Quantitative Decision Making

The Bayesian decision theory

The Bayesian decision theory is a fundamental statistical approach that deals with the issues of pattern classification. The maximus regret entails selecting an alternative with the biggest potential return. It has the advantage of yielding generously if the alternative succeeds but the disadvantage of resulting in a significant loss if the alternative fails. Choosing the types of stock to purchase on the securities markets matches this instance, where the party would chose to buy high-risk equities in the hopes of receiving big returns.

The maximin regret concept

The maximin regret concept works by the choosing of a possibility which will result in the minimum payoff available. Its strength is the fact that it minimizes loss by a great deal while the weakness is that the involved party loses out on making a large profit in event of good outcomes. From a personal as well as business angle, a party would opt for low-risk securities to avoid the potential loss and minimize regrets from failure.

The minimax regret

The minimax regret is a risk-neutral approach to decision making. This means that the person would avoid risk at all costs. The strength is that the person avoids any possible losses while its weakness is that the person/institution misses out on opportunities to make profits. Using the securities example, such a person or institution would trade in securities that have been known to be stable over a lengthy period of time (Berger, 1993).

Reference

Berger , J.O. (1993). Statistical decision theory and bayesian analysis. New York: Springer.