A Model of Consumer Choice With Bounded Rationality and Reference Quantity

In trying to understand the SUPPORT study, it may be useful to think of contemporary bioethics reform in terms of the principles of consumer protection. The central tendency of that reform (particularly in my own field--the law) has been to employ the model of consumer choice. That model sets as its purpose to allow consumers to choose the kinds of products they prefer. It seeks to accomplish that purpose primarily by supplying consumers the information they need to make choices and by insisting that they are given what they chose. Thus, for example, merchants may be required to reveal the actual terms under which they sell goods on credit, so that customers may decide whether they wish to pay the price. And once the customer has chosen a purchase, the merchant is held to the terms originally agreed upon. In short, the consumer choice model seeks to allow customers to make successful choices by providing them with a market that works in the economist's sense, efficiently. Bioethical reform may be understood along these lines. The doctrine of informed consent is, plainly, intended to provide patients the information they need to make wise choices that express their preferences. The patient is thus to become a consumer well-enough informed to make sensible choices in the market for medical care. The patient's choices are then given more binding effect by, for example, various provisions for advance directives. The consumer choice model is hardly startling and surely makes a good deal of sense. The free-market prescription was, for instance, quite successful when it was employed during the New Deal to improve the operation of the securities market. But of course the consumer choice model rests on some consequential assumptions. It assumes, for one thing, that human preferences differ so much that it is best to let people make purchasing decisions for themselves. It assumes an engaged and energetic purchaser. Finally, it assumes that people's preferences are clear and strong enough to drive them to act on the information they receive to make choices and follow up on them. Thus the New Deal reforms of the securities markets were plausible because many of the people who trade in that market know their way around it, have ready access to the information that is provided, have commanding incentives to use it, and know how to do so. The SUPPORT study does not speak directly to all these assumptions. Nor could any single study definitively establish whether such assumptions arc true. Nevertheless, this ambitious and impressive research raises some fruitful questions about the consumer choice model as it has been applied in medicine. The SUPPORT enterprise followed that model in trying to increase the communication between doctor and patient so that both could make better decisions. But what is striking about the study is, despite a commitment of resources (and, one suspects, enthusiasm) unlikely to be even matched, much less sustained, in the dullness of daily life, the SUPPORT instantiation of the model quite failed to produce statistically significant benefits. What are we to make of this unsettling negative? To put the point simply, a strong reading of the study raises questions about the usefulness of continuing to emphasize the consumer choice model. In saying this, I am far from suggesting that efforts to implement the consumer choice model in bioethics have been wasted. I suspect that that model has helped spur changes in medical attitudes which were necessary to make doctors' behavior more tolerable to contemporary American mores. My point, rather, is that--strongly read--the SUPPORT study provokes doubts about the profit of sustaining the consumer choice model at its present preeminence. But how strongly should we read the SUPPORT data? Our answer will depend on many things. But one reason to read it strongly is that SUPPORT seems to confirm what we already know about the effects of some other bioethical reforms in the consumer choice model--that those reforms often seem to have fewer consequences than the consumer choice model seems to call for. …

This document presents supporting materials for the following publication:Farias, Jagabathula and Shah (2012), 'A Nonparametric Approach to Modeling Choice with Limited Data,' Management Science, Articles in Advance, pp. 1-18.Choice models are today ubiquitous across a range of applications in operations and marketing. Real world implementations of many of these models face the formidable stumbling block of simply identifying the 'right' model of choice to use. Since models of choice are inherently high dimensional objects, the typical approach to dealing with this problem is positing, a-priori, a parametric model that one believes adequately captures choice behavior. This approach can be substantially sub-optimal in scenarios where one cares about using the choice model learned to make fine-grained predictions; one must contend with the risks of mis-specification and over/under-fitting. Thus motivated, we visit the following problem: For a 'generic' model of consumer choice (namely, distributions over preference lists) and a limited amount of data on how consumers actually make decisions (such as marginal information about these distributions), how may one predict revenues from offering a particular assortment of choices? An outcome of our investigation is a non-parametric approach in which the data automatically selects the 'right' choice model for revenue predictions. The approach is practical. Using a data set consisting of automobile sales transaction data from a major US automaker, our method demonstrates a 20% improvement in prediction accuracy over state-of-the art benchmark models, which can result in a 10% increase in revenues from optimizing the offer set. We also address a number of theoretical issues, among them a qualitative examination of the choice models implicitly learned by the approach. We believe that this paper takes a step towards 'automating' the crucial task of choice model selection.

Problem definition: Reference prices arise as price expectations against which consumers evaluate products in their purchase scenarios. We investigate what will happen when prospect theory (e.g., reference prices) meets consumer choice models from the perspectives of both the consumers and the firm. Academic/practical relevance: Consumers see multiple relevant products on a particular purchase occasion and often compare their prices to form the willingness to pay when considering whether to purchase a particular product. Reference prices, which are not included in many choice models, may impact consumer choice behavior, so we incorporate reference prices into consumer choice models and investigate the operations management problems. Methodology: We take the widely used multinomial logit model as a showcase to examine the effects of reference prices through analytical and empirical study. We consider the optimization problems on assortment planning and pricing under consumer choice models with a variety of reference prices, including the lowest price and the assortment variety. Results: Our empirical study on a real data set demonstrates that incorporating reference prices into choice models can significantly improve goodness of fit and prediction accuracy of consumer choice behavior. Moreover, we characterize the optimal policies for the assortment planning and pricing problems under the consumer choice models with various reference prices. In particular, for the pricing problems under the reference prices defined by either the lowest price or assortment variety, we show that the optimal pricing policy has the following structure: products can be divided into several groups based on their costs; the products in the same group charge either the same markup or the same price. Managerial implications: In practice, reference prices should be taken into account in model estimation and operations management. Ignoring reference prices may lead to substantial losses. The online appendix is available at https://doi.org/10.1287/msom.2017.0688 .

Problem Definition: Reference prices arise as price expectations against which consumers evaluate products in their purchase scenarios. We investigate what will happen when prospect theory (e.g., reference prices) meets consumer choice models from the perspectives of both the consumers and the firm. Academic/Practical Relevance: Consumers see multiple relevant products on a particular purchase occasion, and often compare their prices to form the willingness to pay when considering whether to purchase a particular product. Reference prices, which are not included in many choice models, may impact consumer choice behavior, so we incorporate reference prices into consumer choice models and investigate the operations management problems. Methodology: We take the widely used multi-nomial logit model as a showcase to examine the effects of reference prices through analytical and empirical study. We consider the optimization problems on assortment planning and pricing under consumer choice models with a variety of reference prices, including the lowest price and the assortment variety. Results: Our empirical study on a real data set demonstrates that incorporating reference prices into choice models can significantly improve goodness-of-fit and prediction accuracy of consumer choice behavior. Furthermore, we characterize the optimal policies for the assortment planning and pricing problems under the consumer choice models with various reference prices. In particular for the pricing problems under the reference prices defined by either the lowest price or assortment variety, we show that the optimal pricing policy has the following structure: products can be grouped into several categories based on their costs; the products in the same category charge either the same profit markup or the same price. Managerial Implications: In practice, reference prices should be taken into account in model estimation and operations management. Ignoring reference prices may lead to substantial losses.

In this paper, we introduce a consumer choice model in which each consumer's utility is affected by the purchase probabilities of his/her neighbors in a network. Such a consumer choice model is a general model to characterize consumer choice under network effect. We first characterize the choice probabilities under such a choice model. Then we consider the associated personalized assortment optimization problem. Particularly, the seller is allowed to offer a personalized assortment to each consumer, and the consumer chooses among the products according to the proposed choice model. We show that the problem is NP-hard even if the consumers form a star network. Despite of the complexity of the problem, we show that if the consumers form a star network, then the optimal assortment to the central consumer cannot be strictly larger than that without network effects; and the optimal assortment to each peripheral consumer must be a revenue-ordered assortment that is a subset of the optimal assortment without network effect. We also present a condition when revenue-ordered can achieve a provable performance. Then in view of the fact that each node in a network can represent a group of consumers, we propose a novel idea in which the sellers are allowed to offer assortments to each node in the network. We show that allowing for randomized may further increase the revenue, and under a mild condition, the optimal assortment for the central consumer must be a combination of two adjacent revenue-ordered and thus efficient algorithm can be developed. Finally, we extend the results to directed acyclic graphs (DAGs), showing that a mixture of adjacent revenue-ordered is optimal under certain conditions.

Pourquoi pas? Rational Choice as a Basic Theory of HRM** broad spectrum of theories from different disciplines is portrayed in contemporary HRM (as a discipline of business administration). Theories from psychology, sociology and economics correspond to the variety of problems addressed in HRM which are again situated at different levels of analysis, namely on the individual, group and organizational level. narrow focus solely on economic approaches, as sometimes suggested in personnel economics, is therefore not sufficient. Instead, the contemporary choice approach may serve as a new basis for the discipline. The approach stems from economics and sociology and, as an offspring of these, combines elements of action and structure in its basic explanatory models. Also it is able to explain effects that emerge on the system or macro level. Indepth explanations allow model building at different levels of analysis, namely on an environmental, organizational and individual level, which can be seen as a major prerequisite of explanations in HRM. In addition choice is conscious of the ignorance of its underlying action theory and this exposes it to the body of physiological and psychological knowledge. Anomalies of classic economic theory can therefore be restricted, for instance by using the method of decreasing abstraction. The method of decreasing abstraction serves as a basic principle or heuristic device for model building, in order to separate choice from traditional anti-reductionism. This article introduces basic elements of the modern choice approach: the macro-micro-macro model of explanation, homo socio-oeconomicus as a model of man and exchange theory as a baseline model of aggregation. final summary discusses research questions and applications of choice in HRM. Key words: Rational Choice, Economic Sociology, Micro-macro-link, Homo Socio-oeconomicus 1. Approach Ever since Pareto's (1935) distinction, it has generally been agreed, that the field of economics should concentrate on rational action; other areas of social science, i.e. sociology should concentrate on irrational behavior. However, economics is a broad field. Therefore it is hardly surprising that economists who are confronted with irrational human behavior or deviations from the perfect model of purposeful and rational behavior wrangle with Pareto and, in particular, with his line of distinction. Critical calls were confronted with only modest appreciation, if they were heard at all, when they suggested that unrealistic assumptions about behavior were required in order to deliver economic or normatively speaking good explanations (Friedman 1953). Academics in Business Administration, dealing with innovation, marketing, organization or human resources and phenomena like decision behavior, informal organizations or HRM abandoned the economic paradigm and searched for more feasible theories in other disciplines. The historical development of HRM is not the topic of this article. However, we need to stress that we are talking about a process of convergence towards neighboring disciplines, notably (social) psychology and (organizational and industrial) sociology, which represent a long wave of historical ideas: Simon's (1955) revolutionary work A behavioral model of rational choice was published half a century ago. Since then, considerable progress has been made in both theory and empirical knowledge. And this holds true for all levels of HRM research - for the individual, group and organizational level. Worth mentioning amongst others are motivation, learning and stress theories, theories about group dynamics, power and management as well as theories about organizations and their environments. Furthermore, it is worth emphasizing tendencies towards empirical social research which evolved at the same time as behavioral science. Economic theory was exported once again into HRM with the rediscovery of institutions in economic research and the development of appropriate models, such as property rights, transaction costs and information asymmetry, which all facilitated the explanation of the failure of the free market model of traditional economics (Coase 1960, Alchian/Allen 1972). …

In new product design, risk averse firms must consider downside risk in addition to expected profitability, since some designs are associated with greater market uncertainty than others. We propose an approach to robust optimal product design for profit maximization by introducing an α-profit metric to manage expected profitability vs. downside risk due to uncertainty in market share predictions. Our goal is to maximize profit at a firm-specified level of risk tolerance. Specifically, we find the design that maximizes the α-profit: the value that the firm has a (1 − α) chance of exceeding, given the distribution of possible outcomes. The parameter α ∈ (0,1) is set by the firm to reflect sensitivity to downside risk (or upside gain), and parametric study of α reveals the sensitivity of optimal design choices to firm risk preference. We account here only for uncertainty of choice model parameter estimates due to finite data sampling when the choice model is assumed to be correctly specified (no misspecification error). We apply the delta method to estimate the mapping from uncertainty in discrete choice model parameters to uncertainty of profit outcomes and identify the estimated α-profit as a closed-form function of decision variables for the multinomial logit model. An example demonstrates implementation of the method to find the optimal design characteristics of a dial-readout scale using conjoint data.

Choice models today are ubiquitous across a range of applications in operations and marketing. Real-world implementations of many of these models face the formidable stumbling block of simply identifying the “right” model of choice to use. Because models of choice are inherently high-dimensional objects, the typical approach to dealing with this problem is positing, a priori, a parametric model that one believes adequately captures choice behavior. This approach can be substantially suboptimal in scenarios where one cares about using the choice model learned to make fine-grained predictions; one must contend with the risks of mis-specification and overfitting/underfitting. Thus motivated, we visit the following problem: For a “generic” model of consumer choice (namely, distributions over preference lists) and a limited amount of data on how consumers actually make decisions (such as marginal information about these distributions), how may one predict revenues from offering a particular assortment of choices? An outcome of our investigation is a nonparametric approach in which the data automatically select the right choice model for revenue predictions. The approach is practical. Using a data set consisting of automobile sales transaction data from a major U.S. automaker, our method demonstrates a 20% improvement in prediction accuracy over state-of-the-art benchmark models; this improvement can translate into a 10% increase in revenues from optimizing the offer set. We also address a number of theoretical issues, among them a qualitative examination of the choice models implicitly learned by the approach. We believe that this paper takes a step toward “automating” the crucial task of choice model selection. This paper was accepted by Yossi Aviv, operations management.

Choice models are widely applied in psychology, economics, transportation, marketing, and operations studies. We review the existing developments on the modeling of consumers’ choices, including the attraction model, the utility‐based model, the temporal model, and the rank‐based model. The relationships among different classes of structural models are established and analyzed. Moreover, an operational data analytics (ODA) framework is presented to estimate the general consumer choice model using data. This framework, generalizing the existing estimation methods for specific structural models, strikes a delicate balance between the (likely imprecise) structural knowledge and the data. This is achieved by articulating the domain of validation through extending the structural knowledge and by formulating the data‐integration model based on the associated structural properties. We demonstrate the implementation of the ODA framework to identify the appropriate consumer choice models. The ODA estimate outperforms the existing parametric and nonparametric methods, particularly over the choice sets that are not covered in the data. We also discuss potential future research of developing ODA approaches to study the related aspects of consumer choice models.

Reference‐dependent consumer preference and choice have been the focus of marketing and behavioural decision researchers for decades. Researchers have proposed different ways of incorporating reference‐dependent consumer preference into quantitative models. No extant research, however, systematically examines and evaluates these modelling methods. As more empirical research on reference‐dependent preference emerges in different areas, this review is both timely and of great importance. The paper pays special attention to empirical articles that incorporate reference points into consumer choice models and classifies the models in terms of reference point operationalization, model framework and reference point heterogeneity. By comparing and evaluating various conceptualizations of reference points and extant empirical choice models, we seek to provide guidance and directions in terms of theory, contexts and methodology (TCM) to future researchers with different application areas and focus of studies. The paper synthesizes the previous findings and identifies current research gaps, which allow us to provide future directions in this research domain.

This paper examines the impact of omitted variable bias (OVB) within consumer choice models on engineering design optimization solutions. Engineering products often have a multitude of attributes that influence consumers’ purchasing decisions, many of which are difficult to include in revealed-preference models due to a lack of data. Correlations among these omitted variables and product attributes included in the model can bias demand parameter estimates. However, engineering design optimization studies typically do not account for this bias. We examine the influence consumer choice OVB can have on design optimization results. We first mathematically derive how OVB propagates into optimal design solutions and characterize properties of optimization problems that affect the magnitude of the resulting error in solutions. We then demonstrate the impact of OVB on optimal designs using a case study of automotive powertrain design optimization. In the case study, we estimate two sets of choice models: one using only “typically observed” vehicle attributes commonly found in the literature, and one with an additional set of “typically unobserved” attributes gathered from Edmunds.com. We find that the model with omitted variables leads to, in some scenarios, substantial bias in parameter estimates (5–143%), which propagates up to 21% error in the optimal engine size.

Two processes of the university are modeled – teaching students, which are indicators of performance characteristics, and the process of harmonization of control figures of the budget for the next fi scal year. The first of them is presented with regression equations, the second – a model of consumer choice. Both models are interlinked and that allowed the scholarship fund to evaluate in terms of performance.

The economic models that prescribe Pigovian taxation as the first-best means of reducing energy-related externalities and argue that taxes are superior to energy efficiency standards are typically based on the neoclassical model of rational consumer choice. Yet, observed consumer behavior with regards to energy use and the purchase of energy-using durable goods is generally thought to be far from efficient, giving rise to the concept of the “energy-efficiency gap.” In this paper, we present a welfare analysis of Pigovian taxes and energy efficiency standards that is based on an alternative, time-consistent behavioral model. We adapt the model of temptation and self-control of Gul and Pesendorfer (2001, 2004) to the context of the purchase of energyusing durable goods. Our results suggest that (i) temptation or self-control might be a contributing factor in explaining the energy-efficiency gap, (ii) standards might be used as a commitment device to address inefficiencies in consumer choice that stem from temptation, and (iii) in the presence of temptation, a policy that combines standards with a Pigovian tax can yield higher social welfare than a Pigovian tax alone.

Chapter 5 - Consumer choice modeling: The promises and the cautions

E-retailing versus physical retailing: A theoretical model and empirical test of consumer choice