The context & data layer for TACITUS
The trust graph
for conflict intelligence
DIALECTICA is the context, foundation, and data layer for TACITUS. It structures any conflict — workplace friction, commercial disputes, geopolitical crises — into a deterministic knowledge graph grounded in 15 peer-reviewed frameworks. Other apps build on top. DIALECTICA is the foundation they reason over.
Layer 1
Context Layer
We extract actors, interests, power dynamics, emotions, trust states, and causal chains from unstructured text.
Layer 2
Foundation Layer
25+ deterministic symbolic rules fire against the Conflict Grammar ontology (15 node types, 20 edge types) — treaty violations, escalation thresholds, ripeness conditions are computed, never hallucinated.
Layer 3
Reasoning Layer
6 AI agents reason over structured graphs, not raw text. Neural predictions are firewalled — deterministic conclusions are never overridden by probabilistic ones.
TACITUS Ecosystem
DIALECTICA is the trust graph
DIALECTICA is not a standalone tool — it's the context and data layer for the entire TACITUS platform. It extracts, structures, and validates conflict data into a deterministic knowledge graph. Other applications in the TACITUS ecosystem build on top of this foundation to deliver specialized capabilities.
Think of it like this: DIALECTICA is to conflict intelligence what a database is to an application. It provides the structured, queryable, deterministic truth that every upstream tool — mediation assistants, risk dashboards, policy simulators, negotiation trainers — can reason over with confidence.
Architecture
Apps
Mediation tools, risk dashboards, negotiation aids, policy simulators
DIALECTICA
Trust graph · Context layer · Deterministic foundation
Data Sources
Documents, transcripts, reports, news, HR complaints, legal filings
Why LLMs need a deterministic foundation for conflict
Language models guess. DIALECTICA computes. Here's the difference.
| Question | LLM Alone | LLM + DIALECTICA |
|---|---|---|
| "What stage is this conflict at?" | Guesses based on keywords | Computes Glasl stage from event chains + escalation velocity |
| "Are there treaty violations?" | Summarizes text about violations | Deterministic pattern matching against norm graph |
| "Who has leverage?" | Describes who seems powerful | French & Raven power analysis with magnitude scores |
| "Is this conflict ripe for resolution?" | General assessment | Zartman ripeness model: MHS + WO computed from graph |
| "What are the underlying interests?" | Extracts stated positions | Maps stated vs unstated interests with BATNA analysis |
| "How might this escalate?" | Speculative narrative | Causal chain analysis with mechanism tagging (escalation, retaliation, contagion) |
Grounded in peer-reviewed conflict research
Every node type, edge type, and symbolic rule in DIALECTICA traces to published academic work.
Glasl, F. (1982). “The Process of Conflict Escalation and Roles of Third Parties.”
9-stage model, basis for escalation detection
Fisher, R. & Ury, W. (1981). “Getting to Yes.”
Interest-based negotiation, BATNA framework
Zartman, I.W. (2000). “Ripeness: The Hurting Stalemate and Beyond.”
Ripeness theory for intervention timing
Galtung, J. (1969). “Violence, Peace, and Peace Research.”
Direct/structural/cultural violence triangle
French, J. & Raven, B. (1959). “The Bases of Social Power.”
8 power domains
Mayer, R., Davis, J., & Schoorman, F. (1995). “Model of Organizational Trust.”
Trust = f(ability, benevolence, integrity)
Plutchik, R. (1980). “A General Psychoevolutionary Theory of Emotion.”
8 primary emotions wheel
Pearl, J. (2009). “Causality.”
Causal inference for event chain analysis
Beyond RAG
Why ontology-augmented generation outperforms RAG
for structured domains
RAG retrieves chunks of text. OAG retrieves structured knowledge with typed relationships and computed properties.
In conflict domains, relationships between entities ARE the data. A vector search can find mentions of ‘Iran’ and ‘sanctions’ — but it cannot compute that sanctions CAUSED enrichment breach which CAUSED escalation to Glasl stage 6.
Deterministic symbolic rules provide guarantees that probabilistic models cannot: if a treaty exists and an event violates its terms, that violation is a fact, not a prediction.
Academic backing
Pan et al. (2024). “Unifying Large Language Models and Knowledge Graphs: A Roadmap.”
Argues for structured knowledge integration over retrieval-only approaches. LLMs + KGs = complementary strengths.
Ji et al. (2022). “Survey on Knowledge Graphs: Representation, Acquisition, and Applications.”
Ontology-driven knowledge representation outperforms unstructured retrieval for domains with rich relational semantics.
RAG
- Retrieves text chunks
- No relationship types
- Cannot compute causality
OAG
- Typed knowledge graph
- 20 edge types, computed
- Deterministic reasoning
One ontology, every conflict domain
The same Conflict Grammar powers analysis from HR desks to the UN Security Council.
HR & Workplace
Detect escalation patterns before they become litigation. Map power dynamics, communication breakdowns, and trust erosion.
Commercial Mediation
Structure complex multi-party disputes. Quantify interests, map BATNAs, identify zone of possible agreement.
Geopolitical Analysis
Track alliance networks, causal chains, and norm compliance across macro-scale conflicts.
Peacebuilding
Assess ripeness for intervention. Model trust trajectories. Design dispute resolution systems.
Architecture Preview
A neurosymbolic pipeline from raw text to decision support in seconds.
Unstructured Text
Input
GLiNER NER
Pre-filter
Gemini Extraction
Structured
Pydantic Validation
Type-safe
Neo4j Knowledge Graph
Graph DB
Symbolic Rules
Deterministic
AI Agents
6 specialists
Decision Support
Output
Built for Developers
Install the ontology, hit the API, or connect via MCP.
Python
pip install dialectica-ontologyNode / TypeScript
npm install @tacitus/dialectica-sdkClaude Desktop (MCP)
MCP Server for Claude Desktop