Decisions
© Copyright 2009, 2012 Herbert J. Bernstein

Decision making is a very complex subject. See the Wikipedia article on Decision Making as a starting point for further study.

• What is a decision?
• A decision is a choice among alternatives
  • e.g. turn left vs. turn right
  • e.g. get up vs. stay in bed
  • e.g. paint that wall bright white, beige, cream, off-white, ....
  • e.g. get married, stay single, have children, don't, ...
  • e.g. invest in mortgage back securities or get a CD
  • e.g. hire this person or that person
  • e.g. keep working for your boss or quit
  • e.g. produce trucks or produce cars
  • e.g. start a war or make peace
• Someone or something must be the decision maker (the decider?)
• Multiple people or entities (the stakeholders) may be impacted by a decision
• Not making a decision is itself a decision
• What makes a decision "good"?
  • "good" may not be quantifiable
  • "bad" may not be quantifiable
  • What actually happens after the decision may be very different from what we thought would happen before we made the decision
  • Suppose we can quantify the decision
    • The elements of the decision may be deterministicly quantifiable or may be random variables.
    • If elements of the decision are random, we may need statistical techniques to make the decision.
    • Possibly some greater benefit is derived from the chosen alternative than from the other alternatives?
    • Possibly the losses (the costs) from the chosen alternative are less than from the alternatives
    • Our choices may be influenced by and influence choices made by others.
• Strategies in Making Decisions
  • Ignore the problem
    • Maybe it will go away
    • Maybe it won't
  • von Neumann and Morgenstern Game Theory
    • Minimize maximal losses
  • Cost-Benefit Analysis
    • Reduce all elements of the problem to dollars
    • Try to maximize the ratio of overall benefit to total cost
  • Try an alternative, then try another, ... see how they come out
  • Get a time machine
  • Model (simulate) the system and try the alternatives in the simulation
    • Models may be physical or conceptual (logical).
    • Diagrams can help in conceptual models Process maps
    • Models are intended to extract the salient features of the system
    • Models may miss important aspects of the system
    • Models may be qualitative or quantitative.
    • Qantitative models may be deterministic or stochastic (based on random variables).
    • To make a decision, we try to maximize (or minimize) an objective function (e.g. profit)
    • For uncertain variables, probability distributions are used
    • Instead of making one decision, we may need to make a continuing series of decisions based on changing varaibles, see Statistical_process_control.
    • For linear sets of inequalities, the simplex method is used