Probability management

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The discipline of probability management communicates and calculates uncertainties as vector arrays of simulated or historical realizations and meta data called Stochastic Information Packets (SIPs). A set of SIPs, which preserve statistical relationships between variables is said to be coherent and is referred to as a Stochastic Library Unit with Relationships Preserved (SLURP). SIPs and SLURPs allow Stochastic Simulations to Communicate with each other. See for example, Analytica (Wikipedia), Analytica (SIP page), Oracle Crystal Ball, Frontline Solvers, and Autobox.

The first large documented application of SIPs involved the exploration portfolio of Royal Dutch Shell in 2005 as reported by Savage, Scholtes, and Zweidler, who formalized the discipline of probability management in 2006.[1] The topic is also explored at length in.[2]

Vectors of simulated realizations of probability distributions have been used to drive stochastic optimization since at least 1991.[3] Andrew Gelman has described such arrays of realizations as Random Variable Objects in 2007.[4]

In 2013 ProbabilityManagement.org was incorporated as a 501(c)(3) non-profit that supports this approach through education, tools, and open standards. The Executive Director, Sam Savage, is author of The Flaw of Averages: Why we Underestimate Risk in the Face of Uncertainty and Adjunct Professor at Stanford University. He is joined on the board by Harry Markowitz, Nobel laureate in Economics. The nonprofit has received financial support from Chevron Corporation, General Electric, Lockheed Martin, PG&E and Wells Fargo Bank. The open SIPmath 2.0 Standard supports XLSX, CSV and XML Formats[5]

References[edit]

  1. ^ Probability Management, Sam Savage, Stefan Scholtes and Daniel Zweidler, OR/MS Today, February 2006, Volume 33 Number 1 http://probabilitymanagement.org/library/Probability_Management_Part1s.pdf
  2. ^ Savage, Sam (2009). The Flaw of Averages, Why we Underestimate Risk in the Face of Uncertainty. Hoboken: John Wiley & Sons. ISBN 978 0-471-38197-6.
  3. ^ Dembo, Ron (1991). "Scenario Optimization". Annals of Operations Research. 30: 63–80. doi:10.1007/BF02204809.
  4. ^ Gelman, Andrew (2007). "Manipulating and summarizing posterior simulations using random variable objects". Statistics and Computing. 17 (3): 235–244. doi:10.1007/s11222-007-9020-4.
  5. ^ SIPmath Standard