Linganisha mbinu

	Policy Scenario Genetic Algorithm	Uchambuzi wa Matukio ya Sera
Nyanja	Uigaji	Uigaji
Familia	Process / pipeline	Process / pipeline
Mwaka wa asili	1975 (GA); 2000s (policy scenario application)	1967–1990s
Mwanzilishi	Holland, J. H. (GA foundation); Lempert, Popper & Bankes (policy scenario search)	Kahn, H. & Wiener, A. J. (seminal); adapted for policy by RAND Corporation and OECD
Aina	Evolutionary metaheuristic for policy scenario exploration	Qualitative-quantitative hybrid scenario method
Chanzo asilia	Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor, MI. ISBN: 9780262581110	Swart, R., Raskin, P., Robinson, J. (2004). The problem of the future: sustainability science and scenario analysis. Global Environmental Change, 14(2), 137–146.
Majina mbadala	PSGA, Policy-GA, Policy Optimization Genetic Algorithm, Evolutionary Policy Scenario Search	PSA, Policy Scenarios, Policy Impact Scenario Analysis, Counterfactual Policy Analysis
Zinazohusiana	4	5
Muhtasari	The Policy Scenario Genetic Algorithm applies evolutionary search to systematically explore large, combinatorial policy alternative spaces under multiple future scenarios. Rather than exhaustively enumerating options, it breeds successive generations of candidate policies, retaining those that perform well across scenario conditions, yielding robust, high-performing policy recommendations.	Policy Scenario Analysis is a structured method for evaluating how different policy interventions perform across a range of plausible future states. By pairing specific policy levers with alternative scenarios, analysts can assess robustness, trade-offs, and unintended consequences of policy choices before implementation — making it a cornerstone of evidence-based policy design in fields from climate to public health.