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Related Concept Videos

Bias01:22

Bias

Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
Stability of Equilibrium Configuration: Problem Solving01:13

Stability of Equilibrium Configuration: Problem Solving

The stability of equilibrium configurations is an important concept in physics, engineering, and other related fields. In simple terms, it refers to the tendency of an object or system to return to its equilibrium position after being disturbed. The stability of an equilibrium configuration can be analyzed by considering the potential energy function of the system and examining its behavior near the equilibrium point.
Problem-solving in the context of the stability of equilibrium configuration...
Stability of Equilibrium Configuration01:23

Stability of Equilibrium Configuration

Understanding the stability of equilibrium configurations is a fundamental part of mechanical engineering. In any system, there are three distinct types of equilibrium: stable, neutral, and unstable.
A stable equilibrium occurs when a system tends to return to its original position when given a small displacement, and the potential energy is at its minimum. An example of a stable equilibrium is when a cantilever beam is fixed at one end and a weight is attached to the other end. If the weight...
BIBO stability of continuous and discrete -time systems01:24

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.
Contingency Table01:29

Contingency Table

A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...

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Related Experiment Video

Updated: May 9, 2026

New Variations for Strategy Set-shifting in the Rat
09:45

New Variations for Strategy Set-shifting in the Rat

Published on: January 23, 2017

Bet-hedging in stochastically switching environments.

J Müller1, B A Hense, T M Fuchs

  • 1TU München, Centre for Math. Sciences, Boltzmannstr. 3, D-85747 Garching, Germany; Helmholtz Center Munich, Institut für Biomathematik und Biometrie, Helmholtz Zentrum München, Ingolstädter Landstr. 1, D-85764 Neuherberg, Germany.

Journal of Theoretical Biology
|August 1, 2013
PubMed
Summary
This summary is machine-generated.

Bet-hedging strategies evolve differently based on environmental change frequency. Rapid changes favor adaptation to the average environment, while slow changes favor adapting to the current conditions.

Keywords:
Adaptive dynamicsBet-hedgingLyapunov exponentStochastic switching systems

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Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
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Published on: September 19, 2012

Area of Science:

  • Evolutionary biology
  • Theoretical ecology
  • Mathematical modeling

Background:

  • Bet-hedging is a strategy where organisms reduce environmental variance in fitness.
  • Understanding bet-hedging is crucial for predicting population persistence in fluctuating environments.

Purpose of the Study:

  • To investigate the evolution of bet-hedging strategies in populations experiencing stochastically switching environments.
  • To extend existing knowledge on bet-hedging and clarify the conditions under which different strategies are favored.

Main Methods:

  • Adaptive dynamics modeling was employed to analyze evolutionary trajectories.
  • Lyapunov exponents were calculated for stochastically switching systems to analyze stability.
  • Heuristic arguments and simulations were used to explore specific environmental change frequencies.

Main Results:

  • Three distinct evolutionarily stable strategies (ESSs) were identified, contingent on the rate of environmental change.
  • Rapid environmental changes favor a monomorphic phenotype adapted to the mean environment.
  • Intermediate environmental change rates favor a bimorphic bet-hedging phenotype, while slow changes favor a monomorphic phenotype tracking the current environment.

Conclusions:

  • The frequency of environmental fluctuations is a key determinant of bet-hedging strategy evolution.
  • Different bet-hedging strategies, including monomorphic and bimorphic phenotypes, can be evolutionarily stable under distinct environmental dynamics.
  • The findings provide insights into the adaptive potential of populations in variable environments.