Forest BORG, a novel framework using the BORG multi-objective evolutionary algorithm, optimizes policy trees for socio-economic systems. Tested on real-world case studies, Folsom lake and RICE IAM, it addresses multi-objective challenges under deep uncertainty, offering effective and explainable decision support for climate change policies.

The 'Forest BORG' framework, based on the BORG multi-objective evolutionary algorithm (MOEA), is introduced as a method for optimizing policy trees in socio-economic systems. This framework accommodates tree-structured decision variables through specialized evolutionary operators, enabling multi-objective and explainable decision support. Tested on the Folsom lake model and the Regional Integrated model of Climate and the Economy (RICE) Integrated Assessment Model (IAM), Forest BORG addresses the challenges of optimizing multi-objective problems under deep uncertainty, primarily driven by climate change.

The Folsom lake model and RICE IAM serve as suitable case studies due to their reflection of real-life problems affected by climate change and involving multiple stakeholders. The XLRM framework is employed to operationalize the conceptual models of these case studies, categorizing uncertainties (X), levers (L), relations (R), and metrics (M).

Positioned between current MOEA capabilities and the need for explainable policies in real-world multi-objective problems under deep uncertainty, Forest BORG undergoes rigorous testing. Analyses include run-time dynamics, Forest BORG specific evolutionary operator dynamics, controllability, exposed trade-offs, and highlighted policy trees.

The framework employs seven mutation operators, crossover with prune and bloat control operations as the recombination operator, and an adaptive tournament size function as the selection operator. The performance and behavior of Forest BORG are evaluated through the five analyses mentioned.

Run-time dynamics demonstrate the algorithm's effective convergence based on hypervolume. Search operator dynamics investigate novel mutation operators, showcasing their contribution. Controllability analysis explores the impact of hyperparameter settings on algorithm performance. Exposed trade-offs are visualized through pareto-optimal policy alternatives, emphasizing the selection of compromise policies. The highlighted policy trees provide insight into actionable decisions based on different objectives.

Forest BORG proves effective, efficient, and reliable across case studies. Comparative analysis with the original policy optimization tree (POT) algorithm reinforces its effectiveness. Although the expected shift from subtree to point mutations was not observed in all cases, the search operators enhance overall search capability. Hyperparameters are case study-dependent, with modest maximum depths favored to avoid overfitting.

Forest BORG successfully produces pareto fronts for each case study, revealing inherent trade-offs. The selected policy trees align with performance metrics, providing valuable insights for decision-makers. However, some results exhibit counter-intuitive behavior, notably in the RICE model trade-offs and small peaks in run-time analysis, attributed to epsilon values.

The research suggests further exploration of other IAMs for a more realistic representation of reality. Forest BORG's societal contribution lies in its development and application to real-world case studies, expanding the range of multi-objective decision-making tools for socio-environmental systems. The implications extend to addressing climate change through policy development, emphasizing the potential for broader applications beyond the tested case studies.