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Kelly criterion for bug hunting?

A half-formed hunch: allocating research time across targets is a bankroll problem, and Kelly might be the right lens.

planted April 22, 2026 · last tended April 22, 2026

A captured spark. Unverified, unpolished, possibly wrong.


A seed, planted fast before it blew away. Possibly nonsense.

The Kelly criterion sizes bets to maximize long-run growth of a bankroll given your edge and odds. Security research has the same shape: my bankroll is attention, each target is a bet with some probability of a finding and some payout (bounty, knowledge, write-up), and I can size positions by the hours I spend.

Things Kelly would predict, if the analogy holds:

  • Never go all-in on one target, even a juicy one (ruin risk = burnout + zero findings).
  • Edge matters more than payout. A boring target where I have deep prior knowledge beats a glamorous one where I’m a tourist.
  • Fractional Kelly (betting less than the formula says) is wise when your edge estimate is noisy, and my edge estimates are very noisy.

Suspicious wrinkle: research payoffs aren’t independent bets. Knowledge compounds across targets, which Kelly doesn’t model. Maybe that’s the interesting part.

Related muscle memory from ctf-field-notes-web: rotating hypotheses on a timer is basically fractional Kelly for a single afternoon.

Next action: re-read the Kelly chapter of Fortune’s Formula, then try actually logging a season of time-allocation decisions and outcomes. If the data is fun, this sprouts.

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