http://ProbabilityManagement.org advocates a corporate framework in which organizations maintain libraries of official distributions for key uncertain quantities, which can be exchanged between model builders so that all models rely on common assumptions. Each univariate Monte Carlo sample is collected into a unit called a Stochastic Information Packet (SIP), which is a textual XML snippet containing a compressed representation of the Monte Carlo sample. When additional indexes are present, the result is an array of SIPs, which is termed a Stochastic Library Unit with Relationships Preserved (SLURP).
SIPs and SLURPs of probability management, which represent distributions as vectors of realizations and metadata which support addition, multiplication, and any other algebraic calculation, while capturing any possible statistical relationship between variables.
A SID is a database of simulation values for one or more variables that can be used to generate a pre-defined sequence of random samples within your ModelRisk model.
The primary uses of SIDs are:
Consistency between models sharing the same variables
The same SID can be used in several different models, where the variables within the SID are directly used in these models. By using SIDs we can be certain that the models are consistent. More importantly, we can be sure that a specific sample of a simulation of one model will exactly correspond with the same particular sample of other models using the same SID. This means that we can combined the models together or, for example, directly compare two different options (built in two different models) sample by sample.
Distributing or updating corporate stochastic forecasts of important variables
Corporate planning and treasury departments, etc. can create SIDs of their up-to-date official stochastic forecasts. Sharing those SIDs with colleagues makes it quick and simple to incorporate stochastic forecasts using the most up-to-date information.
Importing data from applications like OpenBUGS and other simulation programs
Many simulation tools have the ability to export their simulation data directly to Excel, or into a format that Excel can then read. In ModelRisk, you can create a SID from these data and thereby incorporate the simulated variable results into your model, preserving and correlation pattern that is present in the simulated data.
It can be very useful to share SID files with other modellers in your organisation. This ensures that you are building models on the same quantitative uncertain assumptions.
The Stochastic Information Packet (SIP) represents a probability or frequency
distribution as a data structure that holds an array of values and some metadata.
The values are realizations of possible values of an uncertain variable. The
array for a probability distribution is composed so that the probability of each
element is 1/N where N is the number of elements in the array.
The key benefit of using SIPs is that they are actionable, in that they may be
used, as-is, in calculations. If X is a random variable represented by SIP(X),
and F(X) is a function of X, then SIP(F(X))=F(SIP(X)). That is, the function F
is applied sequentially to each element of SIP(X). This means in effect that
SIPs and the arithmetic, relational, and logical operators comprise a group
Click to access SIP+Standard+Version+2.1.1.pdf
Find out more in the article Sam Savage published https://www.probabilitymanagement.org/blog/2019/1/22/virtual-sips
Check out other risk management books
RISK-ACADEMY offers online courses
Informed Risk Taking
Learn 15 practical steps on integrating risk management into decision making, business processes, organizational culture and other activities!
ISO31000 Integrating Risk Management
Alex Sidorenko, known for his risk management blog http://www.riskacademy.blog, has created a 25-step program to integrate risk management into decision making, core business processes and the overall culture of the organization.
Advanced Risk Governance
This course gives guidance, motivation, critical information, and practical case studies to move beyond traditional risk governance, helping ensure risk management is not a stand-alone process but a change driver for business.
