Decision Making in Systems Engineering and Management, 3rd Edition

Chapter 9Decision Making via Tradespace Analysis

There are no solutions. There are only tradeoffs.

—Thomas Sowell, Author

9.1 INTRODUCTION

Among the various methodologies specifically developed to address challenges associated with systems decision making exists a broad class whose members include multiple attribute value theory (MAVT) [1], multiple criteria value modeling (MCVM) [2], stochastic MCVM [3, 4], multiple criteria decision analysis (MCDA) [5, 6], multiple objective decision analysis (MODA) [7], multi‐attribute decision theory (MADT) [8], and stochastic multicriteria acceptability analysis (SMAA) [9, 10] and their extensions. While not equivalent in their specific modeling elements and results, all assess a subset of viable alternatives guided by a set of preferences, criteria, and ideals that retain consistency throughout each method's model building process.

In MCVM in particular, a Cartesian plot that displays each alternative's total costs and total value return in a two‐dimensional tradespace within which each alternative's geometric positioning relative to one another defines dominance conditions that help identify a choice set of efficient alternatives. From this choice set, a decision maker selects a best “bang‐for‐the‐buck” alternative given the expressed cost and value structures underscoring each alternative's position. Reducing the set of competing system alternatives to a smaller choices set accommodates the cognitive limits of human working memory ...