Choice Set Research Articles

A variational inequality formulation for stochastic user equilibrium with a bounded choice set

This study develops a variational inequality (VI) formulation for the stochastic user equilibrium with a bounded choice set, known as the bounded choice model (BCM). A novel mapping is devised to facilitate the development of the VI formulation. The VI formulation offers a new avenue for analysing and solving the BCM. It is shown that all paths in the considered choice set should have an equal mapping at the optimal solution, which resembles the equilibrated travel cost in the user equilibrium (UE). The new formulation has two algorithmic benefits. First, an algorithm that solves a VI problem (i.e., the extragradient method) can outperform the method of successive averages (MSA) in large networks under a UE-type convergent measurement. Second, it is possible to avoid the column reduction procedure when finding the choice set that satisfies the BCM requirement because a projection-type algorithm that solves the VI formulation can assign zero flow to a path. This is more efficient than the MSA, which uses diminishing step sizes to update solutions over iterations, leading to a sublinear rate of convergence and small residual flows on paths that should carry zero flows. Numerical experiments are conducted to demonstrate the properties of the model and compare the performance of different algorithms using the Sioux Falls, Eastern Massachusetts, and Anaheim networks.

How different attributes are weighted in professionals' decision-making in Pediatric Dentistry-a protocol for guiding discrete choice experiment focused on shortening the evidence-based practice implementation for dental care.

Important evidence has been constantly produced and needs to be converted into practice. Professional consumption of such evidence may be a barrier to its implementation. Then, effective implementation of evidence-based interventions in clinical practice leans on the understanding of how professionals value attributes when choosing between options for dental care, permitting to guide this implementation process by maximizing strengthens and minimizing barriers related to that. This is part of a broader project investigating the potential of incorporating scientific evidence into clinical practice and public policy recommendations and guidelines, identifying strengths and barriers in such an implementation process. The present research protocol comprises a Discrete Choice Experiment (DCE) from the Brazilian oral health professionals' perspective, aiming to assess how different factors are associated with professional decision-making in dental care, including the role of scientific evidence. Different choice sets will be developed, either focusing on understanding the role of scientific evidence in the professional decision-making process or on understanding specific attributes associated with different interventions recently tested in randomized clinical trials and available as newly produced scientific evidence to be used in clinical practice. Translating research into practice usually requires time and effort. Shortening this process may be useful for faster incorporation into clinical practice and beneficial to the population. Understanding the context and professionals' decision-making preferences is crucial to designing more effective implementation and/or educational initiatives. Ultimately, we expect to design an efficient implementation strategy that overcomes threats and potential opportunities identified during the DCEs, creating a customized structure for dental professionals. https://osf.io/bhncv .

Preference of primary care patients for home-based healthcare and support services: a discrete choice experiment in China.

This research, utilizing discrete choice experiments, examines the preferences and willingness to pay for home-based healthcare and support services among residents in China, a country grappling with severe aging population, an area often underexplored in international scholarship. This study aims to solicit the preferences of primary care patients for home-based healthcare and support services in China. A discrete choice experiment (DCE) was conducted on 312 primary care patients recruited from 13 community health centers in Wuhan and Kunming between January and May 2023. The experimental choice sets were generated using NGene, covering five attributes: Scope of services, health professionals, institutions, insurance reimbursements, and visiting fees. The choice sets were further divided into three blocks, and each participant was asked to complete one block containing 12 choice tasks. Mixed logit models were established to estimate the relevant importance coefficients of and willingness to pay for different choices, while Latent Class Logit (LCL) modeling was conducted to capture possible preferences heterogeneity. The relevant importance of the scope of services reached 67.33%, compared with 19.84% for service institutions and 12.42% for health professionals. Overall, respondents preferred physician-led diagnostic and treatment services. LCL categorized the respondents into three groups: Group one (60.20%) was most concerned about the scope of services, prioritizing disease diagnosis and treatment over preventive care and mental health, while group two (16.60%) was most concerned about care providers (hospitals and medical doctors were preferred), and group three (23.20%) was most concerned about financial burdens. Primary care patients prefer physical health and medical interventions for home-based healthcare and support services. However, heterogeneity in preferences is evident, indicating potential disparities in healthcare and support at home services in China.

Justification within and between social contexts with the possibility of choice deferral

This paper provides axiomatic characterizations for a class of models where a decision maker (henceforth, DM) maximizes a single preference ranking subject to a shortlist that depends on the underlying choice set and social context. Thus, the primitive is a dataset of choices from every choice set – social context pair that is called the context. The DM is supposed to choose the most preferred option from the shortlist of each context. However, due to existence of conflicting norms (or norms uncertainty), he/she might choose no option in some contexts. Therefore, unlike existing justification models, choice deferral is possible in our framework.

Choiceless Polynomial Time with Witnessed Symmetric Choice

We extend Choiceless Polynomial Time (CPT), the currently only remaining promising candidate in the quest for a logic capturing Ptime , so that this extended logic has the following property: for every class of structures for which isomorphism is definable, the logic automatically captures Ptime . For the construction of this logic, we extend CPT by a witnessed symmetric choice operator. This operator allows for choices from definable orbits. But, to ensure polynomial-time evaluation, automorphisms have to be provided to certify that the choice set is indeed an orbit. We argue that, in this logic, definable isomorphism implies definable canonization. Thereby, our construction removes the non-trivial step of extending isomorphism definability results to canonization. This step was a part of proofs that show that CPT or other logics capture Ptime on a particular class of structures. The step typically required substantial extra effort.

Choice overload interferes with early processing and necessitates late compensation: Evidence from electroencephalogram.

Having a multitude of choices can be advantageous, yet an abundance of options can be detrimental to the decision-making process. Based on existing research, the present study combined electroencephalogram and self-reported methodologies to investigate the neural mechanisms underlying the phenomenon of choice overload. Behavioural data suggested that an increase in the number of options led to negative evaluations and avoidance of choice tendencies, even in the absence of time pressure. Event-related potential results indicated that the large choice set interfered with the early visual process, as evidenced by the small P1 amplitude, and failed to attract more attentional resources in the early stage, as evidenced by the small amplitude of P2 and N2. However, the LPC amplitude was increased in the late stage, suggesting greater investment of attentional resources and higher emotional arousal. Multivariate pattern analysis revealed that the difference between small and large choice set began at around 120 ms, and the early and late stages were characterised by opposite activation patterns. This suggested that too many options interfered with early processing and necessitate continued processing at a later stage. In summary, both behavioural and event-related potential (ERP) results confirm the choice overload effect, and it was observed that individuals tend to subjectively exaggerate the choice overload effect.

A Predictive Model Based on TripAdvisor Textual Reviews: Early Destination Recommendations for Travel Planning

Although many studies have considered the effects of online reviews on tourists’ decisions, none have directly investigated how to leverage open data analyses to create early choice sets and facilitate destination planning. This paper illustrates how salient characteristics can be mined from the shared experiences embedded in review data and incorporated into a predictive model to build a travel counseling approach. The model is designed by first defining a prediction-based mechanism from online reviews and then generating a multinomial classification problem on all candidate destinations of interest. The model is implemented by applying Natural Language Processing (NLP) and Deep Learning (DL) technologies to review textual features. The model is validated using 75,315 reviews from TripAdvisor along with destinations from 257 U.S. national parks. Empirical results indicate a best classification accuracy of 67%, outperforming two previous approaches. Findings shed light on how to exploit past tourists’ experiences to generate early destination recommendations to identify items for choice sets and reduce tourists’ travel-planning effort. Theoretical and managerial implications regarding social media analytics are provided based on online review meta-data in touristic management.

Using multiobjective optimization to reconstruct interferometric data

Context. In very long baseline interferometry (VLBI), signals recorded at multiple antennas are combined to form a sparsely sampled virtual aperture with an effective diameter set by the largest separation between the antennas. Due to the sparsity of the sampled aperture, VLBI imaging constitutes an ill-posed inverse problem. Various algorithms have been employed to deal with the VLBI imaging, including the recently proposed multiobjective evolutionary algorithm by decomposition (MOEA/D) described in the first paper of this series. Aims. Among the approaches to the reconstruction of the image features in total intensity from sparsely sampled visibilities, extensions to the polarimetric and the temporal domain are of great interest for the VLBI community in general and the Event Horizon Telescope Collabroration (EHTC) in particular. Based on the success of MOEA/D in presenting an alternative claim of the image structure in a unique, fast, and largely unsupervised way, we study the extension of MOEA/D to polarimetric and time dynamic reconstructions in this paper. Methods. To this end, we utilized the multiobjective, evolutionary framework introduced for MOEA/D, but added the various penalty terms specific to total intensity imaging time-variable and polarimetric variants, respectively. We computed the Pareto front (the sample of all non-dominated solutions) and identified clusters of close proximities. Results. We tested MOEA/D with synthetic data sets that are representative for the main science targets and instrumental configuration of the EHTC and its possible successors. We successfully recovered the polarimetric and time-dynamic signature of the ground truth movie (even with relative sparsity) and a set of realistic data corruptions. Conclusions. MOEA/D has been successfully extended to polarimetric and time-dynamic reconstructions and, specifically, in a setting that would be expected for the EHTC. It offers a unique alternative and independent claim to the already existing methods, along with a number of additional benefits, namely: it is the first method that effectively explores the problem globally and compared to regularized maximum likelihood (RML) methods. Thus, it waives the need for parameter surveys. Hence, MOEA/D is a novel, useful tool to characterize the polarimetric and dynamic signatures in a VLBI data set robustly with a minimal set of user-based choices. In a consecutive work, we will address the last remaining limitation for MOEA/D (the number of pixels and numerical performance), so that MOEA/D can firmly solidify its place within the VLBI data reduction pipeline.

“It’s a Chance, Not a Choice”: Black Families, School Choice, and Gentrification in Washington, D.C.

Background: Public education reforms, such as expanded school choice, have become a critical lever for remaking urban landscapes. These reforms often aim to attract and retain affluent and White families in urban schools, so scholars have examined how these parents navigate the perceived risk of choosing these schools for their children. Purpose: This paper extends this scholarship to understand how other families experience these reforms in gentrifying landscapes. We ask: (1) How do Black parents navigate school selection in a gentrifying and expansive education marketplace? (2) How do Black parents’ perceptions of schooling shape their approach to school selection? (3) How do parents’ positionalities (e.g., gender, class, place attachments, and tenure in the city) influence their experiences? Research Design: We leverage a qualitative meta-analysis design that pools data from three separately conducted studies of gentrification in Washington, D.C. For this analysis, we center on 34 Black parents’ experiences as they navigate school selection. We reanalyzed data through the lens of critical spatial and racial theories. We paid particular attention to participants’ attachments to place, their perceptions of their choices, the school and neighborhood attributes participants valued, and how they navigated school selection. Findings: Parents considered a broad range of school and neighborhood characteristics as they constructed their choice sets. As they searched for schools, Black parents made a series of racialized compromises to find schools they perceived to be racially, physically, and socially safe for their children. Parents, for example, negotiated their desire for academic rigor with their perception of schools’ social climates and their perceptions that schools would be racially affirming and inclusive. Place and space were essential to parents’ choice set construction. Schools’ physical locations and perceptions of safety influenced whether parents viewed schools as viable options for their children. Conclusion: Our study underscores the multiple factors that bound choice set construction. Critically, Black parents’ experiences as they navigated school selection suggest that the expansive educational marketplace offered a “chance, not a choice” at high-quality educational opportunities.

A Random Consideration Set Model for Demand Estimation, Assortment Optimization, and Pricing

Random Consideration Set Model We operationalize a microfounded consumer choice model—the random consideration set (RCS) choice model of Manzini and Mariotti [Manzini P, Mariotti M (2014) Stochastic choice and consideration sets. Econometrica 82(3):1153–1176]—that captures the limited attention of consumers, assuming that purchases are based on fixed preference orderings with consideration sets formed from independent attentions. We provide a condition for uniquely identifying model parameters and design an efficient algorithm for model parameters estimation. We offer a greedy-like algorithm for assortment optimization, adaptable for optimal assortment subject to cardinality constraint or discovering efficient sets. We extend the model to consider random product preferences, with a 1/2 performance-guaranteed approximation algorithm. Using data from a major U.S. airline, we find that the RCS model outperforms the mixed multinomial logit model in approximately half of the markets, particularly with smaller, less varied data sets.

Improved Metric Distortion via Threshold Approvals

We consider a social choice setting in which agents and alternatives are represented by points in a metric space, and the cost of an agent for an alternative is the distance between the corresponding points in the space. The goal is to choose a single alternative to (approximately) minimize the social cost (cost of all agents) or the maximum cost of any agent, when only limited information about the preferences of the agents is given. Previous work has shown that the best possible distortion one can hope to achieve is 3 when access to the ordinal preferences of the agents is given, even when the distances between alternatives in the metric space are known. We improve upon this bound of 3 by designing deterministic mechanisms that exploit a bit of cardinal information. We show that it is possible to achieve distortion 1+sqrt(2) by using the ordinal preferences of the agents, the distances between alternatives, and a threshold approval set per agent that contains all alternatives for whom her cost is within an appropriately chosen factor of her cost for her most-preferred alternative. We show that this bound is the best possible for any deterministic mechanism in general metric spaces, and also provide improved bounds for the fundamental case of a line metric.

The influence of choice set size and time-lapse accuracy on E-commerce Decision-Making under different product types

The influence of choice set size and time-lapse accuracy on E-commerce Decision-Making under different product types

Understanding the route choice preferences of private and dock-based public bike users using GPS data in Seoul, South Korea

Analyzing route preferences is crucial for comprehending how individuals choose their travel paths, assessing road facilities, and anticipating upcoming traffic conditions. This study aimed to explore the factors influencing route selections made by private and public bike users and compare their decisions during peak and off-peak hours. Global positioning system (GPS) traces of 12,106 private bike users and 74,397 public bike users from a single weekday in Seoul, South Korea, were used. A map-matching process aligned the GPS data with a comprehensive cycling network encompassing bikeways, sidewalks, neighborhood roads, and their street attributes such as urban, park, and river streets. Choice sets composed of the shortest and 10th alternative routes were created to compare various road features with actual routes. By employing a path-size logit model to address overlapping issues among the routes, the results showed that buffered bike lanes separated from car flows positively influenced the route selection of bicyclists. Bike boulevards and shared sidewalk bikeways negatively impacted route decisions made by public bike users, with a more pronounced effect during peak hours. Furthermore, private bike users showed a decreased preference for routes in residential areas during peak hours, while an increased preference during off-peak hours. Moreover, public bike users preferred selecting routes in the presence of shopping-stores or amenities as well as public transit stations, with these effects being more pronounced during peak hours. These findings are expected to contribute to capturing valuable insights into the route preferences of bicyclists for the improvement of bicycle infrastructures.

History Dependence in Drug Demand: Identification and Implications for Entry Incentives

Abstract I use temporal discontinuities in choice sets to identify history dependence in drug demand, finding large and long-term effects. Quasi-randomly assigning a patient to a drug today increases the probability she is taking the same drug four years later by 54 percentage points. History dependence is stronger in patients taking multiple medications and weaker in drugs that are significantly more effective than substitutes. It is also weaker in generics and line extensions, driven by switching from the reference branded drug. I use a pair-specific switching-cost model to capture these patterns and provide suggestive evidence that they affect manufacturers’ entry timing incentives.

An Agent-Based Modeling Approach for the Diffusion Analysis of Electric Vehicles With Two-Stage Purchase Choice Modeling

Abstract Diffusion research of innovative technologies is crucial for new product positioning and strategic planning in product design. As a versatile system simulation method, agent-based modeling (ABM) has been used in many previous studies on the diffusion analysis of electric vehicles (EVs). In these simulations, modeling consumers' purchase decisions is a significant step. Previous studies often adopt simple rule-based decision criteria in this step, while an accurate purchase decision model can contribute to a more reasonable diffusion analysis of EVs. To fill this gap, this brief presents an agent-based modeling approach for the diffusion analysis of electric vehicles with two-stage choice modeling. The core idea is to separate consumers' decision-making process for purchasing cars into two stages. Consumers first form a small choice set from the whole auto market. Then, they make the final choice from the choice set built in the first stage. In addition, the word-of-mouth (WOM) effect and consumers' social networks are also considered in the ABM, which can further improve the accuracy of the diffusion analysis. A case study using data collected from Shanghai, China, is presented to demonstrate the proposed approach. Our approach outperforms other ablation models as well as traditional statistical models in the prediction accuracy of EV's market share. The influence of factors such as government policy and technological improvement on the diffusion of EVs is also discussed. These insights can assist automakers in improving their product design and enhancing their market competitiveness.

Testing a decoy donation incentive to improve online survey participation: Evidence from a field experiment.

This study introduces a new randomized field experiment exploring the impact of offering a decoy charity donation incentive together with a monetary reward to increase response rates in an online survey about coronavirus fears. The study used a two-stage approach, starting with a preliminary survey to investigate participant attitudes toward different types of donations. Subsequently, an experiment was conducted wherein a less desirable £2 donation (the decoy) was introduced as an alternative to a £2 Amazon voucher (the target) within the choice set. The study sample consisted of 431 university students. They were split into three groups: a control group with a standard £2 Amazon voucher incentive (216 participants), a decoy group with the target shown first (108 participants), and a decoy group with the decoy shown first (107 participants). We found significantly higher survey completion rates in the decoy than in the control condition (82.3% vs. 74.5%). Notably, an order effect was observed-presenting the target before the decoy led to a higher completion rate (89.8%) compared to presenting the decoy first (74.8%). Importantly, the inclusion of the decoy incentive did not introduce any response bias. This study offers a proof of principle that incorporating a decoy charity donation incentive into the choice set can have a positive impact on survey participation without adversely affecting response behaviour. It demonstrates the potential of such incentives to encourage participants to complete online surveys, even when a small monetary reward is offered.

Optimal choice designs for the main effects plus specified two-factor interaction effects model for binary attributes

Unlike optimal utility-neutral binary choice designs requiring a large number of choice sets to estimate the main effects and all two-factor interactions, we construct optimal binary choice designs with fewer choice sets for estimating the main effects plus specified two-factor interactions.

Words or code first? Is the legacy document or a code statement the better starting point for complexity-reducing legal automation?

Law is a critical tool that humans have created to assist them in managing complex social interactions. Computational Law holds the potential to significantly enhance our capacity to express and manage legal complexity, and a number of advantages can result from restating public and private legal rules in computable form. Capturing that potential depends in part on the approaches taken to automation. One set of choices involves whether to translate directly into code from existing natural language statements of laws, regulations and contracts or whether to step back, envision the basic structure underlying those statements and build a software approach that reflects that structure in a code-native manner. We argue that many advantages can flow from the second approach, and we present a specific use case of a simplified insurance policy as an example of this approach. Large language models may assist in this process, but are not yet a replacement for a code-native utility. This article is part of the theme issue 'A complexity science approach to law and governance'.

Voluntary blood donation preferences in China: A discrete choice experiment among experienced and inexperienced donors.

Due partly to an ageing population, China faces an increasingly dire blood shortage crisis requiring greater voluntary blood donations. A better understanding of blood donation preferences can inform blood donation policies and potentially increase donations. We used an online survey and discrete choice experiment to achieve our study objective: identify the most influential structural facilitators and barriers to voluntary blood donation in China. First, we identified six structural attributes (travel time, venue, donation volume, paid leave, scheduling and gifts) that were hypothesized to influence voluntary blood donation; attribute selection was based on a literature review and qualitative interviews. Second, a d-efficient design with 36 choice sets and 9 blocks was developed. Participants were asked to complete four choice sets, and in each choice set, they were asked to choose from three options: two voluntary blood donation scenarios and a 'Do not donate blood' option. Study participants were recruited through an online survey platform company in China. Voluntary blood donation preferences and preferences by blood donation history were estimated with random-parameter logit models and interaction terms. In 2022, 1185 individuals enrolled in the study. Most participants had college education (92%). Generally, participants preferred longer paid leave, lower blood donation volumes and gifts after donation. Based on interaction analyses, experienced and inexperienced donors exhibited similar preferences. Campaigns to increase voluntary blood donation rates in China should consider implementing paid leave after voluntary blood donation, lower blood donation volumes and small gifts conferred after donation.

The role of geographic market definition in analysis of grocery retailing

AbstractWe examine how estimates of household food demand elasticities and store profit margins vary with alternative geographic market extents using structural models of household store choice and retailer competition. Our consumer store choice model is novel, simultaneously accounting for the heterogeneity of store choice sets, households' travel distance to stores, and their store‐specific shopping basket prices. We estimate the models using a unique combination of datasets on grocery purchases. We find that the geographic market extent is positively associated with household demand elasticity and negatively associated with store profit margins. The maximum market extent at which changes in demand elasticities become statistically insignificant varies by retailers, ranging between 10 and 16 km. These findings are robust to alternative assumptions of store competition. Our results imply that overlooking the locality of retail competition can result in overestimating the magnitudes of household demand elasticities while underestimating store profit margins, characterizing a relatively more competitive market.