is estimated using attribute data of a national sample and an event-based retrospective dataset collected in 2009 and 2011 respectively. Findings suggest that homophily has a strong effect on the formation of new ties. However, heterophily also plays a role in maintaining existing ties. Although the motivation of this research stems from incorporating social network dynamics in large-scale travel behaviour micro-simulation models, the research can be used in a variety of fields for similar purposes.

Incorporating Mental Representations in Discrete Choice Models of Travel Behavior: Modeling Approach and Empirical Application

We introduce an extension of the discrete choice model to take into account individuals' mental representation of a choice problem. We argue that, especially in daily activity and travel choices, the needs of an individual have an influence on the benefits he or she pursues in the choice of an alternative. Activated benefits and mental costs determine which attributes are considered in evaluating alternatives. The extended model considers the formation of a mental representation of a choice problem as an integral part of the choice process. We show how formation of a mental representation and making a choice can be modeled jointly in an integrated random utility maximization framework. We further show how the integrated model can be estimated based on combined observations of mental representations and choice outcomes using maximum likelihood estimation. A comparative analysis shows that observations of the mental representations may significantly improve predictions and enhance insights into situation-dependent motivations underlying preferences. We illustrate the approach using a data set that involves measurements of mental representations and choice behavior in the area of transport mode choice.

Accommodating Risk Attitudes in Freight Transport Behaviour Research

Behavioural choice modelling is growing in interest as a framework to understand the decision-making of shippers, freight forwarders and other freight agents. Since the 1980s, we have witnessed an increasing number of freight behaviour studies, primarily the freight mode choice, where the roles of one or more freight decision-makers has been addressed, which was neglected in the traditional aggregate approach to freight demand modelling. Stated preference techniques have become a main approach to establishing the role of attributes that define the key drivers in the freight distribution chain. The underlying theory of choice is based on the neoclassical economic assumption that a decision-maker, in choosing, acts as if they are a utility maximizer (working to deliver a profit maximization or cost minimization outcome for the freight business), and this utility maximizing behaviour engenders a population theory of Random Utility Maximization (RUM). Despite the continuing appeal of the RUM framework in applied travel choice studies, a number of specific application assumptions have been questioned by studies in psychology and behavioural economics, arguing that the decisions made by agents are often conditioned on a number of underlying psychological components, one of which is risk attitude. This paper reviews recent freight behaviour studies established on RUM, and presents a major limitation of adopting a risk-neutral assumption through its linear utility specification. Using an existing freight stated choice data set, a nonlinear utility model is estimated which reveals risk-taking attitudes of transporters and shippers. An alternative behavioural paradigm, Rank-Dependent Utility Theory (RDUT), is introduced and incorporated to better accommodate trip time variability, a feature of growing importance in transport systems (passenger and freight). The proposed attribute-specific extended RDUT framework that accommodates the attitude towards risk and preference in freight transport behaviour modelling requires new data, which we detail.

Comparing Fuzzy and Probabilistic Approaches to Preference Uncertainty in Non-Market Valuation

In seeking to value environmental amenities and public goods, individuals often have trouble trading off the (vague) good or amenity against a monetary measure. Valuation in these circumstances can best be described as fuzzy in terms of the amenity being valued, perceptions of property rights, and the numbers chosen to reflect values. In this paper, we apply fuzzy logic to contingent valuation, employing a fuzzy clustering approach for incorporating preference uncertainty obtained from a follow-up certainty confidence question. We develop a fuzzy random utility maximization (FRUM) framework where the perceived utility of each individual is fuzzy in the sense that an individual’s utility belongs to each cluster to some degree. The model is then applied to a Swedish survey that elicited residents’ willingness to pay for enhanced forest conservation and to a Canadian survey of agricultural landowners that elicited their willingness to accept compensation for a tree planting program. Both the WTP and WTA measures we obtain using the fuzzy approach are well below those obtained using standard probability methods. Based on goodness of fit measures and Monte Carlo experimentation, a case can be made for using a fuzzy preference approach for modeling preference uncertainty as opposed to incorporating respondent uncertainty within the random utility maximization framework.

Generalized Reverse Discrete Choice Models

Marketing practitioners and academics have shown a keen interest in the processes that drive consumers’ choices since the early work of Guadagni and Little (1982). Over the past decade or so, a number of alternative models have been proposed, implemented and analyzed. The common behavioral assumption that underlines these models of discrete choice is random utility maximization (RUM). The RUM assumption, in its simplest form, posits that a consumer with a finite set of brands to choose from chooses the brand that gives her the maximum amount of utility. An alternative approach would be to assume that consumers choose the alternative that offers them the least disutility. Our paper proposes and tests a broad class of generalized extreme value models based on this hypothesis. We model the decision process of the consumer the assumption random disutility minimization (RDM) and derive a new class of discrete choice models based on this assumption. Our findings reveal that there are significant theoretical and econometric differences between the discrete choice models derived from a RUM framework and the RDM framework proposed in this paper. On the theoretical front we find that the class of discrete choice models based on the assumption of disutility minimization is structurally different from the models in the literature. Further, the models in this class are available in closed form and exhibit the same flexibility as the GEV models proposed by McFadden (1978). In fact, the number of parameters are identical to and have the same interpretation as those obtained via RUM based GEV models. In addition to the theoretical differences we also uncover significant empirical insights. With the computing effort and time for both models being roughly the same this new set of models offers marketing academics and researchers a viable new tool with which to investigate discrete choice behavior.

Modeling survey response bias – with an analysis of the demand for an advanced electronic device

Survey data are often subject to a number of biases. In this article several models are proposed to detect and adjust for survey response bias. The approach is then applied to the analysis of a market survey data on the demand for an advanced electronic device in a developing country. Substantial differences in take rates and price elasticities are found between the estimates derived from the conventional random utility maximization framework and the biased response model.

Note—Consumer Store Choice and Choice Set Definition

Consumer store choice results from a process whereby information on various alternatives is evaluated by the consumer prior to the selection of one of these alternatives. In the application of store choice models it is often assumed that the information-processing strategy underlying store choice is a simultaneous one in which all possible alternatives are evaluated by an individual. A competing assumption, increasingly recognized in aspatial choice, is that individuals initially evaluate clusters of alternatives and then only evaluate alternatives within a chosen cluster. The nested logit model is a well-known formulation for capturing this type of choice process. This paper adds to the above understanding of consumer spatial choice in the following ways: (i) It describes an alternative to the nested logit model, known as a competing destinations model, which can be used to model hierarchical spatial choice processes. While this model has been described previously in the geography and planning literature, this paper contains a novel derivation of it from within the random utility maximization framework. (ii) A general choice formulation is derived from which the logit, nested logit, and competing destinations models can be obtained. This allows the behavioral assumptions embedded in each model to be seen clearly and it allows a comparison of the nested logit and competing destinations formulations. The latter is shown to be preferable in most spatial choice situations. In particular, the latter can be used when restricted choice sets are fuzzy. (iii) The errors that arise when spatial choice results from a hierarchical process and when a simultaneous choice structure is assumed are demonstrated and are related to the presence of consumer competition/agglomeration effects between stores which are thought to be important in store choice.