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

Multimachine Stability

Multimachine Stability

Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:

BIBO stability of continuous and discrete -time systems

BIBO stability of continuous and discrete -time systems

System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system.

Alternative Sets of Equilibrium Equations

Alternative Sets of Equilibrium Equations

When analyzing the behavior of structures, engineers often rely on the concept of equilibrium. This refers to the state where all forces and moments acting on a system balance each other, resulting in no net movement or rotation. In many cases, equilibrium can be described by a set of standard equations. However, in some situations, alternative sets of equilibrium equations must be used to describe the system's behavior accurately.
One example of such a situation can be observed in a...

Multi-input and Multi-variable systems

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...

Genetic Drift

Genetic Drift

Natural selection—probably the most well-known evolutionary mechanism—increases the prevalence of traits that enhance survival and reproduction. However, evolution does not merely propagate favorable traits, nor does it always benefit populations.Life is not fair. A deer grazing contentedly in a field can have her meal cut tragically short by a bolt of lightning. If the doomed doe is one of only three in the population, 1/3 of the population’s gene pool is lost. Random events like this can...

Gaussian Elimination: Problem Solving

Gaussian Elimination: Problem Solving

Systems of linear equations in several variables are pivotal in modeling complex scenarios involving multiple unknowns and constraints. Such systems are widely used in various fields to represent relationships where several conditions must be simultaneously satisfied. Each variable in the system corresponds to an unknown quantity, while each equation imposes a linear constraint, leading to a structured approach for analyzing and solving real-world problems.A system of three equations with three...

You might also read

Related Articles

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

Sort by

Same author

Evolution of dispersal in a spatially heterogeneous population with finite patch sizes and catastrophes.

Journal of theoretical biology·2026

Same author

Evolving genealogies in cultural evolution, the descendant process, and the number of cultural traits.

Theoretical population biology·2025

Same author

How social learning enhances-or undermines-efficiency and flexibility in collective decision-making under uncertainty.

Proceedings of the National Academy of Sciences of the United States of America·2025

Same author

Many-strategy games in groups with relatives and the evolution of coordinated cooperation.

Journal of theoretical biology·2025

Same author

Conditions for the establishment of creole languages from an evolutionary game theoretic perspective.

Journal of theoretical biology·2025

Same author

Evolution of dispersal under spatio-temporal heterogeneity.

Journal of theoretical biology·2023

Search research articles

Related Experiment Videos

Stochastic evolutionary dynamics of bimatrix games.

Hisashi Ohtsuki¹

¹PRESTO, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan. ohtsuki.h.aa@m.titech.ac.jp

Journal of Theoretical Biology

|January 26, 2010

Summary

This study explores evolutionary game dynamics in finite populations, analyzing asymmetric games with alpha and beta roles. It provides a criterion for equilibrium selection in general bimatrix games under weak selection.

Related Experiment Videos

Area of Science:

Evolutionary Game Theory
Population Genetics
Mathematical Biology

Background:

Asymmetric games in finite populations are crucial for understanding social and biological evolution.
The interplay of natural selection and mutation shapes strategy frequencies.
Bimatrix games, defined by payoff matrices, model interactions between two distinct player roles (alpha and beta).

Purpose of the Study:

To formulate and analyze the evolutionary game dynamics of two-player asymmetric bimatrix games in finite populations.
To investigate the impact of natural selection and mutation on strategy evolution.
To derive a criterion for equilibrium selection under weak selection.

Main Methods:

Formulation of a stochastic evolutionary game dynamics model.
Application of a frequency-dependent Moran process with mutation.
Analytical derivation of the stationary distribution of strategies.

Main Results:

The study analytically derives the stationary distribution of strategies for asymmetric bimatrix games.
A criterion for equilibrium selection is established under conditions of weak selection.
The model captures the combined effects of selection and mutation on strategy dynamics.

Conclusions:

The derived criterion offers a novel approach to understanding equilibrium selection in evolutionary game theory.
This work provides a theoretical framework for analyzing evolutionary dynamics in finite populations.
The findings are applicable to diverse fields modeling strategic interactions and evolution.