ChanceCalc™ Beta 1.1 Now Available

by Sam L. Savage

 
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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:

  1. Watch our videos on ChanceCalc and Frontline’s Analytic Solver below.

  2. Download the latest version here.

  3. Look through the Getting Started guide for a quick overview of what ChanceCalc can do.

  4. Explore the Tutorial to get hands-on tips for using ChanceCalc to cure the Flaw of Averages.

© Copyright Sam Savage, 2021

The SIPmath™ 3.0 Standard and Analytic Solver V2021.5

The AC Current Standard and First Industrial Power Plant of Chancification

by Sam Savage

 

ProbabilityManagement.org is proud to announce the first general release of the SIPmath 3.0 Standard for storing virtual SIPs in the universal JSON format. And we are delighted that the latest Analytic Solver from Frontline Systems both reads and writes this format.  

The discipline of probability management represents uncertainties as data that obey both the laws of arithmetic and the laws of probability. SIPmath 2.0 accomplished this by storing arrays of thousands of Monte Carlo trials. SIPmath 3.0 accomplishes this with a tiny fraction of the storage. If probability were electricity, then SIPmath 2.0 would be direct current and SIPmath 3.0 would the alternating current that we all use today.

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The SIPmath 3.0 Standard uses Doug Hubbard’s HDR random number generator to maintain statistical coherence, generating identical streams of pseudo random numbers across platforms, including native Excel.

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These random numbers feed Tom Keelin’s Metalog Distributions, a flexible system for creating an extremely wide range of continuous random variates, including multi-modal.

The Analytic Solver encompasses optimization, machine learning, simulation, and powerful techniques. Its “Deploy Model” allows you to

“create, test and refine probability distributions that should be used across your company -- say for exchange rates or commodity prices -- using Analytic Solver's 60+ classical, Metalog, and custom distribution creation tools -- then deploy and share them as probability models, following the open Probability Management SIPmath 3.0 standard.”

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Using SIPmath 3.0 ensures that you will get the same Monte Carlo trials in ChanceCalc, Python, R, or, if you have the patience, on an abacus. And going the other way, you may generate probability distributions in a wide variety of simulations, which may be imported into Analytic Solver to use with its powerful stochastic optimization engines.  

I expect this package to play as central a role in Chancification as the 1895 Tesla/Westinghouse hydro power station at Niagara Falls played in electrification.


© Copyright 2021 Sam L. Savage