Neuroscience experiment applied to investigate decision-maker behavior in the tradeoff elicitation procedure

The Tradeoff Elicitation Procedure is a Multi-Criteria Decision Making/Aiding method which is responsible for eliciting scaling constants and presents a robust axiomatic structure. As to its axiomatic structure, this procedure requires the decision-maker to identify the exact indifference point which induces a large number of inconsistencies in the process. In order to evaluate Decision Maker behavior in the Tradeoff elicitation and explore inconsistency in this process, a Neuroscience experiment was conducted using neuro tools, such as an Eye Tracking and an Electroencephalography (EEG). The experiment was applied in a sample of 52 management engineering students. After the data were collected, analyses were developed in order to suggest decision-makers’ behavior in the steps of this procedure. In summary, the responses of the pupils are increased during the process indicating a cognitive effort, and EEG data confirmed this result considering frontal alpha asymmetry and theta power in the frontal electrodes as variables for analysis.

[1]  Sabine Hügelschäfer,et al.  Reinforcement, Rationality, and Intentions: How Robust Is Automatic Reinforcement Learning in Economic Decision Making? , 2017 .

[2]  Erik Kropat,et al.  Operations research in neuroscience , 2017, Ann. Oper. Res..

[3]  Jakub Berčík,et al.  The impact of parameters of store illumination on food shopper response , 2016, Appetite.

[4]  Andreas Glöckner,et al.  The Dynamics of Decision Making in Risky Choice: An Eye-Tracking Analysis , 2012, Front. Psychology.

[5]  P. Ekman,et al.  Approach-withdrawal and cerebral asymmetry: emotional expression and brain physiology. I. , 1990, Journal of personality and social psychology.

[6]  Angelika Dimoka,et al.  On the Use of Neuropyhsiological Tools in IS Research: Developing a Research Agenda for NeuroIS , 2012, MIS Q..

[7]  Gernot R. Müller-Putz,et al.  Electroencephalography (EEG) as a Research Tool in the Information Systems Discipline: Foundations, Measurement, and Applications , 2015, Commun. Assoc. Inf. Syst..

[8]  S. Drummond,et al.  Neural correlates of decision-making during a Bayesian choice task , 2017, Neuroreport.

[9]  Martin Weber,et al.  Behavioral influences on weight judgments in multiattribute decision making , 1993 .

[10]  Leanne M Williams,et al.  Resting EEG theta activity predicts cognitive performance in attention-deficit hyperactivity disorder. , 2005, Pediatric neurology.

[11]  Ana Paula Cabral Seixas Costa,et al.  A new method for elicitation of criteria weights in additive models: Flexible and interactive tradeoff , 2016, Eur. J. Oper. Res..

[12]  R. L. Keeney,et al.  Decisions with Multiple Objectives: Preferences and Value Trade-Offs , 1977, IEEE Transactions on Systems, Man, and Cybernetics.

[13]  V. Rasoulzadeh,et al.  A comparative stationarity analysis of EEG signals , 2017, Ann. Oper. Res..

[14]  Jordan J. Louviere,et al.  Consumer neuroscience: Assessing the brain response to marketing stimuli using electroencephalogram (EEG) and eye tracking , 2013, Expert Syst. Appl..

[15]  Matthias Ehrgott,et al.  Multiple criteria decision analysis: state of the art surveys , 2005 .

[16]  Edmundas Kazimieras Zavadskas,et al.  Decision making on business issues with foresight perspective; an application of new hybrid MCDM model in shopping mall locating , 2013, Expert Syst. Appl..

[17]  Matthew D. Lieberman,et al.  Social cognitive neuroscience: a review of core processes. , 2007, Annual review of psychology.

[18]  Ayşe Özmen Robust Optimization of Spline Models and Complex Regulatory Networks , 2016 .

[19]  M. Fiorani,et al.  Frontal Alpha Asymmetry and Theta Oscillations Associated With Information Sharing Intention , 2018, Front. Behav. Neurosci..

[20]  Louisa Kulke,et al.  Implicit reward associations impact face processing: Time-resolved evidence from event-related brain potentials and pupil dilations , 2017, NeuroImage.

[21]  Jun Wu,et al.  Selection of optimum maintenance strategies based on a fuzzy analytic hierarchy process , 2007 .

[22]  Gerhard-Wilhelm Weber,et al.  A Stochastic Maximum Principle for a Markov Regime-Switching Jump-Diffusion Model with Delay and an Application to Finance , 2018, J. Optim. Theory Appl..

[23]  Adiel Teixeira de Almeida,et al.  Neuroscience Experiment for Graphical Visualization in the FITradeoff Decision Support System , 2018, GDN.

[24]  Pauline van der Wel,et al.  Pupil dilation as an index of effort in cognitive control tasks: A review , 2018 .

[25]  Jonghun Park,et al.  A semi-supervised inattention detection method using biological signal , 2017, Annals of Operations Research.

[26]  Tzyy-Ping Jung,et al.  Mind-Wandering Tends to Occur under Low Perceptual Demands during Driving , 2016, Scientific Reports.

[27]  Soyoung Q. Park,et al.  Neurobiology of Value Integration: When Value Impacts Valuation , 2011, The Journal of Neuroscience.

[28]  W. Klimesch EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis , 1999, Brain Research Reviews.

[29]  Theodor J. Stewart,et al.  Multiple Criteria Decision Analysis , 2001 .

[30]  Adiel Teixeira de Almeida,et al.  Visualization for Decision Support in FITradeoff Method: Exploring Its Evaluation with Cognitive Neuroscience , 2017, ICDSST.

[31]  Fabio Babiloni,et al.  Adaptive Automation Triggered by EEG-Based Mental Workload Index: A Passive Brain-Computer Interface Application in Realistic Air Traffic Control Environment , 2016, Front. Hum. Neurosci..

[32]  Kao-Yi Shen,et al.  Financial modeling and improvement planning for the life insurance industry by using a rough knowledge based hybrid MCDM model , 2017, Inf. Sci..

[33]  Adiel Teixeira de Almeida,et al.  Multicriteria and multiobjective models for risk, reliability and maintenance decision analysis , 2015 .

[34]  H. Heuer,et al.  Frontal theta activity reflects distinct aspects of mental fatigue , 2014, Biological Psychology.

[35]  Chao-Che Hsu,et al.  An integrated MCDM model for improving airline operational and financial performance , 2017 .

[36]  Todd A Hare,et al.  Enhanced Neural Responses to Imagined Primary Rewards Predict Reduced Monetary Temporal Discounting , 2015, The Journal of Neuroscience.

[37]  T. Ramsøy,et al.  Frontal Brain Asymmetry and Willingness to Pay , 2018, Front. Neurosci..

[38]  John J. B. Allen,et al.  Frontal asymmetry as a mediator and moderator of emotion: An updated review. , 2018, Psychophysiology.

[39]  P. Binda,et al.  Pupil size reflects the focus of feature-based attention. , 2014, Journal of neurophysiology.