People use heuristics to control extreme complexity. Heuristics are rules or strategies for information processing, which help to find a quick but not necessarily optimal decision. Heuristics are used when people are overwhelmed by information processing, and help to find a quick, but not necessarily optimal, solution.
SIMPLIFYING THE FACTS: although this helps to simplify decision- making situations, ignoring small differences adds the risk of arriving at non-rational conclusions.
IGNORING POSSIBLE RELATIONSHIPS BETWEEN COMMITMENTS AND PROJECTS: ignoring risk interrelationships may result in risk assessed wrongly.
AVAILABILITY OF INFORMATION: there is always the risk of receiving important information late or perhaps not at all. People always respond emotionally, or in an exaggerated fashion, to highly visible recent information, e. g. surprising news. Investors on the stock exchange market tend toward a positive price prediction when in a good mood following some gain, and they see the current market situation in a pessimistic light when they are in a bad mood following a run of bad luck.
+ People tend to ignore information not only consciously, but also subconsciously, when it "does not suit them or when they expect to receive completely different information.
+ Information which is presented against a contrasting background is often perceived disproportionately.
+ Information which is mentioned last stays uppermost in the mind and therefore is considered most frequently.

When the information is reduced to a manageable level, there comes the necessity to resolve the decision problem as quickly as possible. People tend to base their estimations on a first source of reference value (anchor) and subsequently to adjust their decisions as they receive more information. Empirical research, however, shows that this is often not the case. The adjustment process is regularly cut short and the original value (anchor) is given too much weight.

People also tend to confuse cause and effect, to overestimate empirical and causal relationships. Imagine there are two analysts who daily issue forecasts on the development of the dollar price and Analyst 1 issues two correct forecasts in two consecutive days. Analyst 2 issues two wrong predictions. A client will assume that Analyst 1 offers correct forecasts and Analyst 2 offers wrong forecasts. Thus, an empirical relationship is turned into a causal one.

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