Decision Modeling discusses the elements of decision making and strategy formulation from a broad systems-analytic perspective. The aim is to contribute to the advancement of managerial competence by disseminating a theoretically sound foundation of knowledge geared to enhance professionalism, expertise and prudent foresight.
The philosophical basis espoused in this Website asserts that the interdisciplinary framework which characterizes the field of systems analysis, when properly employed, produces accurate representations of decision and strategy problems, fosters a thorough understanding of the fundamentals underlying those problems, enables systematic risk assessment, and expedites identification, evaluation, selection and subsequent implementation of practicable optimal solutions.

Systems analysis is, in essence, a rational approach to problem solving encompassing a wide range of specialized tools derived from a variety of disciplines. It enjoys a proven track record in prescribing effective design parameters, efficient operational guidelines, and optimal control methods to all manner of challenging technical and administrative systems, including the Apollo moon program. Progressive managers are well advised to avail themselves of the comprehensive worldview and powerful resources afforded by systems analysis.

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In their book, Think Again, Professor Sydney Finkelstein and strategy consultants Jo Whitehead and Andrew Campbell put it succinctly: “Decisions are the lifeblood of action.” They then conclude that two factors are at play in a flawed decision: (1) an individual or a group of individuals made an error of judgment, and (2) the decision process failed to correct the error. “Both have to be present to produce a bad decision.” They add: “What is needed is a diagnostic for knowing when the risk of being wrong is at its highest—when the decision makers need to step back and ‘think again.’ ”  (pp. 199 & xi-xii)

Formally modeling a decision problem provides a concrete means to check subjective judgments coherently.  Models, by virtue of their requisite structure, facilitate the singling out of possible errors of judgment and intuition, assist in the diagnosis of any such errors, and promote their rectification before a commitment is made to a course of action. Decision modeling allows the decision maker to assess when the risk of being wrong is at its lowest. It complements and enriches traditional decision making by allowing practitioners to “think again” the problem continually through methodical analysis conducted in parallel with the usual judgmental approach.

Finkelstein, Whitehead and Campbell have this to say: “If our brains naturally questioned and challenged our assessments and judgments or normally compared multiple options—as we are advised to do by most decision scientists—we would be much better at spotting errors in our thinking and correcting them.” More importantly, “ ‘Objective strategic analysis’ is close to useless if the key decision makers are not part of that analysis.” “Leaders have the responsibility to ensure that those involved in an important decision are part of the analysis.”  (pp. 58 & 203)

Decision makers who employ modeling do not renounce use of their judgment. On the contrary, they wisely endeavor to augment it.

Reference: S. Finkelstein, J. Whitehead & A. Campbell, Think Again: Why Good Leaders Make Bad Decisions and How to Keep It From Happening to You (2008, Harvard Business Press)

In their book, Competing on Analytics: The New Science of Winning, Professor Thomas H. Davenport and researcher Jeanne G. Harris contend that the key to success in todays ultra-competitive business environment is to transform the organizations culture from one based on functional silos to one focused on enterprise-wide analytics. By analytics we [the authors] mean the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions.”  (p. 7)

Analytics, to the authors, comprises four quantitative approaches to analyze business performance: statistical methods, forecasting and trend extrapolation, predictive modeling, and optimization. As decision makers progress from statistical analysis to optimization they increase their companys business intelligence capability, leading a sustainable competitive advantage.

Attaining this level of performance requires a commitment from senior management. “The adoption of a broad analytical approach to business requires changes in culture, process, behavior, and skills for multiple employees. Such changes don’t happen by accident; they must be led by senior executives with a passion for analytics and fact-based decision making. Ideally, the primary advocate should be the CEO.” “Without the push from the top, it’s rare to find a firm making the cultural changes necessary to become an analytical competitor.”  (p. 30)

The authors go on to say: “How does an executive develop a passion for analytics? It helps, of course, if they learn it in school.” However, “What is necessary is a willingness to delve into analytical approaches, the ability to engage in discussions with quantitative experts, and the fortitude to push others to think and act analytically.”  (p. 31)

The purpose of this Decision Modeling Website is to provide current and prospective organizational decision makers with the basic knowledge needed to develop, if not unbridled passion, a working understanding of “managerial analytics”. Keep in mind that systems analysis as applied to decision making should not be construed as a “Think versus Blink” proposition. Rather, it is an eclectic approach intended to complement reason and intuition. Systems analysis makes it easier for you to use both sides (hemispheres) of your brain.

Are you ready to delve into analytical decision making? Check out the characteristics of analytical executives given by the authors and decide for yourself:  (p. 135-6)

• They should be passionate believers in analytical and fact-based decision making.

• They should have some appreciation of analytical tools and methods. The senior executives of analytical competitors dont necessarily have to be analytical experts (although it helps!). However, they do need to have an awareness of what kinds of tools make sense for particular business problems, and the limitations of those tools.

• They should be willing to act on the results of analyses.

• They should be willing to manage a meritocracy. Of course, the leaders of such meritocratic firms have to live and die by this same analytical sword. It would be quite demoralizing for a CEO to preach the analytical gospel for everyone else but then to make excuses for his or her own performance as an executive.

Reference: T.H. Davenport & J.G. Harris, Competing on Analytics: The New Science of Winning (2007, Harvard Business School Press)

Mathematics prerequisites:
The material presented in this Website presumes familiarity with college-level algebra. Prior exposure to probability theory is desirable; however, a review of the basic concepts of probability is included in the Probability Theory Review module. Knowledge of linear algebra and calculus should prove beneficial but, with the exception of certain optional material, is not required.

Preliminary Concepts:
Thematic Modules:
Web Resources (Links):
• Game Theory

Future Development:
• Cost-Benefit Analysis
• Discrete System Simulation
• System Dynamics
• Risk Analysis
• Behavioral Factors
• Ethical Decisions  •••>  Smart Strategies

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e-mail: DecisionModeling@gmail.com

© 2001-2009 Alvin J. Martínez. All rights reserved. Permission is granted to copy, download and print material from this site for personal, noncommercial use. This authorization does not extend to publishing material obtained from this site in other printed or electronic publications of commercial or noncommercial nature, including Internet and intranet sites, without the prior written consent of the author.