Neural networks

Software that is designed to imitate the structure of brain cells and connections among them.

Neural networks are the next step beyond expert systems. They use computer software to imitate the structure brain cells and connections among them. Sophisticated robotics use neural networks for their intelligence. Neural network are able to distinguish patterns and trends too subtle or complex for human beings. For instance, people can’t easily assimilate more than two or three variables at once, but neural networks can perceive correlations among hundreds of variables. As a result, they can perform many operations simultaneously, recognizing patterns, making associations generalizing about problems they haven’t been exposed to before, and learning through experience. For instance, most banks toady use neural networks to flag potential credit cards fraud. In the past they relied on expert systems to track millions of credit card transactions, but these earlier systems could look at only a few factors, such as the size of a transaction. Consequently thousands of potential defrauding incidents were flagged most of which were false positives. Now with neural networks, significantly fewer numbers of cases are being identified as problematic – and it’s more likely now that the majority of those identified will be actual cases of fraud. Furthermore, with the neural network system, fraudulent activities on a credit card can be uncovered in matter of hours rather than the two to three days it took prior to the implementation of neural networks.

Decision making styles:

Every decision maker brings a unique set of personal characteristics to his or her problem solving efforts. For example, a manager who is creative and comfortable with uncertainty is likely to develop and evaluate decision alternatives differently from someone who is more conservative and less likely to accept risk. As a result of this information, researchers have sought to identify different making styles.

The basic promise for this decision making model is the realization that individuals differ along two dimensions. The first is the way they think. Some decision makers are logical and rational. Being such, they process information in a sequential manner. In contrast, some individuals think creatively and use their intuition. These decision makers have a tendency to see matters from a big picture perspective. The second dimension focuses on individual tolerance for ambiguity. Some individuals have a high need for consistency an order in making decisions so that ambiguity is minimized. Others, however, are able to tolerate high levels of uncertainty and can process many thoughts at the same time.

The directive style represents a decision making style characterized by low tolerance for ambiguity and a rational way of thinking. These individuals are logical an efficient and typically make fast decisions that focus on the short term. The analytic decision making style is characterized by high tolerance for ambiguity combined with a rational way of thinking. These individuals prefer to have complete information before making a decision. As a result, they carefully consider many alternatives .The conceptual style of decision making represents some one who tends to be broad in outlook and to look at many alternatives. These decision makers tend to focus on the long run and often look for creative solutions. The behavioral style reflects an individual who thinks intuitively but has a low tolerance for uncertainty. These decision makers work well with others, are open to suggestions and are concerned about the individuals who work for them.

Although the four decision making styles appear independent, most managers possess characteristics of more than in the style. That is, although they usually have a dominant style, the other three styles can be alternatives to be used when a situation may be best resolved by using a particular style.