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Targeted Audience

We envision three separate, parallel "tracks" for EvoLab development: a track for informal education, a track for teachers and students, and a track for researchers. 

Models for informal education are designed to be experienced at a museum kiosk, where a user would typically interact with a model for a few minutes.  Such models are designed to show qualitative relationships and not quantitative measures.

Models for students and teachers are designed to be explored in specific sequences or following a particular exploration pattern for a portion of a learning or teaching activity.  These models need to support transitions from scripted or guided exploration to independent inquiry.  They also need to support advancement from discovery of qualitative relationships to developing more quantitative measures.  Lastly, they must be transparent enough to allow motivated students or teachers to change underlying modeling rules.

Models for researchers are designed to explore quantitative relationships in greater detail and compare various mechanisms of how different natural selection rules might lead to similar/dissimilar emergent patterns in individual and population dynamics.

Modeling Design Principles

  1. Each model should show emergence of either a complex or a non-intuitive outcome related to evolution.
  2. Such emergence should result from simple interactions amongst agents or, better yet, between the agents and the user(s). 
  3. Models should be stripped down to minimal interface elements to support clear understanding of the interrelationships between agents, model parameters, and user interactions.  If additional interface elements would help show more complex interrelationships, break the model into as many sub-models as necessary to give the user a continuum of model complexity to explore the related ideas.
  4. Build models for phenomena that currently have example “black box” simulations (applets, shockwave animations) available on the internet, but which are either non-emergent or not transparent. 
  5. Build models for traditional textbook topics that lend themselves to demonstrating emergent outcomes if represented in an agent based environment.
  6. Develop models that include genotypic representations more complex than a one-to-one mapping to the phenotype.