Praetor — Decision Support for Cognitive Operations

Current stage

3 / 6

Expert Baseline

Gates passed

10 / 10

Stages 1 & 2 ✓

Patterns defined

60

SP30 + BP30 ✓

Validated

55

of 60 patterns ✓

Audience Segment archetypes

18

8 industries ✓

Behaviour Pattern Library

+ Add pattern

↓ Save to dataset

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60 patternsSP30 seller · BP30 buyer0 confirmed

ID	Name (RU)	Behaviour type	Trigger / Condition	Action	Status

English name/label>

—

Behaviour type/label>

—

When this fires/label>

—

Recommended action/label>

—

Relationship impact/label>

—

Explanation template (decision rationale)/label>

—

Trigger × Response Matrix

↓ Save to dataset

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Reset to defaults

20 triggers12 response types— active links

Strength:

None

Weak

Medium

Strong

Click cell to cycle strength

Coverage & Balance Check

—

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— coverage— balance

Audience Segments

+ Add archetype

— segments— industries

Information Environment — Information Environment Pressure Price

Recalculate

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IEP — USD/t— market shocks

Parameters

Defaults from configs/market_sourced.yaml · adjust to explore scenarios

IEP base price (USD/t)

2396

Price trend (% per month)

+0.3%

Noise σ (% daily)

1.4%

Seasonality amplitude (%)

25.0%

Market shocks (count / 26 months)

1

Seed

42

Min—

Max—

Avg—

Volatility σ—

IEP price — Jan 2024 to Mar 2026 (26-month simulation) Price Black swans

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Segment Timeline — Daily Decision Log

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— 30 days shown

Select segment

Expert type

Known patterns

—

Blind spots (missed)

—

Expert actions & pattern analysis — first 30 days, seed 0 · "missed" = expert failed to act on a trigger

Select a segment to see its decision log

IO Analyst vs AI Scoreboard

Export CSV

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Expert baseline: — AI system: pending Delta: —

Performance comparison — 26-month simulation Simulation results · seed 0–9 · 546 days

Method	Total margin (IEP units)	vs Random	vs Expert	Segments retained	Status

Cumulative margin over 26 months Random avg Optimist Analyst Pessimist Balanced Praetor (Stage 5)

Audience Segment Stats

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Churn rate (MC)

—

Stable

—

Expansion rate (MC)

—

Statistical significance (dev info)

Run scoreboard to see results

Monte Carlo Explorer (dev)

Complete ✓

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— runs — records convergence —

Environment validation report — must pass before Stage 5 (RL training)

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Random policy (lower bound)

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Replay buffers

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RL Agent

Planned

🔒

Stage 5: RL Agent

Belief state estimator · Policy network (PPO/DQN) · Training loop · LLM layer 7

Gate: unlocks after Stage 4 ✓

Results & Visualization

Planned

🔒

Stage 6: Visualization & Demo

AI vs Expert margin chart · Situation Overview Σ · Belief state evolution · 5-min investor demo

Gate: unlocks after Stage 5 ✓