Free lesson · Prediction strategies

Decision buffers for probability uncertainty

Open interactive lessonPractice calculationsExplore labs

If your forecast could reasonably be lower, evaluate the trade using that lower value before spending capital.

A lower probability bound is a decision input, not automatically a formal confidence interval. State whether it comes from a calibration study, a posterior quantile or a subjective scenario.

Subtract ask and cost from the lower bound. A positive result gives a conservative expected-value scenario under that bound; it is still exposed to realized losses and model misspecification.

Apply it in a strategy

• Make model uncertainty an explicit acceptance hurdle rather than reporting only a favorable point forecast.
• Record the input timestamp, executable quantity, currency and horizon. Reconcile the result with a cash-flow or state table.
• Stress this failure condition: A miscalibrated lower bound or selected confidence level can provide false comfort.

Research deliverable

Build and explain a decision buffers for probability uncertainty worksheet. Make model uncertainty an explicit acceptance hurdle rather than reporting only a favorable point forecast.

Evidence boundary: Synthetic arithmetic and scenarios illustrate mechanics. They are not historical returns, a paper replication, or evidence of an executable edge. Research sources and their access limitations are recorded at the end of this module.

# Python 3.10+; standard library unless NumPy is imported below.
# Inputs and outputs use the units defined in this lesson. Synthetic teaching example.
def conservative_edge(lower_probability,ask,cost):
    if not 0<=lower_probability<=1 or not 0<=ask<=1 or cost<0: raise ValueError("Invalid forecast scenario")
    return lower_probability-ask-cost

print(conservative_edge(.59,.57,.01))