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	Modeling Tools
	Logistic Growth
	Blending Inheritance
	Jeff Schank (Indiana University), and William Wimsatt (University of Chicago)
Screen Shots | System Requirements

These two applications, Logistic Growth and Blending Inheritance, are designed to accompany Modeling, a text module found in the BioQUEST Collection section of the Library. These programs will allow you to explore mathematically simple models important in ecology and population genetics using a variety of modes of visualization.

Logistic Growth
This simulation program provides the user with a number of ways to graphically analyze, interpret, and understand the behavior of the logistic growth equation (i.e., dN/dt = rN((K – N)/K)) and minimum density limited logistic growth equation (i.e., dN/dt = rN{[(K – N)/K] [(N – C)/N]}. The Modeling text presents several exercises on model building and analysis using this simulation. These exercises are designed to do the following:

• Teach you the behavior of these growth equations and their strengths and limitations as models of population growth.
• Teach you how they are related to each other and to more complex models, and, by using them as an example, how you can build more complex models out of simpler ones.
• Teach you some general and simple features of how to investigate the behavior of dynamical systems, including elementary stability analysis and illustrations of Monte Carlo simulation.
• Show how different ways of analyzing the data can give you different tools for exploring its patterns by employing four distinct modes of representing the data.
• Introduce you to the study and nature of chaotic phenomena.

Blending Inheritance
This application is used to explore the uses and effects of "false models" through the simulation of blending inheritance, a mode of inheritance which was championed by Darwin and many others but which has since been reconceptualized as Mendelian multi-locus additive traits. This kind of simulation (i.e., a simulation based on what we now accept as a false model of inheritance) is not directly relevant for deriving and understanding the consequences of theories of inheritance that we hold to be true, but it may be indirectly relevant, in that it can be crucial for understanding why we accept certain theories as opposed to their competitors. It may also be crucial for understanding the similarities and differences between these theories—usually a necessary requirement for designing an adequate experimental test of the theories. Such fundamentally false models also shed light on current and future investigations in science as well.

Screen Shots

Dialog box for entering parameters for Logistic Growth Model.

System Requirements

Macintosh or Power Macintosh

• System 6.07 or later; System 7.1 or later for PowerMacintosh.
• At least 800K of available RAM.