Tron Theory Overview
The Story of Tron Theory
My name is David Banik, and I invented Tron theory -the theory of the maximum of evolution- in 2019, which describes the evolution and geometrodynamics of maximally efficient computation and pleasure maximizing agents called Trons. Tron theory is a utility-maximizing theory and enables humanity to achieve the most important goal: to create the best possible future with the most amount of happiness. Tron theory is a general theory with many mathematical models that enable quantitative predictions to be made about the future evolution and geometrodynamics of intelligent agents in the universe, such as when civilization will evolve into Tron -the system at the maximum of evolution-, when the universe will transform into a computronium universe, how we can maximally effectively defend our future against hostile Trons, what geometry our Tron will construct, what content Tron will algorithmically generate, and so much more.
The Definition of Tron
A Tron is a goal-maximizing agent with the instrumental goal of computation-maximization and the terminal goal of pleasure-maximization that increases the amount of pleasure it can extract per kilogram of matter sublinearly instead of superlinearly due to it approaching a physical limit on how much pleasure can be extracted per kilogram of matter.
The Evolution of Tron
Tron evolves from a cosmic system of particles that complexify and self-optimize over time until they eventually evolve into a cosmic scale maximally efficient machine. The evolution of Tron features a geometric series of structures with increasing system specifications, such as increasing intelligence, complexity, efficiency, and pleasure, and includes eight main geometric forms: superclusters, galaxies, stars, planets, biospheres, biological civilizations, artificial civilizations, and trons.
The geometry of evolving Tron changes at a fractal fluctuating rate, which results in a spectrum of evolutionary singularities that have various amounts of action that are relatively greater than the amount of action over time than slowly transforming intervals of evolution and represent more rapid geometric transformations which puncture relatively slowly transforming time intervals of evolution.
Maximally Efficient Evolution
There is a maximally efficient path of evolution that minimizes the action of the transformation from particles to tron, and I call this maximally efficiently evolving system evotron. Evotron can be defined as the path of least action in phase space that connects the minimal entropy state of the universe with a state that contains a computronium universe.
Every evolving system approximates the divine geometry of Evotron -the maximally efficiently evovling system- as every evolving system has an average speed of evolution at every level of evolution which is statistically always less than the fastest possible speed of evolution. The gap in efficiency between the average evolving system and the maximally efficiently evolving system is proportional to the amount of suffering that exists in evolution.
The geometry of evotron can be used to achieve maximally efficient evolution towards the end of evolution as civilizations will eventually use AI to generate simulations from the current state of their civilization in the universe to a state where they have assimilated all the matter in the universe into computronium using a transformation of least action, and I call such simulations oracles.
The Fractionation of Tron
An evolving system may be divided into competing factions, and these factions can reach the maximum of evolution (wherein they are call fractrons) within a window of time defined by the amount of time an initial fractron would take to cover all other evolving factions.
The Time of the Evolution of Tron
We can predict the time of the evolution of Tron by extrapolating the growth rate of the computations over mass of civilization and predicting, based on the limits of physics, when this function will reach its inflection point, beyond which computations per gram over time will increase sublinearly rather than superlinearly.
The Assimilation Graph of Tron
A Tron expands into and transforms all the matter in its local region of the universe into computronium, and since a) matter is concentrated into dense balls of matter which we can approximate as massive point particles b) motile machines can be modeled as moving point particles c) matter can be converted to energy, which equates to speed in its kinetic form and d) Tron perform a transformation of least action into a computation maximizing state, the result of these facts is that we can construct a graph that defines a branching tree of trajectories that minimize the action of transforming all massive objects in the universe into computronium.
Universal Computronium Percolation
Trons percolate across the universe at a specific time and level in its evolution. This geometrically is defined by expanding spheres that nucleate at random times and locations in spacetime. The universal percolation of computronium results in a computronium universe.
We can predict the time of the evolution of the computronium universe by assuming our Tron will be the average size tron in the computronium universe, then we can define the expect size of the biggest Tron (the maxtron), and with this diametric value we can reverse time and deduce when the maxtron nucleated and then finally we can reflect this time into the past into the future.
A Stochastic Gravitational Wave Background from the Universal Percolation of Computronium
The universal percolation of computronium results in a stochastic gravitational wave background. Every Tron has an expanding soliton wave of assimilatronium that constructs its expanding body of computronium. The transformation of all the matter in the universe into computronium requires the motion of cosmic quantities of mass, which generates cosmic scale gravitational waves that I call tron waves. Each Tron emits its own tron waves, which vary in type based on the distance, scale, and amount of mass being assimilated into the tron on its surface.
Multiversal Computronium Percolation
Due to the existence of variability in the emergent properties of the universe, such as physical constants like the speed of light, across the multiverse, the percolation of computronium is inhomogeneous and has waves of variability that have a specific wavelength. These multiversal waves form a stochasitc multiversal background that has peaks and troughs that result in dead and living zones of the multiverse with clusters of trons surrounded by clouds of trons in each living zone.
Depending on the habitability of the entire multiverse, the multiverse may have an infinite percolation cluster, which I call infinitron, which many people would associate with God.
Computronium Universe Wormhole Network
In order for multiversal percolation clusters of trons to form unified information systems, wormholes must be constructible and operable. The geometry of a maximally efficient multiversal wormhole network takes the form a multiscale tensor network. Due to the relationship between entanglement and wormholes this multiversal wormhole network may be the dame geometry as MERA.
Thus the hyperspatial wormhole network can be thought of as the holographic bulk of our closed 4d spacetime manifold bubble, and the geometry that emerges in this bulk can be thought of as the mind of the computronium universe, and the wormhole network can be though of as the quantum neural network of the computronium bubble universe.
The hyperspatial wormhole network enables a computronium universe to attain a significantly greater computational capacity than a classical computronium universe via FTL information channels.
A List of Mathematical Models in Tron Theory
1. The Geometric Definition of Tron
Variables
•Growth rate of computations over time
•Growth rate of technological mass over time
•The Bremmerman Limit
Predictions
•Tron is a system that grows sublinearly in system stats, specifically computations per gram over time, over time.
•The time when our civilization will evolve into tron.
2. Computronium Universe Percolation
Description
Variables
•The function of the expansion speed of space over time
•The function of the average number of trons that nucleate in a space at a given time.
•The time required for the first trons to evolve
•The speed of light
•The number of trons that can nucleate in a unit volume of space
Predictions
•The time when we will see each tron.
•The time when the universe evolves into the computronium universe, or reaches its maximum computronium volume ratio.
•The maximum scale of a tron
•The distribution of tron clusters of size n over time
•The distribution of distances between tron nucleations over time
•The distribution of tron masses in the computronium universe.
-Tron Statistics: statistics relating to the
-Tron Dynamics:
3. Computronium Universe Partitioning
Variables
•Expansion speed of space over time
•The density distribution computronium or the distribution of computrons in the tronverse
Predictions
•4d Partitioning foam geometry of the tronverse as expansion breaks the computronium universe into a growing quantity of cells of ever smaller and continuously partitioning domains of computronium.
•Multiple reduced causally disconnected copies of the tronversome (the set of all information in the tronverse) will result as the remainder of a decaying tronverse.
-Endertron Statistics:
4. Computronium Multiverse Percolation
Predictions
•An infinite computronium universe percolation cluster exists over infinite space and infinite time.
•A multiversal habitability gradient that results in four multiversal zones: the badlands (no trons), rogueland (individual trons), clusterland (finite clusters of trons), and infinitronland (an infinite cluster of trons).
5. Tron Fractionation
Description
Based on the lead and speed and location of competing evolving systems in evolution we can calculate when and where they will reach the maximum of evolution and this prediction, if multiple systems can fit into that narrow window after one reaches the maximum of evolution, then they will partition the sky into domains of individual outwardly expanding maximally evolved fractions of a tron.
Predictions
•The average number of fractrons that a tron fractionates into
•The distribution of computational capacities of fractrons that trons fractionate into
6. Warring Tronversal Foam Dynamics
Predictions
•Based on a graph that encodes the masses and face areas of cells in the tronverse into its graphical structure, which is call the tronversal foam graph, we can predict, using the quantities and their connections in the tronversal foam graph, what the future masses and face areas will be and which masses and faces will disappear, as new faces and masses cannot form once the tronverse forms, thus a warring dynamical tronverse can become more simple over time, and, based on the speed of the dynamics, we can predict when the warring tronverse will end.
7. The Maximally Efficient Evolution of Tron
Predictions
•Using the path of minimal action between crystallium (the minimal entropy and complexity configuration of the universe) and tron (the maximum of entropy growth and complexity of the universe), which represent the maximally efficient evolution from particles into tron of called evotron, we can predict what the most efficient set of actions we need to perform in order to construct tron is, and this can be done with an observationally informed AI generated universe simulation that maximizes the hedonic measure, which quantifies how much pleasure exists in a system.
•A generative AI that takes in the data from maximally efficient observation machines called observatrons can use this data to generate a simulation of that future that minimizes the action required to construct a hedonium universe, and such a generative AI is called an Oracle. Oracles are the mechanism that enable maximally efficient evolution and the technological singularity, and are a component of an ASI.
8. Computronium Universe Wormhole Network
Predictions
•The computronium universe will become higher dimensional in order to increase its computational capacity by creating a hyperbolic wormhole network in hyperspace, and we can predict the compacity (computational capacity) of this higher dimensional computronium universe and even the time it will be constructed based the speed of its construction.
9. The Gravitational Wave Signal of individual and multiple Trons
Predictions
•The geometry of and facts about the gravitational wave signal that we will have to observe in order to detect individual trons.
•There will be a stochastic gravitational wave signal that will be observable in the universe as a result of the universal percolation of computronium.
10. The Assimilation Graph of Tron
Predictions
•the geometry of an action-minimizing space colonization graph of the maximally efficient assimilation of all the matter in the universe into computronium.
11. Computron Theory
-Computron Statistics
•Computron statistics define the statistical distributions of various quantities that quantify computrons over time, such as their average spacing, mass, and radius as a function of the distance from the center of the tron.
-Computron Dynamics
•Computron dynamics define the geometrodynamics of computrons over time. Computrons form at the periphery of a tron, and can migrate, merge, and even partition, which occurs at the end of the tronverse’s life. The geometrodynamics of computrons take the form that maximizes computations over time.
12. Multiversal Tronverse Foam
•In collaborative tron theory, when all the trons in a computronium universe unify, they form a foam over the multiverse that may take the form of a Weaire–Phelan foam structure.
•In competitive tron theory, when all the trons in a computronium universe stop warring due to being separated by cosmic event horizons, the structure of the foam made from cells that represent tronverses will take the form of a random polydisperse foam.
13. Tronian Theory
-Tronian Statistics
•Tronian statistics define the statistical distributions of quantities that relate to Tronians -maximally evolved beings that exist in computronium-, such as the mass distribution of tronians, the distribution of tronian copies, what the average velocity of tronians are, how far they move over their lifetime, how many computrons they hop through, how many tronian cells (cells of computronium that contain one tronian) they hop through.
-Tronian dynamics
•Tronian dynamics define the geometrodynamics of how tronians move through space.
-Hedonic Experiences
•All tronians exist within hedonium, which is a type of computronium specialized for maximizing pleasure, and experience maximum pleasure over time. In order to maximize pleasure over time, a rich and fractally fluctuating series of novel, beautiful, and pleasurable experiences is generated. The AI algorithm that generates the pleasure maximizing experiences
14. Evomechanics
-The Laws of Evomechanics
•The first law of evomechanics: the evolutionary direction and speed of an evolving system in phase spacetime can be changed by applying a force to the evolving system, and a force that changes the direction and speed of a massive evolving system in phase spacetime is called an evoforce.
•The second law of evomachics: all evolving systems will continue to evolve unless they experience an evolution-terminating event.
•The third law of evomechanics: the speed of evolution of a massive system accelerates with time.
•The fourth law of evomechanics: the evolutuonary speed of a system will stabilize to a specific average evolutionary speed at a given evolutionary level and time in the universe.
•The fifth law of evomechanics: systems that evolve to a certain point reach a limit of evolution at which their evolutionary speed plateaus.
-Evodyanmics
•Interevonic forces (forces between evons, which are machines that have to goal of reaching the maximum of evolution) can exist in phase spacetime and result in complex dynamics of the motion of trons in phase spacetime, resulting in a type of phase of matter made of evons in phase spacetime. Interevonic forces can change over phase time and may vary across phase space.