Uncertainty as Data
In the late 1980s, Financial Engineers used databases of simulation trials to manage their books of derivatives. By 2012, technology made it possible to apply these methods directly in native Excel, without add-ins. Dr. Sam Savage and Nobel Laureate Dr. Harry Markowitz co-founded 501(c)(3) nonprofit ProbabilityManagement.org to democratize and standardize this powerful approach to decision-making under uncertainty.
The result is the Open SIPmath™ Standard, which runs natively in Excel, Python, R, or JavaScript - no black boxes, just transparent, auditable uncertainty.
Coherent Stochastic Data Obeys Both the
Laws of Arithmetic and the Laws of Chance
Stochastic Data may be stored in Vector Arrays called Stochastic Information Packets (SIPs). SIPs contain hundreds or thousands of potential future values. For example, the SIP of a coin toss might start out like this:
Heads
Heads
Tails
:
Coherent Stochastic Data keeps related events matched across trials, so uncertainty makes sense, one future at a time. Coherent SIPs may be referred to as a Stochastic Library Unit with Relationships Preserved (SLURP).
As an example, suppose your house has 1 chance in 1,000 of burning down. Then a 10,000 trial SIP would display 10 fires at random trials. A Coherent SIP of your insurance policy would pay out on exactly the trials with fires. An Incoherent SIP of insurance would pay off at 10 random times like winning the lottery.
