by Sam L. Savage
The SIPmath™ Standard communicates uncertainty unambiguously and coherently from data scientists and statisticians to decision makers. The standard created the groundwork for Chancification, which takes computer simulation from siloed applications to collaborative networks in which managers need nothing more than a simple application like ChanceCalc to make chance-informed decisions. Applications include:
Linking national weather simulations to power grid simulations to estimate the chance of collapse due to failed equipment, excess heating, or air conditioning load.
Aggregating risks across infrastructure networks to mitigate the chance of safety risks at minimal cost.
Using crowdsourced data on forecasting errors to estimate the chance of achieving projected tax revenues.
Linking the results of ensembles of COVID-19 models at the CDC to local models to predict the chance of exceeding ICU capacity.
The models created with ChanceCalc are standalone Excel files that perform hundreds of simulation trials per second and do not require the add-in to run.
We have made the following changes to ChanceCalc since the first beta version in May 2021:
Numerous bug fixes.
We have frozen the SIPmath 3.0 Standard, which stores uncertainties as JSON objects. Now ChanceCalc can read libraries created in Python, R, or Analytic Solver from Frontline Systems (see below).
Frontline’s Analytic Solver has become the first commercial software package to support the new standard. It can easily create SIPmath 3.0 Libraries for export and can import them to do powerful stochastic optimization.
Here are some ways you can learn more about ChanceCalc, which is available now in beta test:
Watch our videos on ChanceCalc and Frontline’s Analytic Solver below.
Download the latest version here.
Look through the Getting Started guide for a quick overview of what ChanceCalc can do.
Explore the Tutorial to get hands-on tips for using ChanceCalc to cure the Flaw of Averages.
© Copyright Sam Savage, 2021