We explore how organizations can use system dynamics as the core analytical technology to achieve mission-critical goals.
In the The Basics of System Dynamics course we build and apply several basic System Dynamics models in the first 5 weeks of the course. In the last two weeks, 6 and 7, we will apply our inventory of skills and models to the problem of simulating a B (Benefits) Corporation in the context of a practical Business Canvas approach. For this portion of our studies we will use paper, pencil, and the VensimPLE platform for all of our work.
In the System Dynamics Inference course we apply Bayesian data analytics to calibrate, fit, simulate, and infer conclusions consistent with the causal model of the decisions we are studying. The causal model will be built in VensimPLE, read into R, where we can mash the model with data and use Stan to make probabilistic inferences. During the first 5 of 7 weeks we will build basic system dynamics causal models of various business decision, estimate probability models of decisions based on the causal models, and infer probabilistic results using Machine Learning and information criteria.
For these courses we will cross three computing platforms. VensimPLE will help us build and simulate generative causal models, visualize results, and develop scenarios for decision makers. The R programming language (with R Studio - Posit), the tidyverse of data, optimization, numerical integration, and visualization packages will provide a platform for analysis, inference, and visualization of results. The Stan (for Stanislaus Ulam) probabilistic programming library with its ability to estimate systems of differential equations (the underlying mathematics of system dynamics) using Hamiltonian Monte Carlo simulation will allow us to estimate the uncertainty within the causal models we have built.
(During the Spring 2024 session from January 16th to March 4th, the Basic System Dynamics course is offered by the Manhattan College MBA Program as MBA 645 Special Topics: Strategic Management Science. From March 4th through April 21st, the System Dynamics Inference course is offered as MBA 611 Advanced Data Analysis. Please contact Dr. Marc Waldman, Program Director, at marc.waldman@manhattan.edu for more information about the program.)1
We begin our last week together. I suspect we will be spending a little more than a week as we spill over into the grading period next week to finish up all of our shares and assignments. The Weeks 6 and 7 assignment is simply a set of questions about already modeled results. You will need to consult previous weeks to answer the questions. There will be no extra programming, unless of course you want to test your assertions.
Videos, a Vensim mdl, an R RMD and rendered HTML files are dropping into the Week 6 slot.
Enjoy.
We are exactly one week behind my original schedule. The needed give and take of a course like this is hampered by the short 7 week schedule. Nonetheless, even though we are in Week 5, never fear, we will complete the ultimate goal of this course, that is, to infer statistical insights from a dynamic interaction model. Week 5 will take a day only to work out the installation and solution of the ODE model we already built, simulated, and explored.
There will be a live session tomorrow, Saturday, April 13th from 10 to noon on my Zoom channel. I hope to see you then.
Thanks for your patience!
We are still journeying through Week 4. My body and its most recent viral ailment is holding us back a bit! But we will continue to steer into the wind with the rest of the videos, notes, Rmd file, some readings to keep us cheerily occupied. The week’s Share is also posted. I will begin to formulate an assignment question based on the Laffer data for us to mull over which will we due by this coming Sunday April 14th. We will bleed into Week 5 with Cassiopeia and a cmdstanr installation. We will apply our new found wealth of inference to our interactive MooseCo-WolfCo data this week. My goodness I miss them so!
Well, quite a week! I got one video to you, another is on its way, and both readings are available. More tomorrow!
We conclude Week 3 with our assignment. This will be due by this coming Sunday, April 7th. No fooling!
You may access the Week 3 Assignment Workbook here (as well as in the Week 3 tab for Add Inference).
We begin Week 4 tomorrow with topics from Acting on Bayes and a new item called Counting on Laplace where we will finally get to that information criterion we have been promising ourselves. Of course we will run ODE and linear WolfCo projections.
We begin Week 3 in earnest! Here is a flexdashboard rendering we can look forward to playing with. It has interactive sliders to move graphical results. Check out the source code in the left hand side of the navigation menu.
https://wgfoote.shinyapps.io/moose-meets-wolf-live/
Much to do:
Week 3 Share is up and running. We spend some time with William of Ockham and Richard McElreath.
Focus on Foote’s readings. Some portion of the videos will use the ideas and models here. Ultimately we want to build a model of our generated customer bases from Week 2.
Duggan’s Chapter 4 will offer an interesting model of stock management and cohort analysis we should begin to look into.
As always encourage each other daily, while it is still today!
Welcome back to Week 2. Videos await you as we build more texture and story into our predator-prey interaction model. This week we examine capacity sourced limits to growth and the logistic model of market dynamics. Enjoy! Here are some notes to put a point on the discussion:
You can access our System Dynamics Inference: Week 1 Workbook here.
Even on Spring Break … yikes!
Here is an experiment in simulating our Moose-Wolf customer interactions. Click to access Moose Meets Wolf - R Shiny interactive simulation.
Access the app code here: app.R
Welcome to Spring Break.
The Registrar has graciously bestowed a week off as we begin our term of work together. We embrace this week with time to download, play around with, and complete the massive pile of work we have called Week 1!
The Week 1 assignment and share will be due on Monday, March 19th by hours ending 1159 (UTC-5). There will be no Live Session on Saturday, March 16th, although there may be much partying that day and on into the 17th in my neighborhood as vats, cylinders, gallons of green beer will magically appear.
Much refreshed we will march gloriously into our Week 2 mounds of learning on Tuesday, March 19th.
Enjoy, and always encourage one another daily, while it is still today!
Two videos are available on the System Dynamics: Bayesian Inference and Calibration playlist on my Youtube channel. Here is the link to the playlist.. More today and tomorrow. Check Week 1!
Some Tips.
Thanks for the opportunity to work with all of you this term.
And always remember to encourage each other daily, while it is still today!
Bill
For those in MBA 611: Welcome! Check out the Add Inference tab to begin wandering into the course. Stay tuned for overview and week 1 videos, notes, reading, sharing, and doing.
For those in MBA 645: Your course has been renamed The Basics tab on this site to make room for the MBA 611 course (Add Inference). This is the last day of the MBA 645 course! Please deposit your final project work in the Project Workbook. I have 48 hours to issue a grade. If you do not complete the course in the time allotted by the College, and in the interests of working with you to attain your educational goals, I will issue you an Incomplete grade.
We formally complete our journey together next Sunday. The Week 6 and Week 7 shares and Project Workbook are deployed. You are all doing a marvelous job of keeping up with implementations, shares, and, most importantly, interpretations of your results. Thank you so much for the opportunity to work with you!
William G. Foote, Ph.D. | Visiting Scholar | Business Analytics | O’Malley School of Business | Manhattan College | Riverdale, NY 10471
Mobile/Text: 917-767-7980
Zoom: https://us06web.zoom.us/j/4915046043?pwd=lRcmR6raK50vawabuxzl3fWECDGZYO.1
GitHub: https://github.com/wgfoote/
Fax: 718-862-8032
Office hours (MBA645 Spring 2024):
Online on Zoom, Saturdays 10am to 12 noon
Other times by appointment, text me to arrange a time
At the end of these courses students can expect to demonstrate progress in meeting the following goals, proposed here as actions with verbs in the imperative mood.
Pose a researched business question, model the causal influences implicit in the question, simulate potential causal relationships and counterfactual inferences and their sensitivities, and align inferences with decision alternatives and plausible choices for stakeholders.
Deploy analyses which produce interactive analytical products using an industry-grade computational platform engineered according to a tradition of design principles.
Using endogenous generative models, summarize experience and beliefs about stakeholders, their data, and the processes that the generated data used, to infer potential outcomes to answer business questions.
Practice quantitative critical thinking skills through a compound of statistical and normative problem solving which links strategic policies and practices with stakeholders.
Understand the role of the analyst and the analytics process in the decision-making context of complex organizations and their environments.
Communicate analytical decision results to decision makers and other consumers of analytical products effectively using interactive tables and graphs.
For my part this curriculum emanates from over 45 years of learning from and teaching managers system dynamics and statistical inference at Fordham University, Clarkson University, Syracuse University and LeMoyne College. I have used SD techniques and simulations at a variety of financial institutions, high tech, energy, retail, governmental and not-for-profit organizations world-wide.
I have taken liberally materials and ideas (some might say I curated materials) from several extant courses. They all flow from the avowed discoverer of the systems dynamics methodology, Jay W. Forrester, and his decades of work, and students, at the Sloan School of Management, MIT.
First, the maths: Harry Hochstadt’s Differential equations : a modern approach (1963-4) in the first 84 pages details the math behind system dynamics, namely solving systems of simultaneous differential equations.
Second, the numerics: Joel Ferziger’s Numerical Methods for Engineering Applications, 1978. Yes, FORTRAN. I moved most of these routines to MATLAB and APL2 in the 80’s.
Jay Forrester’s 1998 MIT Introduction to System Dynamics self-study course Everything you will need to know about the formulation and interpretation of System Dynamics models from the inventor.
George Richardson’s 2013 Albany University Public Policy courses.
John Sterman’s 2013 Introduction to System Dynamics MIT-OCW course
Ventana System’s VensimPLE Modeling Guide and Tutorial along with Tom Fiddaman’s MetaSD model library
The premise of this curriculum is that learning is inference. Learning can be reading, understanding, reflecting whether in our heads or with complex computing environments. We begin with the following chain of reasoning:
All events, and data collected from events, have a truth value.
Probability is the strength of plausibility of a truth value.
Inference is a process of attaining justified true belief, otherwise called knowledge; learning is inference.
Justification derives from strength of plausibility, that is, the probability distribution of a hypothesis conditional on the data and any background, prior, and assumptive knowledge.
The amount of surprise, or informativeness, of the probability distribution of data given our experiences, is the criterion for statistical decision making – it is the divergence between what we known to be true and what we find out to be true.
All statistical analysis, and reasoning within analysis, begins from a disturbance in the status quo. The disturbance is the outlier, the error, the lack of understanding, the inattentiveness to experience, the irrationality of actions that is the inconsistency of knowledge and action based on knowledge.
We are surprised when the divergence between what we used to know and what we come to know is wider than we expected, that is, believed. The analysis of surprise is the core tool of this course. In a state of surprise we achieve insight, the aha! moment of discovery, the eureka of innovation.
The course will boil down to the statistics (minimum, maximum, mean, quantiles, deviations, skewness, kurtosis) and the probability that the evidence we have to support any proposition(s) we claim.
The evidence is the strength (for example in decibels, base 10) of our hypothesis or claim. The measure of evidence is the measure of surprise and its complement informativeness of the data, current and underlying, inherent in the claim.
Copyright 2024, William G. Foote, all rights reserved.↩︎