Your Competitor's Next Move Is a Probability Distribution, Not a Headline

Every competitive-intelligence tool can tell you that your competitor raised $200M. Most can tell you the same day. None of them can answer the three questions your CRO actually asks in the room: what will they do with it, how much does that cost us, and what should we do first?

Those are not retrieval questions. They are decision-theory questions, and answering them takes a different kind of machinery than a feed — it takes a model of the game you are actually in: incomplete information, beliefs instead of certainties, and payoffs that depend on what both sides do.[2]

Step one: the move is a distribution

PYRAMYD's engine starts by treating "what do they do next?" as a probability distribution over concrete candidate moves — ship an agent tier, cut entry pricing, push into a new segment, acquire the capability they lack. Each move starts from a confidence derived from signals: hiring patterns, filings, release cadence, pricing-page diffs, review language.

Then the distribution gets reweighted by something most CI programs never formalize: Porter's Four-Corner analysis: the competitor's goals, current strategy, assumptions, and capabilities.[1] A move that fits their declared strategy and their org chart can carry up to four times its signal-derived weight; a move that contradicts both can be cut to a quarter. The result is a proper simplex: the moves compete for one unit of belief, so raising one lowers the rest.

Step two: exploitability, not likelihood

Knowing a move is likely tells you nothing about whether you should care. The number that matters is different: if they make this move and you respond well, how much better off are you than if you had done nothing? The engine computes that as the best-responder's expected gain over the status quo, weighted by the posterior — so a near-certain move you cannot profit from scores near zero, which is the correct answer.

A 90%-probable move you can't exploit is trivia. A 40%-probable move with a wide-open counter is the thing to prepare for this quarter.

Step three: the blind-spot amplifier

This is the part with no equivalent in any feed. Porter's assumptions corner asks what the competitor believes. Sometimes the evidence contradicts the belief: they price as if mid-market churn is a cost problem, while their own reviews name onboarding time as the reason people leave. When a predicted move rests on a belief like that, the engine makes it more exploitable, not less — because a competitor committed to a mistake keeps funding it.

Step four: a band, not a point

A single expected payoff hides the number that actually decides whether you act: how badly this can go. The engine runs four thousand seeded draws over the joint move-and-response tree and reports the P5, the median, the P95 — and an explicit probability that you end up net worse. The mean of the simulation converges on the enumerated expectation, which doubles as a built-in cross-check that the tree and the sampler agree.

The output is a ranked list of counter-moves — prescriptions — ordered by posterior-weighted expected exploitable value, each with the moves it blocks and the payoff it forfeits if you skip it. Not a list to read. An order to act in.

The part that keeps it honest

A forecast you cannot score later is entertainment. Weather forecasting learned this from Glenn Brier in 1950: write the probability down before the outcome, score it after.[3] Every prediction the engine issues is recorded with the leading indicators that would confirm it, so it can be scored against what actually happened. The engine also refuses in the other direction — a move with no assessed payoff contributes exactly zero exploitability rather than an optimistic default, and every synthesis ships with its epistemics attached: this is calibrated decision-support under imperfect information, not certainty.

Run it yourself

The formulas in this post are not a whitepaper abstraction — the production engine's pure core runs client-side, and we put it on a public page with sliders. Drag a move's confidence and watch the posterior renormalize, the exploitability re-band, and the prescriptions re-rank. The scenario is illustrative; the math is the real thing.

Run the engine at pyramyd.ai/game-theory →