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As agile processes are increasingly adopted for product design, and as consumer preferences are rapidly evolving with increasing information available from digital media, there is a need for a demand model that can accommodate the dynamics of product development. However, existing models of demand estimation, such as the discrete-choice models, do not capture the dynamics of product development and decision-making processes and thus are unable to effectively capture the effect of product updates and the release of information. To address this gap, we present a dynamic demand model and demonstrate how it can be used to determine the optimal time to release product updates. The demand model is based on decision field theory (DFT), which enables the modeling of the dynamic behavior of human decision makers. The contributions of this article are as follows. First, we formulate a computational model for demand modeling built on DFT and demonstrate the viability of using the model to determine product release strategies. Second, we provide analytical approximations of the demand model and compare the accuracy of the approximated demand against the demand predicted by the dynamics model. Third, we show an example of a game played by competitors trying to optimize demand for their products by choosing the optimal update time relative to each other. Finally, we demonstrate the feasibility of parameter estimation using only the demand data. [ABSTRACT FROM AUTHOR] more...

At present, the flight safety work of civil aviation in China mainly investigates the probable causes of accidents and analyzes flight data after air accidents, causing numerous problems such as passive safety management and delayed risk control. To realize the early warning of flight risk during flight, a dynamic method for the evaluation of landing risk and early warning under the condition of future air–ground data real-time transmission was proposed. The landing stage, which has the most complex operation program and the highest accident rate during a flight, was taken as the research object, and future air-to-ground high-throughput interconnection scenarios comprising 5G and satellite networks were considered to solve the problem of advanced intelligent warnings and aircraft alarms in abnormal flights. First, according to the accident causation theory, the human factor reliability model, the system model, and other theories or models, a landing warning index system based on multisource real-time operation data and the integration of historical statistics and expert knowledge was established. Then, a grounding parameter prediction model was established to solve the problem of lag in the acquisition of four grounding parameters, namely ground pitch angle, ground speed, ground vertical rate, and 50 ft-ground horizontal flight distance in actual flight. This model classified the pilot’s landing operation mode by clustering ARJ21 historical landing data and determined the attribute mean value of the four parameters for each type of operation mode. Furthermore, according to decision field theory, the model discussed the landing mode selection of pilots with different personalities in different scenarios and calculated the selection probability of the pilot’s landing operation mode, thereby obtaining the predicted values of the four above-mentioned indicators. According to the above, aiming at the complexity and uncertainty of the landing risk early warning system, a reasoning method of the multilayer confidence rule base was proposed. This method improved the traditional reasoning method of the single-layer confidence rule base and adopted the bottom-up hierarchical reasoning method considering the complexity characteristics of the landing process, effectively integrating different sources and forms of qualitative or quantitative data. Thus, the dynamic assessment and reasoning of the landing risk were realized. Finally, using the reasoning-based calculation of the landing process for the “2020.10.16 Panzhihua runway grounding event” and “2010.8.2 Yichun air disaster,” the results verified the effectiveness of the method. It was found that the early warning time of the Panzhihua event can reach 13 s. more...

Discrete choice models are almost exclusively estimated assuming random utility maximization (RUM) is the decision rule applied by individuals. Recent studies indicate alternative behavioral assumptions may be more appropriate in health. Decision field theory (DFT) is a psychological theory of decision‐making, which has shown promise in transport research. This study introduces DFT to health economics, empirically comparing it to RUM and random regret minimization (RRM) in risky health settings, namely tobacco and vaccine choices. Model fit, parameter ratios, choice shares, and elasticities are compared between RUM, RRM and DFT. Test statistics for model differences are derived using bootstrap methods. Decision rule heterogeneity is investigated using latent class models, including novel latent class DFT models. Tobacco and vaccine choice data are better explained with DFT than with RUM or RRM. Parameter ratios, choice shares and elasticities differ significantly between models. Mixed results are found for the presence of decision rule heterogeneity. We conclude that DFT shows promise as a behavioral assumption that underpins the estimation of discrete choice models in health economics. The significant differences demonstrate that care should be taken when choosing a decision rule, but further evidence is needed for generalizability beyond risky health choices. [ABSTRACT FROM AUTHOR] more...

When the information conditions of the crashed aircraft are default, locating the aircraft search area is extremely important to carry out the follow-up rescue work. In order to improve the positioning accuracy, through the relevant theory of the decision field, the pilot's emergency behavior decision-making during the forced landing process was analyzed, and an aircraft search range setting method that coupled the pilot's emergency behavior decision-making and flight dynamics was proposed. On this basis, the flight environment was established according to the meteorological and topographical conditions in the high plateau area. Taking the B737-700 model as an example, taking into account the differences in the pilot's decision-making results, the forced landing flight process of the aircraft with some default information conditions was simulated. The scope and priority of the search area were determined accordingly. The simulation results show that in the case of unstable factors or default information, the method of coupling the pilot's emergency behavior decision-making with the dynamic model is reliable. [ABSTRACT FROM AUTHOR] more...

In decisions from experience (DFE), people sample from twoor more lotteries prior to making a consequential choice. Althoughexisting models can account for how sampled experiencesrelate to choice, they don’t explain decisions abouthow to search (in particular, when to stop sampling information).We propose that both choice and search behavior inthis context can be understood as a sequential sampling processwhereby decision makers sequentially accumulate outcomeinformation from each option to form a preference forone alternative over the other. We formalize this process ina new model, Choice from Accumulated Samples of Experience(CHASE). The model provides a good account of choicebehavior and goes beyond existing models by explaining variationsin sample size under different task conditions. Thisapproach offers a process-level framework for understandinghow interactions between the choice environment and propertiesof the decision maker give rise to decisions from experience. more...

Decision field theory (DFT) is a model originally developed in cognitive psychology to explain behavioural phenomena such as context effects and decision-making under time pressure. Given this focus, the model has primarily been used to explain choices observed under controlled laboratory settings, with little attention paid to generalisability. Recent work has improved the mathematical foundations of DFT, making it a tractable model that is easier to apply to a wider variety of choice contexts. In particular, the inclusion of attribute importance parameters has led to successful applications to multi-alternative multi-attribute choice settings, notably with stated preference data in transport. However, thus far, implementations to real-life behaviour (i.e., revealed preference, RP, data) have been limited. The aim of this paper is to extend DFT for larger and more real-world applications, where data may be more 'noisy' and prone to larger variances of the error term. A theoretical extension for the model is presented, relaxing the assumption of independent normal error terms to capture heteroskedasticity. We apply the new model specification to two large-scale revealed preference datasets, also incorporating a range of sociodemographic variables. The new 'heteroskedastic' DFT model substantially outperforms the original version of DFT, as well as choice models based on econometric theory, in both estimation and validation subsets. [ABSTRACT FROM AUTHOR] more...

Judgment and decision making in sporting officials is a challenging task that involves the use of context. Although process models of decision making describe decision contexts, none of the existing models explains when sports officials use rule-driven decision making, or game management. The basic idea of our work is that referees use a subjective threshold to apply game management, which may explain this decision behavior. We propose a new dynamic threshold model that is based on concepts derived from Decision Field Theory. The model includes two thresholds of game management (high/low) and two contact situations (foul/no foul) as approaching one of these thresholds. Using the example of soccer refereeing, we argue that if the game hits a subjective threshold of aggressive play, then the referee shifts from applying the rules to managing the game. This new approach changes the scientific discussion from one focused on what referees should decide in one situation or the other, to a dynamic model that explains the basic psychological mechanism underlying the referee's change in behavior during the game, both at the intra-individual as well as inter-individual level. [ABSTRACT FROM AUTHOR] more...

• We examine the underlying structure of both decision field theory (DFT) and the multi-attribute linear ballistic accumulator (MLBA) and consider how to operationalise these models for use in transport modelling. • We provide a number of methodological improvements for both models as well as a detailed section on the identification of both models. • Both models perform well on typical transport choice datasets. • Results from simulated datasets demonstrate that both models can incorporate alternative specific coefficients and constants. • We test both DFT and MLBA on revealed preference data for the first time, finding that both models have a better model fit than standard choice models in both estimation and out-of-sample validation. Interest in behavioural realism has gradually led to the introduction of alternatives to random utility models (RUMs) as a paradigm for representing choice behaviour, with notable interest, for example, in random regret minimisation (RRM). These more general models continue to rely on a framework where a single value function is calculated for each alternative in each choice setting, and the choice probabilities are calculated by comparing these value functions across alternatives. By contrast, research in mathematical psychology has used a more dynamic approach, where the preference value of each alternative updates over time in a given situation while the decision maker is deliberating about the choice to make. These accumulator models are well suited to accommodating a variety of context effects, and have been shown to give good performance for data collected in laboratory-based settings. The present paper considers two such accumulator models, namely decision field theory (DFT) and the multi-attribute linear ballistic accumulator (MLBA), and addresses limitations that have prevented their use in travel behaviour research. The methodological additions include the ability to capture the influence of socio-demographics, the presence of underlying preferences for specific alternatives, and/or the representation of attributes that have opposite effects on choice probabilities. We develop what we believe to be the first in-depth simultaneous comparison of DFT and MLBA with typical discrete choice models, and test both DFT and MLBA on a revealed preference dataset. We find that each model outperforms typical RUM and RRM implementations for both in-sample estimation and out-of-sample prediction, including in a large scale simulation experiment. [ABSTRACT FROM AUTHOR] more...

The hesitant fuzzy set (HFS) is widely applied in actual multi-attribute group decision-making (MAGDM) problems. It can depict experts' hesitant evaluation information with the membership degree consisting of several possible values. Most existing methods based on HFSs only focus on the final integrated information by different kinds of aggregation operators but fail to provide detailed comparisons between alternatives. They are essentially result-oriented static decision-making methods, based on which, the decision-making results may be inconsistent with reality. However, there is no process-oriented research on hesitant fuzzy information. The decision field theory (DFT) is a dynamic decision-making method and can better simulate the uncertain decision-making process. Thus, this paper integrates the HFS into the DFT and proposes a new decision-making method named as hesitant fuzzy decision field theory (HFDFT) to fill this vacancy. First, we define the hesitant fuzzy momentary preference function and other parameters in HFDFT. After that, for the MAGDM problems with incompletely known attribute weight information, the programming model is used to determine the weights of attributes. Then, the group decision-making method based on HFDFT is presented. Moreover, we apply the proposed HFDFT method to a case about route selection of the Arctic Northwest Passage. Two traditional methods based on the score function and the correlation coefficient, respectively, are further implemented for comparisons to illustrate the validity of the proposed HFDFT method. more...

Research on individual decision-making process is fundamentally critical to explore the macroscopic behavioral rules for travel mode choice. In this paper, a behavioral experiment under different contexts was designed by a process-tracing method to obtain data regarding repetitive travel mode choices. Based on the Decision Field Theory, a stochastic, dynamic model was proved to be reliable and used to reproduce and analyze the repeated decision-making process. It is concluded that in a stable context, travelers would gradually establish and use some new decision rules to make a travel mode choice during the repetitive decision-making process. When travelers have developed a travel mode habit, environmental cues become the key factors that trigger travelers to make travel mode choices. Context change and traffic policies can make travelers consider, weigh and compare the relevant information again and interrupt their previous habitual choice behavior, enhancing the use of Park and Ride. Meanwhile, travelers with a faster learning speed and better memory develop a travel mode habit in a stable context and change the existing car use habit in a new context more quickly. These results would help to enrich the existing theoretical study of travel behavior and provide an interesting starting point for the development of practical strategies to promote the use of public transport instead of a private car. Traffic management techniques such as congestion pricing, along with behavior intervention and guidance strategies for different groups can strengthen this effect. • This paper extended the traditional research method for repetitive travel mode choices and designed a behavioral experiment using a process-tracing method. Meanwhile, a computer programming language was used to implement the user interface design of the experimental contents. The psychological decision-making process data in different hypothetical contexts was automatically obtained. • For a deeper understanding of the mechanisms of repetitive travel choice behaviors, a dynamic, computational model was established to reproduce the forming process of mode choice preference or habit in a stable context and the behavioral changes in a changed context. • Compared to previous studies, our research pays more attention to analyze some key psychological factors, aiming to attract more car travelers to use public transport. • The influences of context change and traffic policies on travel mode choice behavior were identified and some policy recommendations were given to promote a switch from car commuting to public transit. • ∙The conclusions can enrich the existing theoretical study of travel behavior and provide an interesting starting point for the development of practical strategies to promote the use of public transport instead of a private car. [ABSTRACT FROM AUTHOR] more...

Traditionally, descriptive accounts of intertemporal choice have relied on static and deterministic models that assume alternative-wise processing of the options. Recent research, by contrast, has highlighted the dynamic and probabilistic nature of intertemporal choice and provided support for attribute-wise processing. Currently, dynamic models of intertemporal choice—which account for both the resulting choice and the time course over which the construction of a choice develops—rely exclusively on the framework of evidence accumulation. In this article, we develop and rigorously compare several candidate schemes for dynamic models of intertemporal choice. Specifically, we consider an existing dynamic modeling scheme based on decision field theory and develop two novel modeling schemes—one assuming lexicographic, noncompensatory processing, and the other built on the classical concepts of random utility in economics and discrimination thresholds in psychophysics. We show that all three modeling schemes can accommodate key behavioral regularities in intertemporal choice. When empirical choice and response time data were fit simultaneously, the models built on random utility and discrimination thresholds performed best. The results also indicated substantial individual differences in the dynamics underlying intertemporal choice. Finally, model recovery analyses demonstrated the benefits of including both choice and response time data for more accurate model selection on the individual level. The present work shows how the classical concept of random utility can be extended to incorporate response dynamics in intertemporal choice. Moreover, the results suggest that this approach offers a successful alternative to the dominating evidence accumulation approach when modeling the dynamics of decision making. [ABSTRACT FROM AUTHOR] more...

There is a growing interest in the travel behaviour modelling community in using alternative methods to capture the behavioural mechanisms that drive our transport choices. The traditional method has been Random Utility Maximisation (RUM) and recent interest has focussed on Random Regret Minimisation (RRM), but there are many other possibilities. Decision Field Theory (DFT), a dynamic model popular in mathematical psychology, has recently been put forward as a rival to RUM but has not yet been investigated in detail or compared against other competing models like RRM. This paper considers arguments in favour of using DFT, reviews how it has been used in transport literature so far and provides theoretical improvements to further the mechanisms behind DFT to better represent general decision making. In particular, we demonstrate how the probability of alternatives can be calculated after any number of timesteps in a DFT model. We then look at how to best operationalise DFT using simulated datasets, finding that it can cope with underlying preferences towards alternatives, can include socio-demographic variables and that it performs best when standard score normalisation is applied to the alternative attribute levels. We also present a detailed comparison of DFT and Multinomial Logit (MNL) models using stated preference route choice datasets and find that DFT achieves significantly better fit in estimation as well as forecasting. We also find that our theoretical improvement provides DFT with much greater flexibility and that there are numerous approaches that can be adopted to incorporate heterogeneity within a DFT model. In particular, random parameters vastly improve the model fit. [ABSTRACT FROM AUTHOR] more...

In practice, experts sometimes compare alternatives from different perspectives by rankings to express their preferences. However, it is common that the comparing information they give is incomplete, and experts' preferences are changing during the decision-making process. In this situation, it is not easy to depict experts' preferences and making appropriate decisions. Hence, to handle the problems, this paper proposes a multi-attribute group decision-making (MAGDM) method for incomplete linear ordinal ranking (ILOR) information combined with the decision field theory (DFT) from the perspective of process-oriented decision-making. Firstly, the extended preference map and information energy for ILOR are improved. Based on those, the concept of probabilistic utility set (PUS) and some basic operations are proposed to enhance the computability of ILOR, which can convert incomplete ILORs to PUS and depict the experts' preferences effectively. Then, the framework and the detailed steps of the DFT-combined MAGDM method are presented, in which the psychological difference for PUS is established. The method helps depict the distance felt by experts and the variability of the decision-making process. Finally, the illustrations are conducted to show the usage and features of the proposed method. The illustration shows the good interpretability and accuracy of the method. • Proposing a new way to depict incomplete linear ordinal ranking. • Extending the psychological distance for probabilistic utility set. • Combining with decision field theory for multi-attribute group decision making with incomplete linear ordinal ranking. [ABSTRACT FROM AUTHOR] more...

The intuitionistic fuzzy decision making problems have gained great popularity recently. Most of the current methods depend on various aggregation operators that provide collective intuitionistic fuzzy values of alternatives to be ranked. Such collective information only depicts the overall characteristics of the alternatives but ignores the detailed contrasts among them. Most important of all, the current decision making procedure is not in accordance with the way that the decision makers (DMs) think about the decision making problems. In this paper, we develop a novel intuitionistic fuzzy decision making model in the framework of decision field theory. The decision making model emphasizes the contrasts among alternatives with respect to each attribute that competes and influences each other, and thus, the preferences for alternatives can dynamically evolve and provide the final optimal result. After that, we develop an intuitionistic fuzzy group decision making model based on decision field theory, and then make a practical case study on the application of the developed models to the “one belt, one road” investment decision making problems. Finally, we point out the characteristics and the limitations of our models in detail. [ABSTRACT FROM AUTHOR] more...

The rapid development of multimodal transportation system prompts travellers to choose multiple transportation modes, such as private vehicles or taxi, transit (subways or buses), or park-and-ride combinations for urban trips. Traffic corridor is a major scenario that supports travellers to commute from suburban residential areas to central working areas. Studying their modal choice behaviour is receiving more and more interests. On one hand, it will guide the travellers to rationally choose their most economic and beneficial mode for urban trips. On the other hand, it will help traffic operators to make more appropriate policies to enhance the share of public transit in order to alleviate the traffic congestion and produce more economic and social benefits. To analyze the travel modal choice, a generalized cost model for three typical modes is first established to evaluate each different travel alternative. Then, random utility theory (RUT) and decision field theory (DFT) are introduced to describe the decision-making process how travellers make their mode choices. Further, some important factors that may influence the modal choice behaviour are discussed as well. To test the feasibility of the proposed model, a field test in Beijing was conducted to collect the real-time data and estimate the model parameters. The improvements in the test results and analysis show new advances in the development of travel mode choice on multimodal transportation networks. [ABSTRACT FROM AUTHOR] more...

We examine identifiability and distinguishability in Decision Field Theory (DFT) models and highlight pitfalls and how to avoid them. In the past literature, the models' parameters have been put forward as being able to capture the psychological processes in a decision maker's mind during deliberation. DFT models have been widely used to analyse human decision making behaviour, and many empirical applications in the choice modelling domain rely solely on data concerning the observed final choice. This raises the question if such data are rich enough to allow for the identification of the model's parameters. Insight into identifiability and distinguishability is crucial as it allows the researcher to determine which behavioural and psychological conclusions can or cannot be drawn from the estimated DFT model and how a DFT model can be specified in such a way that resulting parameters have meaningful interpretations. In this paper, we address this issue. To do this, we first show which specifications of DFT are equivalent to conventional probit models. Then, building on this equivalence result, we apply established analytical methods to highlight and explain the identification and distinguishability issues that arise when estimating DFT models on conventional choice data. We find evidence that some of the DFT models' special cases suffer from identifiability issues. Our results warrant caution when DFT models are used to infer psychological processes and human behaviour from conventional choice data, and they help researchers choose the correct specification of DFT models. • Decision Field Theory models are equivalent to structured covariance probit models in some special cases. • Estimating high sensitivity in DFT can lead to identifiability issues. • A zero memory decay DFT model is equivalent to several high sensitivity DFT models. • Parameter interpretation must be taken with caution in DFT. • Inferring contemplation process from choice data is not possible in some special cases of DFT. [ABSTRACT FROM AUTHOR] more...

Most theoretical and empirical research on intertemporal choice assumes a deterministic and static perspective, leading to the widely adopted delay discounting models. As a form of preferential choice, however, intertemporal choice may be generated by a stochastic process that requires some deliberation time to reach a decision. We conducted 3 experiments to investigate how choice and decision time varied as a function of manipulations designed to examine the delay duration effect, the common difference effect, and the magnitude effect in intertemporal choice. The results, especially those associated with the delay duration effect, challenged the traditional deterministic and static view and called for alternative approaches. Consequently, various static or dynamic stochastic choice models were explored and fit to the choice data, including alternative-wise models derived from the traditional exponential or hyperbolic discount function and attribute-wise models built upon comparisons of direct or relative differences in money and delay. Furthermore, for the first time, dynamic diffusion models, such as those based on decision field theory, were also fit to the choice and response time data simultaneously. The results revealed that the attribute-wise diffusion model with direct differences, power transformations of objective value and time, and varied diffusion parameter performed the best and could account for all 3 intertemporal effects. In addition, the empirical relationship between choice proportions and response times was consistent with the prediction of diffusion models and thus favored a stochastic choice process for intertemporal choice that requires some deliberation time to make a decision. [ABSTRACT FROM AUTHOR] more...

Abstract: Park and ride is a kind of traffic management solution to the traffic congestion problem in urban cities. This paper analyzes the decision making behavior of Park and Ride from a psychological point of view. Decision Field Theory is used to establish the decision model of Park and Ride. The proposed decision model is calibrated using real-life experimental survey data and has proved to be able to account for the complex decision behavior processes observed in the experimental survey data. The model demonstrates the psychological decision processes of individual travelers and the decision characteristics, such as simple decision, indecision and preference reversal. The effects of factors, e.g. deliberation time, deliberation threshold and initial preference, for mode choice are also examined. The proposed model demonstrates its capability of analyzing park-and-ride decision behavior and providing policy makers with useful information for future promotion and planning for park-and-ride facilities. [Copyright &y& Elsevier] more...

Many everyday decisions have to be made under risk and can be interpreted as choices between gambles with different outcomes that are realized with specific probabilities. The underlying cognitive processes were investigated by testing six sets of hypotheses concerning choices, decision times, and information search derived from cumulative prospect theory, decision field theory, priority heuristic and parallel constraint satisfaction models. Our participants completed 40 decision tasks of two gambles with two non-negative outcomes each. Information search was recorded using eye-tracking technology. Results for choices, decision time, the amount of information searched for, fixation durations, the direction of the information search, and the distribution of fixations conflict with the prediction of the non-compensatory priority heuristic and indicate that individuals use compensatory strategies. Choice proportions are well in line with the predictions of cumulative prospect theory. Process measures indicate that individuals thereby do not rely on deliberate calculations of weighted sums. Information integration processes seem to be better explained by models that partially rely on automatic processes such as decision field theory or parallel constraint satisfaction models. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR] more...

In this paper we examine the performance of theories of decision making under uncertainty/ambiguity from the perspective of their descriptive and predictive power. To this end, we employ an innovative experimental design which enables us to reproduce ambiguity in the laboratory in a transparent and non-probabilistic way. We find that judging theories on the basis of their theoretical appeal, or on their ability to do well in terms of estimation, is not the same as judging them on the basis of their predictive power. We find that the models that perform better in an aggregate sense are Gilboa and Schmeidler’s MaxMin and MaxMax Expected Utility Models, and Ghiradarto et al.’s Alpha Model, implying that more elegant theoretical models do not perform as well as relatively simple models. This suggests that decision-makers, when confronted with a difficult problem, try to simplify it, rather than apply a sophisticated decision rule. [ABSTRACT FROM AUTHOR] more...

Previous research has developed a variety of theories explaining when and why people's decisions under risk deviate from the standard economic view of expected utility maximization. These theories are limited in their predictive accuracy in that they do not explain the probabilistic nature of preferential choice, that is, why an individual makes different choices in nearly identical situations, or why the magnitude of these inconsistencies varies in different situations. To illustrate the advantage of probabilistic theories, three probabilistic theories of decision making under risk are compared with their deterministic counterparts. The probabilistic theories are (a) a probabilistic version of a simple choice heuristic, (b) a probabilistic version of cumulative prospect theory, and (c) decision field theory. By testing the theories with the data from three experimental studies, the superiority of the probabilistic models over their deterministic counterparts in predicting people's decisions under risk become evident. When testing the probabilistic theories against each other, decision field theory provides the best account of the observed behavior. [ABSTRACT FROM AUTHOR] more...

Abstract: Objectives: The purpose of this article is to provide an introduction to the theoretical, practical, and methodological advantages of applying cognitive models to sports decisions. The use of sequential sampling models, in particular, is motivated by their correspondence with the dynamic, variable processes that characterize decision-making in sports. This article offers a brief yet detailed description of these process models, and encourages their use in research on decision-making in sports. In addition, Appendix A provides the sufficient detail to formulate, simulate, and compute predictions for one of these models. Although the formulation focuses primarily on deliberation among a set of options, incorporating other critical task components (e.g. option generation, learning) is contemplated. Conclusions.: Empirical evidence is reviewed that supports the use of sequential sampling models over other approaches to decision-making. Finally, future directions for fine tuning these models to the sports domain are discussed. [Copyright &y& Elsevier] more...

The goal of this article is to explain empirical risk-taking behavior in sports from an individual cognitive modeling perspective. A basketball task was used in which participants viewed four video options that varied in the degree of associated risk. The participants were independently classified by scores on the Questionnaire for Assessing Prospective Action Orientation and State Orientation in Success, Failure, and Planning Situations as action-oriented or state-oriented decision makers. The results of the experiment show that action-oriented players shoot faster and more often to the basket and that state-oriented players prefer to pass to a playmaker more often. Four versions of a computational model of decision making, Decision Field Theory, were compared to evaluate whether behavioral differences depend on the focus of attention, the initial preferences, threshold values, or an approach-avoidance interpretation of the task. Different starting preferences explained individual choices and decision times most accurately. Risk taking in basketball shooting behavior can be best explained by different preferences for starting values for risky and safe options caused by different levels of action orientation. [ABSTRACT FROM AUTHOR] more...