Decision-Making Environment:

Decision-Making Under Certainty

Decision-making under certainty refers to a situation where the decision-maker has complete information about the alternatives, the outcomes, and their probabilities. In other words, the decision-maker knows with certainty what the consequences of each decision will be.

In this situation, decision-making can be relatively straightforward, as the decision-maker can simply choose the alternative that maximizes their objective function. This can be done using techniques such as:

Maximax: This approach involves choosing the alternative that maximizes the maximum outcome.

Maximin: This approach involves choosing the alternative that maximizes the minimum outcome.

Equally Likely: This approach involves choosing the alternative that has the highest average outcome.

Expected Value: This approach involves calculating the expected value of each alternative, which is the weighted average of the outcomes, where the weights are the probabilities of the outcomes. The alternative with the highest expected value is chosen.

or example, consider a decision-maker who is trying to decide whether to invest in stock A or stock B. The decision-maker knows that if they invest in stock A, they will earn a profit of $10,000 with a probability of 0.6, and a profit of $5,000 with a probability of 0.4. If they invest in stock B, they will earn a profit of $8,000 with a probability of 0.8, and a profit of $3,000 with a probability of 0.2. In this case, the decision-maker can use the expected value approach to calculate the expected profit for each alternative:

Expected Profit of Stock A = ($10,000 x 0.6) + ($5,000 x 0.4) = $8,000

Expected Profit of Stock B = ($8,000 x 0.8) + ($3,000 x 0.2) = $6,800

Based on this analysis, the decision-maker should choose to invest in stock A, as it has the higher expected profit.

In summary, decision-making under certainty involves using techniques such as maximax, maximin, equally likely, and expected value to choose the alternative that maximizes the decision-maker’s objective function, given complete information about the alternatives and their outcomes.