Model Of Choice Behavior Research Articles

Electing in the dark? Voting behavior in light of polarization

This paper develops a behavioral public choice model. It provides testable hypothesis to explain voter shifts in European national elections in the last decade. The model comprises three blocs of parties, the government, the opposition and so-called “profiteers”. Retrospective voters evaluate the performance of each bloc. Furthermore, it introduces an exogenous polarizing event that can affect the government’s and the profiteers’ chance to satisfy voters. Moreover, voters are subject to the negativity bias, which means that negative changes in probabilities to satisfy are stronger than positive changes. This framework yields various results on voting behavior under polarization. Most are robust to the introduction of non-voting. The government only profits from polarization iff sufficiently many positively voters are polarized in their favor to outweigh both the negativity bias and the increased competitiveness by profiteers due to polarization. Profiteers, strengthened by polarization, harm the opposition and increase voter turnout. Additionally, a higher negativity bias impairs the government, decreases voter turnout and benefits the opposition and profiteers.

Game model of high-speed railway delay and passenger choice behavior under incomplete information

The arrival status of high-speed trains is an important factor affecting the choice behavior of railway passengers. In order to specifically analyze the influence of the arrival state of high-speed trains on the choice behavior of railway passengers, evolutionary game theory was used to analyze the arrival state of high-speed trains and the choice behavior of railway passengers, and the game matrix of the arrival state of high-speed trains and the choice behavior of railway passengers was constructed. The location of the equilibrium point and the system evolution mechanism are deeply analyzed. Matlab simulation results show that the evolutionary game behavior finally has two evolutionary stable states. When high-speed trains delay time is less than 30 min , the train tends to arrive on time, and the railway passengers tend to choose the ride strategy. When the delay time is greater than 30min, the train tends to arrive on time, and the railway passengers tend to choose the refund strategy. By improving the facilities in the high-speed railway station, the railway passengers can choose the transfer strategy. Changing the value of the game parameters can adjust the evolution direction of both sides of the game and effectively improve the proportion of railway passengers when the high-speed train is delayed.

Imperfect legislative agreements and judicial policymaking. A theoretical inquiry

ABSTRACT This article examines how imperfect legislative agreements offer an opportunity for higher courts’ intervention in policymaking. In concrete terms, it explains that lawmakers in certain situations avoid resolving apparent conflicts of interest due to high transaction costs in the legislative process, maintaining statutory criteria considered transparently inefficient or by simply developing regulatory arrangements with a significant degree of indeterminacy. Employing a simplified model of rational choice behaviour, the article asserts that those scenarios constitute an opportunity for strategic involvement of the higher courts in crafting public policy. As an unintended consequence of the imperfect legislative agreements, justices look at them as an indicator that members of the other branches of government have reached weak political compromises or are unable to impose their policy preferences. Therefore, justices can exercise discretion, foreseeing prospects to increase their authority while the risk of an overruling or the implementation of other retaliatory measures remains low.

Mass evacuation planning for disasters management: A household evacuation route choice behavior analysis

This paper presents a multi-methodological approach to analyzing household evacuation route choice behaviors and the related decision-making for mass evacuation planning in disaster management. Specifically, the proposed multi-methodological approach integrates a syncretic risk perception conceptual model with a household-based group utility evolution model. The dynamics of household evacuation route choice are explored in two stages, namely the individual utility initiation and accommodation. The unique feature of the proposed approach is that it links individual risk perception, survival psychology, and group behavioral dynamics together to characterize household decision-making about choosing the evacuation routes. An empirical study coupled with experiments is conducted to help validate the proposed household evacuation route choice behavior model. Through various designed disaster scenarios, the applicability and strengths of the proposed model for mass evacuation planning and management are demonstrated. Particularly, the mean relative prediction errors of the proposed model with respect to household-based evacuation route switching probability and path flow splits are 3.79% and 6.88%, respectively, all within an acceptable range of ±10 %. This demonstrates that the proposed model and analytical results align closely with household choice behavior.

Simon’s bounded rationality

This note in the Milestones series is dedicated to the paper “A Behavioral Model of Rational Choice”, written by Herbert Simon and published in 1955 on the Quarterly Journal of Economics.

Research on Behavioral Decision at an Unsignalized Roundabout for Automatic Driving Based on Proximal Policy Optimization Algorithm

Unsignalized roundabouts have a significant impact on traffic flow and vehicle safety. To address the challenge of autonomous vehicles passing through roundabouts with low penetration, improve their efficiency, and ensure safety and stability, we propose the proximal policy optimization (PPO) algorithm to enhance decision-making behavior. We develop an optimization-based behavioral choice model for autonomous vehicles that incorporates gap acceptance theory and deep reinforcement learning using the PPO algorithm. Additionally, we employ the CoordConv network to establish an aerial view for spatial perception information gathering. Furthermore, a dynamic multi-objective reward mechanism is introduced to maximize the PPO algorithm’s reward pool function while quantifying environmental factors. Through simulation experiments, we demonstrate that our optimized PPO algorithm significantly improves training efficiency by enhancing the reward value function by 2.85%, 7.17%, and 19.58% in scenarios with 20, 100, and 200 social vehicles, respectively, compared to the PPO+CCMR algorithm. The effectiveness of simulation training also increases by 11.1%, 13.8%, and 7.4%. Moreover, there is a reduction in crossing time by 2.37%, 2.62%, and 13.96%. Our optimized PPO algorithm enhances path selection during autonomous vehicle simulation training as they tend to drive in the inner ring over time; however, the influence of social vehicles on path selection diminishes as their quantity increases. The safety of autonomous vehicles remains largely unaffected by our optimized PPO algorithm.

Of Rodents and Primates: Time-Variant Gain in Drift-Diffusion Decision Models.

The online version contains supplementary material available at 10.1007/s42113-023-00194-1.

Can competing demands affect pro-environmental behaviour: a study of the impact of exposure to partly related sequential experiments

The study of human behaviour is central to the development of appropriate policies for sustainability. We argue that mathematical models of human choice behaviour may produce biased results if they fail to account for the possibility of spillover effects, in particular the possibility that individual behaviour may change as a result of interventions along with competing demands (multiple demands), such as in the sequential exposure to partly related choice contexts. Using a sample of 751 individuals and a carefully constructed experiment, we develop mathematical models that jointly explain the choice between different pro-environmental actions and the willingness to donate money for environmental causes, and at the same time, allow us to test the indirect effect of exposure to multiple demands. We find that the strength of preferences for behavioural changes leading to greater CO2 reductions is (causally) shaped by participants previously considering other similar behavioural changes. The kind of spillover effects we find are relatively complex and often subtle, and thus warrant further replication studies. Care was taken to account for variation of tastes, formatting, order and learning effects, thus reducing the risk of the spillover-related results being influenced by differences across individuals in environmental preferences. Our study demonstrates the existence of a specific type of spillover effects, namely how prior exposure to related choice contexts may affect behaviour in subsequent settings and showcased the effectiveness of discrete choice models to test for it. Given our results, we believe that spillover effects need to be taken into account in the broader choice modelling literature, and at the same time we showcase a useful experimental framework for environmental psychologists and economists.

Understanding passenger exit choices during airplane emergencies: Insights from hybrid choice modeling

In transport accident rescue operations, the primary focus is on saving lives. However, people's behavior and choices in such dangerous situations are unforeseeable. This paper employs a hybrid choice model to investigate how unobservable human heterogeneity impacts passengers’ decisions in airplane emergency evacuations. Three primary latent factors – fear of flying, knowledge of air safety, and no perception of risk – were identified based on indicators from fifteen statements. Subsequently, the hybrid choice model was expanded to include a time perception variable and interaction effects. The findings revealed that compared to Chinese respondents, individuals from the USA exhibit lower fear of flying, more air safety knowledge, and a higher risk perception in flying. All else being equal, aviation professionals can accept longer distances to exits than other passengers, and individuals with higher fear of flying choose middle exits more frequently. In contrast, those with lower risk perception prefer exits at either end of the cabin. Interaction terms indicated that increased air safety knowledge and lower risk perception contribute to a greater acceptance of more distant exits. A lack of risk perception makes traveling alone a significant factor in choosing an exit other than the frontmost, and people tended to avoid following others in densely populated evacuation flows. Regarding time perception, participants perceived the frontmost exit as the quickest, followed by the backmost exit, while the middle exits were viewed as the slowest. This study provides valuable insights for exit choice behavior modeling and airplane evacuation enhancement.

Reduced effects of social feedback on learning in Turner syndrome

Turner syndrome is a genetic condition caused by a complete or partial loss of one of the X chromosomes. Previous studies indicate that Turner syndrome is associated with challenges in social skills, but the underlying mechanisms remain largely unexplored. A possible mechanism is a reduced social influence on learning. The current study examined the impact of social and non-social feedback on learning in women with Turner syndrome (n = 35) and a sex- and age-matched control group (n = 37). Participants were instructed to earn points by repeatedly choosing between two stimuli with unequal probabilities of resulting in a reward. Mastering the task therefore required participants to learn through feedback which of the two stimuli was more likely to be rewarded. Data were analyzed using computational modeling and analyses of choice behavior. Social feedback led to a more explorative choice behavior in the control group, resulting in reduced learning compared to non-social feedback. No effects of social feedback on learning were found in Turner syndrome. The current study thus indicates that women with Turner syndrome may be less sensitive to social influences on reinforcement learning, than the general population.

Money-metrics in local welfare analysis: Pareto improvements and equity considerations

We identify local Pareto improvements from a valuation equilibrium, and extend the results of Hirshleifer, Arrow-Lind, Milleron, and Radner on the evaluation of small projects to behavioral or nonstandard choice models. We use the sign of directional derivative of the sum of McKenzie-Samuelson money metrics to evaluate small projects, but, rather than assume its differentiability, furnish preference conditions that guarantee it. Our methods yield, as an unintended consequence, (i) a refutation of Samuelson's (1974) conjecture that the money metric is locally concave in a neighborhood of a demand point, thereby settling an issue open for five decades; and (ii) a substantive extension of the 1988 Blackorby-Donaldson theorem that the money metric is concave in consumption only if preferences are quasihomothetic. We explain some equity implications of our local-welfare result, and as part of the rehabilitation of money metrics, suggest a case for using a second-order approximation to a money metric for local welfare. We illustrate when our results hold and don't hold with several non-standard choice models.

The neurophysiological Behavioral Perspective Model of consumer choice and its contribution to the intentional behaviorist research programme.

Cognitive explanations raise epistemological problems not faced by accounts confined to observable variables. Many explanatory components of cognitive models are unobservable: beliefs, attitudes, and intentions, for instance, must be made empirically available to the researcher in the form of measures of observable behavior from which the latent variables are inferred. The explanatory variables are abstract and theoretical and rely, if they are to enter investigations and explanations, on reasoned agreement on how they can be captured by proxy variables derived from what people say and how they behave. Psychometrics must be founded upon a firm, intersubjective agreement among researchers and users of research on the relationship of behavioral measures to the intentional constructs to which they point and the latent variables they seek to operationalize. Only if these considerations are adequately addressed can we arrive at consistent interpretations of the data. This problem provides the substance of the intentional behaviorist research programme which seeks to provide a rationale for the cognitive explanation. Within this programme, two versions of the Behavioral Perspective Model (BPM), an extensional portrayal of socioeconomic behavior and a corresponding intentional approach, address the task of identifying where intentional explanation becomes necessary and the form it should take. This study explores a third version, based on neurophysiological substrates of consumer choice as a contributor to this task. The nature of "value" is closely related to the rationale for a neurophysiological model of consumer choice. The variables involved are operationally specified and measured with high intersubjective agreement. The intentional model (BPM-I), depicting consumer action in terms of mental processes such as perception, deliberation, and choice, extends the purview of the BPM to new situations and areas of explanation.

Integrating a choice experiment into an agent-based model to simulate climate-change induced migration: The case of the Mekong River Delta, Vietnam

Forecasting the future impact of climate change on migration is difficult, for many reasons, including the interactive and dynamic nature of many decisions and the heterogeneity of behavior. One popular solution, agent-based models (ABM) cope well with dynamics and heterogeneity, but often lack rigorous foundations in terms of individual behavior. Moreover, given limited exposure to actual climate change, it can be a challenge to build adequate behavioral models of migration choice based on historical data. To tackle this issue, we build an ABM of future migration using a bespoke choice experiment (CE) designed to examine intention to migrate among farmers living in the Vietnamese Mekong Delta (VMD). In the CE, respondents are asked to make migration choices for scenarios constructed using six attributes: drought intensity, flood frequency, income gain from migration, migration networks, neighbors' choice, and crop choice restriction. The simulation runs to 2050 and is based on two scenarios of future global emissions of greenhouse gases—Representative Concentration Pathway (RCP) 4.5 and RCP8.5. The results suggest potentially high levels of migration as a result of climate change and the particular importance of positive feedback from pre-existing migration and neighbor's choices. The results also suggest that crop-restriction regulations have a significant impact on migration for coastal provinces of VMD. Finally, we find that migration drivers vary significantly across provinces, which suggests the policymakers point to targeted action for each province. In summary, the study demonstrates how integrating CE into ABM can foster the predictive modeling of climate-induced migration.

Enhanced decision-making in nicotine dependent individuals who abstain: A computational analysis using Hierarchical Drift Diffusion Modeling

BackgroundVariability in decision-making capacity and reward responsiveness may underlie differences in the ability to abstain from smoking. Computational modeling of choice behavior, as with the Hierarchical Drift Diffusion Model (HDDM), can help dissociate reward responsiveness from underlying components of decision-making. Here we used the HDDM to identify which decision-making or reward-related parameters, extracted from data acquired in a reward processing task, contributed to the ability of people who smoke that are not seeking treatment to abstain from cigarettes during a laboratory task. Methods80 adults who smoke cigarettes completed the Probabilistic Reward Task (PRT) - a signal detection task with a differential reinforcement schedule - following smoking as usual, and the Relapse Analogue Task (RAT) - a task in which participants could earn money for delaying smoking up to 50min - after a period of overnight abstinence. Two cohorts were defined by the RAT; those who waited either 0-min (n=36) or the full 50-min (n=44) before smoking. ResultsPRT signal detection metrics indicated all subjects learned the task contingencies, with no differences in response bias or discriminability between the two groups. However, HDDM analyses indicated faster drift rates in 50-min vs. 0-min waiters. ConclusionsRelative to those who did not abstain, computational modeling indicated that people who abstained from smoking for 50min showed faster evidence accumulation during reward-based decision-making. These results highlight the importance of decision-making mechanisms to smoking abstinence, and suggest that focusing on the evidence accumulation process may yield new targets for treatment.

Route Choice-Based Socio-Technical Macroscopic Traffic Model

Human route choice is undeniably one of the key contributing factors towards traffic dynamics. However, most existing macroscopic traffic models are typically concerned with driving behavior and do not incorporate human route choice behavior models in their formulation. In this paper, we propose a socio-technical macroscopic traffic model that characterizes the traffic states using human route choice attributes. Essentially, such model provides a framework for capturing the Cyber-Physical-Social coupling in smart transportation systems. To derive this model, we first use Cumulative Prospect Theory (CPT) to model the human passengers’ route choice under bounded rationality. These choices are assumed to be influenced by traffic alerts and other incomplete traffic information. Next, we assume that the vehicles are operating under a non-cooperative cruise control scenario. Accordingly, human route choice segregates the traffic into multiple classes where each class corresponds to a specific route between an origin-destination pair. Thereafter, we derive a Mean Field Game (MFG) limit of this non-cooperative game to obtain a macroscopic model which embeds the human route choice attribute. Finally, we analyze the mathematical characteristics of the proposed model, present simulation studies to illustrate the model behavior and show comparison with existing traffic model.

Quantitative Model of Multi-Subject Quality Responsibility in General Contracting Projects Based on Sailed Fish Optimizer

In order to address the issue of the quantitative allocation of quality responsibility among different subjects in engineering general contracting projects, this paper proposed a quantitative model (M-ResQu) for multi-subject quality responsibility allocation based on quality behavior classification criteria. Firstly, utility theory and game theory were used to establish a behavioral choice model for construction units and general contractors, investigating the quality behavioral choice mechanisms in the general contracting mode. Secondly, the sailed fish optimizer (SFO) was used to screen potential laws across 84 practical judicial cases and obtain the type coefficients of three types of quality risk behaviors: technical defects, non-compliance management and non-standard behaviors. Thirdly, a fuzzy mathematics theory was employed to establish the M-ResQu model for multi-subject quality responsibility allocation in general contracting mode. Finally, a simulation analysis was conducted to demonstrate the applicability of the M-ResQu model, and results suggested that it can provide a valuable quantitative tool for quality dispute resolution in the general contracting mode.

Why is Japan’s carbon emissions from road transportation declining?

Average fuel efficiency of vehicles improved substantially over the last three decades in Japan. Yet, the carbon emissions from on-road passenger vehicles continued to increase until 2000, and then turned to a steadily declining trend. We empirically investigate this disparity. To that end, we apply an analogue of the Copeland-Taylor decomposition, combined with an empirically estimated behavioral model of car ownership and utilization choice, to economically decompose vehicle carbon emissions into the scale, composition, and technique effects over our study period, 1990–2015. We find that exogenous demographic changes such as population size, driver’s license holdings, or labor migration across regions can only explain this disparity partially. After accounting for endogenous changes in household’s geographically-explicit transport demand by the estimated behavioral model, the predicted emissions match the time path of the observed emissions surprisingly well. Of all the factors in the behavioral model, the fuel cost per unit of driving accounts for the largest share of the total variation in the observed emissions. Our result indicates that 60% of the technique effect is offset by the perverse effect of induced transport demand due to the lower fuel cost. Importantly, the induced demand comes from both the intensive margin (driving) and the extensive margin (car ownership).

Willingness to Pay for Greener Electricity Among Non-Subsidized Residential Consumers in Indonesia: A Discrete Choice Experiment Approach

By 2021, fossil fuels still dominated Indonesia’s energy source by generating 87% of electricity and constituting 88.5% of the national energy mix. This achievement is far from the government’s target of 23% renewables in the national energy mix by 2025. The main obstacle is the low electricity price that discourages investors from entering the green electricity business. Increasing the electricity price has been the government’s last option, socially and politically. Indonesia’s electricity industry is highly regulated, including the selling price at retail and upstream (independent power producers) levels. The current pricing is cost-based and does not thoroughly consider consumers’ preferences and willingness to pay (WTP). This research aims to estimate the WTP for greener electricity among non-subsidized residential consumers in Indonesia. Residential consumers have dominated the market by using 46% of the generated electricity. We use the discrete choice experiment assuming a mixed multinomial logit choice behavior model and employ the Bayesian method to estimate the model parameters. We found that more than 95% of respondents are willing to pay up to 40% higher than the current price for greener electricity. We also found that the WTP for greener electricity increases with the proportion of renewables. Using the latent class method, we identified three residential consumer segments: Status Quo, Greenie, and Unsatisfied Median, and found significant WTP differences among them. Considering the WTP alone, the result suggests that increasing the electricity retail selling price is justifiable. Further research is needed to develop a pricing strategy that considers the interrelatedness between retail and upstream pricing.

Analysis of taste heterogeneity in commuters’ travel decisions using joint parking– and mode–choice model: A case from urban India

The concept of transportation demand management (TDM) upholds the development of sustainable mobility through the triumph of optimally balanced transport modal share in cities. The modal split management directly reflects on TDM of each transport subsystem, including parking. In developing countries, the policy-makers have largely focused on supply-side measures, yet demand-side measures have remained unaddressed in policy implications. Ample literature is available presenting responses of TDM strategies, but most studies account mode choice and parking choice behaviour separately rather than considering trade-offs between them. Failing to do so may lead to biased model estimates and impropriety in policy implications. This paper seeks to fill this gap by admitting parking choice as an endogenous decision within the model of mode choice behaviour. This study integrates attitudinal factors and built-environment variables in addition to parking and travel attributes for developing comprehensive estimation results. A mixed logit model with random coefficients is estimated using hierarchical Bayes approach based on the Markov Chain Monte Carlo simulation method. The results reveal significant influence of mode/parking specific attitudes on commuters choice behaviour in addition to the built-environment factors and mode/parking related attributes. It is identified that considerable shift is occurring between parking-types in preference to switching travel mode with hypothetical changes in parking attributes. Besides, study investigates the heterogeneity in the willingness-to-pay through a follow-up regression model, which provides important insights for identifying possible sources of this heterogeneity among respondents. The study provides remarkable results which may be beneficial to planning authorities for improving TDM strategies especially in developing countries.

Travel Mode Choice Behavior for High-Speed Railway Stations Based on Multi-Source Data

The service quality of connecting travel mode at high-speed railway (HSR) stations plays an essential role in the travel chain. Most travel choice behavior studies are based on survey data and focus on special scenarios, such as going to work or school. Few studies have addressed travel mode choices for connecting to HSR stations. Based on multi-source data, this study aims to investigate the influences of travel time, cost, reliability, detour, and land use on connecting mode choice behaviors at HSR stations. Taking the Xi’anbei railway station as an example, this study analyzes the behavior characteristics of connecting modes including conventional buses, subway transit, and taxis, and builds a connecting travel mode choice behavior model for arrival/departure at HSR stations based on a generalized additive model (GAM). The results show that connecting travelers at HSR stations prefer to choose subways and taxis during the morning and evening peak hours. The perceived costs for arriving travelers are less sensitive than those for departing travelers. Arriving travelers are significantly more sensitive to reliability than departing travelers. Connecting travelers are less sensitive to cost and more sensitive to reliability than commuting travelers. The research results can provide suggestions and guidelines for connecting facility planning in HSR stations.