A Deterministic Model of Free Will
Abstract
The issue of whether we make decisions freely has vexed philosophers for millennia, Resolving this is vital for solving a diverse range of problems, from the physiology of how the brain makes decisions (and how we assign moral responsibility to those decisions) to the interpretation of experiments on entangled quantum particles. A deterministic model of free will is developed, based on two concepts. The first generalises the notion of initialisation of nonlinear systems where information cascades upscale from the Planck scale, exemplified by the chaology of colliding billiard balls, and featured in the author's Rational Quantum Mechanics. With `just-in-time'initialisation, such Planck-scale information is only initialised when it is needed to describe super-Planck scale evolution, and not e.g., at the time of the Big Bang. In this way determinism does not imply predestination and a system with finitely many degrees of freedom can shadow a system with infinitely many, over arbitrarily long timescales. The second concept describes the upscale control of such Planck-scale information on super-Planck scales and is illustrated by reference to stochastic rounding in numerical analysis. Using these concepts, a deterministic model is proposed whereby freely-made decisions are made by using past experiences to control the impact of noise in the low-energy brain. It is claimed that such a model has evolutionary advantages, not least preventing paralysis by analysis and encouraging…
Read the paper
Related studies
- Chaos and quantum thermalization
- The Dalton quantum chemistry program system
- Cord Blood Leptin and Adiponectin as Predictors of Adiposity in Children at 3 Years of Age: A Prospective Cohort Study
- Quantitative metabolomics by H-NMR and LC-MS/MS confirms altered metabolic pathways in diabetes
- Stable adiabatic quantum batteries
- A novel self-lipid antigen targets human T cells against CD1c(+) leukemias
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.