Lecture Z1 (2021-04-20): Final Exam Review

In this lecture, we review topics from the semester in preparation for the upcoming final exam.

Lecture G1 (2021-04-08): Randomness and Chaos

In this lecture, we introduce the concepts of stochastic modeling (the use of randomness to simplify the modeling process) and chaos (the extreme sensitivity to initial conditions that makes deterministic systems appear to be random. To demonstrate stochastic modeling, we develop a discrete event system simulation model of a bacterial population. This gives an opportunity to discuss random number generation/random number streams and how they allow non-random computers to approximate randomness and realistic variation (allowing for realistic experimentation). We pivot to discussing chaos -- using the Mackey-Glass and Lorentz systems as key examples (one that has a single stock with delay in the flow, the other that has three stocks with simple flows). We finish by connecting these results to the so-called "butterfly effect."

Lecture F3 (2021-04-06): Chapter 10, Model Validity, Mental Models, and Learning (Morecroft, 2015)

In this lecture, we review topics brought up in Chapter 10 of Morecroft (2015), which focusses on building confidence in models. In particular, we review the definition of a model and the different types of models -- from analog to metaphorical -- and how the lack of realism in illustrative models can have a strength in being more generalizable. We discuss formal models as learning tools -- transitional objects that help someone move from one mental model to another. We also discuss how formal models help to build communities of knowledge among model builders. This discussion borrows both from Morecroft (2015) as well as work by cognitive psychologist Frank Keil and a few others. We close with a few comments about testing, verification, and validation (TV&V) and a more concrete example.

Lecture F2 (2021-04-01): Chapter 9, Public Sector Applications of Strategic Modelling (Morecroft, 2015)

In this lecture, we highlight and expand on topics covered in Chapter 8 of Morecroft (2015) related to public sector applications of strategic modeling. We first focus on Forrester's Urban Dynamics model, which provides a dynamic hypothesis explaining systemic (endogenous) reasons for growth and eventual stagnation (and even decline) of cities. We then transition to a a more complex fisheries model that includes endogenous investment (i.e., a model where investment decisions are generated by the model as opposed to specified by the person operating the model). This allows us to discuss a more specific definition of "tipping points" phenomena. Ultimately, many related discussions (bifurcation analysis and endogenizing variables) are left for auxiliary videos (for the sake of time).