Related Experiment Video
Updated: Jul 6, 2026

13:04
Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
Published on: September 19, 2012
Variations on a theme by Rachlin: Probability discounting
1Arizona State University.
Journal of the Experimental Analysis of Behavior
|December 20, 2022
Summary
This study critiques the equivalence of probabilistic and delayed goods discounting, proposing a new model. The research introduces a conjoint measurement approach to better understand probability discounting in intertemporal choice.
Area of Science:
- Behavioral Economics
- Decision Science
- Cognitive Psychology
Background:
- Previous research treated discounting of probabilistic goods as equivalent to discounting of delayed goods.
- This approach, while influential, has been identified as problematic in its original formulation.
Purpose of the Study:
- To critically evaluate the established framework for probabilistic goods discounting.
- To develop and validate a more accurate model for understanding probability discounting in intertemporal choice.
Main Methods:
- The study re-examines the conversion of probability to delay, identifying the omission of trial duration.
- It employs conjoint measurement, treating discounting as a psychophysical matching experiment.
- A model is proposed where utility of amount is multiplied by the weight of probability.
Main Results:
- The proposed model, incorporating a logarithmic transform for amount and a Prelec function for probability, accurately accounts for diverse probability discounting data.
- Empirical evidence suggests probability and delay discounting are distinct, differing in their functional relationships with variables like outcome magnitude.
Conclusions:
- The findings challenge the direct subsumption of probability discounting under delay discounting.
- A refined model offers a parsimonious and effective account of probabilistic intertemporal choice.
- Integrating this probabilistic model with delay discounting theory provides a comprehensive framework for intertemporal decision-making.
Related Concept Videos
Probability Laws
41.2K
Overview
41.2K
Regression Toward the Mean
6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Probability in Statistics
14.2K
Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
14.2K
Random Variables
13.2K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
13.2K
Probability Distributions
7.8K
The probability of a random variable x is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
7.8K
Unusual Results
3.3K
Unusual results are those that have a very low chance of occurring. Unusual results can be identified using probabilities and the range rule of thumb. In problems involving probability, unusual results can be observed in 2 instances – an unusually high number of successes or an unusually low number of successes.
According to the range rule of thumb, any value above or below two standard deviations, 2σ from the mean, μ is considered unusual.
Maximum unusual value =...
According to the range rule of thumb, any value above or below two standard deviations, 2σ from the mean, μ is considered unusual.
Maximum unusual value =...
3.3K

