The coming to decisions based off observed patterns and past experiences is known as a heuristic. On the other hand, algorithms are step by step procedures that guarantee an outcome, and require lots of trial and error. Heuristics are often much more efficient than algorithms, yet they do not always guarantee a solution. For example, in a grocery store setting, an algorithm would be going up and down every single aisle looking for a specific item. Although there will always be a one hundred percent success rate, there is a lack of efficiency and most would not feel this as the best way to look for an item. A heuristic in this situation would be to look in certain labeled aisles that may contain the specific item that is being looked for. One type of heuristic that impacts decision making is the representative heuristic, or the judging of the likelihood of something by intuitively comparing it to particular prototypes. Although the use of this representative heuristic can make decision making quick, it may have a negative effect on the accuracy of such decisions.
This is shown in the study Khaneman and Tversky 1974. This study attempts to prove the hypothesis that when people evaluate probability by representativeness, prior mathematically probabilities would be neglected. To do this, subjects were told to determine what the likely occupation of a certain person would be (engineer or lawyer?) based off of a description. However, this description would often have little to nothing to do with the occupation at all. To cope for this, the particpants were either told that there were 30 engineers and 70 lawyers, or that there were 30 lawyers and 70 engineers. Theoretically, the ratio of the participants’ guesses should be seventy to thirty because of mathematical probability. However, the study found that the participants of both groups had guesses closer to a ratio of fifty to fifty. Such shows that the subjects used to representative heuristic to make their decisions rather than using the probabilities given to them, causing decision making to become unreliable.
This study is useful in showing how the data does not match what one would expect based off the idea that the subjects were aware of the unequal amount of each profession. However, this does not directly prove that the representative heuristic is responsible for this distortion, as there is lots of room for error in the descriptions that the participants were given for each person. In the end though, the study still supports how decision making can become unreliable and cause errors.
This is very thought-evoking. Thanks for your insight!
This is a very interesting perspective on decision making. The statistics and studies presented really help back up the argument.