Meta-Adaptive Decision Transformers Inspired by Human Fast and Slow Cognitive Mechanisms
Keywords:
dual-process cognition, decision transformer, meta-adaptive architecture, fast and slow thinking, socio-technical systems, AI governance, fairness, robustness, sustainabilityAbstract
The deployment of artificial intelligence in complex socio-technical systems requires decision-making architectures that balance rapid responsiveness with deliberative reasoning. This paper introduces a meta-adaptive framework for decision transformers that emulates the dual-process theory of human cognition, distinguishing between fast, intuitive, and slow, analytical modes of processing. The proposed architecture integrates an adaptive meta-controller that dynamically modulates between a lightweight transformer operating in a fast inference mode and a deeper, computationally intensive transformer dedicated to reflective reasoning. This design is motivated by the cognitive science literature on bounded rationality and the limitations of monolithic neural architectures when faced with shifting environmental demands, adversarial perturbations, or resource constraints. We examine the structural trade-offs inherent in such hybrid systems, including latency-accuracy profiles, memory overhead, and energy consumption in edge and cloud deployments. From a governance perspective, the framework offers interpretability advantages by separating intuitive outputs from reasoned justifications, thereby facilitating audit and oversight mechanisms. Fairness implications are addressed through the meta-controller’s capacity to allocate cognitive resources equitably across diverse user populations and contexts. We further analyze robustness against distributional shift and strategic manipulation, drawing on insights from adversarial machine learning and causal inference. The paper situates the meta-adaptive approach within the broader landscape of infrastructure-scale AI systems, discussing deployment strategies for distributed sensor networks, autonomous fleets, and critical infrastructure monitoring. Sustainability considerations related to computational carbon footprint and hardware lifecycles are evaluated. Finally, we outline policy implications for regulatory frameworks that require adaptive compliance across jurisdictions and operational domains. This work contributes a systems-level blueprint for next-generation decision transformers that are not only performant but also trustworthy, equitable, and sustainable.
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