Skip to main content

Combining holistic and decomposition paradigms in preference modeling with the flexibility of FITradeoff

Abstract

This paper focuses on the flexibility feature of the Flexible and Interactive Tradeoff (FITradeoff) multicriteria method for preference modeling. This method is based on the additive aggregation of criteria and using partial (incomplete; imprecise) information to be obtained from a Decision Maker (DM). The flexibility in FITradeoff for preference modeling has already considered two different perspectives: holistic evaluations and elicitation by decomposition based on the classical tradeoff procedure. This paper introduces a new feature in the flexibility of FITradeoff by combining and integrating these two paradigms: Holistic evaluations and elicitation by decomposition. This combination improves the preference modeling process, since it increases its efficiency and consistency. The use of results from behavioral studies is briefly presented. These results include those that arise from using neuroscience tools in order to modulate changes in the design of the Decision Support System and also from improving the decision process by supporting the way the analyst can interact with the DM.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

References

  1. Bana e Costa CA, De Corte J-M, Vansnick J-C (2005) On the mathematical foundation of MACBETH. In: Figueira J, Greco S, Ehrgott M (eds) Multiple criteria decision analysis: state of the art surveys. Springer, New York, pp 409–437

    Google Scholar 

  2. Barberis N, Xiong W (2009) What drives the disposition effect? An analysis of a long-standing preference-based explanation. J Finance 64(2):751–784

    Google Scholar 

  3. Belton V, Stewart T (2002) Multiple criteria decision analysis: an integrated approach. Springer, Berlin

    Google Scholar 

  4. Borcherding K, Eppel T, von Winterfeldt D (1991) Comparison of weighting judgments in multiattribute utility measurement. Manag Sci 37(12):1603–1619

    Google Scholar 

  5. Camilo DGG, de Souza RP, Frazão TDC, da Costa Junior JF (2020) Multi-criteria analysis in the health area: selection of the most appropriate triage system for the emergency care units in natal. BMC Med Inform Decis Mak 20(1):1–16

    Google Scholar 

  6. Carrillo PAA, Roselli LRP, Frej EA, de Almeida AT (2018) Selecting an agricultural technology package based on the flexible and interactive tradeoff method. Ann Oper Res 270:1–16

    Google Scholar 

  7. Chuang H, Lin C, Chen Y (2015) Exploring the triple reciprocity nature of organizational value cocreation behavior using multicriteria decision making analysis. Math Probl Eng 2015:1–15

    Google Scholar 

  8. Ciomek K, Kadzinski M, Tervonen T (2017) Heuristics for selecting pair-wise elicitation questions in multiple criteria choice problems. Eur J Oper Res 262:693–707

    Google Scholar 

  9. de Almeida AT, Roselli LRP (2017) Visualization for decision support in FITradeoff method: exploring its evaluation with cognitive neuroscience. Lecture notes in business information processing, 282nd edn. Springer, Berlin, pp 61–73

    Google Scholar 

  10. de Almeida AT, Roselli LRP (2020) NeuroIS to improve the FITradeoff decision-making process and decision support system. In: Proceedings of the NeuroIS retreat 2020

  11. de Almeida AT, Cavalcante CAV, Alencar MH, Ferreira RJP, de Almeida-Filho AT, Garcez TV (2015) Multicriteria and multiobjective models for risk, reliability and maintenance decision analysis. Springer, Berlin

    Google Scholar 

  12. de Almeida AT, Almeida JA, Costa APCS, Almeida-Filho AT (2016) A new method for elicitation of criteria weights in additive models: flexible and interactive tradeoff. Eur J Oper Res 250(1):179–191

    Google Scholar 

  13. de Almeida AT, Roselli LRP, Costa APCS, Goncalves JMS, Andrade AL (2018) Decision process improvement based on behavioral experiments of multi-attribute choices with graphical visualization. In: Society of NeuroEconomics, 16th, proceedings, Philadelphia, US

  14. de Almeida A, Roselli L, Costa Morais D, Costa A (2020a) Neuroscience tools for behavioural studies in group decision and negotiation. In: Kilgour DM, Eden C (eds) Handbook of group decision and negotiation. Springer, Berlin

    Google Scholar 

  15. de Almeida A, Frej EA, Costa Morais D, Costa A (2020b) Multiple criteria group decisions with partial information about preference. In: Kilgour DM, Eden C (eds) Handbook of group decision and negotiation. Springer, Berlin

    Google Scholar 

  16. de Gusmao APH, Pereira Medeiros C (2016) A model for selecting a strategic information system using the FITradeoff. Math Prob Eng. https://doi.org/10.1155/2016/7850960

    Article  Google Scholar 

  17. de Loof E, Vassena E, Janssens C, de Taeye L, Meurs A, Van Roost D, Verguts T (2019) Preparing for hard times: scalp and intracranial physiological signatures of proactive cognitive control. Psychophysiology 56:10

    Google Scholar 

  18. de Macedo PP, de Miranda Mota CM, Sola AVH (2018) Meeting the Brazilian energy efficiency law: a flexible and interactive multicriteria proposal to replace non-efficient motors. Sustain Cities Soc 41:822–832

    Google Scholar 

  19. Dell’Ovo M, Frej EA, Oppio A, Capolongo S, Morais DC, de Almeida AT (2017) Multicriteria decision making for healthcare facilities location with visualization based on FITradeoff method. In: International conference on decision support system technology. Springer, Cham, pp 32–44

  20. Dimoka A, Pavlou PA, Davis FD (2007) Neuro-IS: the potential of cognitive neuroscience for information systems research. In: Proceedings of the 28th international conference on information systems, pp 1–20

  21. Camara e Silva L, Daher SDFD, Santiago KTM, Costa APCS (2019) Selection of an integrated security area for locating a state military police station based on MCDM/A method. In: 2019 IEEE international conference on systems, man and cybernetics (SMC). IEEE, pp 1530–1534

  22. Edwards W, Barron FH (1994) SMARTS and SMARTER: improved simple methods for multiattribute utility measurement. Organ Behav Hum Decis Process 60(3):306–325

    Google Scholar 

  23. Eisenführ F, Weber M, Langer T (2010) Rational decision making. Springer, Heidelberg

    Google Scholar 

  24. Fehr E, Camerer CF (2007) Social neuroeconomics: the neural circuitry of social preferences. Trends Cogn Sci 11(10):419–427

    Google Scholar 

  25. Fossile DK, Frej EA, da Costa SEG, de Lima EP, de Almeida AT (2020) Selecting the most viable renewable energy source for Brazilian ports using the FITradeoff method. J Clean Prod 260:121107

    Google Scholar 

  26. Frej EA, de Almeida AT (2016) Multicriteria group decision model for supplier selection in a food industry. In: Proceedings of international conference on group decision and negotiation, vol 1, Bellingham, US, pp 60–62

  27. Frej EA, Roselli LRP, Araújo de Almeida J, de Almeida AT (2017) A multicriteria decision model for supplier selection in a food industry based on FITradeoff method. Math Probl Eng. https://doi.org/10.1155/2017/4541914

    Article  Google Scholar 

  28. Frej EA, de Almeida AT, Costa APCS (2019) Using data visualization for ranking alternatives with partial information and interactive tradeoff elicitation. Oper Res 19:1–23

    Google Scholar 

  29. Frej EA, Ekel P, de Almeida AT (2021) A benefit-to-cost ratio based approach for portfolio selection under multiple criteria with incomplete preference information. Inf Sci 545:487–498

    Google Scholar 

  30. Glimcher PW, Rustichini A (2004) Neuroeconomics: the consilience of brain and decision. Science 5695:447–452

    Google Scholar 

  31. Górecka D, Roszkowska E, Wachowicz T (2016) The MARS approach in the verbal and holistic evaluation of the negotiation template. Gr Decis Negot 25(6):1097–1136

    Google Scholar 

  32. Goucher-Lambert K, Moss J, Cagan J (2017) Inside the mind: using neuroimaging to understand moral product preference judgments involving sustainability. J Mech Des 139(4):041–103

    Google Scholar 

  33. Greco S, Słowiński R, Zielniewicz P (2013) Putting dominance-based rough set approach and robust ordinal regression together. Decis Support Syst 54(2):891–903

    Google Scholar 

  34. Hines WW, Montgomery DC (1990) Probability and statistics in engineering and management science. Wiley, New York

    Google Scholar 

  35. Holm A, Lukander K, Korpela J, Sallinen M, Müller KMI (2009) Estimating brain load from the EEG. Sci World J 9:639–651

    Google Scholar 

  36. Hunt LT, Dolan RJ, Behrens TE (2014) Hierarchical competitions subserving multi-attribute choice. Nat Neurosci 17(11):1613

    Google Scholar 

  37. Jacquet-Lagreze E, Siskos J (1982) Assessing a set of additive utility functions for multicriteria decision-making, the UTA method. Eur J Oper Res 10(2):151–164

    Google Scholar 

  38. Kadziński M, Ciomek K, Słowiński R (2015) Modeling assignment-based pairwise comparisons within integrated framework for value-driven multiple criteria sorting. Eur J Oper Res 241(3):830–841

    Google Scholar 

  39. Kang THA, Júnior AMDCS, de Almeida AT (2018) Evaluating electric power generation technologies: a multicriteria analysis based on the FITradeoff method. Energy 165:10–20

    Google Scholar 

  40. Kang THA, Frej EA, de Almeida AT (2020) Flexible and interactive tradeoff elicitation for multicriteria sorting problems. Asia Pac J Oper Res 37:2050020

    Google Scholar 

  41. Keeney RL, Raiffa H (1976) Decision analysis with multiple conflicting objectives. Wiley, New York

    Google Scholar 

  42. Kenning P, Plassmann H (2005) NeuroEconomics: an overview from an economic perspective. Brain Res Bull 67(5):343–354

    Google Scholar 

  43. Khushaba RN, Wise C, Kodagoda S, Louviere J, Kahn BE, Townsend C (2013) Consumer neuroscience: assessing the brain response to marketing stimuli using electroencephalogram (EEG) and eye tracking. Expert Syst Appl 40(9):3803–3812

    Google Scholar 

  44. Korhonen P, Wallenius J (1997) Behavioral issues in MCDM: neglected research questions. Multicriteria analysis. Springer, Heidelberg, pp 412–422

    Google Scholar 

  45. Lima ES, Viegas RA, Costa APCS (2017) A multicriteria method based approach to the BPMM selection problem. In: 2017 IEEE international conference on systems, man, and cybernetics (SMC). IEEE, pp 3334–3339

  46. Linkov I, Cormier S, Gold J, Satterstrom FK, Bridges T (2012) Using our brains to develop better policy. Risk Anal Int J 32(3):374–380

    Google Scholar 

  47. Loewenstein G, Rick S, Cohen JD (2008) Neuroeconomics. Annu Rev Psychol 59:647–672

    Google Scholar 

  48. Macdonald JSP, Mathan S, Yeung N (2011) Trial-by-trial variations in subjective attentional state are reflected in ongoing prestimulus EEG alpha oscillations. Front Psychol 2:82

    Google Scholar 

  49. Mendes JAJ, Frej EA, de Almeida AT, Almeida JA (2020) Evaluation of flexible and interactive tradeoff method based on numerical simulation experiments. Pesquisa Operacional 40:1–25

    Google Scholar 

  50. Monte MBS, Morais DC (2019) A decision model for identifying and solving problems in an urban water supply system. Water Resour Manag 33(14):4835–4848

    Google Scholar 

  51. Morin C (2011) Neuromarketing: the new science of consumer behavior. Society 48(2):131–135

    Google Scholar 

  52. Nermend K (2017) The implementation of cognitive neuroscience techniques for fatigue evaluation in participants of the decision-making process. In: Neuroeconomic and behavioral aspects of decision making. Springer, Cham, pp 329–339

  53. Özerol G, Karasakal E (2008) A parallel between regret theory and outranking methods for multicriteria decision making under imprecise information. Theory Decis 65(1):45–70

    Google Scholar 

  54. Pergher I, Frej EA, Roselli LRP, de Almeida AT (2020) Integrating simulation and FITradeoff method for scheduling rules selection in job-shop production systems. Int J Prod Econ 227:107669

    Google Scholar 

  55. Riedl R, Davis FD, Hevner AR (2014) Towards a NeuroIS research methodology: intensifying the discussion on methods, tools, and measurement. J Assoc Inf Syst 15(10):4

    Google Scholar 

  56. Roselli LRP, de Almeida AT (2019a) Investigating graphical visualization in FITradeoff method with neuroscience using EEG and eye-tracker. Local proceedings for group decision and negotiation. In: 19th international conference on group decision and negotiation, Loughborough

  57. Roselli LRP, de Almeida AT (2019b) Analyzing graphical visualization for multi-attribute decision making using EEG and eye-tracker. In: NeuroPsychoEconomics conference, Rome. Poster section

  58. Roselli LRP, de Almeida AT (2020a) Analysis of graphical visualizations for multi-criteria decision making in FITradeoff method using a decision neuroscience experiment. Lecture notes in business information processing, 384th edn. Springer, Berlin, pp 42–54

    Google Scholar 

  59. Roselli, L.R.P., de Almeida, A.T. (2020b). Improvements in the FITradeoff decision support system for ranking order problematic based in a behavioral study with NeuroIS tools. In: Davis FD et al (eds) Lecture notes in information systems and organization, LNISO, 43edn. NeuroIS, pp 1–12

  60. Roselli LRP, Frej EA, de Almeida AT (2018a) Neuroscience experiment for graphical visualization in the FITradeoff decision support system. In: Chen Y, Kersten G, Vetschera R, Xu H (eds) Group decision and negotiation in an uncertain world. GDN 2018. Lecture notes in business information processing, vol 315

  61. Roselli LRP, Frej EA, de Almeida AT (2018b) Improving graphical visualization in the FITradeoff DSS using neuroscience experiment. In: 2018 INFORMS international conference. Proceedings of the 2018 INFORMS international conference, Taipei

  62. Roselli LRP, de Almeida AT, Frej EA (2019a) Decision neuroscience for improving data visualization of decision support in the FITradeoff method. Oper Res Int J 19:1–21

    Google Scholar 

  63. Roselli LRP, Pereira LS, Silva ALCL, de Almeida AT, Morais DC, Costa APCS (2019b) Neuroscience experiment applied to investigate decision-maker behavior in the tradeoff elicitation procedure. Ann Oper Res 289:1–18

    Google Scholar 

  64. Silva ALCL, Costa APCS (2019) FITradeoff decision support system: an exploratory study with neuroscience tools. In: NeuroIS retreat 2019, Viena. NeuroIS retreat

  65. Silva MM, de Gusmão APH, de Andrade CTA, Silva W (2019) The integration of VFT and FITradeoff multicriteria method for the selection of WCM projects. In: 2019 IEEE international conference on systems, man and cybernetics (SMC). IEEE, pp 1513–1517

  66. Silva ALCL, Costa APCS, de Almeida AT (2021) Exploring cognitive aspects of FITradeof method using neuroscience tools. Ann Oper Res. https://doi.org/10.1007/s10479-020-03894-0

    Article  Google Scholar 

  67. Siskos E, Askounis D, Psarras J (2014) Multicriteria decision support for global e-government evaluation. Omega 46:51–63

    Google Scholar 

  68. Siskos Y, Grigoroudis E, Matsatsinis NF (2016) UTA methods. In: Greco S, Ehrgott M, Figueira J (eds) Multiple criteria decision analysis. International series in operations research & management science, vol 233. Springer, New York

    Google Scholar 

  69. Smith DV, Huettel SA (2010) Decision neuroscience: neuroeconomics. Wiley Interdiscip Rev Cogn Sci 1(6):854–871

    Google Scholar 

  70. Tikidji-Hamburyan RA, Kropat E, Weber G-W (2020) Preface: operations research in neuroscience II. Ann Oper Res 289(1):1–4

    Google Scholar 

  71. Trepel C, Fox CR, Poldrack RA (2005) Prospect theory on the brain? Toward a cognitive neuroscience of decision under risk. Cogn Brain Res 23(1):34–50

    Google Scholar 

  72. Waegeman W, De Baets B, Boullart L (2009) Kernel-based learning methods for preference aggregation. 4OR 7(2):169–189

    Google Scholar 

  73. Wallenius H, Wallenius J (2020) Implications of world mega trends for MCDM research. In: Ben Amor S, de Almeida A, de Miranda J, Aktas E (eds) Advanced studies in multi-criteria decision making. Series in operations research, 1st edn. Chapman and Hall/CRC, New York, pp 1–10

    Google Scholar 

  74. Weber M (1987) Decision making with incomplete information. Eur J Oper Res 28(1):44–57

    Google Scholar 

  75. Weber M, Borcherding K (1993) Behavioral influences on weight judgments in multi attribute decision making. Eur J Oper Res 67:1–12

    Google Scholar 

  76. Zhao Y, Zhao X, Wang L, Chen Y, Zhang X (2016) Does elicitation method matter? Behavioral and neuroimaging evidence from capacity allocation game. Prod Oper Manag 25(5):919–934

    Google Scholar 

Download references

Acknowledgements

This work had partial support from the Brazilian Research Council (CNPq) and FACEPE (Foundation for Research in the State of Pernambuco).

Funding

This material is based upon work supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico, Fundação de Amparo à Ciência e Tecnologia do Estado de Pernambuco under Grant Nos. APQ-0484-3.08/17, APQ-0370-3.08/14.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Adiel Teixeira de Almeida.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

de Almeida, A.T., Frej, E.A. & Roselli, L.R.P. Combining holistic and decomposition paradigms in preference modeling with the flexibility of FITradeoff. Cent Eur J Oper Res 29, 7–47 (2021). https://doi.org/10.1007/s10100-020-00728-z

Download citation

Keywords

  • FITradeoff method
  • Elicitation by decomposition
  • Holistic evaluation
  • Flexibility
  • Preference modelling
  • Multi-criteria decision making/aiding (MCDM/A)