Jove
Visualize
Contact Us

Related Concept Videos

Expected Value01:15

Expected Value

The expected value is known as the "long-term" average or mean. This means that over the long term of experimenting over and over, you would expect this average. The expected average is represented by the symbol μ. It is calculated as follows:In the equation, x is an event, and P(x) is the probability of the event occurring.The expected value has practical applications in decision theory.This text is adapted from Openstax, Introductory Statistics, Section 4.2 Mean or Expected Value and...
Decision Making: P-value Method01:09

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
Purposive Learning01:22

Purposive Learning

E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a bonus...
Determination of Expected Frequency01:08

Determination of Expected Frequency

Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
Classical Conditioning in Daily Life01:17

Classical Conditioning in Daily Life

Classical conditioning, a fundamental principle of associative learning, explains various phenomena observed in daily life, such as fear development, the placebo effect, taste aversion, and drug habituation. These applications demonstrate the profound impact of associative learning on human behavior and physiological responses.
John B. Watson and Rosalie Rayner famously demonstrated the development of fear through classical conditioning in their experiment with Little Albert. They paired the...
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Measuring utility with diffusion models.

Science advances·2023
Same author

Rational policymaking during a pandemic.

Proceedings of the National Academy of Sciences of the United States of America·2021
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: May 12, 2026

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

Classical subjective expected utility.

Simone Cerreia-Vioglio1, Fabio Maccheroni, Massimo Marinacci

  • 1Department of Decision Sciences and IGIER, Università Bocconi, 20136 Milan, Italy.

Proceedings of the National Academy of Sciences of the United States of America
|April 6, 2013
PubMed
Summary
This summary is machine-generated.

Decision makers can now account for both state and model uncertainty using a novel two-stage subjective expected utility model. This framework integrates objective information about data-generating processes into subjective decision-making settings.

More Related Videos

The Joint Effect of Social Comparison and Social Distance on Evaluation of Intertemporal Choice Outcomes in Event-related Potential Studies
08:24

The Joint Effect of Social Comparison and Social Distance on Evaluation of Intertemporal Choice Outcomes in Event-related Potential Studies

Published on: August 25, 2023

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Related Experiment Videos

Last Updated: May 12, 2026

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
13:04

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods

Published on: September 19, 2012

The Joint Effect of Social Comparison and Social Distance on Evaluation of Intertemporal Choice Outcomes in Event-related Potential Studies
08:24

The Joint Effect of Social Comparison and Social Distance on Evaluation of Intertemporal Choice Outcomes in Event-related Potential Studies

Published on: August 25, 2023

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Area of Science:

  • Decision theory
  • Statistical inference
  • Behavioral economics

Background:

  • Classical decision theory assumes either complete knowledge or complete ignorance of the data-generating process.
  • Subjective expected utility theory (Savage, 1954) allows for subjective beliefs about states of the world.
  • Wald (1950) introduced objective information about the class of possible data-generating processes.

Purpose of the Study:

  • To develop a decision model that incorporates objective information about the data-generating process within a subjective framework.
  • To extend subjective expected utility theory to account for model uncertainty alongside state uncertainty.

Main Methods:

  • Integrating Wald's objective information on the class of models (M) into Savage's subjective framework.
  • Developing a two-stage subjective expected utility model.

Main Results:

  • The proposed model successfully integrates objective constraints on the data-generating process with subjective decision-making.
  • The model explicitly addresses and quantifies both state uncertainty and model uncertainty.

Conclusions:

  • The two-stage subjective expected utility model provides a more comprehensive framework for decision-making under uncertainty.
  • This approach enhances the applicability of decision theory in complex scenarios with partial objective information.