[Complex Systems] Embracing Complexity (Part 1)

Why All Organizations Should Build Internal Networks to Survive in an Increasingly Technological World

introduce

There seems to be a dangerous divide between those organizations that have artificial intelligence (AI) and those that don't. Why would it take months for your organization to add a few fields to your status report when Google can recognize your face, understand your speech, and apparently know your secret desire for a new espresso machine?
The simple answer to this question is that the solution lies not in simplicity at all, but in accepting and respecting complexity. Rather than embrace the latest claims of simple "quick-fix" solutions, organizations such as Google embrace complexity by putting the web at the heart of what they do. Data, computers, and networks of artificial neurons can model complex systems. Any organization wishing to future-proof itself and remain competitive must adopt this "network model" without delay.

• The first part of this paper provides leaders with an insight into the complexity and critical role that networks play in the rapid development of the Information Age.

• The second part provides a high-level overview of the "networking toolset" available to all organizations.

the world is not flat

It's easy to see why people used to believe the world was flat. Without access to satellite imagery or mathematics, they had to rely on evidence from their own eyes, which told them (despite the odd hills and valleys) that the Earth was flat.
Your own eyes can lead you to similar misconceptions, because fundamentally, our brains like to think in terms of straight lines and boxes rather than curves and circles.

Straight lines are simple, and it's easy to predict where they're going. You know where you have a good old straight line, but more complex curves and loops. They're "non-linear" and who knows, curves can go crazy and become exponential, in which case they shoot up (or down) suddenly like a rocket.

But we now have satellites and math, and it turns out the world isn't flat, it's a beautiful teal sphere floating in space. If we embrace this complex non-linearity, the world becomes more nuanced, complex, and interesting.

Still, our love for lines runs deep, and it's easy to understand why: lines are a powerful predictive tool, and they're remarkably efficient with them. But they are also rigid and inflexible. While they work well in gears, clocks and steam engines, the same is not true when running machine learning algorithms on big data in the cloud. Organizations hoping to successfully transition to the Information Age must learn to recognize the boundaries in their business and be willing to transcend them when necessary. Overall, our current infrastructure isn't up to the task very well, and not just because the applications and databases themselves are too rigid and inflexible. The biggest problem is the complex and jarring mess that comes from trying to connect these separate parts into one entity.

We need a new set of coordination tools that will allow us to move beyond the boundaries that divide organizational information into separate silos and see the less obvious but more revealing curves and circles that connect it into a unified system. A toolset that spans information boundaries and takes a systems perspective allows us to embrace the inherent and rich complexity of our organizations.

time loop

Also, there is another dimension to complexity. Not only is information interconnected, but it is constantly changing, and as one thing changes, it often has ripple effects on the things it is connected to. It's worth acknowledging that we tend to think about causation linearly. In other words, we tend to think that one thing affects another, which in turn affects another. Like a row of dominoes, you knock down the first one, and a chain of cause and effect ripples down the line. A causes B to cause C.

Again, reality is more nuanced and complex than that. Causality can also contain circles. A causes B to cause C to cause A. These time loops are called feedback loops. If you imagine a couple in a conversation where one partner raises his voice slightly, causing the other to raise his voice, causing the first partner to raise theirs, before you know it, the couple is having a full blown argument, plate On the fly, you have complete disaster. Hurricanes, bee swarms, and pop-up parties are all manifestations of nonlinear two-way causality.

It's hard to overstate the importance of these feedback loops. The second law of thermodynamics states that in isolated systems, disorder increases with time. Things tend to get messier (anyone with kids will attest to this) and this gives us an arrow that shows the direction in which time is flowing - time is flowing in the direction of one big mess.
However, feedback loops (such as Maxwell's demon) allow information to be recorded. This is important because now we have physical systems and information. Complex hierarchical networks of feedback loops (like plants, animals, and yourself) can learn from the past so that predictions about the future influence current actions. This seems to reverse the "arrow of time" and the entropy goes backwards; the order within the isolated system increases. In a universe where everything is becoming more chaotic, plants, language, and robotic vacuums swim upstream, as if the chaos is organizing and cleaning itself up.
(An important side note is that there are no truly isolated systems, and order created in the local system will be exported as disorder to the wider environment. The second law still holds, as a society facing global warming, We are only just beginning to realize the implications of this.)
There are two types of feedback loops: balancing feedback loops and reinforcing feedback loops.

A balancing feedback loop keeps the system as it is. Think of a thermostat in your home that turns hot water off or on depending on how close the current temperature is to a desired target temperature. Blood sugar regulation, supply and demand, and carbon cycling are all examples of balancing feedback loops. Balancing feedback loops is key to maintaining a sustainable system.

On the other hand, reinforcing feedback loops are like engines of change, they cause growth and decay within the system. Bacterial growth, compound interest, and the couple's escalating arguments are all examples of reinforcing feedback loops. Enhancing feedback loops is key to driving growth. Uncontrolled reinforcement feedback loops can lead to exponential changes that, to our linear thinking brains, sometimes look like a line that goes from horizontal to vertical in an instant.

All organizations are united and moved forward through feedback loops, and so are the wider markets in which they operate. A mindset that embraces complexity allows us to see these feedback loops and appreciate their deep anti-entropic (i.e., self-organizing) capabilities. Organizations now need a new set of enabling tools to model these feedback loops, harnessing their power to maximize new opportunities and avoid internal decline.
We can also use feedback loops to understand change in society, and modern organizations need to recognize a fundamental reinforcing feedback loop that exists in human society today: it lies between complexity and rate of change.

Our world is becoming more and more networked, technology is becoming more and more complex, things are changing faster and faster, which in turn means technology is becoming more and more complex, which in turn... well, I think you get the point up.
Everyone is aware that the pace of technological change is accelerating. Smart organizations should acknowledge the feedback loop of complexity change and allocate appropriate and proportionate resources to its inevitable consequences. Quite frankly, if the rate of change and ability to adapt externally to your organization significantly outpaces the rate of change and ability to adapt internally, then your organization is at risk.

stage transition

A balancing feedback loop (such as the thermostat in your home) creates a stable platform on which we can "business as usual"; but sometimes, a strong reinforcing feedback loop (such as a complexity-changing loop) can overwhelm the system keeping the Balanced balancing loop. In other words, the system quickly transitioned to the new normal. A good example is water getting colder and suddenly turning into a block of ice, or a caterpillar turning into a butterfly. These sudden changes are called phase transitions.
The same principles can be applied to balance the feedback loops of human civilization, although the lines between stages are more subjective and fuzzy because the system is much more complex. So far, our civilization has gone through two major phase transitions: the rise of agriculture and the industrial revolution. We seem to be approaching (or we are already in) the next phase of transition, where we will move from an industrialized, mechanized society to a networked, technological one.

What is the impact on the organization? Behind the hype and noise, is there a guiding north star for organizations to navigate to the other side of technology transformation?
To start answering this question, let's go back to straight lines and curves. A defining feature of industrial society is that it is based on straight lines. If industry were a shape, it would be a rectangular box. We can say that "industrial thinking" is "box thinking". This linear thinking is reflected in the environments we create, and in the way we see and interact with the world. The evidence for this is all around us, right in front of our eyes.

Since the Enlightenment, we have used science, mechanics, and analysis to break things down and understand the complexity of each part in isolation. You'll have far more control if you ignore connections and only examine individual parts under sealed lab conditions. You can develop exact linear equations where known inputs produce known outputs and describe how everything works very accurately and reliably. Analytics is an extremely powerful tool that allows us to build the efficient machines and science of the industrial age. This is the fundamental foundation upon which we stand now, and it must be critical to our progress as we move forward.
However, analysis is also a simplification, a linear simplification of reality. Nothing exists in wonderful isolation because virtually everything is interconnected. As we enter the information age, companies such as Google, Amazon, and Facebook have begun exploring how to connect the dots, examining the relationships between the various parts. These companies are integrating massive data sets and using powerful non-linear machine learning algorithms (AI). They've started thinking outside of the rectangular box, and they're cleaning up -- literally.
Saying that your organization needs to start thinking outside the box is not helpful advice, it's nothing more than a "fluffy" cliché. What organizations need is some hard, fast, practical guidance. We need to understand the fundamental axes over which this technological phase transition occurs, and then we need to use this understanding to build a concrete set of tools.

Understanding Technology Phase Transitions

The three main forces that seem to be driving the technology phase shift are:

• Data: Contains a wider variety of data, arriving at increasing rates

• Cloud: A networked computing facility that provides remote data storage and processing services over the Internet

• artificial intelligence: computer systems capable of performing tasks that normally require human intelligence

These three elements are tightly linked, and the combined power of their feedback loops is perhaps best expressed on the Internet, manifested in the imminent rise of giant Internet platforms.

Many organizations look at Amazon, Google, and Facebook from afar and focus only on the exciting new AI these companies are producing, but the reason AI works so well for them is that they have invested and spent time amassing vast amounts of data from the web — And artificial intelligence requires a lot of data. AI is like the tip of a data iceberg, as behind every cutting algorithm is a vast amount of highly processed data. Unfortunately, for many organizations, becoming a data-rich Internet platform is not a realistic option. However, what most organizations do have is all the internal data they have carefully collected and organized over the past 10 to 100 years. So the name of the game is to connect all of this information; to combine the parts into a whole while leaving the data where it is. The result will be enough data to train your AI, and the model will be highly relevant to your organization's niche and mission. For banks, this could be fraud detection, disease identification in hospitals, supply chain optimization for manufacturers, etc. It is not even possible to predict all potential applications, since the sum of connections of organizational data should be far greater than its parts.
This sense of change is blowing with the wind, and most large organizations are moving parts of their physical infrastructure to the cloud, building data lakes, and investing in data science projects. Unfortunately, things aren't going so well for many organizations beyond moving to the cloud (which simply shifts more power to the internet giants). The problem is that organizational data is currently fragmented, dispersed and hidden in numerous disparate systems, making data integration a nightmare. Data lakes are overwhelmed by complexity, and they start to become swamps, leaving AI dependent on the data it depends on. However, if we stop and take a step back, we can start to see the forest and not just the trees.

• Recent successes in artificial intelligence are the result of artificial neural networks (comprised of connections between artificial neurons).

• The cloud is also a network: a network of connected computers.

• Finally, data can also be networked (think Facebook and all its data about connections in our social network).

We can embrace complexity by examining the connections between parts, which is exactly what the web allows us to do. The incredible but exciting possibility is that we can combine all three separate networks (data, cloud, and artificial intelligence) into one unified network. This changed everything, as it is now possible for all organizations to build their own internal network and start embracing its rich complexity.

The network shape is like a unified theory of technological phase transitions. Cloud, data and AI can merge because they are effectively interlocking parts of a broader process of "networking". This is the North Star we've been looking for! If the industrial age is box-shaped, then the technological age is network-shaped, so the transformation of the technological stage is to get out of the box and enter the network.
Unique among data structures, these networks are also capable of modeling loops, which means we can use them to map feedback loops and never forget their importance. These feedback loops are like powerful "clearing chaos engines"; and using them enables organizations to steer a process that will ultimately result in a well-organized internal system. Feedback loops can be used to transform an organization's "data integration problems" into unique "data integration opportunities" and drive a self-reinforcing process of internal "networking".

In many respects, the shift to a networked form is a technological phase shift, and internalizing this networked form allows any organization to own its own space and become a key player in the information age.

The Internet giants are far ahead in this game, but the game has just begun, and now other organizations, such as some governments, investment banks, retailers, and pharmaceutical companies, are also starting to participate in the network. This article attempts to shed light on this effort and bring it together by showing how we use the network shape as our guiding north star. More pragmatically, we can create a networked toolset that allows all organizations to think outside the box, and the good news is that the next section of this article will give you a broad overview of how this can be done.

This article : https://architect.pub/embrace-complexity-part-1
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