The Subjective Dynamic Decision Model (SDEM): A New Approach to Decision-Making Under Radical Uncertainty
Abstract
This paper develops a hybrid model for strategic decision-making in contexts of radical uncertainty. It combines the formal structure of game Theory with the epistemological principles of the Austrian School of Economics. The Subjective Dynamic Decision Model (SDEM) describes decisions not as the result of rational optimisation, but as the result of subjective perception, heuristic action strategies and iterative learning processes. The focus is on modelling the subjective decision-making state, consisting of information, conviction and expectation. Decisions arise from this through experience-based heuristics and are modified by observed results. In addition to the theoretical derivation, the article also provides concrete applications in the areas of cyber security, market behaviour and intelligence work. Simulation-based analyses are used to show how adaptive decision-making strategies develop in complex environments. While generalisability to real-world settings may be limited due to reliance on simulation, SDEM enables practical scenario modelling in volatile domains where traditional optimisation fails. The aim is not to describe optimal solutions, but to analyse subjective adaptation processes in non-linear decision-making contexts. The model thus stands for an insight-oriented approach to decision research
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Al-Ubaydli, O., & List, J. (2012). On the Generalizability of Experimental Results in Economics. https://doi.org/10.3386/w17957
An, Y., Hu, Y., & Xiao, R. (2020). Dynamic decisions under subjective expectations: A structural analysis. Journal of Econometrics, 222(1), 645– 675. https://doi.org/10.1016/j.jeconom.2020.04.046
Arthur, W. B. (1994). Inductive Reasoning and Bounded Rationality. The American Economic Review, 84(2), 406–411. http://www.jstor.org/stable/2117868
Basov, S. (2004). Bounded rationality: static versus dynamic approaches. Economic Theory, 25(4). https://doi.org/10.1007/s00199-004-0484-6
Bayraktar, E., Zhang, J., & Zhou, Z. (2020). Equilibrium concepts for time‐inconsistent stopping problems in continuous time. Mathematical Finance, 31(1), 508–530. https://doi.org/10.1111/mafi.12293
Castellini, M., Di Corato, L., Moretto, M., & Vergalli, S. (2021). Energy exchange among heterogeneous prosumers under price uncertainty. Energy Economics, 104, 105647. https://doi.org/10.1016/j.eneco.2021.105647
Cooke, R. M. (2017). Validation in the classical model. In International series in management science/operations research/International series in operations research & management science (pp. 37–59). https://doi.org/10.1007/978-3-319-65052-4_3
Czeglédi, P. (2020). The consistency of market beliefs as a determinant of economic freedom. Constitutional Political Economy, 31(2), 227–258. https://doi.org/10.1007/s10602- 020- 09301-x
Dricu, M., Bührer, S., Moser, D. A., & Aue, T. (2023). Asymmetrical Update of Beliefs About Future Outcomes is Driven by Outcome Valence and Social Group Membership. International Review of Social Psychology, 36(1), 2. https://doi.org/10.5334/irsp.647
Epstein, J. M. (2006). Generative social science: Studies in agent-based computational modeling. Princeton University Press. https://www.degruyterbrill.com/document/doi/10.1515/9781400842872/html
Etner, J., Jeleva, M., & Tallon, J. (2010). Decision Theory Under Ambiguity. Journal of Economic Surveys, 26(2), 234– 270. https://doi.org/10.1111/j.1467-6419.2010.00641.x
Fox, J., Cooper, R. P., & Glasspool, D. W. (2013). A Canonical theory of dynamic Decision-Making. Frontiers in Psychology, 4. https://doi.org/10.3389/fpsyg.2013.00150
Georgalos, K. (2021). Dynamic decision making under ambiguity: An experimental investigation. Games and Economic Behavior, 127, 28– 46. https://doi.org/10.1016/j.geb.2021.02.002
Gigerenzer, G., & Selten, R. (Eds.). (2002). Bounded rationality: The adaptive toolbox. MIT press. https://core.ac.uk/download/pdf/210844206.pdf
Gilbert-Saad, A., Siedlok, F., & McNaughton, R. B. (2023). Entrepreneurial heuristics: Making strategic decisions in highly uncertain environments. Technological Forecasting and Social Change, 189, 122335. https://doi.org/10.1016/j.techfore.2023.122335
Gintis, H. (2007). The dynamics of general equilibrium. The Economic Journal, 117(523), 1280–1309. https://doi.org/10.1111/j.1468-0297.2007.02083.x
Gonzalez, C., Fakhari, P., & Busemeyer, J. (2017). Dynamic decision making: learning processes and new research directions. Human Factors the Journal of the Human Factors and Ergonomics Society, 59(5), 713– 721. https://doi.org/10.1177/0018720817710347
Hayek, F.A. (1945) 'The use of knowledge in society,' The American Economic Review, 35(4), pp. 519– 530. https://www.jstor.org/stable/1809376.
He, Y. (2021). Revisiting Ellsberg’s and Machina’s Paradoxes: A Two-Stage Evaluation Model under Ambiguity. Management Science, 67(11), 6897– 6914. https://doi.org/10.1287/mnsc.2020.3835
He, Y. (2024). Recursive two-stage evaluation model for dynamic decision making under ambiguity. Journal of Mathematical Economics, 113, 103022. https://doi.org/10.1016/j.jmateco.2024.103022
Hey, J. D., & Cross, J. G. (1985). A theory of adaptive economic behaviour. Economica, 52(205), 127. https://doi.org/10.2307/2553999
Hüllermeier, E., & Waegeman, W. (2021). Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods. Machine Learning, 110(3), 457–506. https://doi.org/10.1007/s10994-021-05946-3
Kahneman, D. (2011). Fast and slow thinking. Allen Lane and Penguin Books, New York. https://durmonski.com/wp-content/uploads/2021/09/Think_Workbook_005.pdf
Kay, J., & King, M. (2020). Radical uncertainty: Decision-making beyond the numbers. WW Norton & Company https://wwnorton.com/books/9781324004776
Kimbrough, E. O. (2010). Heuristic learning and the discovery of specialization and exchange. Journal of Economic Dynamics and Control, 35(4), 491– 511. https://doi.org/10.1016/j.jedc.2010.10.002
Klibanoff, P., Marinacci, M., & Mukerji, S. (2005). A Smooth Model of Decision Making under Ambiguity. Econometrica, 73(6), 1849–1892. https://doi.org/10.1111/j.1468-0262.2005.00640.x
Knight, F. H. (1921). Risk, uncertainty, and profit. New York: Hart, Schaffner, and Marx. https://cdn.gecacademy.cn/oa/upload/2022-03-10%2011-28-18-knight-uncertainty-and-profit.pdf
Kurdoglu, R. S., Ates, N. Y., & Lerner, D. A. (2022). Decision-making under extreme uncertainty: eristic rather than heuristic. International Journal of Entrepreneurial Behaviour & Research, 29(3), 763–782. https://doi.org/10.1108/ijebr-07-2022-0587
Ma, W., Luo, X., & Jiang, Y. (2017). An Ambiguity Aversion Model for Decision Making under Ambiguity. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1). https://doi.org/10.1609/aaai.v31i1.10569
Marwala, T., Boulkaibet, I., & Adhikari, S. (2016). Probabilistic finite element model updating using Bayesian statistics. https://doi.org/10.1002/9781119153023
Matli, W., & Phurutsi, M. (2023). Extending the use of the Belief Action Outcome model during COVID-19 pandemic: Technology access review on locational disparities and inequalities for knowledge workers. Procedia Computer Science, 219, 977– 986. https://doi.org/10.1016/j.procs.2023.01.375
Moch, E. (2025). Game theory and the dynamics of entrepreneurial decisions in free markets. East African Journal of Business and Economics, 8(1), 306– 327. https://doi.org/10.37284/eajbe.8.1.2877
Monye, S. I., Afolalu, S. A., Lawal, S. L., Oluwatoyin, O. A., Adeyemi, A. G., Ughapu, E. I., & Adegbenjo, A. (2023). Impact of Industry (4.O) in automobile industry. E3S Web of Conferences, 430, 1222. https://doi.org/10.1051/e3sconf/202343001222
Nygaard, A. (2022). The Geopolitical Risk and Strategic Uncertainty of Green Growth after the Ukraine Invasion: How the Circular Economy Can Decrease the Market Power of and Resource Dependency on Critical Minerals. Circular Economy and Sustainability, 3(2), 1099–1126. https://doi.org/10.1007/s43615-022-00181-x
Pan, W., Li, Z., Zhang, Y., & Weng, C. (2018). The new hardware development trend and the challenges in data management and analysis. Data Science and Engineering, 3(3), 263–276. https://doi.org/10.1007/s41019-018-0072-6
Pellis, S. M., Pellis, V. C., & Iwaniuk, A. N. (2014). Pattern in behavior. In Advances in the study of behavior (pp. 127– 189). https://doi.org/10.1016/b978-0-12-800286-5.00004-3
Sarin, R. K., & Winkler, R. L. (1992). Ambiguity and decision modeling: A preference-based approach. Journal of Risk and Uncertainty, 5(4), 389–407. https://doi.org/10.1007/bf00122577
Simon, H. A. (1955). A behavioral model of rational choice. The Quarterly Journal of Economics, 69(1), 99. https://doi.org/10.2307/1884852
Sin, Y., Seon, H., Shin, Y. K., Kwon, O., & Chung, D. (2021). Subjective optimality in finite sequential decision-making. PLoS Computational Biology, 17(12), e1009633. https://doi.org/10.1371/journal.pcbi.1009633
Struckell, E., Ojha, D., Patel, P. C., & Dhir, A. (2022). Strategic choice in times of stagnant growth and uncertainty: An institutional theory and organizational change perspective. Technological Forecasting and Social Change, 182, 121839. https://doi.org/10.1016/j.techfore.2022.121839
Tesfatsion, L., & Judd, K. L. (Eds.). (2006). Handbook of computational economics: agent-based computational economics (Vol. 2). Elsevier. https://www.sciencedirect.com/handbook/handbook-of-computational-economics/vol/2/suppl/C.
Tuckett, D., & Nikolic, M. (2017). The role of conviction and narrative in decision-making under radical uncertainty. Theory & Psychology, 27(4), 501–523. https://doi.org/10.1177/0959354317713158
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