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Briefing on Computational Models of Consciousness and Decision-Making

Briefing on Computational Models of Consciousness and Decision-Making

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 Briefing on Computational Models of Consciousness and Decision-Making Executive Summary This document synthesizes two distinct but complementary perspectives on consciousness and cognition. The first is a rigorous academic framework from computational neuropsychiatry that models Major Depressive Disorder (MDD) as a malfunction within an "algorithmic agent." This model deconstructs cognition into three core modules—a Modeling Engine, an Objective Function (valence evaluation), and a Planning Engine—and defines depression as a state of persistently low valence arising from dysfunction in these components, their associated neural circuits, or a hostile environment. The ultimate goal of this approach is the development of personalized, mechanistic "neurotwin" models to design and optimize therapies. The second perspective outlines a conceptual technological application, the Consciousness Consultation Engine, which aims to elevate human decision-making. This engine externalizes and structures cognitive functions into a tiered system of consultation. Its foundational "Ring of 6" directly mirrors the modules of the algorithmic agent, offering distinct perspectives from logic (MIND), emotion (HEART), action (HANDS), community (LEGS), ethics (EYE), and synthesis (AGENT). Together, these sources present a unified vision. The neuropsychiatry framework provides a detailed, neurobiologically-grounded model for understanding and repairing internal cognitive dysfunctions like MDD. The Consultation Engine provides a practical, technological framework for augmenting these same cognitive functions externally to improve decision-making for all individuals. Both approaches converge on the idea that cognition can be understood, modeled, and influenced through a modular, computational lens, pointing toward a future of personalized therapeutic interventions and technologically-assisted wisdom. -------------------------------------------------------------------------------- Part I: The Algorithmic Agent Framework for Major Depressive Disorder A comprehensive model detailed in "The Algorithmic Agent Perspective and Computational Neuropsychiatry" uses Kolmogorov theory (KT) of consciousness to reframe MDD. This approach moves beyond descriptive symptoms to a mechanistic understanding of the disorder as a systems-level failure in an information-processing agent. The Algorithmic Agent Model The framework defines an algorithmic agent as an information-processing system that interacts with the world to maximize its Objective Function. This agent is deconstructed into three minimal, interconnected modules essential for its function and evolutionary persistence (stasis). Modeling Engine: This module is responsible for building, refining, and running the agent's compressive models of the universe, including the external world and the agent itself. It generates predictions that are compared against incoming data.Objective Function: This core component maps a given model into a scalar quantity defined as valence, which corresponds to affective states like pleasure or pain. The agent's primary goal is to maximize the output of this function, which for natural agents assesses the probability of achieving homeostasis and persistence.Planning Engine: Using the world model and Objective Function, this module formulates and executes plans of action intended to increase future valence. Within this model, depression is formally defined as a pathological state where the output of the Objective Function (valence) is persistently low. This state can emerge from dysfunction within any of the core modules or from an intractable, hostile external environment. Etiological Routes and the Dynamics of Depression The agent framework allows for the dissection of multiple pathways that can lead to a depressed state. Modeling Engine Dysfunction: Failures in producing accurate world models, stemming from errors in model construction (cognitive biases), dysfunctional plasticity, or faulty simulation, can lead to low valence. A malfunctioning Comparator—which assesses prediction errors—can cause the agent to become overconfident in flawed negative models or lose confidence entirely, leading to derealization.Objective Function Dysfunction: A dysfunctional Objective Function can pathologically produce low valence regardless of the accuracy of the world model or the success of plans. This aligns with clinical symptoms like anhedonia (inability to feel pleasure) and persistent anxiety. The function may also fail to recalibrate after traumatic events, a process that normally occurs through mechanisms like REM sleep.Planning Engine Dysfunction: The engine may fail to generate plans that increase valence, or it may produce plans that actively decrease it. This can manifest as hopelessness, where the agent is unable to identify a "target world" of higher valence or formulate a credible plan to reach it.The World: Stressful life events, trauma, and ongoing ...
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