Maximin criterion: decision choice method that provides the best of the worst possible outcomes.

• This criterion states that the decision makes should select the alternative that provides the best of the worst possible outcomes.
• This is done by finding the worst possible outcome for each decision alternative and then choosing the option whose worst possible outcome provides the highest payoff,
• This criterion instructs one to maximise the minimum possible outcome.
• Although the maximin criterion suffers from the obvious shortcoming of focusing on the most pessimistic outcome for each decision alternative, it should not be dismissed as naive and unsophisticated.
• The maximin criterion implicitly assumes a very strong aversion to risk and is quite appropriate for decisions involving the possibility of catastrophic outcomes.
• Similarly, if the state of nature that prevails depends on the course of action taken by the decision maker, the maximum criterion might be appropriate.

Computer simulations allows detailed analysis of a managerial problems involving complex cost and revenue relations.

• Investment analyst identifies probability values for significant factors.
• Using computer simulation software, analyst randomly selects sets of investment characteristics based on their chances of turning up in the future.
• Return on Investment (ROI) is estimated for specific scenario.
• ROI is reestimated hundreds or thousands, of times under alternative scenarios to give clear picture of return distribution (investment risk).

• Game theory: a decision framework for making choices in hostile environments and under extreme uncertainty.
• Decision criterion stressed throughout managerial economics is value maximisation.
• In an uncertain economic environment, value maximisation is achieved using the risk-adjusted valuation models.

• Computer simulation; the use of computer software and workstations or sophisticated desktop computers to create outcome scenarios.
• Sensitivity analysis: limited form of computer simulation that focuses on important decision variables.
• Simulations illustrate a broad range of possible outcomes to help managers assess the possible and probable consequences of decision alternatives.
• Computer simulation require allows managers to make precise judgments concerning the desirability of various choices on the basis of highly detailed probability information.
• Computer simulations require probability distribution estimates for a number of variables.
• Full-scale simulations are expensive and time-consuming.
• Limited-scale simulations are used to project outcomes for projects/strategies.
• Instead of using complete probability distributions for each variable included in the problem, results are simulated based on best-guess estimates for each variable.
• Changes in the values of each variable are then considered to see the effects of such changes on project returns.
• Attention is then focused on the variables to which profitability is most sensitive (sensitivity analysis).

• A map of sequential decision-making process.
• Designed for analysing decision problems that involve a series of choice alternatives that are constrained by previous decisions.
• They illustrate the complete range of future possibilities and their associated probabilities in terms of a logical progression from an initial decision point, through each subsequent constrained decision alternative, to an ultimate outcome.
• Widely employed because many important decisions are not made at one point in time, but rather in stages.
• Subsequent determinations depend on prior judgements.
• Sequence of events can be mapped out to visually resemble the branches of a tree.
• Because demand probabilities are known, the expected value of cash flow can be determined.
• Risk and expected return differentials can be incorporated into the decision-making process.

• Decision trees that follow the sequential nature of the decision-making process are often used to provide a logical framework for decision analysis under conditions of uncertainty.
• When a high degree of uncertainty exists and data is not available, computer simulation is used to provide the basis for an opinion.
• These techniques constitute useful and practical means for risk assessment and effective managerial decision making under uncertain conditions.

• Learning curve contribution is important because it helps achieve and maintain a dominant position in a given market.
• Dominant firms have a greater opportunity of learning than non-dominant firms.
• It may be prudent to relinquish non-leading positions and redeploy assets to markets in which your firm has a dominant position to maintain status quo.
• Learning must be significant, average cost savings of 30% as output doubles.
• Learning is important in industries with new products and new production techniques.
• Learning is important in standardised industries where competition is based on price.
• Ideal under management systems that tightly control costs and monitor potential sources of production efficiency.
• Feedback between production and management is essential.
• Learning-curve advantages are industry-specific.

Learning curve: average cost reduction over time due to production experience.

When knowledge gained is used to increase production methods so that output is produced with increasing efficiency, the resulting decline in average costs is said to reflect the effects of the firm's learning curve.

Learning through production experience permits the firm to produce output more efficiently at each and every output level.

To isolate the effect of learning/experience on average cost, it is necessary to identify carefully that portion of average-cost changes over time which is due to other factors.

Learning curves relate cost differences to total cumulative output for a product.

Reported learning/experience rates include the effects of both learning and economies of scale.

Managers have found that the use of the learning-curve concept can substantially improve their ability to forecast production costs based on projected cumulative output which improves pricing decisions and production strategies.

Average costs decline substantially as cumulative total output increases.

Break-even analysis: determination of product volume where revenues equal total costs or costs associated with two alternative processes are the same.

Two perspectives:

• Company view: refers to determining how much volume of business the company must do in order to break-even, that is, to have neither profits/losses where total revenues equal total costs.
• Operational view: two processes have equal costs for a specific level of volume.

Revenues versus Costs

Determine how much volume of business, a company has to do to break-even. The volume can be stated in either monetary units/product units.

Linear model assumptions:

• The selling price per unit is constant;
• Variable costs per unit remain constant;
• Fixed costs remain constant.

Choice of Processes

Also used to choose from among alternative processes a company can use.

Assume that both variable costs per unit and fixed costs remain constant.

Break-even point as that volume where we are indifferent with respect to the costs of the two alternative processes.