simulation model

(noun)

a model that utilizes mathematical algorithms to predict complex responses in ecosystem dynamics

Related Terms

Examples of simulation model in the following topics:

  • Modeling Ecosystem Dynamics

    • Conceptual models describe ecosystem structure, while analytical and simulation models use algorithms to predict ecosystem dynamics.
    • In these cases, scientists often use analytical or simulation models.
    • Like analytical models, simulation models use complex algorithms to predict ecosystem dynamics.
    • However, sophisticated computer programs have enabled simulation models to predict responses in complex ecosystems.
    • Compare and contrast conceptual, analytical, and simulation models of ecosystem dynamics

  • Studying Ecosystem Dynamics

    • Many different models are used to study ecosystem dynamics, including holistic, experimental, conceptual, analytical, and simulation models.
    • Three basic types of ecosystem modeling are routinely used in research and ecosystem management: conceptual models, analytical models, and simulation models.
    • Analytical and simulation models are mathematical methods of describing ecosystems that are capable of predicting the effects of potential environmental changes without direct experimentation, although with limitations in accuracy.
    • A simulation model is created using complex computer algorithms to holistically model ecosystems and to predict the effects of environmental disturbances on ecosystem structure and dynamics.
    • Differentiate between conceptual, analytical, and simulation models of ecosystem dynamics, and mesocosm and microcosm research studies

  • Chance Models

    • Stochastic modeling builds volatility and variability (randomness) into a simulation and, therefore, provides a better representation of real life from more angles.
    • A stochastic model would be able to assess this latter quantity with simulations.
    • Stochastic models can be simulated to assess the percentiles of the aggregated distributions.
    • In a simulated stochastic model, the simulated losses can be made to "pass through" the layer and the resulting losses are assessed appropriately.
    • Support the idea that stochastic modeling provides a better representation of real life by building randomness into a simulation.

  • Checking for independence

    • We simulated these differences assuming that the independence model was true, and under this condition, we expect the difference to be zero with some random fluctuation.
    • H0: Independence model.
    • HA: Alternative model.
    • Based on the simulations, we have two options. (1) We conclude that the study results do not provide strong evidence against the independence model.
    • A stacked dot plot of differences from 100 simulations produced under the independence model, H0, where gender sim and decision are independent.

  • Instructional Models and Applications

    • Promoting student-ownership, using a particular medium to focus attention, telling stories, simulating and recreating events, and utilizing resources and data on the Internet are among them.
    • Three instructional models that implement problem-based inquiry will be discussed next with particular attention to instructional strategies and practical examples.

  • The Carbon Cycle

    • Plumes of carbon dioxide in the simulation swirl and shift as winds disperse the greenhouse gas away from its sources.
    • The carbon dioxide visualization was produced by a computer model called GEOS-5, created by scientists at NASA Goddard Space Flight Center's Global Modeling and Assimilation Office.
    • The visualization is a product of a simulation called a "Nature Run."
    • The model is then left to run on its own and simulate the natural behavior of the Earth's atmosphere.
    • This Nature Run simulates January 2006 through December 2006.

  • Small sample hypothesis testing for a proportion exercises

    • (c) Based on large sample theory, we modeled ˆ p using the normal distribution.
    • Describe how to perform such a simulation and, once you had results, how to estimate the p-value.
    • (b) Based on large sample theory, we modeled ˆ p using the normal distribution.
    • Describe a setup for a simulation that would be appropriate in this situation and how the p-value can be calculated using the simulation results.
    • The p-value will be two times the proportion of simulations where ≤ 0.57.

  • Generating the null distribution and p-value by simulation

    • Each client can be simulated using a deck of cards.
    • There were 5 simulated cases with a complication and 57 simulated cases without a complication, i.e. = 5/62 = 0.081.
    • One simulation isn't enough to get a sense of the null distribution; many simulation studies are needed.
    • The normal model poorly approximates the null distribution for when the success-failure condition is not satisfied.
    • However, we can generate an exact null distribution and p-value using the binomial model from Section 3.4.

  • Conclusion: Implications for Teaching and Learning

    • One is for the students to learn about history following a conceptual change model.
    • Simulations can be used to present exposing or discrepant events to individual learners or in a group setting.
    • MERLOT (Multimedia Educational Resource for Learning and Online Teaching), located at http://www.merlot.org, contains simulations for the domains of business, physics, genetics, and medical education among others.
    • Most of the simulations are designed for adult learners, but a few are targeted for K-12 education.
    • Interactive Physics (http://www.inspiration.com/) and Geometer's Sketchpad (http://www.keypress.com/sketchpad/)are two popular simulation-construction tools.

  • Steps to Integrating Experiential Learning in the Classroom

    • Simulations and gaming within instruction also involve direct experience and thus are valid examples of experiential learning.
    • In addition, it has been found that simulations which shorten the debriefing period at the end of the game session can diminish their own effectiveness.
    • Thus, it is apparent that the reflective observation and abstract conceptualization portions of simulations and games are vital to learning, which has also been established by the Experiential Learning Theory (Ulrich, 1997).
    • Specifically, there has been an effort to utilize this model to increase the effectiveness of Continuing Professional Development (CPD) e-learning courses.