Pratixa: A Cognitive Framework for Behavioral Decision‐Making and Its Mathematical Formalization

Santhosh Kareepadath Rajan

Futures & Foresight Science, 2025, vol. 7, issue 3

Abstract: The present study introduces pratixa, an internal cognitive structure that functions as a reference architecture guiding human decision‐making. Pratixa is a dynamic, event‐sensitive archive of anticipated outcomes of behavior, learned event‐behavior‐outcome associations, and adaptive behavioral responses, drawing on the theories from decision science, psychology, and behavioral adaptation. Past experiences shape pratixa, and iterative learning reinforces it. It supports predictive mental representations by enabling individuals to anticipate the outcomes of their own behavioral responses and adjust those responses when discrepancies arise between anticipated and actual outcomes. Pratixa supports anticipatory learning and real‐time correction, making it a future‐oriented cognitive structure for decision making. It matures in a spiral progression, from null pratixa, where no prior event‐behavior‐outcome associations exist, through quixotic pratixa, characterized by illusory or arbitrary associations, to realistic pratixa, where causal relationships are adequately approximated. This spiral maturation reflects how individuals adapt through experiential learning and reinforcement, transitioning from effortful reasoning to increasingly automatic and context‐sensitive decision‐making. By positioning decision‐making within this evolving structure, pratixa offers a distinct perspective on predictive cognition in complex and ambiguous contexts, with implications for strategic foresight, behavioral economics, and adaptive behavioral decision making. The study also proposes a mathematical formulation to represent how this reference architecture evolves through reinforcement‐based learning and guides decision‐making, providing a computational basis for modeling human foresight and adaptation.

Date: 2025
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https://doi.org/10.1002/ffo2.70017

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Persistent link: https://EconPapers.repec.org/RePEc:wly:fufsci:v:7:y:2025:i:3:n:e70017

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