Opportunity Cost: The cost of missing out on the next best alternative. The benefits that could have been gained by making a different decision.

  1. Distinguish between scientific decision-making and intuition/hunch based decision-making.
  • Scientific is easier to justify
  • Scientific will usually be more reliable
  • Hunch based is much faster
  • But may be less reliable
  • It is cheaper to use hunch based
  • Scientific is more common due to the rise of Big Data
  1. Not key stages in the decision-making process:
  • Recording data
  • Setting objectives
  1. Scientific decision-making is a lot more trustworthy and reliable. It can be backed up with evidence and data that supports or opposes a decision. Scientific decision-making also makes use of a wider set of information than a hunch. Hunches may be based on 1 or 2 things, whereas a scientific decision may be based upon hundreds or even thousands of data points.
  2. Intuition/hunch based decisions can be useful for when action is required quickly. If you don’t have the time or resources to make a scientific decision, then intuition is your best chance.
  3. In any decision, you will need to account for all possible risks and uncertainty that may come from a decision, and weigh it up against the possible rewards. If you do this correctly, you are unlikely to make any decisions where you lose more than you can afford.
  4. Opportunity cost is the benefits that have been given up by making a particular decision over another.
  5. Opportunity cost helps you to see what your resources are going towards, and what you’re getting back. It allows for a more analytical overview of decision-making.

Decision Trees

  • A mathematical model
  • Used by managers to help make decisions
  • Uses estimates and probabilities to calculate likely outcomes.
  • Helps to decide whether the net gain from a decision is worthwhile.

Expected Value: The financial value of an outcome calculated by multiplying the estimated financial effect by its probability.

Example:

High sales + low sales = Total Expected Value

Net gain = Total Expected Value - Cost


(A):

1.4m cost

40% 2.5m payoff 60% 800k payoff

0.4 * 2500000 = 1000000 0.6 * 800000 = 480000

1000000 + 480000 = 1480000 (1.48m) 1.48 - 1.4 = 0.08m net gain

(B): 0.5m cost

30% 1m payoff 70% 0.5m payoff

0.3 * 1000000 = 300000 0.7 * 500000 = 350000

300000 + 350000 = 650000 (0.65m) 0.65 - 0.5 = 0.15m net gain (C):

Do nothing. Not optimal.

Option B is the most profitable option.

== {Express in a single unit—everything in millions would have been good}

Advantages of using decision trees

  • Choices are set out logically
  • Options are considered in parallel
  • Use of probabilities enables risk analysis
  • Likely costs are considered as well as potential benefits
  • Easy to understand & tangible results

Disadvantages of using decision trees

  • Probabilities are just estimates—can be erroneous
  • Uses quantitive data only—ignores qualitative aspects of decisions
  • Assignment of probabilities and expected values prone to bias
  • Decision-making technique doesn’t necessarily reduce risk

Quality of data inputted is equal to the quality of the data outputted.

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