Complexity
These are aspects of complex systems relates to how people experience, understand and interact with complex interdependencies of the things, people, and systems that we're surrounded with. 5G (read more here), not the least if looking at the use of these future networks and regarding them as socio-technical systems, is one example of complex systems that we're working with. Legibility of sensors and actuators in public space and the relation to privacy is another example of what have kept us busy in the field of complexity. Challenges related to management and operations of networks, connected assets, or even coordination of cities and societal functions can also be added to that list. Complex systems are ever-present in society, and increasingly so as more things get connected and ICT systems spread and evolve. That's why everyone who designs products, systems or services should learn about complexity.
Let's begin with what complexity is before considering if and how it possibly can be operated or managed. No single explicit definition exists, let alone one that's agreed on. However, there're a range of typical characteristics of complex systems, and putting complexity in relation to simplicity, complicatedness, and chaos can facilitate grasping the concept.
A complex system consists of numerous interacting agents and the interactions are key for the complexity, yielding unpredictable emergent behaviors and systems that are changing over time. Complex systems are always non-linear, which means that only because A resulted in B once it won't necessarily do so the next time A happens. There're no well-defined problems so solve, rather a problem space to act in, and there's no such thing as right or wrong, only better or worse. Hence, acting in the complex domain requires being willing to take risks and comfortable with the unknown. And not only comfortable with the unknown, but also to some extent prepared for it. The non-linearity implies that complexity can't be reduced or simplified according to for instance streamlined flow charts or rigid process neglecting influencing factors. Flow chart mentality and inflexible pre-defined processes are suitable for simple and complicated systems with strong causality, but neither for complex nor chaotic. Complex systems are systems of systems that are hierarchically and locally organized. No central point or top down control exists, and the levels below can affect the levels above just like the other way around. This characteristic contributes to the robustness and self-organization properties, which also are dependent on feedback (and feed-forward), yet another characteristic of complexity.
Networkedness, interaction and interconnectivity are all key elements in complex systems, why ICT systems are complex socio-technical systems (i.e. social systems operating on technical bases). Biological cells can be regarded as complex systems, and organs are complex system composed of systems (cells). The human body is a complex system too. A city with all its citizens, technologies, and infrastructures is a magnificent example of a complex, socio-technical system, which even can be chaotic sometimes (but there're also simple and complicated parts). Ericsson's vision of the Networked Society represents a vision of a complex system. Not surprisingly we're interested in how people can interact with and understand these complex systems, and how those potentially can be managed and operated.
What someone considers as complex doesn't necessarily have to be complex from everyone's perspective. Complexity is partly absolute and partly relative, depending on experience, knowledge, framing, context, etc. That brings us back to the difference between simple, complicated, complex, and chaotic (or known, knowable, complex, and chaotic if you rather like). First, it's important to understand that there's no linear progression from simple to chaotic. The simple and the complicated are of a different character compared to the complex and the chaotic. What one experience as simple can be found complicated by someone else, but something that's chaotic can never be simple. It's like comparing apples and pears. Simple and complicated focus on structure, how components are organized, whereas complex and chaotic mainly are about behavior, how agents interact. Simple and complicated systems can be seen as more static and hence more predictable than the complex and chaotic, which are changing over time and therefore more dynamic and hard to foretell. Hence different toolsets and methods are required for handling the complex compared to the complicated. That is also why open-mindedness, flexibility and no reductionism are needed when dealing with complexity, whereas streamlined processes, known best practices, and so on are efficient in simple and complicated systems.
Enough about what's what, and over to the question about management. To manage simplicity isn't an issue, managing what's complicated takes more effort or experience, but shouldn't be too problematic as the complicated can be predicted (and more easily automated). But how about complexity; can a complex system be managed and operated? A system that's inherently self-organizing and can't be predicted with high certainty, right? I'd say that it partly depends on how manage is defined. If manage isn't to control but to deal with, or steer in a certain direction by probing the system, sensing what happens, and then acting, then management of complex systems is possible. But it takes certain mindsets and cognitive flexibility, collaborative approach and holistic perspectives.
As human beings we're used to interface with complexity, yet most people's brains like to simplify things and subconsciously find patterns even if there are no. Most of us search for familiarity. That may help in complex contexts as it can lead to decent approximations and help predicting system behavior, but it might just as well end up far off the mark. Examples are linear modeling, cognitive fallacies such as apophenia and pareidolia, and simplifying heuristics. Common simplifying heuristics (which can be seen as "rules of thumb") include: Representativeness (evaluating the probability that A belongs to class B by the degree of resemblance between A and B), Availability (assessing the frequency of a class or the probability of an event by the ease with which it can be brought to mind), Recognition (considering an event as more likely if it is easy to recognize or process), Anchoring and adjustment (start at an initial value known from an earlier event, make estimates, adjust, to yield a final answer), Simulation (regarding events that easily can be mentally "simulated" as more likely to occur), Proximity (estimating risks and probabilities based on judgments of closeness), and Taking the best (evaluating dimensions one by one when multiple dimensions for judgment are available at once, and making a decision that favors the first dimension that discriminates the other alternatives). Awareness of these common, often subconscious, ways of acting is good when it comes to decision-making in complex contexts.
A common notion related to complexity is that society is getting increasingly complex compared to some decades back. Is that really so? Thinking of how we just describe complexity, with networkedness and interactions, it's a fact that society is much more interconnected today compared to what it used to be. There're more and above all new stakeholders to account for, different ways of doing business, and new ways of collaborating and spreading information. Consequently are networked risks higher, and unforeseen cascade and ripple effects are more likely to happen and become less predictable. In that sense we're living in a more complex society than earlier generations. But at the same time we've developed tools and methods for coping with complexity, why not everyone perceive any increase in complexity. That is, technology makes the world more nested and complex at the same time as it helps reducing the perceived complexity. In the end, the key is to not reduce what's complex to something that behaves as if being simple or complicated, but something that's simpler to understand and interact with but still being complex. Embrace the complexity.
