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In probability theory, an outcome is a possible result of an experiment or trial. [1] Each possible outcome of a particular experiment is unique, and different outcomes are mutually exclusive (only one outcome will occur on each trial of the experiment).
Normally a reduction in the probability of winning a reward (e.g., a reduction from 80% to 20% in the chance of winning a reward) creates a psychological effect such as displeasure to individuals, which leads to the perception of loss from the original probability thus favoring a risk-averse decision.
Classical definition: Initially the probability of an event to occur was defined as the number of cases favorable for the event, over the number of total outcomes possible in an equiprobable sample space: see Classical definition of probability. For example, if the event is "occurrence of an even number when a dice is rolled", the probability ...
The ambiguity effect is a cognitive tendency where decision making is affected by a lack of information, or "ambiguity". [1] The effect implies that people tend to select options for which the probability of a favorable outcome is known, over an option for which the probability of a favorable outcome is unknown.
Expected Utility Theory (EUT) poses a utility calculation linearly combining weights and values of the probabilities associated with various outcomes. By presuming that decision-makers themselves incorporate an accurate weighting of probabilities into calculating expected values for their decision-making, EUT assumes that people's subjective probability-weighting matches objective probability ...
The theory continues with a second concept, based on the observation that people attribute excessive weight to events with low probabilities and insufficient weight to events with high probability. For example, individuals may unconsciously treat an outcome with a probability of 99% as if its probability were 95%, and an outcome with ...
In studies of the bias, options are presented in terms of the probability of either losses or gains. While differently expressed, the options described are in effect identical. Gain and loss are defined in the scenario as descriptions of outcomes, for example, lives lost or saved, patients treated or not treated, monetary gains or losses. [2]
For any sample space with equally likely outcomes, each outcome is assigned the probability . [16] However, there are experiments that are not easily described by a sample space of equally likely outcomes—for example, if one were to toss a thumb tack many times and observe whether it landed with its point upward or downward, there is no ...