Bill MacKenty

Computational History

A practical framework for encoding historical systems as computable models by integrating noetic logic< (belief, perception, intention) with Boolean logic (facts, rules, states).

History as a System, Not a Story

Traditional historical study often centers on narrative: who did what, when, and why. Computational history reframes the past as a complex system—a dynamic network of agents (people, groups, nations), resources (food, technology, territory), and constraints (environment, ideology, communication).

Each element is expressed as data:

• Population growth as numerical time-series.
• Political alliances as graph structures.
• Economic exchanges as weighted edges between nodes.
• Cultural attitudes as variable states within belief models.

By encoding historical information into logical and mathematical forms, we can run simulations that explore how small changes in input conditions—a drought, an assassination, a religious reform—may lead to dramatically different outcomes.

Boolean Logic: The Skeleton of Historical Systems

Boolean logic supplies the formal structure on which these systems operate. At its core, it represents the simplest decision space: true/false, on/off, war/peace.

Illustrative rules:

• Alliance == true increases the probability of coordinated military action.
• ResourceScarcity == true triggers rebellion where economic pressure crosses a threshold.
• CulturalAlignment == false raises tension between adjacent polities.

With these rules we construct state machines—abstract models that change based on logical conditions. In simulation, thousands of transitions unfold over time, revealing patterns that mirror real processes: economic collapse, ideological contagion, or imperial expansion.

Noetic Logic: Modeling Human Thought and Belief

History is not made by systems alone; it is made by minds. Noetic logic (from Greek noēsis, “understanding”) formalizes mental states to describe how agents perceive truth, assign value, and act based on internal reasoning.

Belief-driven dynamics we can model include:

• When religious conviction overrides economic self-interest.
• Why leaders interpret the same data—troop movements, trade reports, omens—differently.
• How shared myths and cognitive biases propagate and alter collective behavior.

In short, a Boolean model constrains what is possible; a noetic model helps explain why actors choose among those possibilities.

Merging the Logical and the Noetic

The most powerful insight appears when we integrate both layers into a single computational framework: Boolean logic defines external mechanics; noetic logic defines internal cognition operating within them.

1. Initialize environment: political borders, economic indicators, climate data.
2. Define agents: rulers, factions, institutions—each with belief matrices and behavioral parameters.
3. Iterate through time: apply Boolean rules to update the world; apply noetic rules to update beliefs.
4. Observe emergence: revolutions, migrations, alliances, collapses—mirroring or diverging from known outcomes.

Predicting Without Pretending

Computational history cannot predict the future as prophecy. History’s complexity and contingency preclude absolute foresight. But it can illuminate trajectories, reveal feedback loops, and identify leverage points where decisions—individual or collective—produce outsized effects.

For education and research, simulations help to:

• Clarify cause and effect in nonlinear systems.
• Bridge humanities and computation in authentic inquiry.
• Explore how much of history is logic—and how much is human imagination.

Toward a New Craft of Historical Inquiry

The aim is not to replace traditional scholarship but to augment it—equipping historians with tools to explore questions that text alone cannot answer. By fusing Boolean precision with noetic subtlety, we can build models that respect both the mechanics and the meaning of human events.

In doing so, we reclaim history not as static record, but as living computation—an ever-evolving simulation of mind, matter, and possibility.

Author’s note: This article outlines my working approach to computational history. If you’re interested in classroom-ready exercises, agent-based demos, or formal specifications for the noetic/Boolean layers, feel free to reach out.