CES Online Learning Tutorials
Dynamic Models of Human Systems: How they help us explain our past and manage our future
Welcome to the educational module Dynamic Models of Human Systems!
This hands-on introduction to dynamic models of human systems consists of eight fully developed, online, easy to use and understand visual flow diagram dynamic system models drawn from across the social sciences. These models explore exponential population growth, basic and technically advanced hunter-gatherer and farming communities, boom and bust civilizations, recent economic growth, and our current environmentally challenged limits to growth.
Each model features:
- A contextual social-science introduction to the model.
- A visual flow diagram of colored-coded icons for stocks and adjustable flow valves, and connecting lines (with arrows) that show the flows of stocks and information between the icons.
- A free online simulator with color-coded controls that allow users to exercise the model by setting their own initial stock values and model parameters as within allowable ranges.
- A color-coded table that lists all the model parameters, their units, and associated equations (which are not required to understand the model or explore its behavior).
- Example model simulations that illuminate key features of the model.
- Ideas for additional model simulations are provided, along with suggested settings for initial stock and parameter values.
- A link to the Stella computer model block diagram and high-level code is provided so those with appropriate Stella software can download and modify the model.
Click here to access module components or use the Contents tab in the top menu.
- Russell Genet, California Polytechnic State University
- Peter Richerson, University of California, Davis
- Cheryl Genet, Cuesta College
- Charles Efferson, University of Lausanne, originally wrote models 3 and 5.
Sponsors
Dynamic Models of Human Systems is one of a series of online teaching modules sponsored by the Cultural Evolution Society and the Center for the Dynamics of Social Complexity through the generous support of the John Templeton Foundation.
This project was supported by Grant #61105 from the John Templeton Foundation to the University of Tennessee, Knoxville (PIs: S. Gavrilets and P. J. Richerson) with assistance from the Center for the Dynamics of Social Complexity and the National Institute for Mathematical and Biological Synthesis at the University of Tennessee, Knoxville.
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