Tribute Lecture for Andrew Ford: Insights from the Preface of Modeling the Environment
Introduction
Hello everyone,
On February 12, I will launch a special lecture series dedicated to Andrew Ford (whom I will refer to as Andy).
Andy was a towering figure in the System Dynamics (SD) community. Though I only met him briefly—just enough to take a photo together at a conference—his warm and humble demeanor left a deep impression on me, as it did on many others who had the privilege of knowing him.
Sadly, Andy passed away just before 2025. To honor his legacy, I have decided to delve into his masterpiece, Modeling the Environment, and share my personal interpretation of its key ideas.
Before diving into the details, let’s first explore the Preface, which sets the tone for the entire book and reveals why this work was necessary.
1. Why Was This Book Written? – The Intersection of Real-World Problems and Modeling
At the very beginning of the Preface, Andy references major global issues dominating the news in 2009: climate change, the real estate bubble, and the swine flu pandemic. This immediately caught my attention.
His point was clear:
"The world we live in is complex, deeply interconnected, and difficult to grasp at a glance. To better understand such problems, we need new tools—specifically, System Dynamics modeling."
This resonates deeply with me. In the SD field, it’s easy to focus too much on modeling the systems rather than the problem. Andy reminds us that the purpose of SD is not just to build models but to analyze and understand the problem.
This perspective is invaluable. As I work on my own writing, I am reminded that a book or lecture for SD should begin with the problems.
2. Confronting Human Cognitive Limitations
One of the most striking statements in the Preface is:
"Human cognitive abilities are limited. Even when analyzing the past, it’s difficult to fully understand what actually happened."
Andy elaborates:
"Our understanding is also limited by the complexity of the feedback processes that control system behavior. Our actions may be partially erased by the system’s internal responses, and the system’s apparent resistance to our interventions is confusing. Sorting out the effect of delays and multiple feedbacks is beyond our cognitive abilities, so we look to the past for lessons."
To me, this passage reflects a remarkable humility in Andy’s approach. He is essentially saying:
"We don’t know everything, and we should be prepared to be surprised when we model systems."
It feels like an invitation to continuous learning—a perspective that I find deeply moving.
Philosophical Angle: Cognitive Limitations in Various Disciplines
This idea is not unique to SD but is widely discussed across different fields:
- Economics: Herbert Simon’s bounded rationality (which won him a Nobel Prize) highlights how human decision-making is always constrained by cognitive limitations.
- Philosophy: Kant’s Copernican Revolution in epistemology shifts focus from what exists to how we perceive and construct reality.
- Phenomenology: Recent philosophical movements emphasize that we never see the world as it is, but rather as it appears to us through interpretation.
Andy’s perspective, however, is uniquely engineering-driven. It’s as if he’s saying:
"I don’t care what philosophers or economists debate. As an engineer, I can clearly observe cognitive limitations within real-world systems. These limitations distort decision-making, which is precisely why System Dynamics is necessary."
Seeing how different disciplines eventually converge on this core insight is fascinating.
Unlike philosophy (which explores cognitive limitations logically) or economics (which focuses on decision-making biases), System Dynamics takes a hands-on, computational approach to reveal these limitations.
This is what makes SD so challenging—but also incredibly valuable.
3. Counterintuitive Behavior and Policy Resistance – The Beauty of Modeling
One of Andy’s key arguments is that modeling often produces results that contradict our initial intuition.
He writes:
"Indeed, the simulations may turn out to be the very opposite of what we expected. Policies thought to make the system better may make it worse than before. Policies thought to produce winners and losers may turn out to deliver win-win results in the long run. These surprises are the key to improved understanding."
This resonates deeply with me because I have become one of the strongest advocates of counterintuitive behavior and policy resistance in SD.
Different scholars emphasize different aspects of SD. For me, policy resistance is one of the field's most crucial insights. And Andy’s work has undoubtedly shaped my perspective.
Take this thought-provoking idea:
"A system that appears to create winners and losers might benefit everyone in the long run."
It’s a radical notion.
It naturally leads to complex ethical questions:
"If a system seems unfair today but proves beneficial in the long run, should we accept it?"
This is precisely the intellectual challenge that makes SD so profoundly connected to philosophy and critical thinking.
Reading Andy, I feel as if he’s personally asking me:
"Dr. Benjamin, what surprises have your research revealed?"
His writing encourages me to let go of my own biases and remain open to unexpected results.
This intellectual openness is what I admire most about Andy’s approach.
4. “It’s Okay If You’re Bad at Math” – Lowering the Barrier to Entry
This is probably my favorite part of the Preface.
Coming from a humanities background, I’ve always found advanced mathematics and engineering jargon intimidating.
But Andy reassures us:
"This book does not require training in calculus, differential equations, partial differential equations, statistical analysis, or computer programming. Knowledge of these topics is not required, nor is it crucial to your ability to put modeling to use. The crucial requirement is your knowledge of the feedback processes in your system."
Andy emphasizes that understanding feedback loops is far more critical than knowing advanced math.
This perspective is incredibly encouraging, especially for students intimidated by SD.
Interestingly, Andy himself had a rigorous mathematical background:
- B.S. in Electrical Engineering (UC Davis, summa cum laude)
- M.S. in Applied Mathematics (Harvard, 1968)
- Ph.D. in Public Policy and Technology (Dartmouth, 1975)
His first edition of Modeling the Environment included more calculus-based SD equations, likely reflecting his technical background.
However, he adapted his approach over years of teaching—removing unnecessary mathematical barriers to make SD more accessible.
This shift demonstrates his deep love for SD and his commitment to making it understandable to a broader audience.
5. Delays: The Most Dangerous Element in System Dynamics
One of Andy’s strongest warnings in the Preface is about delays.
To illustrate, he shares a personal story from his teenage years—learning to drive on icy roads.
"The driving test showed that we had all grossly underestimated the long delay to change a car’s direction when the tires have less frictional grip on the surface."
This simple but powerful anecdote captures a fundamental SD insight:
"In real-world systems, delays make it difficult to align intention with outcome."
For me, feedback is the most important, and delay is the most dangerous in SD.
Andy’s emphasis on delays only strengthens my belief in their critical role.
6. A Brilliant Addition: ‘Joe’
Finally, Andy introduces ‘Joe,’ a fictional student who asks common questions that real students have posed over the years.
This simple addition makes the book even more engaging and accessible.
Reading Joe’s questions, I find myself thinking:
"I would have asked that too!"
It’s a testament to Andy’s incredible teaching ability.
🔹 Link to this post: https://bit.ly/3QhDmTn
Would love to hear your thoughts! 🚀
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