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Related Concept Videos

Decision Making01:20

Decision Making

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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...
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Decision Making: P-value Method01:09

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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...
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Computed Tomography01:10

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Decision Making: Traditional Method01:14

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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.
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Design Example: Traverse Angle Computations01:25

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Traverse angle computations are a critical component of surveying, used to compute the internal angles within a closed traverse. A traverse consists of a series of connected lines forming a closed loop, often used for land boundary delineation or mapping. Calculating the internal angles ensures accuracy in the traverse geometry and is essential for checking survey data integrity.The process begins with known azimuths and bearings of the traverse sides. Internal angles at each vertex are...
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Area Computation by the Alternative Coordinate Method01:24

Area Computation by the Alternative Coordinate Method

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The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...
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Computational modelling for decision-making: where, why, what, who and how.

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Creating effective computational models requires collaboration between commissioners, developers, and users. This guide and checklists aim to improve model development and deployment for wiser decision-making.

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Area of Science:

  • Computational Science
  • Decision Science
  • Systems Engineering

Background:

  • Increasingly complex world necessitates sophisticated computational models for decision-making.
  • Model creation demands collaboration among commissioners, developers, users, and reviewers, beyond just data and technical skills.
  • Understanding the modeling process, its purposes, and technical bases is crucial for effective use.

Purpose of the Study:

  • To provide a comprehensive guide for commissioning, developing, and deploying computational models.
  • To offer practical tools, including checklists, for ensuring successful model development and utilization.
  • To enhance understanding and collaboration in the field of modeling across various domains.

Main Methods:

  • A review of best practices in computational model development and deployment.
  • Development of two checklists to guide model commissioners, developers, and users.
  • Exploration of diverse application domains, including public policy, science, and engineering.

Main Results:

  • Identification of key factors for successful model commissioning, development, and deployment.
  • Provision of practical checklists to aid stakeholders in the modeling lifecycle.
  • Highlighting the need for enhanced collaboration and understanding in modeling.

Conclusions:

  • Reinforcing modeling as a distinct discipline is essential to minimize misconstruction.
  • Increasing domain-wide understanding of modeling will reduce its misuse.
  • Closer engagement between commissioners and modelers will lead to more impactful and useful model outputs.