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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

42
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
42
Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

4.2K
On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
4.2K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

29
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...
29
Quantitative Analysis01:12

Quantitative Analysis

249
Quantitative analysis is a technique for measuring the amount of specific constituents in a sample. When the sample's composition is unknown, qualitative analysis is performed first to identify its components, which ensures that the correct substances are measured during the quantitative phase.
In quantitative analysis, two key measurements are made: the sample quantity and a property proportional to the amount of the analyte (the substance being analyzed). This forms the basis of the...
249
Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

40
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...
40
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

60
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
60

You might also read

Related Articles

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

Sort by
Same author

Association between preserved ratio impaired spirometry and cardiovascular disease subtypes: evidence from NHANES 2007-2012.

BMC cardiovascular disorders·2025
Same author

Dissecting and validation the biomarker of heart failure progression in patients with atherosclerosis by single-cell sequencing, bioinformatics, and machine learning.

Frontiers in genetics·2025
Same author

Structural features of polysaccharides from Physalis pubescens L. stem, and their neuroprotective effects and anti-aging activities.

International journal of biological macromolecules·2025
Same author

Resource recovery from low-strength domestic wastewater via a novel integrated chemically enhanced primary treatment (CEPT)-membrane distillation concentration and dynamic adsorption process: Performance improvement and sustainability implications.

Environmental research·2025
Same author

Neuroprotective peptide OL-FS13 attenuates cardiac apoptosis and myocardial infarction injury through Nrf2/HO-1 pathway.

Clinical and experimental hypertension (New York, N.Y. : 1993)·2025
Same author

Machine-Learning Potential Molecular Dynamics Reveals the Critical Role of Flexibility in Solid-Liquid Nanofluidic Friction.

ACS nano·2025

Related Experiment Video

Updated: Jun 9, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.0K

Dealing with soft variables and data scarcity: lessons learnt from quantification in a participatory system dynamics

Irene Pluchinotta1, Ke Zhou1, Nici Zimmermann1

  • 1Institute for Environmental Design and Engineering, The Bartlett Faculty of The Built Environment, University College London, London, UK.

System Dynamics Review
|October 23, 2024
PubMed
Summary
This summary is machine-generated.

System dynamics (SD) models help decision-making with complex, intangible factors. This study addresses challenges in quantifying soft variables and data scarcity within participatory SD processes.

More Related Videos

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

8.7K
Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.3K

Related Experiment Videos

Last Updated: Jun 9, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.0K
Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

8.7K
Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.3K

Area of Science:

  • Systems Science
  • Decision Science
  • Participatory Modeling

Background:

  • System dynamics (SD) models are vital for complex problem structuring and decision-making, particularly in areas with limited data and nonlinear relationships.
  • Quantifying intangible or qualitative aspects (soft variables) in SD models presents a significant challenge, especially when data scarcity precludes conventional analytical methods.
  • Existing procedures for obtaining and analyzing information using participatory approaches for soft variables are limited.

Purpose of the Study:

  • To review current quantification methods for soft variables and identify open questions, particularly concerning data scarcity.
  • To detail a quantification process developed within a participatory system dynamics framework, addressing data scarcity and soft variables.
  • To propose a novel quantification framework adaptable to varying data availability and stakeholder engagement levels.

Main Methods:

  • Literature review of existing quantification techniques for soft variables in system dynamics.
  • Development and application of a participatory approach to quantify soft variables amidst data scarcity.
  • Framework design based on empirical findings from the participatory process.

Main Results:

  • Identified gaps in current methods for quantifying soft variables, especially under data scarcity.
  • Demonstrated a practical, participatory process for effectively handling soft variables and data limitations in SD modeling.
  • Developed a flexible quantification framework tailored to data availability and stakeholder involvement.

Conclusions:

  • Addressing soft variables and data scarcity in system dynamics requires innovative participatory approaches.
  • The proposed framework offers a structured method for enhancing the quantification of qualitative elements in SD models.
  • Effective stakeholder engagement is crucial for successful quantification in data-scarce environments.