Lecture ZZ - Final Exam Review (2020-04-28)

In this lecture, we review the format and possible content for the Spring 2020 final exam for SOS 212. The exam is comprehensive, covering systems dynamics modeling from causal loop diagrams (and systems thinking) up through stock-and-flow diagrams (and system dynamics models). Example models, such as the SIR model as Bass diffusion model, are briefly discussed. Students are encouraged to revisit information on chaos, stochastic modeling, and tipping points. Questions are asked and answered.

Lecture G1 - Randomness and Chaos (2020-04-16)

In this lecture, we introduce stochastic modeling as well as the topic of chaotic behavior in complex systems. The first half of this lecture focuses on stochastic modeling and the distinction between randomness and stochastic modeling, where "stochastic modeling" is the deliberate choice to use randomness in a model to simplify the modeling process. We then transition to discussing chaos (a particularly extreme sensitivity to initial conditions in models), which gives models an appearance of random behavior despite having no randomness within them. We close with an explanation of the so-called "butterfly effect" and an explanation of "strange attractors" frequently captured in the study of chaotic systems.

Lecture F3 - Morecroft (2015, Ch. 10) - Model Validity, Mental Models, Learning (2020-04-14)

In this lecture, we cover topics related to Chapter 10 of Morecroft (2015) on model validity, mental models, and learning. We revisit the general description of a model and discuss how models can range from analogue to metaphorical, with "illustrative models" balancing the two and providing plausible scaling with generalizable insights. The discussion guided by Morecroft (2015) is interleaved with classic arguments from Frank Keil about the shallows of explanation and illusions of depth. We discuss how formal models are "transitional objects" that allow for deepening the understanding of mental models as well as joining with others in collective cognition and cognitive division of labor. We close the discussion with examples of how to build confidence in mental models through verifying correct boundaries, structure, and calibrated parameter values. Significant confidence in a validated model comes from learning something new about the system being modeled.

Lecture F2 - Morecroft (2015, Ch. 9), Public Sector Applications of Strategic Modeling (2020-04-09)

In this lecture, we cover topics brought up by Morecroft (2015, Chapter 09) is his exploration of public sector applications of strategic modeling. We review the Forrester Urban Dynamics model and how it was synthesized from dynamic hypothesis (as a causal loop diagram) to multi-sector stock-and-flow diagram to a simulation that generated predictions that could be compared to real data (for both validation and scenario testing/planning). We then pivot back to the simple harvested fishery and use it to discuss the more general dynamical systems topic of "tipping points" and the visualization tool of "bifurcation diagrams" that help visualize things like hysteresis in systems (which are often associated with tipping points). The class ends after skipping over some content from the chapter about adding an endogenous investment model to the simple harvested fishery, and so that content is covered in a separate video to be (optionally) watched outside of class.

The separate lecture content on the endogenous investment model can be found at can be found at: https://youtu.be/uXdcHXq7eeQ

Lecture F2 video:

Extra module on fishery with endogenous investment model:

Lecture F1 - Morecroft (2015, Ch. 8), Industry Dynamics - Oil Price and the Global Oil Producers (2020-04-02)

In this lecture, we cover the major modeling topics from Chapter 08 of Morecroft (2010), which covers the development of a dynamic model of oil price that explicitly models global oil producers (including independent producers, OPEC, and the effect of Russia entering the market after the collapse of the USSR). The lecture marches through the selection of major sectors to model and then drills down into each sector to discuss how to structure stocks and flows that capture the dynamics of how variables relate to each other to explain price and production fluctuations over time.