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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 ...
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
Prices can increase over time; Increasing the number of periods can decrease efficiency. Grossman and Perry [4] study sequential bargaining between a buyer and a seller over an item price, where the buyer knows the gains-from-trade but the seller does not. They consider an infinite-turn game with time discounting.
The pragmatic factor usually results in a "pure time preference" factor in the social discount rate, that a pleasurable experience at a certain date is intrinsically more valuable than the exact same experience at a later date, and that the life of a person born sooner has more intrinsic value than the life of a person born later.
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 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]
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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.