Lone Star Announces Four New White Papers
Lone Star Analysis deals in a wide range of topics relative to its highly successful TruNavigatorSM which offers next generation capabilities for analyzing and solving business issues and decisions in the presence of complexity and uncertainty. It finds optimal solutions in seconds or minutes, where others may take hours or days, if they can solve them at all. These white papers demonstrate the wide range of topics that Lone Star deals with.
The first new white paper, “TruNavigatorand OptiSolvTM: Stochastic Optimization in a Probabilistic Simulation Environment”, shows how blending probabilistic simulation and stochastic optimization offers important breakthroughs in managing business and technical challenges.
The second paper, “Efficiently Representing Uncertainty as Probability Distributions”, discusses two means for efficiently representing uncertainty as probability distributions: Stochastic Information Packets (SIPs) and Stochastic Library Units with Relationship Preserved (SLURPs). Lone Star has been actively working with ProbabilityManagement.org in this area.
The last paper, “The “Wisdom of Crowds” is a powerful method to understand a likely value of a quantity. However, extreme “tails” of a distribution are more difficult to understand. A wide range of biases limit human understanding of rare events. This paper describes means to better understand rare events using models and estimates from groups; crowdsourcing distributions, not just specific forecasts. A brief survey of prior work, and some original Lone Star research is presented.
The last paper, “Data Sets and Resources Characterizing Large Numbers of Observations and Rare Events”, is a document provided as a public service by Lone Star Analysis. It is not copyrighted, and users can feel free to modify, copy and re-use as they see fit. Additions to the list are welcomed. Please make suggestions by email to info@Lone-Star.com
All four of these informative papers can be found on Lone Star’s website on the Insights page.