Structuring a Competitive Analysis: Decision Trees, Decision Forests, and Payoff Matrices
13 Pages Posted: 21 Oct 2008
Abstract
Decision trees can be helpful in analyzing the key drivers of profitability and in designing risk-mitigation strategies. When analyzing competitive situations, each player's viewpoint can be represented by a separate decision tree, creating a "decision forest." In some situations, it is also useful to summarize two players' decision trees in a single payoff matrix. This note introduces those concepts step by step.
Excerpt
UVA-QA-0674
Structuring a Competitive Analysis:
Decision trees, Decision Forests, and Payoff Matrices
Decision Trees
Most business decisions involve significant uncertainties and risks. The profitability a newly developed drug, for instance, depends on a variety of factors including the introduction of rival products by competitors, the efficacy of the drug in the field, and demand by customers. Decision trees can be helpful in analyzing the key drivers of profitability and in designing risk mitigation strategies.
In particular, decision trees allow the decision maker to sketch the order in which additional information becomes available as a business project unfolds. For example, pharmaceutical companies often find out about competing new products even before shipping the first units of their own drug to retailers. Figure 1 summarizes one such set of assumptions about the flow of information:
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Keywords: game theory, decision trees, matrix
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