Category Archives: Reductionism

Complex Systems Science: An Informal Overview — Part III: Synthesis of purposeful systems

 This is Part 3 of a multipart series, see Part 1 and Part 2 for additional context.

Purposeful System Synthesis

When we look around the world, we notice that many systems seem to work. That is, they accomplish some task that is useful, they fulfill a purpose or serve a function. The heart beats in order to pump oxygen through the distributed tissues of the body, and inner ear bones play a role in transducing air pressure waves into neural patterns that can inform behavior.

These terms — use, purpose, function — are extremely familiar to us. We live with them as concrete realities. I use the car to get to the store, to get food, to cook a meal, to eat with the family.

Reductionism has no place for these realities. It has no room function or purpose. In a reductionistic universe, nothing happens in order that something else can happen; it just happens. Period. End of story.

Anything that appears otherwise is an epiphenomenon, essentially an illusion.

In biology this deficiency has been most acute. Reductionistic biologists are forced into continuous doublespeak in which they discuss how living systems function — with the implicit understanding that the things they discuss don’t really exist and don’t really have any functions.

This is not mere semantics. This philosophical-blockage has delayed many important conversations in science, and in connecting science to a mature ability to synthesize purposeful systems.

Of course we can salvage a scientific understanding of function as soon as we admit emergent phenomena into our discourse. Functions emerge out of relations. The function of the heart is not something to be found by looking at the heart, but at looking at its network(s) of relationships. For a highly-recommended deep dive into this topic, see Life Itself by Robert Rosen.

Not all emergent properties are functional, but all function is emergent.

Design

For manmade systems, the typical answer to “how did that system get organized so that works?” is “someone put it that way”. That is, the organization is imposed by an external agent who understand how the parts work together to make something useful happen.

This is also how creationists explain where we, as biological creatures, came from, and why we are organized the way we are: an agent arranged us that way. “Intelligent design”.

Of course as a scientific answer to the question of our existence this is no good. But it still sounds pretty good for cars.

Self-organization

How do things get organized when there is no one to organize them? Simply, they organize themselves!

In self-organized systems, all the parts that compose the system just “do their thing”. No part needs to know about the system it’s a part of, its organization, or even that it exists. Each part interacts locally with its neighbors (in physical or abstract space), and often its behaviors are characterized by a simple set of rules. Order that persists is a consequence of there being something globally stable about an arrangement that these parts discover by chance — by wiggling around randomly, essentially. 

A tangible example is the formation so-called micelles. Micelles are physical systems that are similar in many ways to cellular membranes found in living organisms. They are organized in a roughly spherical pattern, embodying a boundary between an ‘internal’ and ‘external’ environment.

 

This arrangement is entirely a consequence of the properties of the molecules and their relations with each other and their local environment. The relevant properties are as follows: some lipid molecules happen to be structured with a head and a tail. Further, the heads of the lipids are attracted to water, whereas the tails are repelled. This polarity doesn’t mean much for a single lipid molecule, but when a bunch of them get near each other, something special happens: the tails, being repelled by the water they are in, find refuge in huddling together, so to speak. The more that bunch together, the less water there is locally, and the less repulsive that environment becomes to the tails. The heads, being attached to the tails, can’t go far. But they don’t mind being wet, so they point out away from the huddled tails. And voila, a membranous sphere.

That’s it. No designer, no constructor, no external agent, but an organized system. Organization for free. (Well, sort of.)

Micelles are not in any direct sense “functional”. But cell membranes are. Every cell relies on the self-organization of its membrane in order that it persists by constraining critical operations within a semi-controlled environment.

Evolution

Not all such arrangements persist, of course. Things break. Cells die. The volatility of the environment tests the fragility of everything, weeding out those patterns that do not withstand the variability.

This is, simply, Natural Selection. With enough time, and therefore enough volatility, the patterns that persist are those which are able to respond to volatility by adjusting their internal patterning and/or modifying their exposure. In other words, things become alive.

These are the two sisters of evolution: creation and destruction. Self-organization provides a rich variety of ordered patterns, environmental stress tests these structures for ability to persist.

Engineering

Engineering is the practice of synthesizing systems to solve human problems. Many of the problems we face today are of enormous complexity. The systems we synthesize in an attempt to address these problems necessarily involve many interacting parts including individuals, organizations, and technologies.

Traditional engineering practices are reductionistic, and assume that a plan of roughly the following form will successfully solve any given problem:

    1. Break problem into pieces
    2. Construct a component that solves each problem-piece
    3. Put pieces together into working whole

The realities that throw a wrench in this process when it comes to large-scale complex systems are myriad, but the regularity of costly failures that result from its application is reason enough to look for a more sufficient way of thinking and doing. 

A figure-ground reversal is needed in the engineering practices in order to facilitate the synthesis of purposeful systems whose complexity is outside the cognitive scope of any individual: a shift in emphasis from the specific structure of a complete solution, to the evolutionary environment in which problem-solving systems can evolve.

Without further argument about the potential for evolution to generate complex adaptive systems with the ability to solve a huge variety of problems, I offer several practical principles informed by evolutionary synthesis for systems engineers and systemic designers to consider in the face of complex real world challenges.

Practical principles:

    • Foster (non-toxic) variety

Evolution happens over ensembles, not individuals. Without variety there is no potential for evolution. Consider how variety is generated in the system, and foster it even when ‘reasonable’ solutions are already discovered. Never put all your eggs in one basket.

    • When resources are abundant, foster the non-obviously-useful

Unlike explicitly designed systems, what is not obviously useful, sometimes is, or can be.  Our inner ear bones that we use to hear evolved from the jawbone of our fishlike ancestors. The reductionistic engineer would have optimized away our ability to hear long ago.

    • Allow for heredity

Systems that show signs of success should be able to pass on their form to subsequent generations. The nature and mechanism of this process will vary from domain to domain.

    • Detect and fail fast, and local, the toxic

Again, harmful varieties should be rooted out as early and as locally as possible, before becoming systemic.

    • Coevolve components

Things work well together when evolved together. The corollary is: don’t expect components that did not coevolve to work well together.

    • Expose to the ecological early

Exposure to the real problem environment the system is supposed to operate in during development/evolution of varieties will buffer against over-designing, and provide an opportunity for the maturation of systems that can handle the true complexity of their task.

    • But not too early

Sometimes it may benefit a system to have some simulated experiences or otherwise explore its range of behaviors with buffered consequences before deployment. This can be seen in the biological world for example in the propensity for play in the most complex organisms. Balancing the potential benefits of playtime with the need to get a big boy job is an art, not a science.

    • Figure-ground reversal: attend more to the selective and generative aspects of the evolutionary environment, less to a specific imagined solution

This does not imply imagined solutions should play no role, but that they should be part of an ensemble of potential solutions. Again: eggs, baskets.

    • Resist the temptation to scale quickly a promising solution

Solutions should prove themselves in time. Often, success can be incidental but look causal; we are fooled by randomness. Moreover some malignancies develop slowly and quietly. We will be thankful when they show up that we moved slowly.

Complex Systems Science: An Informal Overview — Part II: Organization and Scale

This is part 2 of a series informally introducing and discussing ideas in complex systems science, and their relevance to how we build our world. Here is part 1. 

Complex Systems Science and the Special Sciences

We are familiar with science being broken down into different categories depending on what is being studied: particle physics (e.g. electrons), chemistry (molecules), biology (organisms), psychology (minds), sociology (groups of humans), etc. Call these the special sciences as their role is to look into a certain kind of stuff.

Complex systems science is not defined by what the stuff under study is, but rather how one asks and attempts to answer questions about whatever stuff is of interest. Recall that in complex systems, the properties we are interested in might emerge from interactions among components, i.e. emergent properties. For this reason, in complex systems science we pay special attention to the interactions and relationships among the parts, and how they give rise to (emergent) patterns of behavior.

We can do this in physical systems, biological systems, social systems, or any other system of interest. The answers we get will often look remarkably different than those from the special sciences.

Organization and Interdependence

When we attend to the interactions and relationships in a system, the organization of the stuff becomes more central to our understanding than the stuff itself. To illustrate this point, imagine a mad scientist takes each cell of your body one by one and relocates it to a random location — would you feel much like yourself? I think not. When the organization is disrupted, so are the interactions, and the nature of the system changes.

This also means when you change one part of the system, you may affect a larger portion of, or even the whole, system. This is because the behavior of the parts are interdependent. What part A is doing affects what part B & C are doing (and perhaps vice verse) — what my heart is doing affects what my lungs and muscles are doing. Interdependent behavior presents all kinds of challenges to standard statistical approaches which assume the independence of parts of a system.

Whether or not the change in one part of a system has affects on other parts of the system depend on its organization. If you had to choose between losing a kidney or a heart, which would you choose? Would a tree do better off losing ten-thousand leaves or one trunk?

These are hints to be cautious of centralization, and to use redundancy for robustness when possible. When we build systems we should ask ourselves, “what would happen if X failed?” — even if we are pretty sure X won’t fail.

More is Different

‘More is different’ is another way of saying ‘emergence happens’. It is no easy task to predict what the emergent effects will be when we scale a system (i.e. increase its size/number of components), especially when operating under reductionistic assumptions (emergent effects will always surprise the reductionist).

When engineering systems, emergent effects are often detrimental, or even catastrophic, to the integrity of the system, and therefore the purpose it was intended to fulfill. This is because, at the smaller scale, what appear as irrelevant side-effects (which may not have been noticed or attended to at all) are able to be absorbed or dissipated into the system’s environment in some way or another. When we grow the system, these ‘side-effects’ can coalesce and become relevant to the behavior of the system.

This is why we don’t see land animals much bigger than elephants throughout Earth’s history: the mechanical forces that are mere side-effects for smaller critters become causes of failure. Darwin puts a harsh limit on the scale of a design motif.

There are countless engineering failures that are of enormous cost to society (e.g. F-35, USS Zumwalt, the global financial system). Overgrown elephants.

The holy grail of systems engineering is to leverage emergence rather than fighting against it. Nature manages to do this via evolutionary tinkering. Perhaps we can take a cue from her.

Complex Systems Science: An Informal Overview — Part I: Reductionism and Emergence

This is the first in a series of short blog posts where I will discuss some of the major themes in complex systems science in an informal, and hopefully accessible, way. The perspective is my own, and might differ from others’ takes on these concepts.

From these concepts and themes I will distill implications for real-world decision-making, and rather than applying specific results to a specific problem, I will emphasize the big picture and ‘lessons learned’. So, let’s get started with a small dose of philosophy. 

Reductionism

To get a sense of the essence of complex systems science, it is important to understand the philosophy of reductionism. Reductionism asserts that to understand something, it is sufficient to (1) break it down into its ‘natural’ parts, (2) study the properties of those parts.

Said differently, a reductionistic philosophy assumes that any property of a whole is inherited directly from a corresponding property of a part of the system.

Complex systems science recognizes the insufficiency of reductionism to understand and describe our world — especially the most interesting and relevant phenomena: living, social, civilizational, systems.

To be clear, this is not a claim the reductionist methodologies are never appropriate, just that by themselves they are insufficient.

Emergence

Reductionism has become so ingrained in our way of thinking it is sometimes difficult to imagine what the alternative could be. Recall that reductionism assumes that if a system, call it S, has a property, call it P, then P must be present in at least one of the parts that compose S.

Clearly, the alternative is that property P of S is not present in any part of S. How can this be?

The answer is that P emerges from interactions of component parts of S. Let’s make this concrete with an example.

We all know that the brain is involved in recognizing patterns. For example, we see a pattern of light and know it is a face, or we might even recognize the pattern as our Grandmother. So it is safe to say ‘pattern-recognition’ is a property of the system ‘brain’.

The brain is composed of parts called neurons. In a simplified description, a neuron is either ON or OFF. When it is ON, it sends signals to other neurons it is connected to that it is ON. Each neuron, at each moment in time, adds up the signals being sent to it, and makes a simple decision whether to be ON or OFF. That is essentially it. No pattern-recognition property to be found in any neuron.

However, when these neurons are connected into networks, they are able to recognize complex patterns, such as human faces. How those networks come to be and how they function to do cognitive work can be saved for another time, just trust me here that it works.

The important point here is that the property, pattern-recognition, emerges at the scale of the system, a network of neurons (brain), without any part of the system having such a property.

Reductionism fails.