Abstract

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. For a detailed domain application to financial services marketing, see Mehrhoff (2026, “Dynamic Audience Segmentation Across Marketing, Sales, and Product Strategy: An Application of the Integrated Action-Driver Theory (IADT)”).

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the author

Six frontiers. Seventeen years. Three Laws of Decision-Making.

Patrick Mehrhoff is a German entrepreneur, independent researcher, AI engineer, and author. He is the author of the three original theories that constitute the Mehrhoff Research Program. He built the work the studio carries.

Most practitioners spend a career inside one domain. He worked as a practitioner at six of the frontiers that redefined how society organises information, money, and value over the last two decades. The consumer internet, Swiss fintech, European monetary policy, Southeast Asian financial publishing, institutional crypto assets, and the first wave of applied AI.

The same structural observation appeared at every frontier. By 2019, on a whiteboard at crypto infrastructure provider Wyden in Zurich, the pattern had become visible to itself. It did not yet have a name. Four years before any AI language model became publicly available, the complete logic of the Three Laws of Decision-Making was already on the whiteboard.

The studio was built from Bangkok, without institutional affiliation and without external funding. The Rubens workshop model defines how it operates. The founder sets the intellectual direction at every decisive step. The tools and collaborators execute within it.

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