PredictiveCortex

A Theoretical Framework for Foresight-Capable Language Models

"To predict is not to guess — it is to model the unfolding of structured causality across time, agents, and information."
Orest Yatskuliak, Initiator of the PredictiveCortex Research Project*

Abstract

PredictiveCortex is an experimental research framework that explores the theoretical capabilities of Large Language Models (LLMs) to perform predictive reasoning through structured simulation.

Rather than focusing on statistical prediction of text, we investigate whether it is theoretically possible to extend an LLM’s capabilities to simulate causal-temporal systems, construct epistemic scenarios, and perform multi-agent foresight modeling.

We postulate that prediction, in its cognitive essence, is an emergent property of:

• Temporal vectorization
• Ontological knowledge structuring
• Multi-agent dynamics
• Causal chain reasoning

This repository lays the theoretical groundwork, partial implementations, and philosophical rationale for future research on cognitive-level foresight within AI systems.

System Architecture

mermaid 
graph TD
    A[Input Scenario Prompt] --> B[Temporal Vectorizer Module]
    B --> C[Ontology Mapper]
    C --> D[Causal Reasoning Engine]
    D --> E[Multi-Agent Simulator]
    E --> F[Predictive Output Hypotheses]

Each layer contributes to increasing abstraction:

• Temporal Vectorizer: Encodes time-based sequences as predictive tokens
• Ontology Mapper: Connects raw input with structured knowledge graphs
• Causal Engine: Extracts interdependencies and latent triggers
• Agent Simulator: Assigns roles and simulates interactions

Example Use Case

Prompt:

Given rising geopolitical tension in Region X and economic decline in Region Y, what plausible chain of events could unfold in the next 12 months?

Output (Partial):

1. Migration outflows from Region Y increase (Month 3)
2. Resource tensions escalate at the border of X and Y (Month 5)
3. Diplomatic intervention by Z-nation begins (Month 6)
4. Cyber conflict incidents rise globally (Month 9)

This is not a "forecast" — it is a structural simulation of plausible outcomes based on encoded causality.

Sample Code Snippets

Python: Temporal Token Mapper

python
def encode_temporal_vector(event_sequence):
    # Hypothetical vectorization logic
    import numpy as np
    time_factors = [e['timestamp'] for e in event_sequence]
    magnitudes = [e['impact_score'] for e in event_sequence]
    return np.array([t * m for t, m in zip(time_factors, magnitudes)])

Go: Agent Causality Chain

go
package agent

import "fmt"

type Agent struct {
    Name    string
    Beliefs []string
    Goals   []string
}

func (a *Agent) Act(situation string) string {
    return fmt.Sprintf("%s reacts to %s based on beliefs %v", a.Name, situation, a.Beliefs)
}

Ontological Structure Table (Example)

Layer	Domain	Sample Entities	Type
L1	Political	Nation, Treaty, Sanction	Concrete
L2	Economic	Currency, Inflation, Trade Route	Abstract
L3	Sociocultural	Belief, Protest, Cultural Norm	Conceptual
L4	Technological	AI System, Cyberattack, Databreach	Emerging

This schema is injected into the ontology mapper during scenario construction.

References & Inspirations

• Judea Pearl – Causality: Models, Reasoning, and Inference
• Brian Christian – The Alignment Problem
• Joshua Epstein – Agent-Based Modeling and Generative Social Science
• Gärdenfors, Peter – Conceptual Spaces: The Geometry of Thought

Disclaimer This project is a theoretical framework only. We do not claim operational foresight or real-world predictive capability. Our goal is to open interdisciplinary dialogue and inspire new research directions in epistemic AI.

Collaboration If you're a researcher, cognitive scientist, AI engineer, or philosopher interested in predictive reasoning systems — let’s collaborate.

“Prediction is not a number. It's a worldview filtered through information structure.”

Quickstart Demo

Run a minimal temporal forecast with multi-agent consensus:

python3 scripts/temporal_forecast_demo.py