What is a good decision?

A good decision is based on logic, consider all available data and possible alternatives, and is obtained through rational analysis of data and alternatives.

Does a good decision always result in favorable outcome?

No. Remember, at times good decisions can fail and bad decisions can be a success.

Steps in Decision making

1. Clearly define the problem: Do we understand the problem or stuck at symptoms?
2. List the possible alternatives: What options are available? Doing nothing is also an alternative.
3. Identify the possible outcomes (or states of nature): What outcomes are possible for the alternatives figured out in step 2? Identify each outcome, positive or negative.
4. List the payoff or profit of each combination of alternatives and outcomes: Create a matrix for each alternative + outcome combination, and figure out payoff.
5. Select one of the decision theory models
6. Apply the model and make your decision

Let’s say a book company is planning to launch an ebook reader (like Kindle). they have 2 prototypes currently in RnD.

Problem statement: Come up with a ebook reader which can boost sales for ebooks.

• Alternative 1: Launch Prototype 1
• Alternative 2: Launch Prototype 2
• Alternative 3: Do not launch a product

Now say for each alternatives we can have various outcomes

• Huge Success (Sales above 100K in a quarter)
• Moderate Success
• Failure

After this we will analyze each combination of payoffs, for example

Alternative 1 (Prototype 1) + Outcome 1 (Huge Success) = Payoff (Profit 200K, selling 100K readers)

Similarly a matrix is created for each combination possible,.

Before making a decision, one needs to take into account the Risk-taking ability of the person or organization. We can divide risk nature into

• Risk Averese
• Risk Nuetral
• Risk Lovers

Also, the risk appetite will change based on the risk involved, for example, for someone earning 100K, a risk of 1K is low, but when the same risk becomes high when it involves 200K.

In addition, one also needs to take Decision making environment into consideration

Decision making Environments

1. Decision making under certainty: Decision-maker knows with certainty the consequences of every alternative
2. Decision making under uncertainty: decision-maker does not know probabilities of various outcomes
3. Decision-making under risk: Decision-maker knows the probabilities of various outcomes.

In short, when a company needs to make a decision, it will start from a decision under an uncertainty position, and try to move to the decision under risk by associating some probabilities to the outcomes based on past experience or market research. When the probability is straight 1 or 0, it is a decision under certainty, which is almost never possible.

Let’s go back to our previous example, and make it simple with just 2 outcomes, and based on past experience company can predict a 50-50 chance of success or failure.

Alternative	Success Outcome	Failure Outcome
Go with prototype 1	200,000	-180,000
Go with prototype 2	100,000	-20,000
Do nothing	0	0

So considering this a Decision under risk scenario, we use a popular method called Expected Monetary Value, to evaluate the alternatives.

EMV or Expected Monitory Value (alternative i) = (payoff of first outcome) * (probability of first outcome) + (payoff of second outcome) * (probability of second outcome) + ….. +(payoff of Nth outcome) * (probability of Nth outcome)

Going back to our use case, we can say

EMV for prototype 1: (0.5)*(200,000) + (0.5) *(-180,000) = 10,000

EMV for prototype 2: (0.5)*(100,000) + (0.5) *( -20,000)=40,000

EMV for Do nothing: (0.5)*0 + (0.5)*0= 0

So based on our analysis, we can see prototype 2 has the largest EMV and is the best option to go under current circumstances.