PARAIBELCOM reconstructs what is known about an adversary from the reports that actually arrived, says when a decision has to be taken and what single observation would change it, plays every friendly plan against every adversary plan before it ranks anything, and keeps testing the retained plan as each new report arrives. It runs on a laptop, and on a disconnected handset.
Six minutes, one laptop, no network. The reports arriving, the state rebuilt at a chosen instant, every friendly plan played against every adversary plan, the second opinion, the unit-level concepts, and the same engine answering on a disconnected handset. Nothing is sped up and nothing is cut.
The video loads from YouTube only when you press play — nothing is requested from a third party before that. Open it there instead.
Every product is a view over the same reconstructed state. No stage is allowed to invent an input that the stage before it did not supply.
Each answers one question a staff officer actually asks — and each states what it will not claim.
“Why this plan, and not the other one?”
Friendly plans are generated, adversary plans are generated against them, and every friendly plan is played against every adversary plan — a million engagements, without sampling or pruning. What comes out is a portfolio with weights, not a single recommendation. Ranking uses a value function the operator sets and can read, over five measured features.
“Is this position even possible, given everything we have seen?”
The adversary formation state is reconstructed at any point in the reporting timeline. Observed, declared, assessed and unknown are separated. A stale position stays marked stale instead of being presented as current, and where a formation has not been seen recently the system shows where it could still be rather than inventing a position for it.
“What would make me change this plan, and how long do I have?”
Sorted by decision deadline, not by score — an event template is read to find out what has to be settled first. Each row gives the event, the time of contact, what the observation would be worth if it were made, and where the indicator stops discriminating between competing explanations.
“What do I still not know, who can look, and by when?”
The written intelligence requirements are held as written, and what the system’s own state says is unresolved is computed separately — then the divergence between the two lists is published. Where they agree, the plan is confirmed. Where they differ, the difference is the product.
A course of action is retained by a named person. From then on, every batch of reports that arrives is an advance of the clock — and the question is no longer which plan is best, but whether the retained plan still holds, and on what evidence.
“What did we know, and when did we know it?”
Every report carries three clocks — observed, reported, received. A report is new when it was received since the last state, even if it describes an older moment; a late report is shown as late, because it may change what the previous evaluation should have said.
“How old is what we believe about each adversary formation?”
For each formation the engine publishes the age of its last report against a declared staleness horizon, the source of that horizon, and the next moment at which the picture will change by itself because evidence ages out — before anyone asks.
“Where should our units be by now?”
At each advance the retained plan places every friendly unit — route completed, route remaining, position at that clock — from the same derivation the verdict rests on, so the map and the decision cannot tell two different stories. An operator’s declaration replaces the planned position the moment it is made.
“What would the system change — and who decides?”
The engine may propose to continue, to integrate a unit into the frozen plan or to switch concept, with its rule and its ties stated. When there is nothing to decide — no friendly force declared at that time — it says exactly that, instead of presenting an empty plan as an answer.
Four commitments explain most of the design. Each one costs something, and the cost is stated — a principle whose price is not named is a slogan.
Every line is marked observed, declared, assessed or unknown, and the marking travels inside the data rather than living in the interface — so a change of screen cannot strip it.
Cost — products look emptier than a competitor’s. Fields that could be filled plausibly are left visibly empty.
A gap reads as a gap, and the missing input is identifiable by name. A measurement that arrives blank is refused rather than coerced to zero.
Cost — the system regularly declines to answer questions it is asked, including during demonstrations.
Write “62 %” in a doctrinal product and it will be read as a frequency — and nobody downstream will ask again. The system prints an assessment score with its nature attached and takes the explanatory burden instead.
Cost — scores are harder to explain than percentages, and the explanation has to be repeated.
Predicting what an adversary commander will do is not a solved problem and we do not claim it. Identifying the place and the time at which one observation would separate two hypotheses is computable — and it is what a collection manager can act on this afternoon.
Cost — it never produces the confident single forecast that briefs well.
A plan can be optimal against one opponent and dangerously fragile against another. The design problem is not to combine methods; it is to connect different mathematical questions without confusing their answers.
| Method | Question it answers | Failure mode it addresses | What it does not settle |
|---|---|---|---|
| MAP-Elites | Which strategically distinct courses of action exist — not only the highest-scoring one? | Premature convergence on a single family of plans | Whether any of them holds against a responding adversary |
| Lanchester | What happens when this friendly plan meets that adversary plan, under declared assumptions? | Interactions left implicit | The truth of the assumptions: the payoff matrix is a model, not ground truth |
| Nash | What if the opponent responds strategically rather than as a fixed sample? | Ranking plans as if the adversary did not choose | Operational robustness: an equilibrium is conditional on the matrix |
| Monte Carlo | Which conclusions survive when the modelled conditions are perturbed? | Confusing expected performance with ranking stability | Which perturbations matter in the real world |
| NEMESIS | Which adversary plan hurts this plan most — and can the plan be repaired? | Resilience that only holds against sampled opponents | Adversaries the model cannot represent |
Eighteen modules on a consumer phone. Every one works with no network, no account and no cloud. None of them transmits.
None of this is a claim about military effectiveness. They are claims about the engineering — the kind that can be checked by somebody who does not trust us.