Choice Behavior Research Articles

Value of time and port choice: An approach regarding import companies in Brazil

The cargo release time in Brazilian ports is a critical factor that impacts the competitiveness of companies. This paper analyzed the port choice process from companies in two states in the Southeast region of Brazil: Rio de Janeiro and Minas Gerais. We used Stated Preference data. Multinomial logit and mixed logit with error components models were estimated. We analyzed the following attributes: taxation, road transport tariff, ship calls, port tariff, sea freight tariff and cargo release time. The experimental designs were structured using an efficient design for Rio de Janeiro, and a Bayesian efficient design for Minas Gerais. A comparison of the port choice behavior was carried out between the states. For each state, the Value of Time (VOT) was calculated referring to the willingness of companies to pay for the reduction of one unit (day) of cargo release at ports. The estimated VOT for companies in RJ was R$/t.day 387.45 (77.49 U$S/t.day) and for companies in MG, it was R$/t.day 364.93 (72.98 U$S/t.day). The results indicate that the estimated values may vary according to characteristics, such as company size and product type.

Investigating day-to-day route choices based on multi-scenario laboratory experiments, Part II: Route-dependent attraction-based stochastic process model

Laboratory experiments are one of the important means used to investigate travel choice behavior under strategic uncertainty. Many experiment-based studies have shown that the Nash equilibrium can predict aggregated route choices, while the fluctuations, whose mechanisms are still unclear, continue to exist until the end. To understand the fluctuations, this paper proposes a route-dependent attraction-based stochastic process model, which shares exactly the same behavioral foundation introduced in Part I of the study (Qi et al., 2023), i.e., route-dependent inertia and route-dependent preference. The model predictions are carefully compared with the experimental observations obtained from the congestible parallel-route laboratory experiments containing 312 subjects and eight decision-making scenarios (Qi et al., 2023). The results show that the proposed stochastic process model can precisely reproduce the random oscillations both in terms of flow switching and route flow evolution. Subsequently, an approximated model is developed to enhance the efficiency in evaluating the equilibrium distribution, providing a practical tool to evaluate the impacts of transportation policies in both long- and short-term runs. To the best of our knowledge, this paper is the first attempt to model and explain experimental phenomena by introducing stochastic process theories, as well as a successful example of applying experimental economics methodology to improve our understanding of human travel choice behavior.

Determinants of treatment-seeking behaviour and healthcare provider choice in Afghanistan in 2018: a cross-sectional study

IntroductionIn Afghanistan, remarkable achievements have been made in improving access to healthcare and health outcomes since the introduction of essential healthcare packages. Nonetheless, sociodemographic and geographical inequities persist, and out-of-pocket...

More than a wink and a nudge: examining the choice architecture of online government budget simulations

Abstract Following the growing interest in using behavioral theory and choice architecture in the public sector, several new studies have looked at how changes in the choice architecture of budget simulations influence the participants’ budgetary decisions. These studies have also introduced the possible problem that participants may make inappropriate choices in the budget simulation, like creating a budget with unacceptably high budget surpluses. Building on Thaler and Sunstein’s NUDGES framework, we seek to answer the question, ‘How can budgetary choice architects correct for errors such as large ending surpluses at the end of the budget simulation?’ We replicate earlier results on budget starting conditions. Additionally, we test a budget treatment that encourages participants to reduce ending budget surpluses. The budget treatment works as intended and suggests that the large ending budget surpluses stem from errors made by participants in the simulation rather than loss aversion. The need to both nudge and budge participants is important for practicing choice architects, like public budgeters who have to design and implement tools that inform citizens and reveal accurate preferences that conform with legal requirements.

The Effects of Learning Attributes on Students’ Writing Performance

This research investigates how three students’ learning attributes—attitudes, behavior, and English proficiency background contribute to the students’ writing performance. Statistical methods explored three primary areas: (1) the influence of students’ attitudes towards the students’ choice of teaching methods and learning behavior, (2) the benefits of consultations in enhancing students’ writing performance, and (3) the impact of the three factors—English proficiency background, online learning duration, and numbers of consultation—on students’ writing performance. The study involved 29 first-year undergraduate engineering students. The results showed that the students with positive attitude towards English learning exhibited higher satisfactions and engagement, regardless of whether they followed a teacher-directed or self-directed method. This positive attitude had a substantial positive correlation with the satisfactions of both self-directed (r = 0.637) and teacher-directed (r = 0.447) methods. Additionally, the satisfactions of the self-directed method significantly correlated with the satisfactions of the teacher-directed method (r = 0.707) and with learning behavior through the teacher-directed method (r = 0.581). With notable differences in pre-test and post-test scores, the consultations were pivotal in enhancing writing performance of the students who participated in the optional extra-session (t = 8.846) when comparing to those who did not (t = 5.138). The data analysis using techniques namely Feature Importance and Univariate Selection indicated that online learning duration (the time spent on the teaching materials) had the most significant impact on the students’ writing performance.

Reinforcement learning‐based approach for urban road project scheduling considering alternative closure types

AbstractGrowth in urban population, travel, and motorization continue to cause an increased need for urban projects to expand road capacity. Unfortunately, these projects also cause travel delays, emissions, driver frustration, and other road user adversities. To alleviate these ills, road agencies often face two work zone design choices: close the road fully and re‐reroute traffic or implement partial closure. Both options have significant implications for peri‐construction road capacity, traveler costs, and the project duration and cost. This study presents a decision‐making methodology to facilitate the choice between full road closure and partial closure. The presented decision‐making methodology is a bi‐level optimization problem: at the upper level, the road agency seeks to optimally schedule road construction work to minimize net vehicle emissions and road construction costs. The lower‐level of the problem captures two types of travelers’ route choice behaviors: rational travelers who minimize their travel time and path‐loyal travelers who do not change their routes from their pre‐construction routes. The bi‐level mixed integer nonlinear model is solved using a reinforcement learning‐based algorithm (the multi‐armed bandit‐guided particle swarm optimization [PSO] technique). The computational experiments suggest the superiority of the proposed algorithm, compared to the classic PSO algorithm in terms of solution quality. The numerical results suggest that if the percentage of path‐loyal travelers increases, the agency needs to invest more in road project construction to implement under partial closure to avoid a significant increase in vehicle emissions.

ПСИХОЛОГІЧНІ ОСОБЛИВОСТІ ПРОЯВУ КОПІНГ-СТРАТЕГІЙ СТУДЕНТІВ З РІЗНИМ РІВНЕМ СТРЕСОСТІЙКОСТІ

Ukrainian society and every Ukrainian family is currently experiencing social and psychological difficulties as a result of the Russian invasion of Ukraine. All this creates a significant number of stressors for the population. Martial law, hostilities, forced changes in living conditions, and constant stress cause people to experience difficulties in maintaining psychological comfort. Therefore, the problem of optimal choice of coping strategies, especially by students, is becoming particularly relevant. The article deals with the issue of individual's choice of behaviour coping strategies. The special significance of the formation and development of this phenomenon in the participants of the educational process of a higher education institution is emphasized. In particular, the emphasis is placed on the difficulties that students are currently facing, namely the consequences of quarantine restrictions, a sense of isolation, internal discomfort and general psycho-emotional stress caused by the martial law in our country. The article focuses on coping strategies as a subconscious means of psychological defence that shapes human behaviour in certain conditions. A theoretical analysis is carried out and meanings of the following concepts are indicated: «coping», «coping strategy», «coping behaviour» and «coping resources», as well as an analysis of the peculiarities of making behavioural choices in certain conditions. The main strategies of behaviour in a stressful situation and the main approaches to the study of coping behaviour and behavioural strategies are analysed. The article reflects empirical indicators of the ratio of dominant coping strategies at different levels of stress resistance. A correlation between the level of stress resistance and coping strategies has been established: with a low level of stress resistance, passive coping strategy dominates; in the presence of a high level - active coping strategies.

Do you love traveling to the beach or mountain? Predicting personality traits and choice behaviour

ABSTRACT Several personality-predicting tools are being used by managers in their strategic decision-making process. The most common are the Big Five model and the Myers – Myers-Briggs Type Indicator model, respectively. This study deviates and tests a novel personality prediction model. Specifically, it utilizes the TF-IDF (Term Frequency-Inverse Document Frequency) algorithm as a feature extraction method to map personality traits based on consumers’ textual data from the Kaggle MBTI Personality Type dataset. This study uses realistic data gathered from traveller reviews and comments on hotel stays from TripAdvisor’s widely recognised platform to predict different personality traits and their travel destination preferences, i.e. beach or mountain. The contribution of this study lies in the identification of multiple dimensions of peoples’ personality traits and their relationship with their preferences for travel locations. This proposed trait classification mechanism can assist marketing managers in effectively carrying out customer segmentation, setting realistic goals, and optimizing marketing strategies.

Faecal microbiota composition and impulsivity in a cohort of older adults with metabolic syndrome

Impulsivity is an important determinant of human behaviour, affecting self-control, reasonable thinking and food choices. Recent evidence suggests a role for gut microbiota in human behaviour, but the relationship between gut microbiota and impulsive behaviours remains largely unexplored. To address this knowledge gap, the present study aims to explore the associations between faecal microbiota composition with trait and behavioural impulsivity, in a subcohort of the PREDIMED-Plus trial, including older adults presenting overweight/obesity. Fecal samples (n = 231) were profiled for their microbiota composition using 16 S rRNA amplicon sequencing and impulsivity was determined through four different assessments. Adherence to different dietary patterns was estimated through questionnaires. Beta diversity analyses showed a significant association with the Conner’s Performance Test (CPT) in multivariate-adjusted models, and, in total, 13 bacterial genera associated with CPT. Erysipelotrichaceae UCG 003 showed the highest association with CPT and known butyrate producers such as Butyricicoccus spp., Roseburia spp., and Eubacterium hallii were among the identified bacteria. The bacteria Lachnospiraceae UCG 001, Anaerostipes and Blautia were associated with CPT and also the adherence to healthy and unhealthy plant-based diets. In addition, functional analysis showed a significant negative association between the CPT and the glucuronate and galacturonate metabolic pathways. From the other impulsivity assessments, two more associations were identified, for the genus Phascolarctobacterium with the Stroop test, and the genus Lachnospiraceae GAG 54 with the positive urgency subscore of UPPS-P Impulsive Behaviour Scale. Overall, our findings suggest potential links between the faecal microbiota composition and function with behavioural impulsive inattention as determined by the CPT.

Neural reward system reflects individual value comparison strategy in cost-benefit decisions

A core assumption in decision neuroscience is that individuals decide between options by comparing option-specific subjective reward values. Psychological accounts challenge this view and suggest that decisions are better explained by comparisons between choice attributes than by comparisons between option-specific values, casting doubts on the interpretation of activation in the neural reward system as subjective value signals. Here, we provide neuroimaging and pharmacological evidence that value-related neural activity follows the value comparison strategy employed by an individual on the psychological level. Neural model comparisons reveal that activation in the striatum, rather than generally reflecting attribute-wise or option-wise value comparisons, reflects the value comparison strategy that provides the best explanation for an individual’s choice behavior. Strikingly, manipulating activation in the dopaminergic reward system reveals that dopamine antagonism counteracts the engagement in an individual’s dominant value comparison strategy. Together, our findings provide evidence for the biological plausibility of psychological accounts of decision making and emphasize the importance of neural model comparisons to prevent misinterpretations of brain activation.

Slow motion in corona times: Modeling cyclists’ spatial choice behavior using real-time probe data

The recent COVID-19 pandemic has provided a renewed impetus for empirical research on slow and active modes of transportation, specifically bicycling and walking. Changes in modal choice appear to be sensitive to the actual quality of the environment, the attractive land use and built environment conditions, and the ultimate destination choice. This study examines and models the influence of cyclists’ health concerns during the pandemic on their spatial destination and route choices. Using a large real-time dataset on the individual daily mobility of cyclists in the province of Utrecht, the Netherlands, collected through GPS-linked sensors on bikes (VGI, or volunteered geographical information), the analysis employs spatial regression models, Shapley decomposition techniques, and spatial autocorrelation methods to unveil the backgrounds of changes in spatial behavior. The results reveal that the perceived wellbeing benefits of bicycling in green areas during the pandemic have significantly influenced cyclists’ choice behavior, in particular route and destination choice.

Travel route recommendation with a trajectory learning model

This study addresses a critical issue in location-based services: travel route recommendation. It leverages historical trajectory data to predict the actual route on a road network from a starting point to a destination, given a specific departure time. However, capturing the latent patterns in complex trajectory data for accurate route planning presents a significant challenge. Existing route recommendation methods commonly face two major problems: first, inadequate integration of multi-source data, which fails to fully consider the potential factors affecting route choice; and second, limited capability to capture road network characteristics, which restricts the effective application of node features and negatively impacts recommendation accuracy. To address these issues, this research introduces a Trajectory Learning Model for Route Recommendation (TLMR) based on deep learning techniques. TLMR enhances the understanding of user route choice behavior in complex environments by integrating multi-source data. Moreover, by incorporating road network features, TLMR more effectively captures and utilizes the structural and dynamic information of the road network. Specifically, TLMR first employs a Position-aware Graph Neural Network to learn features of intersections from the road network, incorporating context features like weather and traffic conditions. Then, it integrates this information through neural networks to predict the next intersection. Finally, a beam search algorithm is applied to generate and recommend multiple candidate routes. Extensive experiments on four large real-world datasets demonstrate that TLMR outperforms existing methods in four key performance metrics. These results prove the effectiveness and superiority of TLMR in route recommendation.

Time perception and delay discounting in the FMR1 knockout rat.

There is substantial evidence for timing (time perception) abnormalities related to developmental disabilities, particularly autism spectrum disorder. These findings have been reported in humans and nonhuman preclinical models. Our research objective was to extend that work to a genetic knockout (KO) model of fragile X/developmental disability, the FMR1KO rat. We also sought to test delay discounting in the model and assess potential relations between timing and choice behavior. Consistent with previous human and nonhuman work, we found reduced timing precision in the FMR1 KO rats. We also discovered significantly increased smaller, sooner reward choice in the FMR1 KO rats. Performance on the timing task appeared to be unrelated to performance on the choice task for both model and control rats. These results add to what has become increasingly clear: timing is disrupted in humans diagnosed with developmental disabilities and in nonhuman models designed to model developmental disabilities. Our findings are consistent with those of previous work and the first to our knowledge to show such effects in the FMR1 KO rat. We discuss the potential clinical implications and future directions surrounding potential "timing interventions" for individuals diagnosed with developmental disabilities.

Role of Posterior Medial Thalamus in the Modulation of Striatal Circuitry and Choice Behavior.

The posterior medial (POm) thalamus is heavily interconnected with sensory and motor circuitry and is likely involved in behavioral modulation and sensorimotor integration. POm provides axonal projections to the dorsal striatum, a hotspot of sensorimotor processing, yet the role of POm-striatal projections has remained undetermined. Using optogenetics with slice electrophysiology, we found that POm provides robust synaptic input to direct and indirect pathway striatal spiny projection neurons (D1- and D2-SPNs, respectively) and parvalbumin-expressing fast spiking interneurons (PVs). During the performance of a whisker-based tactile discrimination task, POm-striatal projections displayed learning-related activation correlating with anticipatory, but not reward-related, pupil dilation. Inhibition of POm-striatal axons across learning caused slower reaction times and an increase in the number of training sessions for expert performance. Our data indicate that POm-striatal inputs provide a behaviorally relevant arousal-related signal, which may prime striatal circuitry for efficient integration of subsequent choice-related inputs.

Neural mechanisms underlying robust target selection in response to microstimulation of the oculomotor system.

Despite its prevalence in studying the causal roles of different brain circuits in cognitive processes, electrical microstimulation often results in inconsistent behavioral effects. These inconsistencies are assumed to be due to multiple mechanisms, including habituation, compensation by other brain circuits, and contralateral suppression. Considering the presence of reinforcement in most experimental paradigms, we hypothesized that interactions between reward feedback and microstimulation could contribute to inconsistencies in behavioral effects of microstimulation. To test this, we analyzed data from electrical microstimulation of the frontal eye field of male macaques during a value-based decision-making task and constructed network models to capture choice behavior. We found evidence for microstimulation-dependent adaptation in saccadic choice, such that in stimulated trials, monkeys' choices were biased toward the target in the response field of the microstimulated site (Tin ). In contrast, monkeys showed a bias away from Tin in non-stimulated trials following microstimulation. Critically, this bias slowly decreased as a function of the time since the last stimulation. Moreover, microstimulation-dependent adaptation was influenced by reward outcomes in preceding trials. Despite these local effects, we found no evidence for the global effects of microstimulation on learning and sensitivity to the reward schedule. By simulating choice behavior across various network models, we found a model in which microstimulation and reward-value signals interact competitively through reward-dependent plasticity can best account for our observations. Our findings indicate a reward-dependent compensatory mechanism that enhances robustness to perturbations within the oculomotor system and could explain the inconsistent outcomes observed in previous microstimulation studies.Significance Statement Electrical microstimulation has been used to study the causal contributions of certain brain areas or circuits to cognition and behavior. Nonetheless, the overall impact of microstimulation on behavior remains inconclusive, hinting at neural mechanisms that interact with experimental perturbation of neural activity. We hypothesized that this interaction could be driven by the reward feedback animals receive while performing tasks, either with or without external perturbations. Using computational modeling and data from microstimulation during a reward-dependent decision-making task, we found microstimulation and reward-value signals competitively interact within the oculomotor system. This interaction enhances the system's robustness to both internal and external perturbations. Our results have important implications for employing microstimulation in basic and clinical research.

A protocol for linking participants’ retailer ‘loyalty card’ records into the Avon Longitudinal Study of Parents and Children (ALSPAC)

Longitudinal population studies (LPS) have a long history of providing insights into how individual consumption patterns and other lifestyle choices affect health and socio-economic outcomes. LPS typically operate as research databanks, integrating rich and deep phenotypic data - covering diverse aspects of individual, family and household status - with genomic data and linked records on health and socio-economic outcomes. However, individual consumption and behavioural choices are traditionally studied solely using self-report methods which are prone to known biases. We propose to enrich LPS databanks with a new form of digital footprint data – individual shopping history records. These are collected by supermarkets through “loyalty” card schemes and can provide a new perspective on real world behaviours and history of consumption. However, as a novel class of data in the context of longitudinal research, our ability to assess the quality and completeness of the data is unknown, as is our ability to effectively triangulate between self-reported and linked data. This paper describes a protocol for linking individual level shopping history data into a LPS using Avon Longitudinal Study of Parents and Children (ALSPAC) as a testbed. The protocol covers the process of establishing participant fair processing, an ethical and legal basis for the linkage framework itself, and how these data will be integrated into the ALSPAC databank. It does not cover the subsequent research use of these data. The protocol was built on an extensive participant engagement and acceptability work and has been approved by the ALSPAC Law and Ethics committee.

Maladaptive Restrictive Food Choice Behavior in Adolescents With Anorexia Nervosa.

Individuals with anorexia nervosa persistently restrict their food intake while often severely underweight. This maladaptive food choice behavior and related decision-making processes have mainly been investigated in adults. It is unknown whether the same decision-making processes drive food choices in adolescents, given their more favorable treatment outcomes. This study investigated maladaptive food choice behavior in adolescents with AN and examined whether they display the same decision-making processes as adults. Adolescents with AN (n = 42) and age-matched controls (n = 42) completed a computer-based food choice task, rating food images for healthiness and tastiness before choosing between two food items. Adolescents with AN chose high-fat foods less frequently than controls, with food choices more influenced by their perceived "healthiness" and less by their "tastiness" than they did among controls. Relative to controls, adolescents with AN also reported lower overall tastiness ratings and greater habit strength of restrictive eating, which was, however, not related to food choices. Adolescents with AN display the same maladaptive food choice behavior as adults, namely the persistent restriction of high-fat food choices. Their choices were more strongly influenced by the perceived healthiness of a food item, compared to the choices of controls.

A behavioral choice analysis of the role of life events during early nonabstinent natural recovery from alcohol use disorder.

Prior research supported a behavioral choice analysis of the role of life events in posttreatment drinking among abstinence-seeking inpatients with alcohol use disorder (AUD). This study investigated the generality of those relationships among persons attempting "natural" recovery involving moderation drinking. We had two hypotheses: (1) The likelihood of drinking after an event would be related to the degree of alcohol-related disruption in the life-health area of the event. (2) Event-related drinking episodes would be quantitatively greater than event-unrelated episodes. Participants (N = 83) were from a larger integrated data set of prospective natural recovery studies of persons with AUD who had stopped heavy drinking and had 6-month follow-up reports of drinking and events; abstainers were excluded. Alcohol-related disruption before resolution was assessed in four domains (relationships, vocational/financial, living arrangements/legal, physical health). As predicted, postresolution event-related drinking was positively correlated with preresolution vocational/financial disruption (p < .01) and negatively correlated with preresolution physical health problems (p = .06). Event-related drinking episodes involved heavier drinking than event-unrelated episodes (p < .001). These findings indicate strong support for the generality of the latter relationship and qualified support for the generality of the former relationship. The different results in the two samples are attributed to differences in the evolution of their AUD recovery process and the decoupling of the event-drinking relationships. The behavioral choice framework suggests ways to improve the characterization of environmental variables in future recovery research. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Supplemental Material for A Behavioral Choice Analysis of the Role of Life Events During Early Nonabstinent Natural Recovery From Alcohol Use Disorder

Supplemental Material for A Behavioral Choice Analysis of the Role of Life Events During Early Nonabstinent Natural Recovery From Alcohol Use Disorder

Research on healthcare data sharing in the context of digital platforms considering the risks of data breaches.

Within China's healthcare landscape, the sharing of medical data has emerged as a pivotal force propelling advancements in the insurance sector and enhancing patient engagement with healthcare services. However, medical institutions often exhibit reluctance toward data sharing due to apprehensions regarding data security and privacy safeguards. To navigate this conundrum, our research introduces and empirically validates a model grounded in evolutionary game theory, offering a robust theoretical framework and actionable strategies for facilitating healthcare data sharing while harmonizing the dual imperatives of data utility and privacy preservation. In this paper, we construct an evolutionary game model involving medical institutions, big data innovation platforms, and insurance companies within the context of digital platforms. The model integrates exogenous causes of data breaches, endogenous causes of data breaches, compensation payments, government penalties, subsidies, unreasonable fees, claims efficiency, and insurance fraud. The stability analysis of the evolutionary game identifies eight equilibrium points among medical institutions, platforms, and insurance companies. Numerical simulations demonstrate convergence toward strategy E 7 = (0, 0, 1), suggesting a trend for medical institutions to adopt a fully anonymous information-sharing strategy, platforms to implement strict regulation, and insurance companies to opt for an auditing approach. Sensitivity analysis reveals that the parameters selected in this study significantly influence the players' behavioral choices and the game's equilibria. When breaches occur, medical institutions tend to seek co-sharing between platforms and insurance companies. This promotes enhanced regulation by platforms and incentivizes insurance companies to perform audits. If the responsibility for the breach is attributed to the platform or the insurance company, the liability sharing system will push healthcare organizations to choose a fully anonymous information sharing strategy. Otherwise, medical institutions will choose partially anonymous information sharing for more benefits. In case of widespread data leakage, the amount of compensation shall augment, and the role of compensation shall replace the role of government supervision. Then, the government shall penalize them, which shall reduce the motivation of each subject.