Teaching pillar: pluralism
3. Teaching Pillar: Simulation
The traditional economics education is generally static, rather than dynamic, in two important ways. The first is the form the course materials take. Most courses use a textbook, and perhaps some supplementary lecture slides or other written materials, as the main source of information and examples. By their nature, these materials are only able to present stationary diagrams, pictures, and graphs, which can struggle to convey the evolving nature of the real-world economy. The second is that many of the traditional economic models taught in these materials, including the classic, algebra-based supply and demand model, are static in that they specify various equilibrium outcomes without meaningfully describing how we get from one outcome to another.
By leveraging simulation, imagine economics looks to place dynamics front-and-center in the teaching of economics, both in the content itself and in the ways that content is presented. In other words, while the traditional approach focuses on the stable, non-changing resting points predicted by the classic models, we instead focus on the ever-changing dynamics of economic systems. Although these systems—depending on the particulars—may at times yield resting points similar to the classic models, they are better suited to illustrating complex and evolving dynamics that emerge when typical assumptions are modified or removed.
Beyond this distinction between the static and dynamic, imagine economics also strives to introduce learners to elements of programming and computation that are increasingly a critical part of research and professional roles. The traditional economics education often begins purely with concepts, moving to algebra for introductory material, and then moving to calculus for intermediate and advanced material. imagine economics instead takes programming and computation as the foundation on which economic models are built, initially re-creating the traditional models and then moving to change, decompose, and reevaluate those models using the power of simulation. In doing so, learners build competencies that are useful not only for economics and economics research, but also for software development, data science, consulting, and many other domains.