The Free Will Equation: Quantum Field Analogies for AGI

• URL: http://arxiv.org/abs/2507.14154v1
• Date: Fri, 04 Jul 2025 10:25:52 GMT
• Title: The Free Will Equation: Quantum Field Analogies for AGI
• Authors: Rahul Kabali,
• Abstract summary: This paper proposes a theoretical framework, called the Free Will Equation, to endow AGI agents with a form of adaptive, controlledity in their decision-making process.<n>The core idea is to treat an AI agent's cognitive state as a superposition of potential actions or thoughts, which collapses probabilistically into a concrete action when a decision is made.<n> Experiments in a non-stationary multi-armed bandit environment demonstrate that agents using this framework achieve higher rewards and policy diversity compared to baseline methods.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Artificial General Intelligence (AGI) research traditionally focuses on algorithms that optimize for specific goals under deterministic rules. Yet, human-like intelligence exhibits adaptive spontaneity - an ability to make unexpected choices or free decisions not strictly dictated by past data or immediate reward. This trait, often dubbed "free will" in a loose sense, might be crucial for creativity, robust adaptation, and avoiding ruts in problem-solving. This paper proposes a theoretical framework, called the Free Will Equation, that draws analogies from quantum field theory to endow AGI agents with a form of adaptive, controlled stochasticity in their decision-making process. The core idea is to treat an AI agent's cognitive state as a superposition of potential actions or thoughts, which collapses probabilistically into a concrete action when a decision is made - much like a quantum wavefunction collapsing upon measurement. By incorporating mechanisms analogous to quantum fields, along with intrinsic motivation terms, we aim to improve an agent's ability to explore novel strategies and adapt to unforeseen changes. Experiments in a non-stationary multi-armed bandit environment demonstrate that agents using this framework achieve higher rewards and policy diversity compared to baseline methods.

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