Decision field theory: Improvements to current methodology and comparisons with standard choice modelling techniques

Hancock, Thomas O.; Hess, Stephane; Choudhury, Charisma F.

doi:10.1016/j.trb.2017.11.004

Cited by 37 publications

(40 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…What is more, it can also accurately predict the relationship between the probability of selection and preference with time. Besides, it is widely applied in the fields of military command control and traffic control [43].…”

Section: A the Classical Dft Methodsmentioning

confidence: 99%

“…Besides, the DFT method can accurately predict the selection probability and present the relationship between preference intensity and time. It has been applied to stock trading, military command control and traffic control [41]- [43]. However, when the preference information or uncertain knowledge is expressed by the form of HFS, we cannot use the traditional DFT to address it efficiently.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Route Selection of the Arctic Northwest Passage Based on Hesitant Fuzzy Decision Field Theory

Song

Zhang

et al. 2019

IEEE Access

View full text Add to dashboard Cite

The hesitant fuzzy set (HFS) is widely applied in actual multi-attribute group decisionmaking (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. INDEX TERMS Hesitant fuzzy set, decision field theory, multi-attribute group decision making, route selection.

show abstract

Section: A the Classical Dft Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Route Selection of the Arctic Northwest Passage Based on Hesitant Fuzzy Decision Field Theory

Song

Zhang

et al. 2019

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The non-linearity in the specification for the drift rates allows for the explanation of the similarity, attraction and compromise effects. Notably, work such as Guevara and Fukushi (2016) and Hancock et al (2018) demonstrate that models that can account for these context effects can be effective for understanding travel behaviour. The corresponding non-linear expression for the drift rate is the second value function we test in this paper:…”

Section: Value Functions: Linear Difference Asymmetric Decay Soft Plusmentioning

confidence: 99%

“…Batley and Dekker (2019) and for regret models, see Dekker 2014). Departures to more different models, such as decision field theory (Busemeyer and Townsend, 1992), whilst sometimes finding improvements in model fit, additionally result in models that become computationally infeasible for large-scale datasets (Hancock et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Quantum probability: A new method for modelling travel behaviour

Hancock

Broekaert

Hess

et al. 2020

Transportation Research Part B: Methodological

Self Cite

View full text Add to dashboard Cite

There has been an increasing effort to improve the behavioural realism of mathematical models of choice, resulting in efforts to move away from random utility maximisation (RUM) models. Some new insights have been generated with, for example, models based on random regret minimisation (RRM, µ-RRM). Notwithstanding work using for example Decision Field Theory (DFT), many of the alternatives to RUM tested on real-world data have however only looked at only modest departures from RUM, and differences in results have consequently been small. In the present study, we address this research gap again by investigating the applicability of models based on quantum theory. These models, which are substantially different from the state-of-the-art choice modelling techniques, emphasise the importance of contextual effects, state dependence, interferences and the impact of choice or question order. As a result, quantum probability models have had some success in better explaining several phenomena in cognitive psychology. In this paper, we consider how best to operationalise quantum probability into a choice model. Additionally, we test the quantum model frameworks on a best/worst route choice dataset and demonstrate that they find useful transformations to capture differences between the attributes important in a most favoured alternative compared to that of the least favoured alternative. Similar transformations can also be used to efficiently capture contextual effects in a dataset where the order of the attributes and alternatives are manipulated. Overall, it appears that models incorporating quantum concepts hold significant promise in improving the state-of-the-art travel choice modelling paradigm through their adaptability and efficient modelling of contextual changes.

show abstract

“…To date, research and software for experimental design have almost exclusively been based on the (often implicit) assumption that decision-makers make choices using a (linear-additive) Random Utility Maximisation (RUM) decision rule. However, a growing https://doi.org/10.1016/j.jocm.2019.04.002 Received 8 August 2018; Received in revised form 2 April 2019; Accepted 2 April 2019 number of studies have found overwhelming evidence that decision-makers may opt for other types of decision rules when making choices (Kivetz et al, 2004;Hess et al, 2012;Leong and Hensher, 2012;Guevara and Fukushi, 2016;Hancock et al, 2018;Van Cranenburgh and Alwosheel, 2019). In light of this, very recently a method to create efficient experimental designs for one alternative decision rule, namely Random Regret Minimisation (RRM), has been proposed .…”

Section: Introductionmentioning

confidence: 99%

New software tools for creating stated choice experimental designs efficient for regret minimisation and utility maximisation decision rules

Cranenburgh

Collins

2019

Journal of Choice Modelling

View full text Add to dashboard Cite

At the time of creating an experimental design for a stated choice experiment, the analyst often does not precisely know which model, or decision rule, he or she will estimate once the data are collected. This paper presents two new software tools for creating stated choice experimental designs that are simultaneously efficient for regret minimisation and utility maximisation decision rules. The first software tool is a lean, easy-to-use and free-of-charge experimental design tool, which is dedicated to creating designs that incorporate regret minimisation and utility maximisation decision rules. The second tool constitutes a newly developed extension of Ngenea widely used and richly featured software tool for the generation of experimental designs. To facilitate the use of the new software tools, this paper presents clear worked examples. It focusses on practical issues encountered when generating such decision rule robust designs, such as how to obtain priors and how to deal with alternative specific parameters. Furthermore, we analyse the robustness of the designs that we created using the new software tools. Our results provide evidence that designs optimised for one decision rule can be inefficient for another -highlighting the added value of decision rule robust designs.

show abstract

Decision field theory: Improvements to current methodology and comparisons with standard choice modelling techniques

Cited by 37 publications

References 63 publications

Route Selection of the Arctic Northwest Passage Based on Hesitant Fuzzy Decision Field Theory

Route Selection of the Arctic Northwest Passage Based on Hesitant Fuzzy Decision Field Theory

Quantum probability: A new method for modelling travel behaviour

New software tools for creating stated choice experimental designs efficient for regret minimisation and utility maximisation decision rules

Contact Info

Product

Resources

About