Heuristics and Biases:
‘People rely on a limited number of heuristic principles which reduce the complex tasks of assessing probabilities and predicting values to simpler judgmental operations.’ (Kahneman et. al, 1974) Heuristics are cognitive shortcuts or ‘rules of thumb’ used to simplify the decision making process. Heuristics result in good decisions and their main asset is that they save time. Most of the heuristics are used by people with specific cognitive styles of problem solving. However, heuristics can cause biases and systematic errors when they fail. Whilst making decisions, people are typically unaware of the heuristics and biases and when or in what instances they should be used. There are many biases in the use of heuristics but some of the most common include; 1) Availability
2) Adjustment and Anchoring
‘There are situations in which people assess the frequency of a class or the probability of an event by the ease with which instances or occurrences can be brought to mind’ (Kahneman et. al, 1974) Availability can be described as the inability to accurately assess the probability of a particular event happening. The most common factor here is experience. Assessments based on past experience may not be representative e.g. one may evaluate the probability of a new local fish shop in the Letterkenny area, failing, by imagining the various problems in may encounter. The structured review and analysis of objective data can reduce availability bias. 2) Adjustment and Anchoring
‘In many situations, people make estimates by starting from an initial value that is adjusted to yield the final answer’ (Kahneman et. al, 1974) The majority of subjectively derived probability distributions are too narrow and fail to estimate the true variance of the event and perhaps the best way to overcome this is to assess a set of values, rather than just the mean. (I.e. anchoring) 3) Representativeness
This is the process by which an attempt to establish the probability that a person or object belongs to a particular group or class, based on the degree to which the characteristics of that person/object fits into the stereotypical perception of members of that group or class. In the answering of these questions, people generally focus on the similarities with the respective person/object versus the stereotypical perception. The closer the similarity between the two, then there is a high probability that the respective person/object belongs to a particular class. An example from (Kahneman, 1974) shows how representativeness may take place; Q: How do people assess the probability that Steve is engaged in a particular occupation from a list of possibilities (e.g. farmer, salesman, airline pilot, librarian or physician)? ‘Steve is a very shy and withdrawn, invariably helpful, but with little interest in people, or in the world of reality. A meek and tidy soul, he has a need for order and structure, and a passion for detail’. A: In the representativeness heuristic, the probability that Steve is a Librarian, for example, is assessed by the degree to which he is representative of, or similar to, the stereotype of a librarian. Motivational
This is the case when probability estimates are often influenced by incentives and therefore, the estimates do not accurately reflect people’s true beliefs. These incentives can be real or perceived.
Linked Decisions and there complexity
Linked decisions are decisions made today which creates new decisions to be made in the future. There are no time limits on linked decisions and they can be minutes, months, years even decades ahead. In terms of making linked decisions, to choose the correct choice now, you must think and analyze about decisions in the future. Therefore future planning is a massive element, as well as understanding the relationship between the decisions...