What can we Know about Systems?
Some recent comments from readers have inspired me to reflect on the limits to human understanding.
What aspects of systems elude our understanding through observation, and how can we approach them?
Can we truly understand any complex system without accounting for the different ways that individuals perceive those systems?
Here’s a brief glimpse into how I’m currently thinking about these topics.
The limits of observation
Jessie Henshaw, a natural systems scientist with training in mathematics, physics and architecture, sent me an email in response to my post on The State of System Explorers:
“Have you ever thought of talking about the unanswered questions?
Things like how we're naturally blind to the designs of systems because they are internal, and even if you see some parts from the outside, you can't see their relationships. Like looking through a window at a family having dinner. The observer is largely blind to what is going on.”
I gave a well-received recent talk on it.
My initial impulse after reading this comment was to want to push back a bit.
What do you mean you can’t see their relationships? Couldn’t the observer walk inside the house, listen to the conversation, observe and talk with the family members to get more detailed information? Don’t we have an incredible array of tools at our disposal that help us peek inside of systems to observe relationships?
After watching the presentation, however, I could appreciate the broader point being made. There will always be a gap between what we “know” and what’s really happening in nature.
Jessie’s presentation included a couple of great quotes from Nobel Prize-winning theoretical physicists Niels Bohr and Werner Heisenberg. Bohr gave us a fundamental understanding of the atom while Heisenberg created the field of quantum mechanics.
Both were acutely aware of science's limited capacity to elucidate the nature of reality.
Jessie then gave a specific example demonstrating some limits of observation that she discussed in her recent paper — Emergent Growth of System Self-Organization & Self-Control.
In the paper Jessie examines data from gamma ray bursts, the energy that gets released when a star collapses into a black hole. The raw data (on the right in the image below) displaying photon counts gathered by satellite shows what looks to be pure noise. However, after applying an innovative mathematical “smoothing technique” to the data she found six highly organized curves (on the left) indicating the presence of some significant physical phenomenon.
What are we looking at exactly? Jessie theorizes that we might be observing the surface of the star colliding with the black hole and getting pulled in at first, followed by successive inner layers being sucked in one at a time. But since we can’t directly observe all the relevant dynamics, we can’t know for sure.
Later in the presentation, a second example is given of an electron’s “p-orbitals.” What is the electron doing? In what specific ways is it responding to its contexts? Again, our methods of observation can’t tell us much beyond statistical patterns.
From Jessie’s perspective, much of what science can say about nature has been reduced to statistics. But the reality is that nature at its core isn’t statistics; it’s probably relationships of some kind.
The limitations of science in revealing the inner workings of the world is an undeniable fact of life. This isn’t a problem so long as society understands the proper role of scientific inquiry. Science isn’t a static set of immutable facts to be memorized. It’s a never ending process, a grand quest for greater understanding.
However, I believe we are on the cusp of a new era in which our perceptions of what can be observed and known using science will radically shift. The “century of complexity” prophesied by Stephen Hawking in the year 2000 is well upon us.
For example, network science is flourishing as an academic discipline that is all about formally studying relationships. Grounded in the mathematics of graph theory, network science gives us a vast array of measures that go beyond statistics to provide useful information about the dynamic relationships in complex systems. Meanwhile, artificial intelligence is supercharging our capacity to process massive amounts of data.
So, what do I think about the unanswered questions? I find them exciting as they represent new frontiers to be explored!
I think we should be ambitious in aiming to increase our knowledge of the world. We should maintain humility when confronted with the limitations of our understanding. We should always remain curious and strive to achieve a healthy balance between skepticism and trusting those with specific expert knowledge.
Accounting for Individual Perception
Another comment came from a reader,
, who commented on last week’s post outlining a mathematical definition of system. Squeezycakes expressed their belief that systems exist primarily in the minds of observers, and that we can’t really understand complex systems without accounting for individual differences in cognition and perspective.“This is ambitious, but I do believe that every system primarily exists in the mind's eye of an OBSERVER...complex systems especially can't be truly understood without reference to the cognition and perspectives and worldviews of the people who are participating in and trying to understand them...no idea how to begin turning that into maths though!”
I don’t entirely agree with the assertion that systems exist primarily in the mind’s eye of an observer. Yet I absolutely believe that we must account for unique experiences to truly understand complex systems.
How can science account for the hidden information within the minds of observers?
The practical short-term solution is to simply accept this significant limit on our understanding. Don’t make unfounded assumptions about what people may believe or think. Try to unpack what may be happening in people’s minds through observation, interviews, and surveys.
In the long run, I'm optimistic that advancements in systems science and brain imaging technology could elevate psychology to a discipline truly deserving of the label “science”.
This May, a team of neuroscientists and computer scientists at The University of Texas at Austin demonstrated how AI can be used to decipher specific words and sentences from brain scans.1 Last year, scientists showed how brain wave data for experiments can be effectively collected online from users at home using commercial EEG headsets.
So, we can use AI to “read people’s minds” and are simultaneously building the infrastructure necessary to cheaply collect massive amounts of brain wave data from people sitting at home.
Currently, we lack robust methods to reliably determine how individual cognition and perception influence, and are shaped by the behavior of complex systems.
But technological advances, big data, and new research methods have the potential to revolutionize our abilities in this area. It’s fun to imagine what new tools for understanding might be at our disposal ten years from now.
What can we know about systems?
We can know a lot more than we currently do, but I don’t think “total” comprehension of any complex system is within our reach. I suspect if we achieved total comprehension, it would fundamentally alter what it means to be human.
I greatly appreciate the comments and questions, so please keep them coming!