In earlier posts, we described the critical thinking process and how a problem can be broken down into a series of objective questions.  While the questions may be objective, their relative “weight” in the decision process will vary.  For example, if the technical aspects of the problem can be easily addressed and meet the customer needs but the legal requirements forbid shipping the product, the legal requirements far outweigh all other requirements and the project would face significant hurdles (legislative action, exemptions, etc).

Consider the map metaphor. If a company is interested in determining where to promote a new social network product it would deconstruct the strategic question into various components such as technical advantages, social and political impact, and legal requirements. Each of these would be given a relative weight. Within each “plane” of the topography a relative ranking would be imposed. Data would be collected that would allow management to evaluate and score each of these “planes” then compare the results to determine the optimal location of the product launch. It is incumbent on management to maintain objectivity in this assessment. Shifting weights or scores without process leads to a biased decision.

Along with determining the criteria, a decision must be made regarding the relative value or “weight” of each of these factors.  Essentially, not all criteria are equal.  In this case “legal” and “political” have a higher weight than “technical” or “social”.  The addition of weight significantly changes the interpretation of score obtained.  This is evident when the raw data results table is compared to the weighted table.

The use of tools such as weighed order decision-making can only be effective when combined with critical thinking. The establishment of criteria used to evaluate the decision must meet all conditions for critical thinking. While this may lead to greater complexity when evaluating a position, the result is more objectivity in making the decision. The drawback to this approach is that the user may sacrifice accuracy in pursuit of precision. Often times the analyst may go to extremes in breaking down various criteria. The user must understand what values are of appropriate “weight” in determining how precise the question and answer must be. A precise answer may represent an extraordinary amount of work in determining the assigned value. If it has little impact on the overall decision process, then the user as failed to adjust the overall decision it must make.

Modeling decision making is seen in various types of analysis including Sensitivity Analysis, Delphi, TOPSIS, and Analytical Hierarchy Process.  How data is collected and used can differ in these different methodologies.  In sensitivity analysis, uncertainty drives the mathematical modeling.  The more independent variables are applied, the less certain relationships can become between independent variables.  As data models become more complex, the user must recognize the stochastic nature of the “planes” within the business model.  

Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a methodology that is based on the vector representation of how the measured values compare to the positive ideal state and the Negative conditions state.  The closer to the positive ideal, the better the decision.

Delphi models use a panel of experts to systematically formulate answers to a series of questions as forming the comparators in the decision making process.  The components for critical thinking must permeate this process.  Structured group information can lead to decision bias unless diversity is built into the team (and the questionnaire).  This group can suffer from the same bias if all view the data presented from the same perspective.  This type of bias can be overcome with assuring that an array of data sources and expertise is involved in decisions and alternatives. 

Analytical Hierarchy Process is the breakdown of a problem analogous to the critical thinking process then determining the relative weight of each component.  

In all of the above, processes are required that objectively assign the appropriate “weight” to a decision element.  This requires applying the same principles of critical thing that leads to scores based on data, not “feeling”.

NoviLens is an appliance for data driven decision making. NoviLens handles the tedium of the computing components of the process and frees up the decision maker to focus on their problem solving tasks.

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