The best Challenge projects address real-world problems with measurable components — what we call computational science.
Types of projects
Challenge teams tackle a range of interesting problems to solve. The most successful projects address a topic that holds great interest for the team. In recent years, ideas for projects have come from Astronomy, Geology, Physics, Ecology, Mathematics, Economics, Sociology, and Computer Science.
Here are some sample questions teams might wish to answer: Is our county going to run out of water? How are attitudes towards self-care in the prevention of disease changed? What is the likelihood that the deer population in Bandelier will run out of grassland? How can data sensors improve our lives?
Projects fall into a couple of categories. In one category are problems that have clear mathematical models with well-defined variables. These are problems that are usually modeled using Java or Excel.
Other projects study what are called complex systems and examine emergent behavior — the behavior of a system based on the way components of a decentralized system interact with each other. These kinds of projects are modeled using StarLogo or NetLogo.
Learn about StarLogo NOVA (it's free) at education.mit.edu/project/starlogo-nova.
Learn about NetLogo (it's free) at ccl.northwestern.edu/netlogo.
Topic inspiration
Past finalist projects combine rigorous science with creative modeling — see more examples of what teams build.
Focusing your project
After a team has found an idea that is interesting, the members begin the process of focusing on the key questions they wish to examine. Mentors are very useful at this point because they help teams clarify and define precisely the questions to be answered. Often interesting problems are very large and it is essential to think about the parts that make up the whole of the problem. When the parts have been identified, then teams can decide where they wish to begin.
Continuing a project
A team may decide to work on a project a second year. The demonstrated level (quantity and quality) of work invested in the research, modeling, and implementation of a continued project must be comparable to that invested in a new project, for it to be judged competitively.
The best preparation for a follow-on project and potential publication is to do some journal research to see what has been done before. Then do some twist on the work to make it unique — such as doing a model in an agent-based software instead of the languages commonly used in the articles.
Examples
See curated example projects and Expo photos, final reports from recent seasons, and the archive for past team work.