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

Modeling with Differential Equations01:25

Modeling with Differential Equations

Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
Dynamic Equilibrium02:20

Dynamic Equilibrium

A reversible chemical reaction represents a chemical process that proceeds in both forward (left to right) and reverse (right to left) directions. When the rates of the forward and reverse reactions are equal, the concentrations of the reactant and product species remain constant over time and the system is at equilibrium. A special double arrow is used to emphasize the reversible nature of the reaction. The relative concentrations of reactants and products in equilibrium systems vary greatly;...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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)...
Separable Differential Equations01:20

Separable Differential Equations

A separable differential equation is a type of first-order differential equation where the derivative dy/dx can be expressed as a product of two functions: one that depends only on x and another that depends only on y. This allows for the rearrangement of the equation so that all terms involving y are on one side, and all terms involving x are on the other. This process, known as the separation of variables, simplifies the process of solving the equation by enabling the integration of both...
Law of Segregation01:49

Law of Segregation

When crossing pea plants, Mendel noticed that one of the parental traits would sometimes disappear in the first generation of offspring, called the F1 generation, and could reappear in the next generation (F2). He concluded that one of the traits must be dominant over the other, thereby causing masking of one trait in the F1 generation. When he crossed the F1 plants, he found that 75% of the offspring in the F2 generation had the dominant phenotype, while 25% had the recessive phenotype.
Theories of Dissolution: The Danckwerts' Model and Interfacial Barrier Model01:09

Theories of Dissolution: The Danckwerts' Model and Interfacial Barrier Model

Various dissolution theories provide insight into the factors that influence the dissolution rate. Danckwerts' Model suggests that turbulence, rather than a stagnant layer, characterizes the dissolution medium at the solid-liquid interface. In this model, the agitated solvent contains macroscopic packets that move to the interface via eddy currents, facilitating the absorption and delivery of the drug to the bulk solution. The regular replenishment of solvent packets maintains the concentration...

You might also read

Related Articles

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

Sort by
Same author

Individual tolerances and dynamics of intragroup segregation in a extended Schelling model.

Scientific reports·2025
Same author

Fungi as Turing automata with oracles.

Royal Society open science·2024
Same author

Dynamical robustness of a Boolean model for the human gonadal sex determination.

Computational biology and chemistry·2024
Same author

Gene regulatory networks with binary weights.

Bio Systems·2023
Same author

Majority networks and local consensus algorithm.

Scientific reports·2023
Same author

Phase transitions in a conservative game of life.

Physical review. E·2021

Related Experiment Video

Updated: May 31, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Dynamics and complexity of the Schelling segregation model.

Nicolás Goles Domic1, Eric Goles, Sergio Rica

  • 1Departamento de Informática, Universidad Técnica Federico Santa María, Avenida Vicuña Mackenna 3939, San Joaquín, Santiago, Chile.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|July 7, 2011
PubMed
Summary

The Schelling social segregation model shows that even mild discrimination can lead to highly segregated patterns. Segregation dynamics tend to minimize the boundaries between different populations, optimizing spatial arrangements.

More Related Videos

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Related Experiment Videos

Last Updated: May 31, 2026

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Area of Science:

  • Social dynamics
  • Computational sociology
  • Agent-based modeling

Background:

  • The Schelling model is a foundational agent-based model explaining social segregation.
  • Segregation arises from individual preferences and local interactions, not necessarily from strong discriminatory intent.
  • Understanding the parameters influencing segregation patterns is crucial for social science research.

Purpose of the Study:

  • To investigate the dynamical evolution of segregation patterns in the Schelling model.
  • To analyze the impact of key parameters (neighborhood size, tolerance, initial population distribution) on segregation.
  • To explore the relationship between segregation and interface minimization.

Main Methods:

  • Simulation of the Schelling social segregation model with two distinct populations.
  • Systematic variation of model parameters including neighborhood size and individual tolerance.
  • Analysis of emergent spatial patterns and their characteristics.
  • Introduction and analysis of an energy functional to describe system configuration.

Main Results:

  • Segregation patterns generally minimize the interface between different population zones.
  • An energy functional was introduced, showing a strictly decreasing trend for tolerant populations.
  • The study identified that non-strictly-decreasing energy functionals can lead to highly efficient segregation.

Conclusions:

  • The Schelling model demonstrates how local preferences can drive global segregation.
  • Minimizing inter-group boundaries is a key emergent property of segregation dynamics.
  • The energy functional provides a novel way to quantify segregation efficiency and system dynamics.