Beyond Predictions: A Participatory Framework for Multi-Stakeholder Decision-Making

• URL: http://arxiv.org/abs/2502.08542v1
• Date: Wed, 12 Feb 2025 16:27:40 GMT
• Title: Beyond Predictions: A Participatory Framework for Multi-Stakeholder Decision-Making
• Authors: Vittoria Vineis, Giuseppe Perelli, Gabriele Tolomei,
• Abstract summary: We propose a novel participatory framework that redefines decision-making as a multi-stakeholder optimization problem.<n>Our framework captures each actor's preferences through context-dependent reward functions.<n>We introduce a synthetic scoring mechanism that exploits user-defined preferences across multiple metrics to rank decision-making strategies.
• Score: 3.3044728148521623
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Conventional decision-support systems, primarily based on supervised learning, focus on outcome prediction models to recommend actions. However, they often fail to account for the complexities of multi-actor environments, where diverse and potentially conflicting stakeholder preferences must be balanced. In this paper, we propose a novel participatory framework that redefines decision-making as a multi-stakeholder optimization problem, capturing each actor's preferences through context-dependent reward functions. Our framework leverages $k$-fold cross-validation to fine-tune user-provided outcome prediction models and evaluate decision strategies, including compromise functions mediating stakeholder trade-offs. We introduce a synthetic scoring mechanism that exploits user-defined preferences across multiple metrics to rank decision-making strategies and identify the optimal decision-maker. The selected decision-maker can then be used to generate actionable recommendations for new data. We validate our framework using two real-world use cases, demonstrating its ability to deliver recommendations that effectively balance multiple metrics, achieving results that are often beyond the scope of purely prediction-based methods. Ablation studies demonstrate that our framework, with its modular, model-agnostic, and inherently transparent design, integrates seamlessly with various predictive models, reward structures, evaluation metrics, and sample sizes, making it particularly suited for complex, high-stakes decision-making contexts.

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