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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...
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...
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Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...

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Updated: Jun 16, 2026

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

General probability-time tradeoff and intertemporal risk-value model.

Shengxiang She, Chaoqun Ma, Desheng Dash Wu

    Risk Analysis : an Official Publication of the Society for Risk Analysis
    |February 9, 2010
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an intertemporal risk-value (IRV) model, unifying probability-time, time-value, and risk-value tradeoffs for decision-making under uncertainty. The model formally captures psychological distance in evaluating temporal lotteries.

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    Published on: January 9, 2016

    Area of Science:

    • Decision Science
    • Behavioral Economics
    • Psychological Modeling

    Background:

    • Evaluating uncertain outcomes over time involves complex tradeoffs between risk, value, and time.
    • Existing models often address these tradeoffs in isolation, lacking a unified framework.
    • Understanding decision-makers' psychological distance in temporal evaluations is crucial.

    Purpose of the Study:

    • To propose a novel Intertemporal Risk-Value (IRV) model.
    • To integrate probability-time, time-value, and risk-value tradeoffs into a single framework.
    • To formally represent the psychological distance in evaluating temporal lotteries.

    Main Methods:

    • Development of a general probability-time tradeoff.
    • Formalization of the IRV model incorporating value, probability, and time dimensions.
    • Application of the IRV model to a wildcatter problem for structural insights.

    Main Results:

    • A unified framework for analyzing decisions involving risk, value, and time is established.
    • A formal representation of the probability-time tradeoff reflecting psychological distance was derived.
    • Empirical and theoretical support for the probability-time tradeoff intuition was confirmed.

    Conclusions:

    • The IRV model provides a comprehensive approach to understanding complex temporal decision-making.
    • The formalization of psychological distance enhances the predictive power of economic models.
    • The model offers valuable insights into evaluating uncertain future outcomes.