Bootstrapped decision-making models: When

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In practice, bootstrapped models have many uses.  The most scalable way to use these models are for informal analysis “gut checks.”  The human mind works in funny ways.  Simply having a parallel bootstrapped model to casually refer to can quell fears or heighten suspicions, potentially improving even a poorly codified decision-making processes.

Performance benchmarking is another useful area for bootstrapped models.  By removing random error, a bootstrapped model can serve as a useful benchmark for analyzing and evaluating decisions to figure out what went wrong (or what went right).

Risk management is another interesting application.   Hard-coding checks to catch decisions that lie outside a predefined range of outcomes/values (derived from bootstrapped models) is a direct way to catch exaggerated instances of an analyst’s natural human inconsistency.  And it’s fundamentally different from a purely quantitative risk management methodology, since the measure of risk is a deviation from what a human would have theoretically done following her or his own approach.

Finally, one could go all in and build fully employed decision making tools from bootstrapped models.   The resulting tools would loosely resemble a true quant model, though using a model in this manner still requires updating the model as the human perspective changes.