Analytica in the Classroom

Teaching Quantiative Modeling Skills to Students

Teaching your students to think analytically and clearly about messy real-life decision problems is the key challenge for a professor in scientific fields of study, such as economics, engineering, public policy, business, finance, and the standard sciences (physics, chemistry, biology, etc). Appropriate use of software modeling tools can greatly enhance the learning experience as long as you as an instructor keep the emphasis on developing these skills rather than the mechanics of using the software. The use of Excel in the classroom, in particular, is often very damaging in this respect. We often see the emphasis, and the focus of the students, shift from shift from developing the clear thinking and modeling skills to mastery of the spreadsheet mechanics. We believe that the use of Analytica in the classroom really helps to keep the focus on the core modeling skills.

For those of us who have been working in quantitative fields for many years, it is easy to lose sight of some of the most difficult, yet most important, skills that students must learn. These include:

Ability to clearly define variables.

Real-world problems are messy, quantitative, subjective, fuzzy, etc. Yet a formal analytical model must reduce these to unambiguous and quantitative terms -- what we usually refer to as variables.

Identifying how the parameters and variables of a problem influence each other.

Influence isn't always as straight forward as it seems. It often involves notions of causation, but in a decision model, even more fundamental is is how the information flow should proceed, which may not always be strictly causal. Identifying influences may involve abstract reasoning about how things can be computed. It also involves reasoning about what variables could reasonably be obtained or assessed, versus computed (decomposed into) other variables.

Finding information and assessments

Finding facts and assessments in publications, on the web, by contacting subject experts, etc., is a key skill of any model builder, plus greatly impacts the first two steps in terms of how models can be decomposed.

Assessing unknowns

Models always contain estimates, guesses, back-of-the-envelope assessment. Analytica never find everything fact they need already published, and must fill in unknowns, which gets to the next point.

Expressing uncertainty

The importance of explicit representations of uncertainty are now widely recognized across nearly all the quantitative fields of study. Expressing assessments in terms of a distribution, rather than single numbers, can actually speed up model building, plus lead to much deeper insight.

Working with others

Model builders must synthesize knowledge from other domain experts.

Combining quantititive findings from models with unmodeled factors

If you aren't careful, your students may think quantitative analysis will spit out the "correct answer". Since models only capture some of the world, we always have to take insights gained from analyses and combine them with unmodeled considerations, as well as contradictory results. A good curriculum exposes students to exercises along these lines.

Presenting findings

Analytical results are about insights gained from models, not about specific numbers. Developing clear and transparent analyses (and clear and transparent models) is absolute a key skill.