Model Of Choice Behavior Research Articles

Dopamine restores cognitive motivation in Parkinson's disease.

Disorders of motivation, such as apathy, are common in Parkinson's disease, and a key feature of such disorders is a greater aversion to effort. In humans, the experience of cognitive effort is ubiquitous, and cognitive apathy has traditionally been considered distinct and separable from other subtypes. Surprisingly, however, the neurobiology of cognitive motivation is poorly understood. In particular, although dopamine has a well-characterized role in incentivizing physically effortful behaviour, a critical, unresolved issue is whether its facilitatory role generalizes to other domains. Here, we asked how dopamine modulates the willingness of patients with Parkinson's disease to invest cognitive effort in return for reward. We tested 20 patients with idiopathic Parkinson's disease across two counterbalanced sessions-ON and OFF their usual dopaminergic medication-and compared their performance to 20 healthy age-matched controls. We applied a novel task in which we manipulated cognitive effort as the number of rapid serial visual presentation streams to which participants had to attend. After training participants to ceiling performance, we then asked them to choose between a low-effort/low-reward baseline option, and a higher-effort/higher-reward offer. Computational models of choice behaviour revealed four key results. First, patients OFF medication were significantly less cognitively motivated than controls, as manifest by steeper cognitive effort discounting functions in the former group. Second, dopaminergic therapy improved this deficit, such that choices in patients ON medication were indistinguishable from controls. Third, differences in motivation were also accompanied by independent changes in the stochasticity of individuals' decisions, such that dopamine reduced the variability in choice behaviour. Finally, choices on our task correlated uniquely with the subscale of the Dimensional Apathy Scale that specifically indexes cognitive motivation, which suggests a close relationship between our laboratory measure of cognitive effort discounting and subjective reports of day-to-day cognitive apathy. Importantly, participants' choices were not confounded by temporal discounting, probability discounting, physical demand, or varying task performance. These results are the first to reveal the central role of dopamine in overcoming cognitive effort costs. They provide an insight into the computational mechanisms underlying cognitive apathy in Parkinson's disease, and demonstrate its amenability to dopaminergic therapy. More broadly, they offer important empirical support for prominent frameworks proposing a domain-general role for dopamine in value-based decision-making, and provide a critical link between dopamine and multidimensional theories of apathy.

Extended consumer responsibility: Syncretic value-oriented pricing strategies for trade-in-for-upgrade programs

Nowadays, consumers play a central role in shaping the right retailing strategy. Motivated by the popularity of trade-in-for-upgrade (TIFU) programs in consumer electronics industries, this study adopts a multi-methodological approach to comprehensively explore TIFU-related behavioral pricing and market competition challenges. Grounded upon consumer psychology and behavior theories, this study develops a generalized consumer choice behavior model which takes into account the potential effect of reference dependence choice behavior. A behavior-grounded analytical model is established. Both analytical and quantitative empirical analyses using multiple empirical data sources are conducted. One distinctive feature of the proposed model is the conceptualization of vendor-buyer collaborative value orientation (termed as syncretic value orientation in this paper) in business objective functions, where we advocate that the extended consumer responsibility (ECR) should be incorporated into the modeling and analysis. In particular, this study derives a generalized form of the analytical model to determine the syncretic value-oriented prices and trade-in rebates under TIFU market competition. The results further reveal that involving the syncretic value in business objectives favors not only a price-leading firm’s expansion of market share but also gaining additional profits with a low-price-low-rebate strategy. Utilizing a low-price-high-rebate strategy, a price follower can seize the opportunity to lead in market share when the price leader solely sets profit maximization as its goal in a competitive market.

Model of passenger behavior choice under flight delay based on dynamic reference point

Flight delay has always been a concern of scholars, but in previous studies, there has been little discussion of passenger behavior choice after flight delay. The paper based on the prospect theory, taking the Beijing-Shanghai route as an example, constructs the passenger behavior choice model under flight delay, and in order to explore the decision-making behavior of passengers with different attributes under different delay scenarios. The results show that the reference point of passengers' behavioral decision-making is dynamic due to different delay scenarios and passengers' attributes. For the same reason, passengers have different behavioral choices when encountering flight delay. Through empirical analysis, it is found that the choice of passengers based on prospect theory is in line with the actual choice of passengers. The choice model proposed in the paper can effectively describe the passenger's choice behavior.

Factors Affecting the Willingness to Pay for the Protection of the Di River: an Approach Using the Box-Cox Double Hurdle Model

The Di River, located in West Africa between Burkina Faso and Mali, is a subject of concern to its users. Using econometric models of choice behavior, the determining factors of local populations’ willingness to pay (WTP) for the restoration of the riverbanks are either individual or collective variables. The latter variables imply that data collection focused on common characteristics of the population rather than intrinsic characteristics. Most determining factors have a positive effect on willingness to pay, which is especially observed with subjective or individual variables and reflects the very moderate investment that local populations are willing to make. However, that is also indicative of the potential to achieve sustainable management in such a way that personal factors contribute to increasing the WTP. In addition, the variable related to the level of education of a respondent reveals a willingness to pay a nonfinancial contribution for the restoration of the riverbanks and sustainable management of the resource.

Accounting for multi-dimensional dependencies among decision-makers within a generalized model framework: An application to understanding shared mobility service usage levels

Activity-travel choices of individuals are influenced by spatial dependency effects. As individuals interact and exchange information with, or observe the behaviors of, those in close proximity of themselves, they are likely to shape their behavioral choices accordingly. For this reason, econometric choice models that account for spatial dependency effects have been developed and applied in a number of fields, including transportation. However, spatial dependence models to date have largely defined the strength of association across behavioral units based on spatial or geographic proximity. In the current context of social media platforms and ubiquitous internet and mobile connectivity, the strength of associations among individuals is no longer solely dependent on spatial proximity. Rather, the strength of associations among individuals may be based on shared attitudes and preferences as well. In other words, behavioral choice models may benefit from defining dependency effects based on attitudinal constructs in addition to geographical constructs. In this paper, frequency of usage of car-sharing and ride-hailing services is modeled using a generalized heterogeneous data model (GHDM) framework that incorporates multi-dimensional dependencies among decision-makers. The model system is estimated on the 2014–2015 Puget Sound Regional Travel Study survey sample, with proximity in latent attitudinal constructs defined by a number of personality trait variables. Model estimation results show that social dependency effects arising from similarities in attitudes and preferences are significant in explaining shared mobility service usage. Ignoring such effects may lead to erroneous estimates of the adoption and usage of future transportation technologies and mobility services.

Polarization and acculturation in US Election 2016 outcomes – Can twitter analytics predict changes in voting preferences

Elections are among the most critical events in a national calendar. During elections, candidates increasingly use social media platforms to engage voters. Using the 2016 US presidential election as a case study, we looked at the use of Twitter by political campaigns and examined how the drivers of voter behaviour were reflected in Twitter. Social media analytics have been used to derive insights related to theoretical frameworks within political science. Using social media analytics, we investigated whether the nature of social media discussions have an impact on voting behaviour during an election, through acculturation of ideologies and polarization of voter preferences. Our findings indicate that discussions on Twitter could have polarized users significantly. Reasons behind such polarization were explored using Newman and Sheth's model of voter's choice behaviour. Geographical analysis of tweets, users, and campaigns suggests acculturation of ideologies among voting groups. Finally, network analysis among voters indicates that polarization may have occurred due to differences between the respective online campaigns. This study thus provides important and highly relevant insights into voter behaviour for the future management and governance of successful political campaigns.

Spatio-temporal modeling of destination choice behavior through the Bayesian hierarchical approach

Trip purpose inference is critical in transportation demand management (TDM) as well as traffic congestion alleviation. However, destination choice can be affected by a variety of factors, many of which are difficult to determine (e.g. socio-demographics). Besides, the spatio-temporal variation and correlation inherent in travel patterns further intensify the difficulty of understanding destination choice behavior. To this end, this research proposes a Bayesian hierarchical approach for modeling the destination choice behavior through time and space. The proposed method can take into account both the unavailable factors and spatio-temporal correlations by introducing random fields. Moreover, the implementation of the Integrated Nested Laplace Approximations (INLA) combined with the Stochastic Partial Differential Equation (SPDE) makes it computationally feasible to model large-scale spatio-temporal correlation structures. The model is further applied to two-week data from more than 8000 taxis in Harbin. The empirical results indicate that the proposed approach is capable to capture spatio-temporal variability in destination distribution, and the inclusion of spatial and temporal random effects is of great help to improve the model performance. The case study also examines how the land-use types influence the destination choice. It is believed that the modeling method and the exploratory spatial–temporal analysis of destination distribution in this study can enriches the methodologies for travel demand modeling as well as decision support for transport policy development.

Temporal discounting of gains and losses of time: An experimental investigation

This paper investigates “asymmetries” between non-monetary gains and losses in intertemporal choice. We considered gains and losses of spare/working time with respect to a reference duration defined in a working contract. Specifically, we elicited a behavioral model of intertemporal choice that accounts for a gain/loss-dependent discounting function and a reference-dependent utility. Additionally, we did not impose preference for the present (positive discounting) and allowed for both decreasing and increasing impatience. While our results are standard regarding the discount of money (our baseline treatment), our subjects heavily discounted gains of time. More patience was observed for losses of time and a sizable portion of subjects even exhibited negative discounting, i.e. they preferred to expedite losses of time. Our econometric estimations also reveal a much larger heterogeneity of behavior in terms of both utility and discounting for gains and losses of spare time as compared to money.

Equations of mind: Data science for inferring nonlinear dynamics of socio-cognitive systems

Discovering the governing equations for a measured system is the gold standard for modeling, predicting, and understanding complex dynamic systems. Very complex systems, such as human minds, pose stark challenges to this mode of explanation, especially in ecological tasks. Finding such “equations of mind” is sometimes difficult, if impossible. We introduce recent directions in data science to infer differential equations directly from data. To illustrate this approach, the simple but elegant example of sparse identification of nonlinear dynamics (SINDy; Brunton, Proctor, & Kutz, 2016) is used. We showcase this method on known systems: the logistic map, the Lorenz system, and a bistable attractor model of human choice behavior. We describe some of SINDy’s limitations, and offer future directions for this data science approach to cognitive dynamics, including how such methods may be used to explore social dynamics.

A utility-based bicycle speed choice model with time and energy factors

This paper presents a utility-based behavioral model of bicycle speed choice. A mathematical framework is developed with travel time, energy expenditure, and control factors. Observational speed data are used to calibrate the model and estimate marginal rates of substitution between energy expenditure and travel time. The model is validated by applying it to predict speed changes on pedal-assist electric bicycles. This paper lays a foundation for further development of operational active travel speed and joint speed-route choice models, which can lead to more sensitive and behaviorally-grounded operations, microsimulation, and mode choice models. In addition, the findings have implications for modeling the effects of emerging bicycle technologies. Further research is needed to calibrate the model for a broad population of travelers.

Optimal Bus-Bridging Service under a Metro Station Disruption

A station disruption is an abnormal operational situation that the entrance or exit gates of a metro station have to be closed for a certain of time due to an unexpected incident. The passengers’ travel behavioral responses to the alternative station disruption scenarios and the corresponding controlling strategies are complex and hard to capture. This can lead to the hardness of estimating the changes of the network-wide passenger demand, which is the basis of carrying out a response plan. This paper will establish a model to solve the metro station disruption problem by providing optimal additional bus-bridging services. Two main contributions are made: (1) a three-layer discrete choice behavior model is developed to analyze the dynamic passenger flow demand under station disruption; and (2) an integrated algorithm is designed to manage and control the station disruption crisis by providing additional bus-bridging services with the objective of minimizing the total travel time of affected passengers and the operating cost of bridging-buses. Besides, the multimodal transport modes, including metro, bridging-bus, shared-bike, and taxi, are considered as passengers’ alternative choices in face of the station disruption. A numerical study based on the Beijing metro network shows that additional bus-bridging services can significantly eliminate the negative impact of the station disruption.

Economic Decisions and Simon’s Notion of Bounded Rationality

Decision making in economics has been always intertwined with the concept of rationality. However, neoclassical economic literature has been dominated by a specific notion of rationality, namely, perfect rationality, characterized by the assumption of consistency and by the maximization hypothesis. Herbert Simon, in his long research activity, questioned this concept of perfect or global rationality, suggesting a different vision, based on empirical evidence and regarding an individual’s choices. He challenged the neoclassical theory of global rationality, suggesting his notion of bounded rationality, a satisficing (instead of optimizing) behavior, and the relevance of procedural rationality to understand the process of thought of decision makers.Thus, this paper focuses on Simon’s notion of bounded rationality, since bounded rationality remains the hallmark of his theoretical contribution. First, the paper examines the economic decision process in the neoclassical theory and Simon’s notion of bounded rationality. Then, it analyzes in depth Simon’s behavioral model of rational choice, underlining the relevance of satisficing behavior and procedural rationality. Finally, it suggests an assessment of the concept of bounded rationality.

When payoffs look like probabilities: Separating form and content in risky choice.

Paralleling research in perception, behavioral models of risky choice posit "psychophysical" transformations of material outcomes and probabilities. Prospect theory assumes a value function that is concave for gains and convex for losses, and an inverse S-shaped probability weighting function. But in typical experiments, form and content are confounded: Probabilities are represented on a bounded numerical scale, whereas representations of monetary gains (losses) are unbounded above (below). To unconfound form and content, we conducted experiments using a probability-like representation of outcomes and an outcome-like representation of probability. We show that interchanging numerical representations can interchange the resulting psychophysical functions: A proportional (rather than absolute) representation of outcomes leads to an inverse S-shaped value function for gains. This alternative value function generates novel framing effects, a common ratio effect for bounded gains, and a "framing interaction," where gain-loss framing matters less for proportional outcomes. In addition, we show that an absolute (rather than proportional) representation of probability reduces sensitivity to large probabilities. These findings highlight the deeply constructive nature of the psychophysics of risky choice, and suggest that traditional models may reflect subjective reactions to numerical form rather than substantive content. (PsycINFO Database Record

Travel Mode and Travel Route Choice Behavior Based on Random Regret Minimization: A Systematic Review

In recent years, traffic congestion has become increasingly serious and the urban environment has deteriorated, posing a challenge to the modern sustainable transportation system. Sustainable travel behavior is a solution that many scholars recognize as being an important aspect in the development of socially, environmentally, and economically sustainable communities. Increasing numbers of studies analyzed the travel choice behavior based on Random Regret-Minimization (RRM) model. RRM considers multiple attribute compromises to capture the traveler’s choice behavior based on minimizing the perceived regret decision criteria. Travel route choice and travel mode choice are interrelated and mutual restraint when a traveler makes a travel decision. To our knowledge, there are limited literatures that overall considered travel mode and travel route choice behavior based on RRM at present. This paper aims to fill this gap and presents a literature review for the application of RRM on sustainable travel mode and travel route choice behavior from empirical issues, influencing factors, theories and methods to evaluate RRM’s potential and limitations as a discrete model of travel choice behavior. The results will provide reference for researchers to study this field and develop novel strategies to promote the sustainable traffic system in the future.

Efficient Estimation of Risk Preferences

AbstractRisk and the risk attitude of agents are two fundamental elements of decision making under risk and uncertainty. Recent developments in risk and risk preference analyses have raised questions on conventional approaches to estimating risk preferences. This study proposes an estimation procedure that employs a seminonparametric estimator to estimate the density function of risk without imposing distributional assumptions, as well as a numerical integration method to construct closed‐form expressions of conditional moment conditions for efficient estimation. The method achieves a substantial efficiency improvement relative to the conventional GMM approach in Monte Carlo simulations. The proposed approach is general and applies to the estimation of behavioral choice models under risk, or models that require expectation operations and closed‐form equations for estimation.

Structured random walk parameter for heterogeneity in trip distance on modeling pedestrian route choice behavior at downtown area

A nested logit model of mode and walk route choice behavior in a downtown area is developed. The model samples alternatives at the route choice level, which is a lower level of the nesting structure. Alternative routes are drawn using the random walk method. A structured random walk parameter based on trip distance is proposed in order to consider the heterogeneity in trip distance, and a structured scale parameter based on trip distance is also applied for the nested logit model. Dataset is obtained by using mobile phones with GPS functions to track the trajectories in a downtown area at Nagoya, Japan. The results suggest that the proposed structured random walk parameter provides more efficient parameter estimates. Also, heteroscedasticity in the error terms for trips of various distances is found to be significant. Empirical findings include that older pedestrians prefer main shopping streets, and that roadside restaurants affect the route choice differently on weekdays and holidays. The different effects of the roadside environments according to the pedestrian’s attributes and the day of the week may indicate a need for more appropriate policies for specific pedestrian categories and days of the week.

Recreation Costs of Endangered Species Protection: Evidence from Cape Hatteras National Seashore

Management of public lands often involves competing uses and difficult tradeoffs. In this article, we examine the impact of an economically important, policy-relevant public land management regulation designed to protect coastal biodiversity. We focus our attention on the land use conflict at Cape Hatteras National Seashore between off-road vehicle (ORV) access and nesting habitat protection for a number of endangered species. We combine site choice and participation data to estimate a repeated discrete choice model of recreational angler behavior in response to time-varying access restrictions. Our results suggest the economic costs of this policy are relatively modest, ranging from $403,000 to $2.07 million annually. Our results provide general support for the National Park Service’s recently implemented ORV management plan, as an upper bound of recreation costs is likely less than conservative estimates of the benefits associated with endangered species protection.

An online survey to enhance the understanding of car drivers route choices

Abstract The increasing application of advanced behavioral choice models, reflecting the stochasticity of individuals’ preferences and the complex nature of human decision-making process, requires enhanced data collection methods to obtain detailed data without significantly increasing the respondent burden. In this study, we present the development and deployment of a general data collection framework adapted for behavioral route choice studies. The main objectives of the proposed framework are to observe drivers’ route choices, and to identify important factors, including observable attributes and latent behavioral traits, affecting those decisions. More specifically, the survey has been designed to reveal drivers’ consideration set of route alternatives from which they pick their final choices. The detailed analysis of survey’s response behavior will help improve the framework to gather travel data even more efficiently.

Impact of Flight Departure Delay on Airline Choice Behavior

ABSTRACT The Korean aviation industry (KAI) has been expanded significantly, because the Incheon International Airport opened in 2001 and low cost carriers (LCC) entered the KAI market. But following the KAI's growth, flight departure delays (FDDs) have increased six-fold during the past decade. In terms of these situations, this study firstly adds the FDD variable to an airline choice behavior (ACB) model and secondly analyses how FDD impact the ACB. As a result, FDD is relatively less influential on ACB than other attributes. Both the flight service and available schedule are positively related to the choice of full service carriers (FSC). The analysis shows that FSCs’ allotment rate (AR) is 85.3%, while LCCs’ one is 14.7%.

Learning the route choice behavior of subway passengers from AFC data

This paper learns the route choice behavior of passengers from Auto Fare Collection, timetable, and train loading data using a method combined with Bayesian inference and Metropolis-Hasting sampling. First, the influential factors of route choice such as in-vehicle travel time, transfer time, and in-vehicle crowding are given. Next, formulations are established based on AFC, timetable and train loading data, which are merged into a logit model of route choice behavior of subway passengers. Next, an algorithm integrating Bayesian inference and Metropolis-Hasting sampling is designed to calibrate parameters of the logit model. Finally, a case study of Beijing subway is applied to verify the validity of the model and algorithm. A detailed discussion shows that in-vehicle crowding plays a crucial role in passenger route choice behavior.