Prediction Market Intelligence Risk Forecaster

Signal Parameters math.js live

Scenario Presets

Baseline Market Prior ($P_0$) 0.35

Pre-signal market implied probability (0.00 to 1.00).

Signal Strength ($S$) 0.80

Probability of signal occurrence if risk event is true.

Evidence Reliability ($W$) 0.75

Weight/trust placed in official intelligence report vs noise.

Time Horizon (Months) 6 months

Forecast timeframe for market convergence.

Risk Decay / Resolution Rate 0.30

Rate at which market absorbs signal over horizon.

Bayesian Inference Math:
Updates pre-event odds $\Omega_0 = \frac{P_0}{1 - P_0}$ using Likelihood Ratio $K = \frac{P(\text{Signal} \mid \text{Event})}{P(\text{Signal} \mid \neg \text{Event})}$. Produces updated Bayesian posterior probability $P_1$.

Updated Posterior Probability

76.5%

+41.5% shift from baseline

Bayes Factor ($K$)

6.12

Substantial evidence strength

Market Implied Odds

3.25 : 1

Fair value market price: 76.5¢

Volatility Risk Index

High (0.82)

Sensitivity to signal noise

Market Trajectory Curve over Horizon D3 Interactive Curve

Sensitivity Matrix: Signal Strength vs Reliability Weight

Signal Strength \ Weight	W = 0.25 (Low)	W = 0.50 (Med)	W = 0.75 (High)	W = 1.00 (Max)