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Agent-based goal programming

Agent-Based Goal Programming (ABGP) integrates agent-based simulation with goal programming optimization to model systems where multiple autonomous decision-makers pursue competing, prioritized goals. It enables researchers to study how decentralized, adaptive behavior at the agent level leads to system-level outcomes measured against predefined targets, capturing both emergence and multi-criteria satisfaction simultaneously.

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Agent-Based Goal Programming — Hybrid simulation-optimization with decentralized agents and multi-goal satisfaction

Taksonomisk metoderegister · process-pipeline / simulation

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Same method familyAgent-Based Modelingmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketAgent-based multi-objective optimizationmachine-suggested · Relational suggestion, not evidence.See alsoGOAL-PROGRAMMINGmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketMulti-objective goal programmingmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketStochastic Goal Programmingmachine-suggested · Relational suggestion, not evidence.