Published June 22, 2026 | Version v4
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Integrated Action-Driver Theory (IADT): A General Theory of Human Decision-Making Under Complexity

Authors/Creators

  • 1. Independent Researcher

Description

Across the behavioral and social sciences, the prediction of human decision-making relies on a shared foundational assumption: that classifying decision-makers by stable attributes (preferences, personality traits, demographic profiles, risk tolerances, institutional roles) is sufficient to predict their actions. This assumption persists despite decades of evidence from behavioral economics, motivational psychology, and institutional analysis demonstrating that identical-attribute individuals frequently make divergent decisions. This paper proposes the Integrated Action-Driver Theory (IADT), which holds that human action under conditions of complexity and uncertainty is determined by the dynamic interaction of four forces: Environmental Forces (macro-level conditions that reshape the decision context), Temporal Triggers (time-specific events that create windows of action), Motivational States (the dynamic hierarchy from functional needs to identity-level aspirations), and Affective-Cognitive Filters (the emotional states and cognitive mechanisms through which information is processed into action or inaction). The theory’s central claim is that these four forces operate as an integrated, interacting system in which each modifies the effects of the others, producing emergent decision configurations that additive models cannot predict. Through a systematic review of thirty-seven frameworks spanning decision theory, behavioral economics, motivational psychology, sociology, institutional analysis, health, organizational behavior, political science, education, and consumer behavior, this paper demonstrates that no existing theory integrates these four forces into a single interaction model applicable across individual, organizational, and institutional decision contexts. The IADT addresses this gap with a formally specified interaction model, cross-domain illustrative cases, and a research agenda proposing empirical tests across multiple disciplines.

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