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Chapter 12

thinking in systems: a richer life experience

Donella H. Meadow's primer by the title Thinking in Systems provides excellent motivation for anyone writing a book on the subject. The examples she provides in her systems zoo demonstrate the benefits a systems approach to thinking provides, for understanding phenomena in clear categories of systems prototypes. For example, she demonstrates how the time delay of perception by suppliers and retailers in a car manufacturing and sales organization can become a dominant oscillating effect when trying to improve the success of deliveries in the system. She also demonstrates how different the system behaves when renewable versus non-renewable resources are involved. Population dynamics, temperature control dynamics, and economy dynamics all provide classic and yet motivational examples of how system thinking helps understanding to reduce suffering for beings on our planet.

The categorization of system dynamics entities into inputs, processes, and outputs (connected through varying positive and negative feedback loops) just happens to be the same categorization suggested in computer science 101 courses. Though any systems learning in my first year computer science coursework focused solely on computer systems, the intrinsic connection to using categorical sub-systems to study systems of all sorts became more and more useful as my coursework shifted toward all-encompassing systems science and systems engineering — those perspectives outside of the traditional realm of computer science education. As I merged the two together into a single body of knowledge, the computer became a tool to help amplify systems consideration when studying any system of interest. But, it could only amplify what was already provided. And it could only do so through visual, auditory, and interaction strategies the computer displays were capable of producing.

There lies the real heart of the matter: the inputs, processes, and outputs need to be anticipated in order to design an effective use of the computer and its displays in amplifying understanding. Donella expresses well the hours and hours of interviewing, observation, and documentation involved in identifying the component inputs, processes, and outputs involved in a system. Documenting system components and their interactions can be a painstaking process. The importance of doing so magnifies once you have done it for a while and realize the configuration of a system simulation may be moot if a significant component is missed. How do you sleep when you have five million dollars to build a system simulation and understand that every apparent insignificant component might turn out to have a significant effect when involved in processes that have feedback amplifications? It sure seems like a village of experts in system components should be involved before the first line of simulation code is written.

Imagine the life work of a scientist who tries to understand the ecology of a beach cove through food webs by identifying all participating plants and animals (as inputs), their interactions (as processes), and their waste (their outputs). Imagine thirty years of study identifying thousands of inputs, millions of processes, and thousands of outputs. Then, imagine the dismay when a second run through starts to investigate and add parasites to the system and finds that the parasite contribution dominates the ecology in ways that dramatically affect the work done so far. It's an insightful real-world case study — one that got many science teams scrambling to promote a new perspective that states, "If you are not including parasites in food webs, you aren't getting the whole picture. They are consumers like predators, but they are less visible and easy to forget". (a quote by Kevin Lafferty, a marine ecologist with the U.S. Geological Survey, and adjunct professor in the University of California Santa Barbara Department of Ecology, Evolution and Marine Biology). Parasites' complex life cycles — starting out in one form in one animal, and moving on to the next form and host — also give them a wider range of food sources than free-living species, which in turn makes food webs more complex than originally thought.

Or imagine the life work of scientists who tried to study human cognition by building computer systems that could demonstrate aspects of thought similar to human thought (thus suggesting a similarity between the brain and such computers). They provided inputs, developed processes, and fed outputs back into the system until they got outputs that seemed like outputs of human thought. In that approach, the machine becomes a simulated brain comprising of significant brain functions processing significant brain inputs that come from simulating typical human perceptive processes (hearing and sight predominantly). Then someone comes along and hypothesizes that the brain can't really think well without feedback from the body. Locomotion and sensors are added to the simulated brain and huge advances in simulating the human brain are made — advances that many suggest should have come thirty years prior had the scope of the human cognition system been better conceived.

Such anecdotes provide systems thinkers the leeway to become expansive in their thinking — or perhaps the mandate. The good news is that great satisfaction comes with participation in the expansion experience. The bad news is that deadlines are hard to meet when scope is expanding through each new insight. By living in and interacting with the world with an assumption that everything is connected, we gain a sense of abundance and awe in the diversity of potential inputs, processes, and outputs participating in the world's enormous ecosystem of phenomena. We share in the joy of life-long pursuits that look at any one input, process, or output as the basis for a career in research — especially if the world keeps thriving without the participation of those individuals in day-to-day critical human services. We buy into the idea that the strange life forms at the bottom of the ocean might provide some significant input to solving world problems (as a solution to preventing a disease or eradicating a cause of suffering). Each new discovery could be or become a significant component in a system evolved by nature and/or manufactured by humans.

The pursuit of inputs requires careful observation and physical and theoretical evidence of existence. When I see a strange new weed in my garden, I wonder what inputs are involved in keeping that weed alive (or what inputs allowed it to grow in the first place). When I see a plant that had been thriving starting to turn a different color, I wonder what inputs are involved in making that plant sickly (and what inputs might make it healthy again). When I wish to understand the physical state of any natural phenomenon, I attempt to identify all inputs to the phenomenon. That pursuit is an expansive one as a systems thinker — expanding even to the identification of inputs as different perhaps when the same component is in a different state (water as gas, liquid, or solid for example). Identifying inputs is never-ending as we acknowledged when we sequenced the first human chromosomes' DNA in its entirety and then had to face the daunting impact of how many other things were involved in the human body that weren't a direct consequence of DNA.

The pursuit of processes expands exponentially as new inputs are found to participate in a process unseen or unimagined previously. The classification of strands of DNA into genes redefines inputs and each newly identified gene increases the number of known processes the human body performs based on its DNA. The fact that genes are documented to behave differently based on different environmental factors (including time-sensitive behavior) adds to the complexity of the processes being pursued. As the verbs of systems, processes can occur with combinations of all inputs (the nouns) — just as human language gains expansive expression through the mixing of nouns and verbs.

As processes are found, outputs can be identified as new system states brought on by input processing. Often, the outputs are byproducts of the system that have good or bad labels attached to them. The system thinker expands the study of outputs to assume they are likely both good and bad and defers conclusions until all possible feedback loops have been considered (as today's output can be tomorrow's input to the system as a whole). The outputs can be reversed engineered as well to help find processes (by expanding the pursuit through "how did this get here?" investigation), just as inputs can be tracked to suggest processes where they are involved.

The rich life experience comes from expanding the awareness of everything we consider with the knowledge each consideration might be significant to one or more of all system processes taking place in the universe. With a simulaton life perspective there's no time to be bored as we become aware of the systems playing out in front of our eyes or even within the great complexity of our mind-body systems (Buddhist monks suggest they are never bored even as their work appears redundant to others). While some suggest we discuss weather as a default topic to break the ice with others, we can actually contemplate weather at any time to stave off any state of boredom before it even has a chance to emerge. As we live our lives moment by moment, systems thinking can contemplate our existence as a system of components interacting with each other. We can pursue inputs of thought to engage your mind, contemplate mental processes with new inputs to enact them, and identify new outputs of such thinking that might even suggest a compelling action for us to take next. We can simulate any future participation in a system we think e might want to pursue. As system thinking gives us a construct and vocabulary for pursuing inputs, processes, and outputs, our contemplation becomes clearer and more efficient — the process becomes more familiar and aware. We start to explore the expansion of system understanding without any sense of being overwhelmed or overwhelming our minds. It becomes natural as a way to explore nature — human and otherwise.

The simulaton life has a lot going for it in terms of models and tools to support the life. There is the body of evidence coming from neuroscience research that suggests how the human brain works to produce an ongoing simulation in order to provide us a simulated reality we can make sense of to exist as a coherent being. That evidence provides motivation that pursuing the simulaton life will provide rewards from exercising our brain in ways that align well with what the anatomy does best. There are the software tools that let us document our observations and explorations into the objects that our brains consider when we are trying to understand phenomena. There are formal methods from information science that help us document our findings in efficient storage media. There are social media technologies that let us interconnect our documented findings with others to expand our shared knowledge base. There's an evolved systems analysis perspective that provides a systematic approach to observing and documenting our findings in light of their participation in systems that affect us.

Given all the models and tools that continue to empower the simulaton life more as time goes on, there's an opportunity to get better at creating simulation tools that tie the available assets together. Just as Hollywood has refined the process by which movies get better for engaging human emotion and video games have refined the process by which game play motivates players, the simulation community can refine the process by which simulations most effectively produce insights and deepen understanding. If emotion is a key component to the insight process, we can include appropriate advances from moviemakers. If video game consoles, interactive peripherals, and computer graphics techniques motivate video game play, we can pursue simulation experiences that take advantage of advances in those components. If the systems perspective helps suggest how we package content in our simulations, we can pursue evaluation of the system perspective in the simulations we create and experience. Each simulation we provide to augment our thinking is a system that can be evaluated in light of systems thinking — hardware, software, content, user experience, and insight as a system of inputs, processes, and outputs.

We get a double benefit from becoming better systems thinkers through simulation. First we can provide a better simulation and provide a well-defined metric by defining the simulation as a system — one that maps to an observed system we wish to simulate. We can evaluate the simulation by comparing the insights and understanding we get from the simulated system to its applicability to the observed system. Second we can also improve the simulation experience by thinking of the simulation experience as a system. Thinking about the simulation experience as a system allows us to apply our conclusions to the next simulation we design or experience with the intent of improving. Through that second benefit, each simulation provides the opportunity to improve all other simulations. When we have an insight into making any simulation experience better, we can review all other simulations to see if that insight applies there as well.

A hypothesis I entertain often is that the world needs more system thinkers. This book is perhaps the most I've done to date to test that hypothesis, but I suspect I'll find some evidence for or against the hypothesis depending on where the community of readers takes it. It would be hypocritical to not attempt to provide this book as a system and let it evolve by finding better inputs to the material in the book, processes by which it can be explained, and outputs in terms of visualizations based on system simulation software prototypes. A community of readers (and writers) is likely what the book needs to become its best contribution. Perhaps book will not be the right word for it eventually — perhaps it is more a Wiki or other form of repository. It would be great to get involved in a greater pursuit of what this book should be — a pursuit by many who are becoming effective as systems thinkers and evaluate the book as a system for improving a simulaton life. If I am not a very good benevolent dictator for organizing the evolution of this collection of content, I'd gladly pass it on to another and participate as participant instead of coordinator. There's another mildly tested hypothesis I'd like to test better — if I am a useful benevolent dictator for managing the content of this book as it evolves into something I don't yet anticipate (since systems thinking and the simulaton life has taught me to stay nimble in my anticipations).