Quantitative Methods: An Introduction for Business Management

12

Deterministic Decision Models

In the last few chapters we have covered tools to represent some standard forms of uncertainty. Our main aims were to understand the relationship between variables of interest and possibly to forecast their future values. Understanding how a system works is clearly essential in all scientific disciplines, including the social ones. However, in management there is a further step: moving from knowledge discovery to decision making. So far, we have just hinted at decision models every now and then. In this chapter, we move on to a systematic treatment of quantitative models and methods for decision making. In this first step, we disregard uncertainty and deal with deterministic problems. Later, in Chapter 13, we merge decision models with probability and statistics to address the case of decision making under uncertainty. This will open up a world of challenging and rewarding models. Yet, we should always keep in mind that even the best decision model is always based on an approximate description of reality, and it should be regarded as a support tool, not a magical oracle. We will further insist on this in Chapter 14, where we outline a few complications arising in the practical world.

From a technical point of view, this chapter relies on concepts that were introduced in Chapters 2 and 3, such as:¹

• Convex sets and convex/concave functions
• Local and global optimizers
• Quadratic forms and multivariable calculus

There, we covered unconstrained optimization ...