Choice Model Research Articles

Benchmarking numerical simulation of induced fault slip with semi-analytical solutions

AbstractPore pressure fluctuation in subsurface reservoirs and its resulting mechanical response can cause fault reactivation. Numerical simulation of such induced seismicity is important to develop reliable seismic hazard and risk assessments. However, modeling of fault reactivation is quite challenging, especially in the case of displaced faults, i.e., faults with non-zero offset. In this paper, we perform a systematic benchmarking study to validate two recently developed numerical methods for fault slip simulation. Reference solutions are based on a semi-analytical approach that makes use of inclusion theory and Cauchy-type singular integral equations. The two numerical methods both use finite volume discretizations, but they employ different approaches to represent faults. One of them uses a conformal discrete fault model (DFM) while the other employs an embedded (non-conformal) fault model. The semi-analytical test cases cover a vertical frictionless fault, and inclined displaced faults with constant friction and slip-weakening friction. It was found that both numerical methods accurately represent pre-slip stress fields caused by pore pressure changes. Moreover, they also successfully cope with a vertical frictionless fault. However, for the case with an inclined displaced fault with a constant friction coefficient, the embedded method can not converge for the post-slip phase, whereas the DFM successfully coped with both constant and slip-weakening friction coefficients. In its current implementation, the DFM is therefore the model of choice when accurate simulation of local faulted systems is required.

Personalized Pricing and Assortment Optimization Under Consumer Choice Models with Local Network Effects

We introduce a consumer choice model where each consumer’s utility is affected by their neighbors’ purchase probabilities in a network. We first characterize the choice probabilities and then consider the associated personalized assortment optimization problem. Although this problem is NP-hard, we show that for star networks, 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. Then, we propose a novel idea in which the sellers are allowed to offer “randomized assortments” to each node in the network by viewing each node as a consumer group. We show that randomized assortments may further increase the revenue, and for general network setting, under certain conditions, the optimal assortment must be a combination of two adjacent revenue-ordered assortments. Finally, we study the associated optimal pricing problem and develop structural properties and efficient algorithms.

Modeling and testing strategic interdependence and tipping in public policy implementation

We develop a game-theoretic model of strategic interdependence and tipping in public policy choices and show that the model can be estimated by probit and logit estimators. We test its validity and applicability by using daily data on state-level COVID-19 responses in the United States. Social distancing via shelter-in-place (SIP) strategies and wearing masks emerged as the most effective nonpharmaceutical ways of combatting COVID-19. In the United States, choices about these policies are made by individual states. We develop a game-theoretic model of such choices and test it econometrically, confirming strong interdependence in the implementation of these policies. If enough states engage in social distancing or mask wearing, others will be tipped to follow suit. Policy choices are influenced mainly by the choices of other states, especially those of similar political orientation and to a lesser degree by the number of new COVID-19 cases. The choice of mask-wearing policies is more sensitive to peer choices than the choice of SIP policies, and Republican states are much less likely than Democratic to introduce mask-wearing policies. The choices of policies are influenced more by political than public health considerations. These findings emphasize strategic interdependence in policy choice and offer an analytical framework for these complex dynamics.

Learning-by-doing: an insight worthy of the pantheon and how to do it in teams

Abstract In this paper, I pick up on an important theme in Mario Rizzo’s work: that rationality should be understood more broadly than the rational choice model as learning to adjust behaviour in the light of experience and the mistakes that it yields. In particular, I focus on learning-by-doing (LBD). I argue in the first part of the paper that it should be regarded as one of the central insights in economics, alongside those that are more usually recognised like the gains from trade and the importance of unintended consequences. I use Smith and Hirschman’s discussion of LBD to ground this claim. In the second part of the paper, I turn to the determinants of LBD in teams. I argue that the key rule or constitutional/policy design question is how best to embrace the diversity that is central to LBD within teams without this undermining the social origins of co-operation in teams.

The digitalization of emergency department triage: the perspectives of health professionals and patients.

Digitalization in the healthcare sector offers several organizational advantages, ranging from enhanced service quality to cost savings. However, its adoption often progresses slowly and faces challenges, especially in critical settings like emergency departments, requiring prompt, clear, and efficient communication. As such, this study aims to comprehensively assess the factors influencing the preference for digitalized tools over traditional methods from the perspectives of both service providers and users. We employ two ad hoc stated preference surveys in which we ask respondents to reveal their preference in simulated triage scenarios. Three main alternatives are proposed: traditional procedures, a fully digitalized solution with no direct patient-professional interaction, and a hybrid option that combines traditional and digital aspects. Scenarios and alternatives vary according to predetermined attributes, selected among the features acknowledged to impact the triage efficiency and efficacy: the possibility to communicate in a known language, the completeness of information retrieved from the patient, the time dedicated to triage activity, and the level of privacy. Responses are analyzed by means of discrete choice models. Our findings reveal a preference for the hybrid approach, wherein patients use digital tools to input relevant information, followed by an interview with healthcare professionals. Nevertheless, distinct alternative- and case-specific features can favor the preference toward other kinds of triage. Respondents prefer shorter triage times and the opportunity to interact in a known language, while the level of privacy does not significantly impact their choices. Interestingly, the presence of an algorithm assigning urgency code diminishes the probability that healthcare professionals select the fully digitalized option. This study provides important insight into the utilization of digital tools in emergency departments. The results can be used by hospital managers and policy makers to develop digital tools that meet the needs of both users and healthcare professionals. This, in turn, may result in cost savings and improved quality of service.

Spatial overlap: trade-offs in refugees’ residential choices

ABSTRACT Social science research gives rise to what we call the ‘refugee mobility puzzle’: While restrictions on the freedom of residence limit refugees’ socio-economic integration, those who do not face such restrictions often move to areas with high unemployment that similarly hinder their labor market prospects. This study addresses a central element of this puzzle: What draws refugees to high unemployment areas? We propose the spatial overlap hypothesis arguing that refugees in comparatively generous reception systems initially prioritize or are forced to prioritize non-labor-market resources such as affordable housing and proximity to social networks. Thereby they often settle in areas with general economic disadvantage as such areas frequently overlap with said resources. Based on the IAB-BAMF-SOEP Survey of Refugees this study tracks residential histories of more than 2,400 refugee respondents across all German counties and postcodes. Discrete choice models show that preferences for cities, lack of affordable housing, the presence of small, service-oriented companies, and co-ethnic communities explain refugees’ gravitation to disadvantaged areas. These results support the spatial overlap hypothesis and suggest that recently approved refugees face trade-offs: area characteristics that are initially attractive tend to spatially overlap with such that likely inhibit longer-term integration prospects.

Random Forest estimation of the ordered choice model

AbstractIn this paper we develop a new machine learning estimator for ordered choice models based on the Random Forest. The proposed Ordered Forest flexibly estimates the conditional choice probabilities while taking the ordering information explicitly into account. In addition to common machine learning estimators, it enables the estimation of marginal effects as well as conducting inference and thus provides the same output as classical econometric estimators. An extensive simulation study reveals a good predictive performance, particularly in settings with nonlinearities and high correlation among covariates. An empirical application contrasts the estimation of marginal effects and their standard errors with an Ordered Logit model. A software implementation of the Ordered Forest is provided both in and in the package available on and , respectively.

The impact of pension reform on employment, retirement, and disability insurance claims

We evaluate the 2011 comprehensive reform of Norwegian early retirement institutions using a parsimonious random utility choice model. Conditional on employment at age 60, we estimate a three-state conditional logit model to explain the realized labor market state at age 63 among the alternatives of employment, retirement, and disability program participation. The reform radically changed work incentives and/or pension access age for some (but not all) workers, such that the influence of economic incentives can be identified based on reform-generated variation only. We find that improved work incentives caused employment rates to rise considerably at the expense of early retirement and exit through disability insurance. Improved liquidity through a lower age to access own pension funds on actuarially neutral terms caused a small increase in employment and a large drop in disability program participation. Properly designed pension reforms thus need to take the interplay between old-age pension and disability insurance programs into account.

Social preferences and the variability of conditional cooperation

AbstractWe experimentally examine how incentives affect conditional cooperation (i.e., cooperating in response to cooperation and defecting in response to defection) in social dilemmas. In our first study, subjects play eight Sequential Prisoner’s Dilemma games with varying payoffs. We elicit second mover strategies and find that most second movers conditionally cooperate in some games and free ride in others. The rate of conditional cooperation is higher when the own gain from defecting is lower and when the loss imposed on the first mover by defecting is higher. This pattern is consistent with both social preference models and stochastic choice models. In a second study subjects play 64 social dilemma games, and we jointly estimate noise and social preference parameters at the individual level. Most of our subjects place significantly positive weight on others’ payoffs, supporting the underlying role of social preferences in conditional cooperation. Our results suggest that conditional cooperation is not a fixed trait but rather a symptom of the interaction between game incentives and underlying social preferences.

Long-Run Behavior and Convergence of Dynamic Mean Field Equilibria

AbstractWe study the behavior of dynamic equilibria in mean field games with large time horizons in a dynamic consumer choice model. We show that if the stationary equilibrium in the associated infinite horizon game is unique, the dynamic equilibria of the finite horizon games converge to the stationary equilibrium of the infinite horizon game as the time horizon tends to infinity. If the stationary equilibrium is not unique, however, the situation becomes more involved. In this case, we show that in addition to convergence to the stationary equilibria, in the long run, the dynamic equilibria circle around randomized stationary equilibria for certain choices of boundary data.

Investigating the e-learning choice under the learners’ perspective using demand driven learning model: insights from Vietnam

Over the past four decades, e-learning has experienced rapid global growth, revolutionizing higher education. In Vietnam, universities have increasingly turned their attention and resources toward e-learning development, adapting to specific contexts and needs. This study investigates the drivers of e-learning choice within the framework of the demand-driven learning model (DDLM), with a primary focus on three core factors: quality content, delivery, and service. Employing a quantitative approach and the PLS-SEM technique, this research uncovers significant findings. The results of the study highlight the pivotal role of quality content, which exerts the most potent influence on e-learning choice, evidenced by a robust path coefficient of 0.400. Service and delivery, with direct path coefficients of 0.183 and 0.173, respectively, also play substantial roles in shaping e-learning decisions. Moreover, mediate role of delivery in the the e-learning choice model has been confirmed. Quality content leads to delivery, and delivery, in turn, leads to e-learning choice. Similar pathway has been found with service. A higher level of service increases delivery, which positively impacts on e-learning choice. These findings hold critical implications for the formulation of e-learning development policies in Vietnam’s higher education institutions. Universities should prioritize the continuous development of high-quality, engaging, and up-to-date educational content that aligns with industry needs and student interests. Additionally, emphasis should be placed on providing a supportive e-learning experience, characterized by responsive customer service, accessible technical support, and efficient issue resolution mechanisms. Moreover, universities should consider the implementation of user-friendly and interactive content delivery platforms and methods that actively engage learners. In essence, this research serves as a guide for universities in Vietnam, enabling them to enhance their e-learning offerings by ensuring the content quality, support services, and delivery methods, ultimately fostering a dynamic and accessible learning environment that meets the evolving needs of their students and the demands of the modern educational landscape.

Are random price generators useful for health policy processes and analysis? effectiveness of random price generators in health policy processes and analysis

This paper discusses why using random price generators in an economic model for medical markets may be useful. It summarizes the different steps for the development of the model and describes an original contribution of the physicians’ choice model with random drug prices applied to Type 2 diabetes in the US. Pharmaceutical markets have been supply-driven to boost life science and medical technologies; however, with the widening inequalities inside national health systems and the global agenda for universal health coverage, more economic research is being done to strengthen the analysis of demand for healthcare services. Research, especially, examines more disaggregated levels of demand systems to understand the heterogeneity of physicians’ choices and better capture patient needs. It can also be an approach to calibrate supply and demand adjustments in medical markets. This paper argues that choice modeling is particularly relevant, using random price generators in a structural model where a demand approach is useful. If validated by additional experimental studies, this first study by Professors Huttin & Hausman (2021) could be used in advanced value assessment frameworks. Random price generators on medical markets could also be tested with additional models that fit oligopolistic market structures (e.g., models for differentiated product markets such as the Berry–Levinsohn–Pakes model). It may also help with a policy analysis process that addresses major disruptive transformations of market dynamics, evolving boundaries in science, fast digitalization, and artificial intelligence-based information systems.

Факторная модель личностного выбора соучастия в экстремальном добровольчестве и его диагностика

The article is devoted to improving psychological support for volunteering in extreme conditions. The emphasis is placed on the establishment of predictors of personal choice of participation in volunteerism, the development of an integrative model and diagnostic tools for assessing readiness for participating activities. The subject of the study is a model of personal choice of participation in extreme volunteerism. The aim is to substantiate an integrative model of personal choice of participation in extreme volunteerism, to develop an appropriate diagnostic methodology based on it. The methodological basis is based on axiological, holistic, activity-based and subjective approaches. Semantic analysis of diagnostic constructs, as well as expert assessment and psychological testing were used (questionnaire «Scale of dispositional egoism» (K. Muzdybaev); questionnaire «Motivation for help» (S.K. Nartova-Bochaver); questionnaire of value orientations (adaptation of M. Rokich's methodology); indicative questionnaire «Personality orientation» (V. Smekal, M. Kucera adaptation by N.M. Peisakhov); questionnaire on motivation of volunteer activity (K.V. Gil); methodology for diagnosing achievement motivation (A. Mehrabian); methods of diagnosing personal maturity (V.A. Ruzhenkov); test of resilience (S. Muddy, adaptation by D.A. Leontiev, E.I. Rasskazova); questionnaire «Coping behavior» (R. Lazurus, S. Folkman, adaptation by S. Hobfall); value questionnaire (Sh. Schwartz); original author's methods – a questionnaire for assessing the propensity to extreme volunteering, an assessment of manifestations of professional personality deformation, a questionnaire for evaluating the choice of participation in extreme volunteerism). The sample size is 218 people. Applied: descriptive statistics, factor analysis, meta-analysis of data. According to the results of the study, the 16 factor structure of the choice of complicity is justified, which includes: «Prosocial value orientations», «Ideality», «Integrity of worldview», «Voluntary sense orientation», «Motivation», «Interest in volunteerism», «Propensity to extreme activity», «Dedication», «Critical thinking», «Extreme perceptual congruence», «Emotional stenicity», «Self-control», «Activity in extreme conditions», «Extreme vitality», «Self-organization», «Reliability». The novelty lies in the development of an integrative model and a questionnaire for evaluating the choice of participation in volunteerism. The results can be applied by psychologists of law enforcement agencies and volunteer organizations in conducting psychological selection and training of volunteers.

Using discrete choice modeling to understand the drivers of reproductive delay in the United Kingdom

The ideal family size in the UK is, on average, two children. However, there is a fertility gap, the difference between wanted and achieved family size, of around one in three children, which is largely caused by extended delays to reproduction. Standard quantitative methods used to identify what causes these delays have limitations, often relying on macro-level data which conveys little about individual needs, or applying longitudinal methods that produce outdated results because only once people have completed their reproductive years is it possible to infer which life-course factors led to later-age childbearing. This study is the first to overcome these challenges by applying a discrete choice experiment to reveal the barriers that people are facing right now. This innovative methodology allows the estimation of the relative importance of the barriers and the distance between them measured in months of reproductive delay. Among other things, the results show that for men and women, partner support was more important than finances or housing, although support means different things depending on the level of education with more educated women prioritizing hands-on fathers and household gender equality, whereas less-educated women strongly desired stable partnerships. Men favored partner readiness and neighborhood quality. These, and the other findings shown here, provide detailed insight into the contemporary causes of delayed fertility in the UK.

Determinants of receiving government support by small and medium-sized enterprises in Russia

Government support measures help companies overcome various restrictions impeding their growth and develop ment, which is of special relevance for small and medium-sized enterprises (SMEs). These measures, therefore, must reach the target companies and ensure efficient allocation of resources. The article reveals key characteristics of SMEs in Russia that increase the probability of them receiving government support. The research methodology is based on public interest theory, public choice theory and the concept of economic freedom. The research method is a two-step regression analysis, in which the binary choice model is first evaluated, and then the LASSO model is used to assess the impact of the textual content of the companies’ websites. The dataset of the study is represented by data on 2,176 Russian metallurgical enterprises for 2020–2023, of which 57% were receiving government support. The results of the first step of evaluation showed that the organization’s age and size meas ured by revenue were the key factors associated with the probability of receiving government support. At the second step, we found that the mention of certain goods or materials on the websites, the enterprise’s location and orientation towards foreign stakeholders were significantly related to the likelihood of getting state support. The study offers tools for analyzing the determi nants of government support for SMEs and highlights the importance of this analysis for more efficient allocation of government resources.

The effect of trust in neighbourhood on parents' decision to drive their children to school: Insights from a hybrid choice model

IntroductionThe growing trend of children being driven to school has become a cause of concern in developed countries, with parents' perception of trust in neighbourhoods emerging as a key determinant. Previous research has mainly investigated the influence of either the mother's or the father's perception on children's school travel. While the contribution of each parent's perception to the final decision, and the factors affecting this contribution, remains unclear. MethodsThis study leverages data from the Longitudinal Study of Australian Children (LSAC) to examine the distinct roles of mothers' and fathers' perceptions of trust in the neighbourhood on their children's school travel mode. It proposes a hybrid choice model (HCM) to measure the contribution of each parent's perception as a function of their socio-economic status (SES) in the school travel mode decision. ResultsThe results indicate that a higher parental perception of trust in the neighbourhood significantly reduces the likelihood of using private car to take children to school. The study also identifies several factors that influence parental perception of trust, highlighting differing impacts on mothers and fathers. Neighbourhood SES has a more pronounced positive impact on mothers’ perceptions of trust, whereas homeownership and urban residency exhibit stronger effects on fathers' perceptions. The analysis further delineates the contribution of individual parents to the overall parental perception of trust, demonstrating that higher levels of income decrease this contribution. ConclusionThis study highlights the significant role of parental perception of neighbourhood trust in children's school travel mode, with both mothers' and fathers' perceptions playing crucial roles. Neighbourhood SES, homeownership, urban residence, and ethnic diversity shape parents' perceptions, with differing effects on mothers and fathers. Future policy initiatives should focus on fostering trust and social inclusion through community events, multicultural programs, and social activities to enhance neighbourhood cohesion and support sustainable school travel choices.

Logistics service sharing in cross-border e-commerce

While the demand for cross-border e-commerce has grown rapidly, challenges have emerged for both retailers and consumers participating in this global market. Retailers have been struggling with high logistics costs to fulfill cross-border demand, while the lack of trust in foreign retailers is a major issue for consumers. In this paper, we study a cross-border collaboration scheme between a domestic and a foreign retailer to mitigate these challenges. This entails a co-opetition framework where the domestic retailer performs the last-mile delivery of the foreign retailer’s orders in exchange for a logistics service fee. We model demand via a Multinomial Logit (MNL) choice model where the consumers are trust- and price-sensitive. We compare the market outcomes of the two retailers in “pre-collaboration” and “post-collaboration” settings. We find that there exist win-win outcomes where both retailers benefit from collaboration under realistic settings. We also show that a cooperative mechanism can lead to higher profits for both retailers compared to the non-cooperative mechanism for setting the logistics service fee, if the contract terms are decided carefully.

Frequency Range Optimization for Linear Viscoelastic Characterization of Burger's Model

The linear viscoelastic behavior of materials is represented using mechanical models of choice, which are further utilized in different numerical investigations, such as finite element simulations and discrete element simulations. Burger's model is one of the widely adopted mechanical models and remains highly favored in contemporary research due to its multiple advantages. Specifically, it excels in representing long-term creep and stress relaxation behavior in a relatively simplified manner. Accurate identification of the long-term behavior for the viscoelastic material, particularly asphalt concrete, is crucial, as it serves as a key indicator of asphalt pavement performance over its service life. However, past research studies show that the parameters of Burger's model should be back-calculated from experimental data only within a limited range of frequency, otherwise, the parameters fail to represent the true material behavior. To the best of the authors’ knowledge, there is no approach for researchers to obtain the critical frequency range in which the experiments should be performed. Therefore, this study proposes a novel framework to find the critical frequency range to obtain appropriate model parameters of Burger's model, to better characterize the viscoelastic behavior of the materials. To examine the framework, asphalt concrete mixtures are used as examples in this study. Necessary laboratory tests including complex modulus tests and stress relaxation tests, are performed on two distinctive types of asphalt concrete mixtures. The generalized Maxwell model with different number of Maxwell chains are used to evaluate the performance of Burger's model. Furthermore, since commercially available finite element packages generally do not have a direct built-in Burger's model, the article shows a way of implementing Burger's model in finite element simulation. The simulations corresponding to the laboratory tests are carried out in both frequency domain and time domain to thoroughly evaluate the performance of Burger's model. The optimal frequency range of 0.1-20 Hz for the examined mixtures is found to significantly improve the accuracy of the descriptive master curve. The results also suggest that the generalized Maxwell model requires a minimum of four Maxwell chains to maintain good performance in accurately characterizing the behavior of asphalt mixtures. However, adding more Maxwell chains beyond a critical limit may not provide significant benefits. Finite element simulations demonstrate that the stress relaxation behavior predicted by the obtained Burger's model parameters aligns more closely with experimental data over longer time intervals. This makes Burger's model a strong choice for aiding in the design of simulations for studies focused on the long-term behavior of materials.

Welfare measurement with income-dependent discrete choice

The expectation of the random compensating variation is the welfare change measure that is used in discrete choice models. The expectation of the equivalent variation is equally founded theoretically. When choices are income independent and income enters utilities linearly, the two measures are identical. The case of income-dependent choices remains an area for exploration. The paper provides the equivalent variation counterparts of the formulas that are available for the expectation of the compensating variation.

Designing employee benefits to optimize turnover: A prescriptive analytics approach

Employee turnover significantly impacts organizations, particularly those with substantial investments in training their workforce. To mitigate these effects, we propose a Prescriptive Human Resources Analytics approach that optimizes employee benefits to minimize total costs, focusing on turnover management The methodology models employee decision-making using a discrete choice model, with parameters estimated through maximum likelihood. We solve the resulting nonlinear optimization problem with a heuristic tailored to the problem’s complexity. We applied this methodology to a hospital case study, which was used to enhance the transportation system as an employee benefit, considering the associated turnover costs. The results demonstrate that our approach can reduce total costs, optimize the usage level of the designed benefits, and increase employee satisfaction. This research provides a robust framework for data-driven decision-making in HR, offering practical tools for improving employee retention strategies.