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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.
It is calculated as the present discounted value of future utility, and for people with time preference for sooner rather than later gratification, it is less than the future utility. The utility of an event x occurring at future time t under utility function u, discounted back to the present (time 0) using discount factor β, is
Revised and reprinted in 1995. – Psychology Today book club alternate selection; The Healing Power of Dreams, Simon & Schuster, 1991, with two foreign editions. Women's Bodies, Women's Dreams, Ballantine Books, 1988. Garfield, P. (June 1987). "Nightmares in the sexually abused female teenager". Psychiatric Journal of the University of Ottawa.
The phenomenon of hyperbolic discounting is implicit in Richard Herrnstein's "matching law", which states that when dividing their time or effort between two non-exclusive, ongoing sources of reward, most subjects allocate in direct proportion to the rate and size of rewards from the two sources, and in inverse proportion to their delays. [8]
In real world situations, "discounting makes sense because of the inherent uncertainty of future payoffs". [55] One study looked at how reward discounting is context specific. [18] By differing the time and space between small and large rewards, they were able to test how these factors affected the decision making in tamarins and marmosets ...
The Good Housekeeping Book Club chooses one feel-good book every month to feature as our monthly book club pick. Here's why you'll love them, too. Year in Review: Check Out Every Feel-Good Read ...
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.