Probabilistic Machine Learning Driven Decision Support System for Enhancing Policy Decision-Making Under Uncertainty
Keywords:
Uncertainty Modeling, Probabilistic Machine Learning, Policy Decision-Making, Risk Analysis, Decision Support SystemsAbstract
This research examines the role of uncertainty modeling in enhancing the quality, reliability, and adaptability of policymaking. Traditional policy decisions often rely on fixed assumptions that fail to account for the inherent volatility of social, economic, and environmental systems. By integrating probabilistic techniques, scenario analysis, and sensitivity-based evaluation, the study demonstrates how policymakers can better anticipate variability in economic, social, and environmental outcomes. The findings indicate that uncertainty modeling not only improves predictive accuracy but also strengthens policy resilience by revealing hidden risks, alternative pathways, and the range of possible impacts under differing conditions. The research contributes a structured framework for incorporating uncertainty into policy design and evaluation, providing practical tools for evidence-based decision-making. In practice, the model enables policymakers to make more adaptive, transparent, and risk-aware decisions, ultimately transforming traditional deterministic approaches into dynamic strategies capable of responding effectively to complex and unpredictable real-world challenges.
Downloads
References
K. Maani, Multi-stakeholder decision making for complex problems: A systems thinking approach with cases. World Scientific, 2016.
T. Coito, B. Firme, M. S. E. Martins, S. M. Vieira, J. Figueiredo, and J. M. C. Sousa, “Intelligent sensors for real-Time decision-making,” Automation, vol. 2, no. 2, pp. 62–82, 2021.
D. Bacciu, P. J. G. Lisboa, A. Sperduti, and T. Villmann, “Probabilistic modeling in machine learning,” in Springer Handbook of Computational Intelligence, Springer, 2015, pp. 545–575.
M. Ghavamzadeh, S. Mannor, J. Pineau, and A. Tamar, “Bayesian reinforcement learning: A survey,” Found. Trends® Mach. Learn., vol. 8, no. 5–6, pp. 359–483, 2015.
L. Yu, S. Wang, and K. K. Lai, “An integrated data preparation scheme for neural network data analysis,” IEEE Trans. Knowl. Data Eng., vol. 18, no. 2, pp. 217–230, 2006.
L. E. Sucar, Probabilistic graphical models. Springer, 2021.
R. K. Behera, P. K. Bala, and R. Jain, “A rule-based automated machine learning approach in the evaluation of recommender engine,” Benchmarking An Int. J., vol. 27, no. 10, pp. 2721–2757, 2020.
A. Bousdekis, K. Lepenioti, D. Apostolou, and G. Mentzas, “A review of data-driven decision-making methods for industry 4.0 maintenance applications,” Electronics, vol. 10, no. 7, p. 828, 2021.
J. C. Stephens, E. J. Wilson, and T. R. Peterson, “Socio-Political Evaluation of Energy Deployment (SPEED): An integrated research framework analyzing energy technology deployment,” Technol. Forecast. Soc. Change, vol. 75, no. 8, pp. 1224–1246, 2008.
R. Heijungs, J. Guinée, R. Kleijn, and V. Rovers, “Bias in normalization: causes, consequences, detection and remedies,” Int. J. Life Cycle Assess., vol. 12, no. 4, pp. 211–216, 2007.
S. C. Bankes, “Tools and techniques for developing policies for complex and uncertain systems,” Proc. Natl. Acad. Sci., vol. 99, no. suppl_3, pp. 7263–7266, 2002.
M. Yarandi, H. Jahankhani, and A.-R. H. Tawil, “An adaptive e-learning Decision support system,” in 2012 15th International Conference on Interactive Collaborative Learning (ICL), IEEE, 2012, pp. 1–5.
J. W. Hansen, “Integrating seasonal climate prediction and agricultural models for insights into agricultural practice,” Philos. Trans. R. Soc. B Biol. Sci., vol. 360, no. 1463, pp. 2037–2047, 2005.
N. K. Newlands et al., “An integrated, probabilistic model for improved seasonal forecasting of agricultural crop yield under environmental uncertainty,” Front. Environ. Sci., vol. 2, p. 17, 2014.
M. Ghil et al., “Extreme events: dynamics, statistics and prediction,” Nonlinear Process. Geophys., vol. 18, no. 3, pp. 295–350, 2011.
C. I. Eke, A. A. Norman, and L. Shuib, “Multi-feature fusion framework for sarcasm identification on twitter data: A machine learning based approach,” PLoS One, vol. 16, no. 6, p. e0252918, 2021.
A. Cogato, F. Meggio, M. De Antoni Migliorati, and F. Marinello, “Extreme weather events in agriculture: A systematic review,” Sustainability, vol. 11, no. 9, p. 2547, 2019.
J. R. Kasprzyk, S. Nataraj, P. M. Reed, and R. J. Lempert, “Many objective robust decision making for complex environmental systems undergoing change,” Environ. Model. Softw., vol. 42, pp. 55–71, 2013.
H. Eakin, E. L. Tompkins, D. R. Nelson, and J. M. Anderies, “Hidden costs and disparate uncertainties: trade-offs in approaches to climate policy,” Adapt. to Clim. Chang. Threshold. values, Gov., vol. 212, 2009.
Z. Ma and G. Chen, “Bayesian methods for dealing with missing data problems,” J. Korean Stat. Soc., vol. 47, no. 3, pp. 297–313, 2018.
A. Elliott-Graves, “The value of imprecise prediction,” Philos. theory, Pract. Biol., vol. 12, no. 4, 2020.
R. B. Myerson and E. Zambrano, Probability models for economic decisions. Mit Press, 2019.
P. J. H. Schoemaker, “Forecasting and scenario planning: The challenges of uncertainty and complexity,” Blackwell Handb. Judgm. Decis. Mak., pp. 274–296, 2004.
Z. Ghahramani, “Probabilistic machine learning and artificial intelligence,” Nature, vol. 521, no. 7553, pp. 452–459, 2015.
A. Azari, M. Ozger, and C. Cavdar, “Risk-aware resource allocation for URLLC: Challenges and strategies with machine learning,” IEEE Commun. Mag., vol. 57, no. 3, pp. 42–48, 2019.
U. Bhatt et al., “Uncertainty as a form of transparency: Measuring, communicating, and using uncertainty,” in Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 2021, pp. 401–413.
I. Sanderson, “Complexity,‘practical rationality’and evidence-based policy making,” Policy Polit., vol. 34, no. 1, pp. 115–132, 2006.
J. S. Clark and O. N. Bjørnstad, “Population time series: process variability, observation errors, missing values, lags, and hidden states,” Ecology, vol. 85, no. 11, pp. 3140–3150, 2004.
A. Chatzimparmpas, R. M. Martins, I. Jusufi, K. Kucher, F. Rossi, and A. Kerren, “The state of the art in enhancing trust in machine learning models with the use of visualizations,” in Computer Graphics Forum, Wiley Online Library, 2020, pp. 713–756.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Adskhan Reyhan Ekrem
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
