Why Is Decision-Making More Normative - Essay Example

According to Bayesians understanding, probability is arrived at due to some personal level of confidence. An individual can convey confidence that an identified event will occur, and this in turn, is seen as the probability of the identified event occurring. On the other hand, frequentists’ understanding of probability goes in line with a reference class such as an immeasurable number of coin tosses. It is evident that single occurrences do not belong to a definite reference class, and thus the occurrences are not bound to a relative frequency or probability.

In essence, the normative theory of probability is the Bayes theorem. Decision making is more normative when it is based on frequencies than when it is based on probabilities. Decision-making is more normative when it is based on frequencies other than probabilities since the total number of occurrences is an indication of its reliability. A decision is also normative if it is looked at in a frequency format instead of a probability format. In accordance with heuristics and biases literature, people do not comprehend concerning probabilities in a normative way.

Contrary to the above expression, it is essential to note that evolutionary psychologists argue that people’s reasoning goes hand in hand with Bayes theorem. This is so when the information has been presented in a format that people have known and evolved to process. Evolutionary reason shows how Bayesian reasoning developed. Various problems have been considered when analyzing if decision making in terms of frequencies and probabilities. The problems were from Tversky and Khaneman (1982) and Casscells et al (1978).

Both problems presented by Tversky and Khaneman (1982) and Casscells et al (1978) were in a probability format. The problem by Tversky and Khaneman involved a witness identifying a cab involved in a hit and run incident at night. In that city, two cab companies operate the Green and the Blue. The witness identified the cab in the incident as blue. However, due to probability format, in general people are bound to think that the cab in the incident is more likely to be blue other than green. It is evident that an error in decision making based on probabilities has an error, which Tversky and Khaneman relate to representative heuristic.

In order to make a normative decision, it is important to look at the witness’ accuracy as the focal point and at the same time neglect the base rate of the cabs in the city. The second problem presented by Casscells et al (1978) was on medical diagnosis. This problem involved a test directed towards detecting a disease with prevalence of 1/1000 and a false rate of 5%. A question was posed to medical students asking them the chances of an individual with a positive result truly having the disease.

The question took the assumption of students not knowing anything regarding the individual’s signs and symptoms. The results from the medical students showed that they ignored base rates for diagnosis problems. This problem also affected by representative heuristic. Cosmides & Tooby (1996) took an interesting direction by presenting their problem in both probability and frequency formats. The problem involved a test, which was developed to detect when a person has disease X. The test comes out positive whenever it is given to an individual with the disease that is the true positive is 100%.

However, from 1000 healthy individuals, 50 turn out to be positive from the test that is false positive is 5%. The test was applied randomly to a sample of 1000 Americans selected through a lottery. The data Cosmides & Tooby arrived got