Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Population Growth

Population Growth

Population size is dynamic, increasing with birth rates and immigration, and decreasing with death rates and emigration. In ideal conditions with unlimited resources, populations can increase exponentially, which plots as a J-shaped growth rate curve of population size against time. This type of curve is characteristic of newly-introduced invasive species, or populations that have suffered catastrophic declines and are rebounding.

Modeling with Differential Equations

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...

Exponential Equations for Modeling Growth

Exponential Equations for Modeling Growth

Exponential models are essential for describing rapid, multiplicative changes in natural systems, such as population growth. When a population doubles at regular intervals, the process can be modeled using a suitable base. For instance, a bacterial culture that doubles every three hours follows the model n(t)=n0⋅2t/3, where n(t) is the population at the time t.A more general model uses the natural base e, especially for continuous growth. This takes the form n(t)=n0⋅ert, where r is the relative...

Gene Flow

Gene Flow

Gene flow is the transfer of genes among populations, resulting from either the dispersal of gametes or from the migration of individuals.

What is Population Genetics?

What is Population Genetics?

A population is composed of members of the same species that simultaneously live and interact in the same area. When individuals in a population breed, they pass down their genes to their offspring. Many of these genes are polymorphic, meaning that they occur in multiple variants. Such variations of a gene are referred to as alleles. The collective set of all the alleles within a population is known as the gene pool.

Growth Models with Integration: Problem Solving

Growth Models with Integration: Problem Solving

In population modeling, integration provides a systematic way to determine accumulated quantities from known rates of change. One such application arises in ecology, where the total weight of a fish population in a body of water is referred to as its biomass. When the rate of growth of this biomass is known as a function of time, calculus can be used to determine the total biomass at a future date.Growth Rate and Biomass FunctionLet the growth rate of the fish population be represented by a...

You might also read

Related Articles

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

Sort by

Same author

Identification of Non-Tuberculous Mycobacteria in Iberian Lynx (<i>Lynx pardinus</i>) and Their Impact on Its Health.

Veterinary sciences·2025

Same author

Using Digital Art and Attachment Priming in a Web-Based Serious Game to Reduce Pain and Social Disconnection in Individuals With Chronic Pain and Loneliness: Randomized Controlled Trial.

JMIR serious games·2024

Same author

Do institutions evolve like material technologies?

Current opinion in psychology·2024

Same author

The Effects of Playing Versus Watching an Interactive Video Game Featuring an Older Adult Protagonist on State Empathy and Ageism Reduction.

Games for health journal·2024

Same author

The evolution of environmentally mediated social interactions and posthumous spite under isolation by distance.

PLoS computational biology·2024

Same author

Insights into the spatial ecology of severely injured free-living felids: Iberian lynx, bobcat, and snow leopard.

Ecology and evolution·2024

Search research articles

Related Experiment Video

Updated: May 18, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

Bipartite graphs as models of population structures in evolutionary multiplayer games.

Jorge Peña¹, Yannick Rochat

¹Faculty of Social and Political Sciences, Institute of Applied Mathematics, University of Lausanne, Lausanne, Switzerland. jorge.pena@unibas.ch

|September 13, 2012

Summary

Bipartite graphs better model multiplayer games on networks than unipartite graphs. This research uses bipartite graphs to study cooperation evolution, revealing how network structure impacts evolutionary dynamics.

More Related Videos

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

Related Experiment Videos

Last Updated: May 18, 2026

The HoneyComb Paradigm for Research on Collective Human Behavior

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

Area of Science:

Evolutionary Game Theory
Network Science
Computational Biology

Background:

Games on graphs analyze evolutionary dynamics in networked populations.
Unipartite graphs are standard for modeling interactions, including multiplayer scenarios.
Implicit assumptions in unipartite models can influence evolutionary outcomes.

Purpose of the Study:

To propose bipartite graphs as a superior framework for modeling population structures in evolutionary multiplayer games.
To investigate the evolution of cooperation in N-person prisoner's dilemma using bipartite graphs.
To highlight the impact of network construction on evolutionary dynamics.

Main Methods:

Utilizing bipartite graphs to represent population structures in evolutionary multiplayer games.
Conducting computer simulations to analyze the evolution of cooperation.
Applying concepts from social network analysis, including centrality and clustering.

Main Results:

Bipartite graphs offer a more accurate representation for multiplayer games compared to unipartite graphs.
Implicit assumptions in unipartite graph models significantly affect evolutionary dynamics.
The study demonstrates the importance of network construction and analysis in understanding evolutionary processes.

Conclusions:

Bipartite graphs are more suitable for computational modeling of evolutionary multiplayer games.
Network structure, particularly when modeled with bipartite graphs, is crucial for understanding cooperation dynamics.
Social network analysis concepts enhance the study of evolutionary processes on networks.