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In behavioral economics, time preference (or time discounting, [1] delay discounting, temporal discounting, [2] long-term orientation [3]) is the current relative valuation placed on receiving a good at an earlier date compared with receiving it at a later date. [1] Applications for these preferences include finance, health, climate change.
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]
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
In economics, a discount function is used in economic models to describe the weights placed on rewards received at different points in time. For example, if time is discrete and utility is time-separable, with the discount function f(t) having a negative first derivative and with c t (or c(t) in continuous time) defined as consumption at time t, total utility from an infinite stream of ...
When considering longer time periods, a fixed "pure time preference" discount rate becomes extremely counterintuitive; a 1% rate implies that Tutankhamun should ethically value a single day of his life over the sum total entire lives of everyone living today. Over long periods, peoples' stated preferences become extremely hyperbolic. [4]
Time value of money problems involve the net value of cash flows at different points in time. In a typical case, the variables might be: a balance (the real or nominal value of a debt or a financial asset in terms of monetary units), a periodic rate of interest, the number of periods, and a series of cash flows. (In the case of a debt, cas
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.
A key aspect of the exponential discounting assumption is the property of dynamic consistency— preferences are constant over time. [1] In other words, preferences do not change with the passage of time unless new information is presented. For example, consider an investment opportunity that has the following characteristics: pay a utility ...