Dallas, TX (8/13/14) Lone Star Analysis, internationally recognized business and technology decision support, modeling, and simulation, and a trusted provider of advisory services that address highly complex client issues announces the launch of a series of articles on demystifying decision analysis (DA).
The fact is, decision analysis is mystery to some and not fully understood by others. Over the next few weeks a series of articles on the evolution of decision analysis, simple explanations of what it is and how it can provide a better understanding of how to address the complex questions that don’t seem answerable will be published in the Enhanced Decision Analysis pages of Lone Star’s website.
Throughout the series the goal is to help executives and managers gain a better understanding of decision analysis and Lone Star’s contributions to the field of decision analysis. Lone Star has been advancing the state-of-the-art to enable clients to peer into the future and gain a true understanding of the range of potential outcomes for any decision that matters to them, regardless of size or complexity.
The challenges faced by executives and managers today have staggering analytical and organizational complexity. This complexity is exacerbated by the fact that even the smallest of business decisions can have millions or even billions of dollars of impact depending on the challenge being faced.
“Just think of the benefit that could be gained if you could accurately look into the future and truly understand the outcomes of critical and complex decisions, said Matthew Bowers, Lone Star’s Vice President, Corporate Development. “That capability is available. Lone Star has developed cutting edge processes and tools that have enabled our clients to “see the future”, to gain competitive advantage, and to make the right decision with confidence.”
Decision analysis is a mathematical process supporting decision making. In particular, DA deals with making decisions in the presence of uncertainty. The discipline stems from foundational work at MIT and Stanford in the 1960’s. DA is Bayesian form of mathematics that includes the use of diagrams to represent the topics within a DA model, and uses Monte Carlo methods.