doi: 10.17586/2226-1494-2023-23-1-121-135

Value-based modeling of economic decision making in conditions of unsteady environment

V. Y. Guleva, A. N. Kovantsev, Сурков А.Г., P. V. Chunaev, G. V. Gornova, A. V. Boukhanovsky

Read the full article  ';
Article in Russian

For citation:
Guleva V.Yu., Kovatsev A.N., Surikov A.G., Chunaev P.V., Gornova G.V., Boukhanovskiy A.V. Valuebased modeling of economic decision making in conditions of unsteady environment. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2023, vol. 23, no. 1, pp. 121–135 (in Russian). doi: 10.17586/2226-1494-2023-23-1-121-135

The changing environment creates the conditions for changing people’s behavior which together can lead to crisis situations. In the case of making economic decisions, the emerging non-stationarity of the dynamics of different components of the system can represent an economic crisis. The possibility of universal human values consideration in modeling decision-making under conditions of changing environment has been studied. The internal processes of an agent prior to making a decision are reflected in the concept of Beliefs-Desires-Intentions-Actions-Reactions (BDIAR). The article reviews the regularities and existing approaches to modeling economic decisions, and proposes a new, author’s approach. The mechanisms of influence of stress on decision-making, factors of rationality limitation, and risk assessment in the context of behavioral economics are revealed. The well-known theories of the influence of values on the change in the structure of consumption in a crisis situation are considered. Ways of taking into account emotions in the architecture of the agent are shown. In the proposed model, values are considered as a social factor in decision making. Due to their subjectivity, they are presented mathematically as the basis for assessing environmental objects. The subjectivity of the objects of choice assessments is reflected in the functions of attractiveness of the objects, the functions of the agent’s state dynamics, and in the subjectivity of the decision influence on the satisfaction of the agent. A possible modification of the components of the agent model is shown, taking into account the influence of values on the dynamics of consumption. A method is proposed for taking into account values in the BDIAR architecture when modeling an agent’s decision-making where the levels of the architecture correspond to values, preference functions, and functions of the agent’s state dynamics. The pseudonymized transactional data on debit cards of the partner bank customers were analyzed separately for 2017–2019 and 2020. The subjectivity of the environment influence in a crisis situation on the dynamics of changes in values and needs for different consumer groups is demonstrated, taking into account the clustering of their behavior types. Differences in the dynamics of values at the individual level and the level of groups are shown; an increase in the priority of survival values and a different rate of return to the pre-crisis state for different behavioral groups are also shown. The results obtained are useful for developing methods for modeling economic behavior in a non-stationary external environment, in particular, in the case of crises.

Keywords: decision making model, crisis, values, needs, non-stationarity, behavioral economics, BDIAR

Acknowledgements. This research is financially supported by the Russian Science Foundation, Agreement No. 17–71–30029 with co-financing of the Bank “Saint Petersburg”.

  1. Pospelov I. Modeling russian economy in the crisis period. Voprosy Ekonomiki, 2009, no. 11, pp. 50–75. (in Russian).
  2. Hawkins D. Some conditions of macroeconomic stability. Econometrica, 1948, vol. 16, no. 3, pp. 309–322.
  3. Bengtsson M., Alfredsson E., Cohen M., Lorek S., Schroeder P. Transforming systems of consumption and production for achieving the sustainable development goals: moving beyond efficiency. Sustainability Science, 2018, vol. 13, no. 6, pp. 1533–1547.
  4. Ashraf S., Félix E.G.S., Serrasqueiro Z. Do traditional financial distress prediction models predict the early warning signs of financial distress? Journal of Risk and Financial Management, 2019, vol. 12, no. 2, pp. 55.
  5. Edison H.J. Do indicators of financial crises work? An evaluation of an early warning system. International Journal of Finance & Economics, 2003, vol. 8, no. 1, pp. 11–53.
  6. Demoulin N.T.M., Zidda P. Drivers of customers’ adoption and adoption timing of a new loyalty card in the grocery retail market. Journal of Retailing, 2009, vol. 85, no. 3, pp. 391–405.
  7. Gabaix X. Power Laws in Economics and Finance. Annual Review of Economics, 2009, vol. 1, no. 1, pp. 255–294.
  8. Squartini T., Caldarelli G., Cimini G., Gabrielli A., Garlaschelli D. Reconstruction methods for networks: the case of economic and financial systems. Physics Reports, 2018, vol. 757, pp. 1–47.
  9. Houthakker H.S. Revealed preference and the utility function. Economica, 1950, vol. 17, no. 66, pp. 159–174.
  10. Kalman P.J. Theory of consumer behavior when prices enter the utility function. Econometrica, 1968, vol. 36, no. 3-4, pp. 497–510.
  11. Mehta S., Saxena T., Purohit N. The new consumer behaviour paradigm amid COVID-19: permanent or transient? Journal of Health Management, 2020, vol. 22, no. 2, pp. 291–301.
  12. Loxton M., Truskett R., Scarf B., Sindone L., Baldry G., Zhao Y. Consumer behaviour during crises: Preliminary research on how coronavirus has manifested consumer panic buying, herd mentality, changing discretionary spending and the role of the media in influencing behavior. Journal of Risk and Financial Management, 2020, vol. 13, no. 8, pp. 166.
  13. Jeffrey H.J., Putman A.O. The irrationality illusion: A new paradigm for economics and behavioral economics. Journal of Behavioral Finance, 2013, vol. 14, no. 3, pp. 161–194.
  14. Poole K.B. Adaptability and Decision Making under Stress in the Workplace. A thesis submitted in partial fulfillment of the requirements for the degree of Master of Arts in Psychology. Middle Tennessee State University, 2018. 66 p.
  15. Yilmaz S., Kafadar H. Decision-making under stress: Executive functions, analytical intelligence, somatic markers, and personality traits in young adults. Applied Neuropsychology: Adult, 2022.
  16. Pope J., Hein M., Russell M.A., Burkholder Ch. A Continuation of research: Student decision making under stress in a flight control center simulation. Proc. of the 20th International Symposium on Aviation Psychology (ISAP), 2019, pp. 91–96.
  17. Coleman J.S., Farraro Th.J. Rational Choice Theory: Advocacy and Critique. SAGE Publications, 1992, 232 p.
  18. Behavioral Economics. Great Russian Encyclopedia. Available at: (accessed: 19.10.2022). (in Russian)
  19. Kahneman D., Knetsch J.L., Thaler R.H. Fairness and the assumptions of economics. The Journal of Business, 1986, vol. 59, no. S4, pp. S285.
  20. Kahneman D., Tversky A. Prospect theory: An analysis of decision under risk. Handbook of the fundamentals of financial decision making: Part I. World Scientific, 2013, pp. 99–127.
  21. Tversky A., Kahneman D. Judgment under uncertainty: Heuristics and biases. Science, 1974, vol. 185, no. 4157, pp. 1124–1131.
  22. Egeth H., Kahneman D. Attention and effort. The American Journal of Psychology, 1975, vol. 88, no. 2, pp. 339–340.
  23. Maćkowiak B., Matějka F., Wiederholt M. Dynamic rational inattention: Analytical results. Journal of Economic Theory, 2018, vol. 176, pp. 650–692.
  24. Sims C.A. Implications of rational inattention. Journal of Monetary Economics, 2003, vol. 50, no. 3, pp. 665–690.
  25. Luo Y. Consumption dynamics under information processing constraints. Review of Economic Dynamics, 2008, vol. 11, no. 2, pp. 366–385.
  26. Bergstrom T.C. Evolution of social behavior: Individual and group selection. Journal of Economic Perspectives, 2002, vol. 16, no. 2, pp. 67–88.
  27. Taborsky B. The Evolution of Social Behaviour. Ethology, 2021, vol. 127, no. 10, pp. 751–757.
  28. Maslow A.H. Preface to motivation theory. Psychosomatic Medicine, 1943, vol. 5, no. 1, pp. 85–92.
  29. Mitchell A. The Nine American Lifestyles: Who We are and where We’re Going. Scribner Book Company, 1983, 302 p.
  30. Kahle L.R. Social Values and Social Change: Adaptation to Life in America. Praeger Publishers, 1983, 324 p.
  31. Schwartz S.H. An overview of the Schwartz theory of basic values. Online Readings in Psychology and Cultur, 2012, vol. 2, no. 1.
  32. Boroel B., Aramburo V., Gonzalez M. Development of a scale to measure attitudes toward professional values: An analysis of dimensionality using Rasch measurement. Procedia - Social and Behavioral Sciences, 2017, vol. 237, pp. 292–298.
  33. Gawel J.E. Herzberg’s theory of motivation and Maslow’s hierarchy of needs. Practical Assessment, Research, and Evaluatio, 1996, vol. 5, pp. 11.
  34. Lester D., Hvezda J., Sullivan Sh., Plourde R. Maslow’s hierarchy of needs and psychological health. The Journal of General Psychology, 1983, vol. 109, no. 1, pp. 83–85.
  35. Wilson S.R., Mihalcea R., Boyd R.L., Pennebaker J.W. Cultural influences on the measurement of personal values through words. AAAI Spring Symposia, 2016.
  36. Inglehart R. et al. World Values Survey. 2005.
  37. Inglehart R. Postmodemity: Changing values and changing societies. Polis. Politicheskie issledovanija, 1997, no. 4, pp. 6–32. (in Russian)
  38. Maćkowiak B., Matějka F., Wiederholt M. Rational Inattention: A Review. ECB Working Paper, 2021.
  39. Usher M., Tsetsos K., Yu E.C., Lagnado D.A. Dynamics of decision-making: From evidence accumulation to preference and belief. Frontiers in Psychology, 2013, vol. 4.
  40. Poltawski L., van Beurden S.B., Morgan-Trimmer S., Greaves C. The dynamics of decision-making in weight loss and maintenance: A qualitative enquiry. BMC Public Health, 2020, vol. 20, no. 1, pp. 573.
  41. Negre E., Arru M., Rosenthal-Sabroux C. Toward a modeling of population behaviors in crisis situations. How Information Systems Can Help in Alarm/Alert Detection, Elsevier, 2018, pp. 199–218.
  42. De Silva L., Meneguzzi F.R., Logan B. BDI agent architectures: A survey. Proc. of the 29th International Joint Conference on Artificial Intelligence (IJCAI), 2020, pp. 4914–4921.
  43. Rao A.S., Georgeff M.P. BDI agents: from theory to practice. Proc. of the First International Conference on Multiagent Systems, 1995, pp. 312–319.
  44. Lin C.-E., Kavi K.M., Sheldon F.T., Daley K.M., Abercrombie R.K. A methodology to evaluate agent oriented software engineering techniques. Proc. of the 40th Annual Hawaii International Conference on System Sciences (HICSS’07), 2007, pp. 60.
  45. Puica M.-A., Florea A.-M. Emotional belief-desire-intention agent model: Previous work and proposed architecture. International Journal of Advanced Research in Artificial Intelligence, 2013, vol. 2, no. 2.
  46. Pereira D., Oliveira E., Moreira N. Modelling Emotional BDI Agents. Proc. of the Formal Approaches to Multiagent Systems (FAMAS) Workshop, 2006.
  47. Van Dyke Parunak H., Bisson R., Brueckner S., Matthews R., Sauter J. A model of emotions for situated agents. Proc. of the fifth International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS '06), 2006, pp. 993–995.
  48. Jiang H., Vidal J.M., Huhns M.N. EBDI: An architecture for emotional agents. Proc. of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS'07), 2007, pp. 38–40.
  49. Jones H., Saunier J., Lourdeaux D. Personality, emotions and physiology in a BDI agent architecture: The PEP→BDI model. Proc. of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2009). V. 2, 2009, pp. 263–266.
  50. Hernández D.J., Deniz Ó., Lorenzo J., Hernández M. BDIE: A BDI like architecture with emotional capabilities. AAAI Spring Symposium - Technical Report, 2004, vol. 2, pp. 60–67.
  51. Allais M. The so-called allais paradox and rational decisions under uncertainty. Expected Utility Hypotheses and the Allais Paradox, 1979, pp. 437–681.
  52. Beresford B., Sloper P. Understanding the dynamics of decision-making and choice: A scoping study of key psychological theories to inform the design and analysis of the panel study. Social Policy Research Unit, University of York York, 2008.

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Copyright 2001-2023 ©
Scientific and Technical Journal
of Information Technologies, Mechanics and Optics.
All rights reserved.