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.

Lecture E5 (2022-10-27): Assignment E5 – Creating Limited, Coupled Population Models

In this lecture, we discuss an upcoming assignment in SOS 212 that will provide practice in creating more complex, multi-sector system dynamics models. We review how to create rate formulas for processes like population growth. We review lookup tables. We review ghost primitives/shadow variables. We also introduce how to use modular arithmetic (mod/modulo/modulus) that, when combined with a lookup table, makes seasonal patterns easy to introduce in system dynamics models. This is also covered for both Vensim (from Ventana Software) and Insight Maker.

Lecture E4 (2022-10-25): Chapter 6, The Dynamics of Growth and Diffusion (Morecroft, 2015)

In this lecture, we cover topics discussed by Morecroft (2015, Chapter 6) on the dynamics of growth and diffusion and relate them to other systems with S-shaped growth that we've seen in the past – a simple fishery model as well as epidemic growth. The main focus of this chapter is on the Bass model of innovation diffusion, which includes a contagion-like word-of-mouth loop (similar to the "SI" in an "SIR" model, or similar to population growth in a fishery) as well as an advertising loop to get the process started (like inoculating a population with its first infectious individuals). We then cover embellishments of the Bass model and do a strategic thinking example on one of those embellishments, which relates to strategy for the entry of easyJet as a low-cost airline into an existing marketplace of major carriers.

Lecture E3 (2022-10-20): Epidemic Dynamics

In this lecture, we start to introduce more complex system dynamics models (SDM), as would be implemented in Vensim or Insight Maker, for more complex systems. We focus on the classical SIR (susceptible–infectious–recovered) multi-compartment model from epidemiology. We build up this model as a stock-and-flow diagram from scratch, justifying the expressions/equations that we use and then using simulation to inform us when the equations might have significant flaws. Ultimately, we get to a working SIR model that matches dynamics of basic disease spread, and we go through a strategic thinking/scenario-planning example that shows that the value of quarantine policy is, in most cases, not to reduce spread of a disease but "flatten the curve" and lower (but widen) the infection peak to keep it under a manageable public-health threshold (set by university capacity/etc.).

Lecture E2 (2022-10-18): Making Simulations More Realistic, Part 2 – Delays, Fixed and Smoothing

In this lecture, we continue to add complexity to system dynamics models in Vensim and Insight Maker by introducing two different forms of delays – fixed delays and smoothing/averaging delays. We spend some of the lecture discussing the fundamental difference between these delays, and we spend much of the rest of the lecture discussing how to implement these delays in both Vensim and Insight Maker. We also discuss some other functions (like STEP, for step responses, and PULSE/PULSE TRAIN) as well as how to use lookup tables to insert more arbitrary functions over time as inputs to systems. Finally, we spend some time discussing how higher-order smoothing/averaging delays help mix aspects of both fixed delays and pure smoothing delays.

Lecture E1 (2022-10-14): Making Simulations More Realistic, Part 1 – Units, Sliders, and Lookup-Table Converters

In this lecture, we discuss how to embellish basic System Dynamics Modeling (SDM) simulation models with additional complexity and more efficiently interact with working simulation models. In particular, we introduce units (in both Vensim and Insight Maker) as a tool for the verification and validation of simulation models. We also discuss how to use sliders (in both Vensim and Insight Maker) to quickly adjust parameters (constants) and generate new outputs. We close with a discussion of lookup tables (converters) as well as how to implement them in both Vensim and Insight Maker. 

Lecture D-E (2022-09-29): Midterm Review

Midterm review session for ASU SOS 212 for Fall 2022.

Lecture D4 (2022-09-27): Chapter 3, Modelling Dynamic Systems (Morecroft, 2015)

In this lecture, we demonstrate how to draw and simulate stock-and-flow diagrams in Insight Maker (a web-based System Dynamics Modeling (SDM) tool), and then we discuss the third chapter of Morecroft (2015), which introduces the reader to stock-and-flow diagrams and numerical simulations of dynamical systems. We go through a simple example modeling the flows of instructors into and out of a university, and then we move to a more complex multi-sector simulation of a community attempting to regulate drug use through the use of police. This latter simulation helps illustrate how to form equations for flows and converter variables, and it also demonstrates how anomalous behaviors that are generated by simulations can help indicate where problems may be in simulation formulas (which should then be updated to make the simulation outputs more realistic).

Lecture D3 (2022-09-23): Stock-and-Flow Diagrams in Vensim and Insight Maker

In this lecture, we start by reviewing numerical integration methods (Euler's method) for approximating solutions to ordinary differential equations in spreadsheets. We use a filling toilet tank as an example of a balancing/negative-feedback system that we can simulate this way. We then pivot to discussing stock-and-flow diagram representations of these systems and then end with a short demonstration about how to use Vensim to draw and simulate these stock-and-flow diagrams, thereby greatly accelerating the numerical integration process done by hand in the spreadsheet. We ran out of time before being able to cover the example in Insight Maker, but we will start with that at the beginning of the next lecture.

Lecture D2 (2022-09-20): Introduction to Numerical Simulation of Dynamical Systems, Part 2

In this lecture, we review the fundamentals of numerical simulation (and Euler's method) for a simple clonal bacteria population system with only births. We then add a death flow in and explore how changing the numerical simulation time step ("dt") affects the results. We then transition to a numerical simulation of a toilet tank (a classic balancing feedback loop).

Lecture D1 (2022-09-15): Introduction to Numerical Simulation of Dynamical Systems, Part 1

In this lecture, we introduce numerical simulation of dynamical systems (coupled ordinary differential equations) within the context of stock-and-flow diagrams for System Dynamics Modeling in strategic thinking. After covering how systems of ODE's capture the underlying "forces" driving change in a system, we review different ways to think about compound interest. This compound interest example motivates "Euler's method" for numerical integration over time. That is, we describe the product of flows and time-step durations as a sort of "interest" earned over each simulation time step ("compounding period" in the bank analogy). We use a bacterial growth example to show how this perspective can let us simulate the (average) growth characteristics of a bacterial population without having to simulate the discrete events where each bacteria reproduces independent of each other bacteria. Overall, this lecture relates the "time step" parameter in tools like Vensim and Insight Maker to calculus-based topics like the definition of the derivative. Furthermore, this lecture uses spreadsheet tools (like Microsoft Excel and Google Sheets) to provide a picture of what goes on inside simulation tools like Vensim and Insight Maker.

Lecture C2 (2022-09-13): "Applying Systems Archetypes" (Kim and Lannon, 1997)

In this lecture, we review the perspectives of Kim and Lannon (1997) toward applying Systems Archetypes in strategic modeling. This involves four approaches – structural pattern templates, lenses, dynamical theories, and predicting future behavior – that capture most of the different ways that Systems Archetypes can be used to propose interventions (or predict hypothetical outcomes from those interventions) in the iterative process of using models to make changes in the world.

Lecture C1 (2022-09-08): Feedback Systems Thinking with CLDs

In this lecture, we start to introduce "systems archetypes" as representing more complex aggregations of loops that give rise to complex (but predictable) behaviors over time. We introduce S-shaped growth as a modification to reinforcing loops, balancing loops with delay as a modification to balancing loops, and growth with overshoot as a combination of the two. We then expose several of the other more complex archetypes as an introduction to the upcoming article on using systems archetypes in the next lecture. The lecture experience closes with groups working on building their own S-shaped growth examples and "shifting goals" examples.

Lecture B3 (2022-09-06): Chapter 2, Introduction to Feedback Systems Thinking (Morecroft, 2015)

In this lecture, we review Chapter 2 (Introduction to Feedback Systems Thinking) from Morecroft (2015). This chapter contrasts event-driven thinking with feedback systems thinking, which are two perspectives on analyzing possible interventions in the world. The former focuses on reacting to problems as they occur, with their causes (as well as the long-term intervention consequences) being outside of the decision-making model. The latter focuses on considering not only the intervention but the possible side effects related to other dynamic forces that are distal from the main problem of interest. A focal road congestion example is used throughout, with the chapter closing on an example about dueling showers ("accidental adversaries") and how they can be viewed as a model for dueling sub-units of a single company with limited capacity to operate both simultaneously.

Lecture B2 (2022-09-01): Drawing Causal Loop Diagrams in Vensim

In this lecture, we review causal loop diagram (CLD) fundamentals and go through a few examples of building and annotating CLDs. We also go over how to draw CLD's in the Vensim PLE system dynamics modeling tool.

Note that due to atypical technological limitations in the classroom at the time of the recording, the video quality is not optimal.

Lecture B1 (2022-08-30): Introduction to Causal Loop Diagrams

In this lecture, we motivate the use of "causal loop diagrams" as a bridge for building system dynamics models as well as analyzing models already built. We then introduce the fundamental structures within CLDs – the links (positive and negative, with and without delays) and the feedback loops (positive/reinforcing and negative/balancing/counteracting/regulating, and annotations denoting polarity as well as application-specific context). We do a few examples of drawing and analyzing simple CLD's and discuss rules and conventions for choosing variables to include in CLD's.

Lecture A3 (2022-08-25): Chapter 1, The Appeal and Power of Strategic Modeling (Morecroft, 2015)

In this lecture, we discuss topics from Chapter 1 of Morecroft (2010) surrounding strategic modeling for analysis of various different scenarios that can emerge from a single system dynamics model. After some philosophical discussion of the continuous modeling spectrum from metaphorical to analog, we transition to more concrete examples with a simple harvested fisheries model. This gives us the opportunity to use the SDM to show how important functional response is and to use the example to motivate approaches for regulating a sustainable fishery.

Lecture A2 (2022-08-23): Introduction to Modeling

In this lecture, we introduce modeling broadly, including the different types of models (mental models, physical models, animal models, mathematical/analytical models, computational/numerical models, and simulation models) and how they are used. We focus on how modeling is as much about leaving the right things out as it is choosing the right things to keep in.

Lecture A1 (2022-08-18): Course Introduction

Introduction to the course and the start of an introduction to quantitative simulation modeling.

Lecture Z1 (2022-04-26): Final Exam Review

This lecture reviews material for the upcoming Spring 2022 Final Exam in SOS 212. The lecture covers topics related to System Dynamics Modeling (SDM) of systems related to sustainability problems.

Lecture G1 (2022-04-14): Randomness and Chaos

In this lecture, we introduce two concepts related to the predictability of dynamical systems -- randomness and chaos. Randomness is introduced as a modeling tool to help reduce the number of dynamical variables that need to be considered to model a system. This approach is known as "stochastic modeling", where "stochastic" comes form the Greek word for "guess" or "conjecture." Stochastic modeling makes the conjecture that a system is random even if the real-world version of the system is not random but is instead complicated. Randomness simplifies model building. We then introduce chaos, which is a very strong sensitivity to initial conditions that creates deterministic behavior over time traces that appear random. We show how that chaos can be caused by (nonlinear) feedback with delay (as in the Mackey-Glass system) with as little as one state variable (stock). We then show that without delay, chaos can occur when there are 3-or-more state variables (stocks). To demonstrate this latter point, we show the Lorenz system and its corresponding Lorenz attractor (an example "strange attractor"). We discuss how the so-called "butterfly effect" relates to this extreme sensitivity to initial conditions (with Jurassic Park references).

Lecture F3 (2022-04-12): Chapter 10, Model Validity, Mental Models, and Learning (Morecroft, 2015)

In this lecture, we review the key points of Chapter 10 from Morecroft (2015), with some additional connections to literature from Frank Keil, George E.P. Box, and a few others. The chapter focuses reviews the purpose of models that fall all over the modeling spectrum -- from realistic, analog models to less realistic (but highly generalizable), simplistic, metaphorical models. We discuss how the process of building models (even simple models) helps us "transition" our mental models to more sophisticated and deeper levels of understanding and move ourselves away from the "illusion of depth" (or "shallows of explanation") that we might have before forming such models/formal theories. We extend this idea to using models to help achieve shared understanding with other experts whose expertise might differ from our own. We then pivot to discussing how we build confidence in the formal models we build -- ensuring that they have the right boundaries, structures, and equations and that they produce the right behaviors and even allows us to learn about the original system through experimenting with the modeled system.

Lecture F2 (2022-04-07): Chapter 9, Public Sector Applications of Strategic Modelling (Morecroft, 2015)

In this lecture, we discuss topics from Chapter 9 of Morecroft (2015). These topics discuss how to use system dynamics modeling in setting policies for urban growth and renewable resource management. We explore a basic model of the growth (and limits to growth) of a city before we then switch to our progressively growing model of a fishery. This fishery exhibits a "tipping point", which gives us an opportunity to discuss tipping points and bifurcation diagrams (for simulation) We then close with a discussion about how to "close the loop" in the fishery model and use modeled human behavioral responses to changes in fish density. This discussion lets us introduce policy lever as well and explore how well they might accomplish our sustainability goals.

Lecture F1 (2022-03-31): Chapter 8, Industry Dynamics – Oil Price and the Global Oil Producers (Morecroft, 2015)

In this lecture, we discuss highlights of Chapter 8 from Morecroft (2015) on the industry dynamics linking oil price to global oil producers, with a particular focus on the effect of OPEC (Organization of Petroleum Exporting Countries) and independent producers. The goal is to explain patterns of subtle oscillations, relative stability, and massive fluctuations in the market over time. The model starts with a basic negative feedback model of a free-market economy and then adds a component related to OPEC's cartel influence (the effect of a swing producer in a cartel) and even the eventual addition of Russian oil reserves after the breakdown of the Soviet Union/USSR. System dynamics models help provide insight into the less observable latent variables that are necessary to explain otherwise puzzling patterns in behavior over time.

Lecture E5 (2022-03-24): SOS 212 Assignment E5 – Creating Limited, Coupled Population Models

This lecture provides assistance and introduction to the final two homework assignments in SOS 212. We start with some tips about Assignment E2 (a Vensim PLE/Insight Maker assignment that makes use of sliders, units, and delays). We then discuss Assignment E5 (which is a two-sector model of a coupled population/water dynamics system). The class ends early for students to do work on the assignments during the class period.

Lecture E4 (2022-03-22): Chapter 6, The Dynamics of Growth and Diffusion (Morecroft, 2015)

In this lecture, we cover highlights of Chapter 6 (Dynamics of Growth and Diffusion) from Morecroft (2015). This chapter focuses on S-shaped growth in consumer markets (and innovation diffusion in general), and it uses the Bass diffusion model (with advertising) as a central dynamical hypothesis. We draw connections between the contagion in the Bass model to limiting processes in simple logistic growth models as well as the infection part of the SIR model. After showing variants of the Bass model for the cases of repeat purchases and durable goods, we move on to a scenario planning/strategic thinking example for the case of easyJet (a low-cost air carrier).

Lecture E3 (2022-03-17): Epidemic Dynamics

In this lecture, we introduce the use of System Dynamics Modeling (particularly, stock-and-flow simulation models) to the analysis of the spread of infectious disease. We focus our investigation around the SIR   (Susceptible–Infectious–Recovered) model from epidemiology. We build up the SIR model from scratch (using a stock-and-flow model) and derive the basic reproduction number (R0) and discuss how quarantines are most practical for "flattening the curve" (the infection peak) and not totally stopping the spread of disease. We close with an introduction to other compartmental infectious disease models, such as SIS, SIRS, SEIR, SEIRS, MSEIR, and MSEIRS.

Lecture E2 (2022-03-15): Making Simulations More Realistic, Part 2 – Delays, Fixed and Smoothing

In this lecture, we discuss how to implement two types of delays (fixed delays and smoothing delays) in stock-and-flow diagram dynamical systems models using two System Dynamics Modeling tools – Vensim PLE (by Ventana Systems) and Insight Maker. Along with covering fixed delays and smoothing delays, we also introduce higher-order smoothing delays (which are a type of delay in between the two other purer forms of delays) and some functions for perturbing systems (pulses and steps).

Lecture E1 (2022-03-03): Making Simulations More Realistic, Part 1 – Units, Sliders, and Lookup-Table Converters

In this lecture, we introduce Units, Sliders, and Lookup-Table Converters in both Vensim PLE (by Ventana Systems) and Insight Maker. This also allows us to introduce shadow variables in Vensim (and access "Time" in both Vensim and Insight Maker). These constructs help us make more realistic simulations or help make interacting with simulations more convenient.

Lecture D-E (2022-02-22): Midterm Review

In this lecture, the Spring 2022 midterm for SOS 212 is discussed and we review topics from Units A, B, C, and D. Some practice problems are also completed.

Lecture D4 (2022-02-17): Chapter 3, Modelling Dynamic Systems (Morecroft, 2015)

We start this lecture with very brief tutorials of building, executing, and analyzing stock-and-flow diagrams in both Vensim PLE (from Ventana Systems) and Insight Maker. We then move on to cover Chapter 3 from Morecroft (2015), which is an introduction to dynamical systems modeling (i.e., using stock-and-flow diagrams to simulate dynamical system behavior-over-time trajectories). The chapter coverage is truncated due to lack of time, but we cover the identification of stocks and how to draw links between them that help us visualize the dependencies that emerge from the flow models used.

Lecture D3 (2022-02-15): Stock-and-Flow Diagrams in Vensim and Insight Maker

In this lecture, we review how to simulate a the behavior over time of simple negative feedback dynamical system (the filling of water in a toilet tank) using spreadsheet numerical methods. We compare and contrast this with bacterial growth models simulated in similar ways and discuss two basic categories of flow equations that will be used in a variety of different models. We then pivot to discussing how to build stock-and-flow diagrams and implement them in Vensim so that behavior over time trajectories can be generated more quickly.

Lecture D2 (2022-02-10): Introduction to Numerical Simulation of Dynamical Systems, Part 2

In this lecture, we continue to discuss numerically integrating (simulating) dynamical systems in a spreadsheet tool such as Microsoft Excel or Google Sheets. We go over our previous example (bacterial growth with both reproduction and death modeled) and explore the effect of changing the time step size ("dt"). We then pivot to introducing a negative feedback example (filling of a toilet tank).

Lecture D1 (2022-02-10): Introduction to Numerical Simulation of Dynamical Systems, Part 1

In this video, we begin the discussion of using computer-based automation tools (in this case, spreadsheets) to generate numerical simulations of dynamical system behaviors over time (BOT). We start with a simplistic example of compound interest in a bank and show how the small-time scale behavior of any dynamical system can be approximated by a "bank account model" so long as each "compounding period" ("dt") is sufficiently small to assume that the variables are not changing much during that period. This is equivalent to the "secant approximation" of a derivative (where a derivative, which is strictly defined at a point, is approximated by the slope of a line connecting that point to a point just a little later in the trajectory). We close with an exercise where the exponential growth of bacteria is numerically simulated within a spreadsheet.

Lecture C2 (2022-02-03): "Applying Systems Archetypes" (Kim and Lannon, 1997)

In this lecture, we discuss how Kim and Lannon (1997) describe the four uses of Systems Archetypes for feedback systems thinking -- as structural pattern templates, lenses, dynamic theories, and for predicting future behavior. This allows us to describe several very common systems archetypes as well.

Lecture C1 (2022-02-01): Feedback Systems Thinking with CLDs

In this lecture, we start to introduce methods to embellish CLD's to form more complex models of dynamical systems with multiple feedback loops. This is an introduction to the next lecture, where we go over a wide range of systems archetypes discussed by Kim and Lannon (1997).

Lecture B3 (2022-01-27): Chapter 2, Introduction to Feedback Systems Thinking (Morecroft, 2015)

In this lecture, we cover chapter 2 from Morecroft (2015), which introduces feedback systems thinking -- a shift of mind away from an event-oriented perspective to a more endogenous viewpoint that takes into account the (often long-term) side effects of solutions as they feed back into future problems. This discussion is motivated by a traffic congestion example. The discussion provides the opportunity to discuss Causal Loop Diagrams (CLD's) and the conventions/rules for drawing them that make them useful for either finding systems archetypes that predict behavior or coming up with dynamic hypotheses to test later with simulation. That provides an opportunity to bring up Behavior Over Time (BOT) plots and how to use them in real-world data as compared to simulation data to test and refine plausible causal hypotheses about dynamic behavior.

Lecture B2 (2022-01-25): Causal Loop Diagrams in Vensim

In this lecture, we introduce (via hands-on tutorial) the program Vensim PLE by Ventana Systems and how to use it to draw Causal Loop Diagrams (CLDs). We then review how to draw and edit CLD's in general and introduce a homework assignment where students will have to draw and annotate complex CLD's in Vensim.

Lecture B1 (2022-01-20): Introduction to Causal Loop Diagrams

In this lecture, we pivot from our introduction to system dynamics modeling in general to discuss the broader endogenous perspective, where macroscopic observables of a system are a function of internal, not external, causes. That motivates the rest of the lecture, which introduces the "Causal Loop Diagram" (CLD). A CLD includes a network of links (representing positive or negative causal relationships) that may form loops (that are positive or negative). We can use CLD's to build stock-and-flow diagrams (dynamical systems models) or to gain insights into the trajectories produced by existing stock-and-flow diagrams.

Lecture A3 (2022-01-18): Chapter 1, The Appeal and Power of Strategic Modeling (Morecroft, 2015)

This lecture discusses topics from Chapter 1 of the course textbook by Morecroft (2015). The focus of the chapter is an introduction to strategic modeling, particularly in the case of strategic system dynamics models. We initially discuss the modeling spectrum -- from analog to metaphorical -- and when it is beneficial to leave aspects out of models. We then pivot to discussing Forrester's Limits to Growth/World Dynamics model and its limitations. We then finish by showing how a simple dynamical systems model of a fishery can lead to actionable suggestions about regulation that can help restore a fishery to sustainability.

Lecture A2 (2022-01-13): Introduction to Modeling

In this lecture, we dig more deeply into the definition of a model and the types of models. We focus on how anything can be used as a model so long as it is being used to answer a "What If" question. In other words, models are defined by how they are used and not what they are made of. When then discuss the differences between mental and quantitative models (pros and cons of each). We finish with a description of the different ways that quantitative models are used, the different types of quantitative models, and the different types of simulation.

Lecture A1 (2022-01-11): Course Introduction

In this first lecture of the semester, we discuss the course policies, motivation, and topic outline. We then start to discuss modeling in general -- leading up to a simple definition of a "model" as a tool for answering a "What If" question. The unavoidable modeling degrees of freedom may be open for misuse, even inadvertent. That said, "reality" is not the goal of modeling. All models are wrong, but good models (even if wrong) are crafted to be illuminating and useful; their lack of reality can actually make them more generalizable.