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Temporal discounting (also known as delay discounting, time discounting) [12] is the tendency of people to discount rewards as they approach a temporal horizon in the future or the past (i.e., become so distant in time that they cease to be valuable or to have addictive effects). To put it another way, it is a tendency to give greater value to ...
Time discounting or temporal discounting is a wide range of ideas involving the connection between time and the extent to which an object, situation, or course of action is seen as valuable. The overall theory is that people put more value and worth into immediate events and outcomes, and apply less value to future outcomes or events.
Exponential discounting yields time-consistent preferences. Exponential discounting and, more generally, time-consistent preferences are often assumed in rational choice theory, since they imply that all of a decision-maker's selves will agree with the choices made by each self. Any decision that the individual makes for himself in advance will ...
Hyperbolic discounting, where discounting is the tendency for people to have a stronger preference for more immediate payoffs relative to later payoffs. Hyperbolic discounting leads to choices that are inconsistent over time—people make choices today that their future selves would prefer not to have made, despite using the same reasoning. [52]
Given two similar rewards, humans show a preference for one that arrives in a more prompt timeframe. Humans are said to discount the value of the later reward, by a factor that increases with the length of the delay. In the financial world, this process is normally modeled in the form of exponential discounting, a time-consistent model of ...
The first experiment in delayed gratification was conducted by Walter Mischel and Ebbe B. Ebbesen at Stanford University in 1970. [11] The purpose of the study was to understand when the control of delayed gratification, the ability to wait to obtain something that one wants, develops in children.
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Preference learning is a subfield of machine learning that focuses on modeling and predicting preferences based on observed preference information. [1] Preference learning typically involves supervised learning using datasets of pairwise preference comparisons, rankings, or other preference information.