You have already come across uncertainty about values, and you learned about methods for addressing these in Chapters 3 and 4. In this chapter you will learn about three methods for dealing with uncertainty about outcomes. One is game theory, which offers a number of strategies that reflect different attitudes to uncertainty. The second is robustness analysis, which is based on the idea that in an uncertain world it is better, if possible, to make a sequence of decisions that preserve flexibility by keeping as many good options open as you can, than to make one inflexible decision that may be overtaken by events. The third is AIDA (analysis of interconnected decision areas).
By the end of this chapter, you will be better able to:
• distinguish between different aspects of uncertainty in decision making,
including uncertainty about the working environment, about values and
about related agendas
• describe strategies from game theory for dealing with uncertainty arising
from lack of knowledge or factors beyond the decision maker's control
• describe the principles of robustness analysis when there is uncertainty
about the future and the decision problem can be broken down into a
sequence so as to keep options open
• describe the principles of analysing interconnected decision areas when
there is uncertainty about related agendas
Decision area Any area of choice within which decision makers can conceive of an alternative
course of action that might be adopted, now or at some future time.
Payoff In a single-criterion decision problem, the outcome or value of a given decision option
for a given state of nature.
Regret For a given state of nature, the loss of payoff associated with a given decision option
when compared with what would have been the payoff from the best decision option.
States of nature Possible combinations of events and circumstances that are beyond the
control of the decision makers (i.e. determined by exogenous variables).