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Related Concept Videos

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...
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
Decision Making01:20

Decision Making

Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
Experimental Designs01:16

Experimental Designs

An experimental design is a systematic process that allows researchers to evaluate the relationship between dependent and independent variables. There are three widely used types of experimental design - pre-experimental design, true experimental design, and quasi-experimental design. In pre-experimental design, the researcher compares the data before and after some interventions or treatments. The true-experimental design has more than one purposefully created group, a commonly measured...
Framing Effects03:26

Framing Effects

Information is everywhere and its presentation—such as how and when items are presented—can impact our perceptions and decisions surrounding the info. This broad concept umbrellas framing effects—influences that occur due to the way information is framed in its appearance, whether it’s purely the order or the specific wording of a message. Let’s take a look at numerous ways in which two versions of something can objectively say the same thing, yet we respond in different ways based on the...
Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
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Related Experiment Video

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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

Modeling choice and valuation in decision experiments.

Graham Loomes1

  • 1Department of Economics, University of Warwick, Coventry, United Kingdom. g.loomes@warwick.ac.uk

Psychological Review
|July 28, 2010
PubMed
Summary
This summary is machine-generated.

This study presents a simple model explaining how people make choices under risk. It reveals that perceived probabilities and payoffs drive observed decision-making patterns, organizing more data than previous models.

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

  • Behavioral Economics
  • Decision Theory
  • Cognitive Psychology

Background:

  • Decision-making under risk and uncertainty is a key area of research.
  • Numerous experimental regularities have been observed in this field.
  • Existing models struggle to comprehensively explain these observed patterns.

Purpose of the Study:

  • To develop a parsimonious descriptive model of individual choice and valuation.
  • To explain common experimental regularities in decision-making under risk.
  • To propose novel, testable implications for future research.

Main Methods:

  • Development of a novel descriptive model of choice and valuation.
  • Analysis of how participants perceive probabilities and payoffs.
  • Empirical examination of the model's predictions using new data.

Main Results:

  • The proposed model accounts for observed regularities in decision-making experiments.
  • Perceptual biases in probabilities and payoffs are identified as key drivers.
  • The model demonstrates superior data organization compared to extant models.

Conclusions:

  • A parsimonious model effectively explains individual choice under risk and uncertainty.
  • Perception of probabilities and payoffs is crucial for understanding decision-making.
  • The model offers new, empirically supported insights and testable hypotheses.