Identification of Logical Errors through Monte-Carlo Simulation

TL;DR

Monte Carlo simulation not only assesses risk but also helps detect hidden logical errors in spreadsheet models, offering a valuable secondary benefit for model validation.

Contribution

This paper highlights the use of Monte Carlo simulation as a tool for identifying logical errors in spreadsheets, a novel secondary application beyond risk analysis.

Findings

Monte Carlo simulation reveals logical errors in models.

Warning signs can be recognized with training.

Secondary benefit enhances model validation processes.

Abstract

The primary focus of Monte Carlo simulation is to identify and quantify risk related to uncertainty and variability in spreadsheet model inputs. The stress of Monte Carlo simulation often reveals logical errors in the underlying spreadsheet model that might be overlooked during day-to-day use or traditional "what-if" testing. This secondary benefit of simulation requires a trained eye to recognize warning signs of poor model construction.