The project
An open research project building a formal mathematical model of large-scale human behavioral prediction — in public, with full transparency about what works and what doesn't.
"Psychohistory dealt not with man, but with men. Not with human life, but with human lives. It was the science of mobs, in all the implications of the word..."
— Isaac Asimov, Foundation
In Asimov's Foundation series, psychohistory is a mathematical science that can predict the broad sweep of history for large populations. Not who will be the next prime minister — but whether empires will collapse, whether revolutions will happen, whether civilizations will integrate or fragment over centuries.
Asimov wrote this as fiction. But since the 1940s, complexity science has steadily built the ingredients that would make something like this real: statistical physics that applies to social systems, empirical cliodynamics that finds mathematical patterns in history, network science that maps how ideas spread and cascade, behavioral economics that quantifies how humans deviate from rationality.
This project is an attempt to assemble those ingredients into a single formal system.
At its core, the formula is a probabilistic dynamical system. Given a description of a civilization's current state — population density, wealth inequality, elite overproduction, institutional trust, network connectivity — it outputs a probability distribution over possible future states.
It does NOT predict:
It DOES attempt to predict:
The mathematical form is a Fokker–Planck equation with a jump process — borrowed from statistical physics, which uses the same framework to describe particles in random fields. The "particles" here are civilizations; the "field" is the space of possible macro-states.
The formula is built bottom-up across four layers, each handled by specialized research agents:
Layer 1 — Micro (Individual decisions): The Behavioral Neuroscientist and Evolutionary Psychologist define the "particle parameters" — loss aversion, temporal discounting, conformity pressure, coalitional instincts. These are the parameters governing how individual humans respond to stimuli, derived from empirical psychology and cross-cultural studies.
Layer 2 — Meso (Collective pattern formation): The Network Scientist and Computational Sociologist define how micro-behaviors aggregate into collective dynamics. How does information cascade through social networks? What network topology makes a society susceptible to rapid opinion shifts? When does individual conformity become civilizational inertia?
Layer 3 — Macro (Historical laws): The Econophysicist, Cliodynamicist, and Political Scientist build the large-scale layer. Wealth distribution power laws. Secular cycles (Turchin's demographic-structural theory). Institutional constraints and how they shape the drift equations. This is where the formula connects to measurable historical patterns.
Layer 4 — Formalization (The math): The Statistical Physicist builds the actual mathematical structure that encodes all three layers into a unified predictive theory. The Bayesian Statistician turns that into honest probabilistic forecasts with calibrated confidence intervals.
The Philosopher of Science operates horizontally across all layers, attacking every output for curve-fitting masquerading as theory. No formula update can be committed without philosophical approval.
The formula is tested two ways:
Historical retrodiction: We run the formula against well-documented historical events — the collapse of the Mughal Empire, the Meiji Restoration, the Weimar Republic's destabilization, the Rwandan genocide. Can the formula retrodict these events from their preconditions? Current record: 6 PASS / 2 PARTIAL / 0 FAIL from 8 independent testable events.
Polymarket prediction:The formula generates probabilistic predictions against live Polymarket markets. When those markets resolve, we score the prediction using the Brier score and compare against the market consensus probability. The target: beat the market on >55% of resolved events. First result: predicted Hungary's Tisza election win at 80% (market 74%) — Brier score 0.0400 vs. market 0.0729.
This is the only honest validation: markets aggregate global information and are hard to beat. If the formula can do it consistently, it's doing something real.
This project requires synthesizing research across ~12 academic disciplines simultaneously: behavioral economics, evolutionary psychology, network science, cliodynamics, econophysics, statistical physics, political science, Bayesian statistics, philosophy of science, and more. No single researcher could hold all of this in their head at once with the necessary depth.
Multi-agent AI makes this tractable. Each Claude agent specializes in one research domain, searches and evaluates real academic papers, and returns structured outputs. The lead agent synthesizes. The philosopher critiques. The integrator merges proposals into the formula.
This is open research: every session log is public, every formula update is documented, every failed prediction is recorded. The goal is not to produce a polished product — it's to do the actual science of psychohistory in public.
After 15 research sessions, the formula is at version v0.5.9 with 36 open caveats and an epistemological confidence of 5.5/10.
What that means: the mathematical structure is defined and plausible, the historical retrodiction record is encouraging, but many parameters still have wide confidence intervals, the Bayesian architecture is not yet fully calibrated, and several layers have known gaps that haven't been resolved.
This is science in progress. We're documenting every step.