Extending the Subjective Dynamic Decision Model: A Heuristic and Meta-Cognitive Framework for Strategic Adaptation

Abstract

This paper introduces a novel extension of the Subjective Dynamic Decision Model (SDEM) by integrating multiple context-sensitive heuristics, adaptive decision thresholds, and a meta-cognitive uncertainty parameter. Unlike classical decision models, this enhanced framework explicitly captures feedback learning, subjective uncertainty, and self-reflective judgment, enabling a more realistic simulation of strategic behaviour under radical uncertainty. Traditional decision theories often rely on assumptions of rational agents, stable preferences, and complete information. However, in real-world contexts such as crises, complex markets, or adversarial environments, such conditions are rarely met. The enhanced SDEM integrates three core extensions to reflect cognitive and strategic diversity: (1) the availability of multiple context-sensitive heuristics, (2) a dynamic adjustment of the decision threshold based on experience, and (3) a meta-cognitive uncertainty parameter that regulates decision-making based on subjective confidence. These additions allow for the formal inclusion of adaptive learning, bounded rationality, and self-reflective judgement in decision processes. The paper presents the theoretical underpinnings of the model, discusses its implications for cognitive modelling and agent-based simulations, and illustrates its functionality through practical examples. Potential applications include cybersecurity, strategic intelligence, supply chain resilience, and energy markets. The proposed framework enables realistic modelling of heterogeneous behaviour in multi-agent environments and offers new insights into dynamic adaptation under uncertainty.