Corruption Deterrence: Detection vs. Penalty

Deterrence levers

Detection probability 25%

Penalty severity 25

Bribe population

Average bribe payoff 18

Live readout

Expected corruption rate

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Officials accepting

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Officials declining

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Avg expected cost

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Avg bribe payoff

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How it works

Each dot is one public official facing one bribe offer. The official is modelled as a rational expected-value calculator: accept the bribe if its certain payoff exceeds the expected cost of getting caught — detection probability multiplied by penalty severity.

expected cost = P(detection) × penalty
accept bribe  if  payoff > expected cost
decline bribe if  payoff ≤ expected cost

Detection probability — audits, transparency rules, whistleblower protection. Raising it alone raises the expected cost for every official.
Penalty severity — fines, prison time, career loss if caught. Raising it alone also raises the expected cost, even with detection unchanged.
Each official has slightly different odds of being audited and a slightly different penalty (some cases are easier to prove, some judges harsher) — that's why the scatter is a cloud, not a single line.
The heatmap sweeps every combination of the two levers and shades the resulting corruption rate, so you can see the whole policy space at once: either lever, pushed far enough on its own, drives corruption toward zero.

Policy space — corruption rate by lever combination x: detection probability · y: penalty severity

Bribe decisions — this batch x: bribe payoff · y: expected cost of being caught