Jove
Visualize
Contact Us
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 Concept Videos

Decision Making: P-value Method01:09

Decision Making: P-value Method

5.8K
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...
5.8K
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

4.4K
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...
4.4K
Probability Laws01:49

Probability Laws

29.7K
Overview
29.7K
Decision Making01:20

Decision Making

1.2K
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...
1.2K
Probability Distributions01:32

Probability Distributions

10.0K
 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...
10.0K
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

391
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
391

You might also read

Related Articles

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

Sort by
Same author

Predicting the replicability of social and behavioural science claims in COVID-19 preprints.

Nature human behaviour·2024
Same author

Predicting and reasoning about replicability using structured groups.

Royal Society open science·2023
Same author

Predicting reliability through structured expert elicitation with the repliCATS (Collaborative Assessments for Trustworthy Science) process.

PloS one·2023
Same author

The Monash Autism-ADHD genetics and neurodevelopment (MAGNET) project design and methodologies: a dimensional approach to understanding neurobiological and genetic aetiology.

Molecular autism·2021
Same author

Towards open, reliable, and transparent ecology and evolutionary biology.

BMC biology·2021
Same author

Predicting the causative pathogen among children with osteomyelitis using Bayesian networks - improving antibiotic selection in clinical practice.

Artificial intelligence in medicine·2020

Related Experiment Video

Updated: May 3, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.3K

Exploring risk judgments in a trade dispute using Bayesian networks.

Bonnie C Wintle1, Ann Nicholson

  • 1Environmental Science, School of Botany, University of Melbourne, Melbourne, 3010, Australia.

Risk Analysis : an Official Publication of the Society for Risk Analysis
|February 6, 2014
PubMed
Summary

Bayesian networks (BNs) offer a transparent and mathematically sound method for analyzing trade disputes, aiding World Trade Organization (WTO) settlement. They provide a more accessible alternative to complex simulations for exploring disagreements under uncertainty.

Keywords:
Bayesian networkdecision supportimport risk analysisrisk perceptiontrade

Related Experiment Videos

Last Updated: May 3, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.3K

Area of Science:

  • Decision Analysis
  • Risk Assessment
  • International Trade Law

Background:

  • Bayesian networks (BNs) are graphical tools for scenario exploration and stakeholder communication.
  • Trade disputes, such as the WTO's Australia-New Zealand apples case, involve complex risk assessments.
  • Current methods for import risk analysis (IRA) range from basic qualitative approaches to complex simulations.

Purpose of the Study:

  • To investigate the utility of Bayesian networks (BNs) for analyzing trade disputes.
  • To model different approaches to import risk analysis (IRA) using BNs.
  • To assess the potential of BNs in aiding World Trade Organization (WTO) dispute settlement.

Main Methods:

  • Developed a series of Bayesian networks (BNs) of increasing complexity.
  • Modeled various import risk analysis (IRA) methodologies, from qualitative to quantitative simulations.
  • Applied BNs to the specific case study of the Australia-New Zealand apples dispute.

Main Results:

  • BNs proved useful for exploring disagreements under uncertainty due to their probabilistic nature and transparent representation of analytical steps.
  • The sensitivity of risk outputs to different judgments, like trade volume, was effectively explored.
  • BNs were found to be more accessible and mathematically sound than basic qualitative and semi-quantitative risk analyses.

Conclusions:

  • Bayesian networks (BNs) offer a transparent and accessible aid for complex decision-making in trade disputes.
  • While current BN tools have computational limitations compared to complex simulations, they provide superior transparency for stakeholders.
  • Technological advancements in BN software show promise for future applications in dispute resolution.