Lecture Z1 (2022-11-29): Final Exam Review

In this lecture, we prepare for the final exam and review topics from the whole semester.

Lecture G1 (2022-11-17): Randomness and Chaos

In this lecture, we introduce two very different concepts – randomness and chaos. These two terms are often mistakenly used as synonyms, but they are far from it.

We introduce randomness as a modeling tool that helps us make sense of the world and reduce the complexity of models that we use to describe the world. This approach – using randomness to simplify descriptions of otherwise very complicated small-scale behavior – is called "stochastic modeling." "Stochastic" here comes from the Greek for guessing or conjecturing, thus exposing that "stochastic" is not a synonym for randomness but is actually an approach for assuming randomness even when there is no reason to believe that randomness is actually playing a role in the "real" system. We then describe how to use numerical approximations of randomness (from mathematical functions implemented within a computer) to generate stochastic computer simulation models within Vensim and Insight Maker. Traditionally, these kinds of models would be built within "Discrete Event System simulation" tools (like Arena or AnyLogic or others), but we show how our system dynamics modeling (SDM) tools can be co-opted to have random outputs too.

We then pivot away from randomness to describe chaos, which is an extreme sensitivity to initial conditions that can be present in even very simple single-stock system dynamics models (so long as they have delay and nonlinear feedback). This extreme sensitivity to initial conditions often leads to behavior-over-time plots that appear to be random even though they are determined entirely by the internal state of the system (i.e., they are "locally predictable"). We demonstrate this with the Mackey–Glass system. We then show how chaos can emerge without delay in systems with three (or more) stocks, and this is demonstrated with the Lorenz system. By plotting the three stocks against each other, we can see that the apparently random behavior-over-time plots actually have structure, which is captured by the "Lorenz attractor", an example "strange attractor" in chaotic systems. We use this new understanding of chaos to better explain the popularized "butterfly effect", which is NOT about butterflies "causing" weather events but more about how a universe with a butterfly in one position might unroll very differently than a universe with a butterfly in a different position.

So, randomness is a tool we use to make models simpler (or have fewer variables and parameters), and chaos is a tricky phenomenon that makes modeling and analysis harder.

Lecture F3 (2022-11-15): Chapter 10, Model Validity, Mental Models, and Learning (Morecroft, 2015)

In this lecture, we review the Chapter 10 of Morecroft (2015), which revisits a discussion of the function of models and discusses methods of building confidence in (simulation) models. We connect Morecroft's message to similar messages from Frank Keil (on formal models/theories and the shallows of explanation). We also discuss how tangible models that Morecroft describes as "transitional objects" can also be viewed as "boundary objects" on interdisciplinary teams. We discuss different ways of verifying, validating, and calibrating models. This lets us discuss things like boundary adequacy and structural adequacy, which are important to designing the high-level architecture of a model. We close with a discussion of how ultimately we build the most confidence in models when those models result in us learning something about a system.

Lecture F2 (2022-11-10): Chapter 9, Public Sector Applications of Strategic Modelling (Morecroft 2015)

In this lecture, we review topics from Chapter 9 of Morecroft (2015) on public sector applications of strategic modelling (i.e., system dynamics modeling, SDM). We start by walking through Forrester's Urban Growth Dynamics model and how it helps act as a lens for thinking about the drivers of stagnation in cities. Then we shift to thinking about regulation in a fishery. We take this opportunity to introduce the notion of a "tipping point" as well as the tool of a "bifurcation diagram." We do not have enough time to show how to endogenize exploitation decisions within the fishery model, but details of this are presented by Morecroft (2015).

Lecture F1 (2022-11-04): Chapter 8, Industry Dynamics – Oil Price and the Global Oil Producers (Morecroft, 2015)

In this lecture, we cover examples and a case study explored by Morecroft (2015, ch. 8) relating to building and using system dynamics models of the global oil industry. At a high level, the salient points are how to model an apparently large and complex system with a tractable set of (relatively small) stocks and how to build models sector by sector to reduce the modeling burden. At a lower level, we focus on modeling the effects of OPEC (Organization of Petroleum Exporting Countries) on the global oil industry.