Luhmann's Model Of Complexity, Selection Force, And Influence

Note: This is the first of a couple of articles I’m planning to write about this rough cluster of topics and themes, strongly influenced by Luhmann. For those well-versed in Luhmann, I need to caution that I’m not by any means an expert on Luhmann’s work. These articles will primarily draw from his posthumous publication Macht Im System1, as well as some papers and interviews. If I drag out writing this for long enough, there’s a good chance it will also contain material from his magnum opus, The Society Of Society.

Niklas Luhmann was a German sociologist, best known for two2 things: Being very influential to sociology at large (notably not as much in the English-speaking world, but Europe and Russia, his ideas came to the anglosphere later and more slowly.) and, to be polite, making full use of the German language.3

To me, some of his most interesting work and what I want to talk about here is his model and conceptualisation of influence, selection, complexity and how organisations fit into that. All models are wrong, including this one, but this one is useful in providing insight into how complexity is managed across organisations, how coping with complexity happens, and how decisions are transferred and transformed.

Let’s start bottom-up, and go from simpler and more elementary definitions and relationships to more complex and emergent ones.

What’s A System?

Luhmann defines a system in a characteristically terse way, namely that “a system is defined by its boundary between itself and the environment”. This is as broad of a definition as a useful one, a system in this notion is formed any time someone tries to make a distinction between inside and outside. Inside the system, the level of complexity is reduced compared to the outside, mostly by means of agreements and influence (more on this below) as to what to do, and what the priorities are. This can be as loose as “shared terminology”, i.e. when a group of experts agree to name a common object4, and as specific as an employment contract.

Systems are often loose and overlapping, and like in more classically-known systems theory (referring here to the MIT branch of Donella Meadows and Jay Forrester), the main use of defining them is to define the unit of analysis.

Inside the system, the complexity of the world is reduced by there being agreed-upon, shared definitions, goals and values, compared to outside of the system, where they are not.

What’s Complexity?

Complexity, in Luhmann’s model, is the amount of aspects or concerns that have to be considered for potential actions any one actor could take at any one point. This generally is considered infinite outside of the system, and the whole point of the system boundary is to define a space where it is manageable, by reducing the amount of actions possible to a point where it is possible to act.

This is generally done by agreements on goals, values, functions, delegation, and so on: If we agree that I’m cooking dinner tonight, the number of potential actions for what I’m doing this evening are now much smaller, and most of them should include cooking dinner.

It’s important here that complexity inside a system is variable and influenceable, you can choose how complex the inside of your system is. You can make it very simple and the next action is always very clear, the trade-off is that it can’t represent the environment/rest of the world very well, you have disregarded many dimensions and aspects of the environment to get to your simple system.5

The reward for taking on more complexity is being closer aligned with the environment and therefore being able to react to changes in the environment (i.e. having less adaptation problems), the reward for taking on less complexity is being able to decide on a reasoned course of action.6

To use an example: If I’m cooking dinner, I can choose to care or not care about dietary preferences my guests have. Doing so increases complexity (Now if vegetarians are present, I may need to cook a second main course), but reflects the world better: some guests might still be vegetarian, no matter if I choose to care about it, or not. Choosing to not care might lead me to being surprised by the outcome of my actions, but it makes choosing that action easier.

Why Do People Want To Reduce Complexity?

Because, in brief, they are bad at dealing with it. If you give someone a large number of potential options and ask them to pick the best one for them to do by a reasoned process (i.e. not just picking one at random, based on aesthetics, and so on), the amount of complexity they can successfully do that with is quite low.

People can adapt to deal with this complexity, but the trade-off is time and cognitive load. Adding complexity means that the process of deciding what to do is slower, as people’s intuition-based capability for making off-the-cuff decisions is exceeded, and they have to use slower, more deliberate processes to decide on just exactly what to do, much less actually doing it.

To illustrate further, consider asking someone that’s a reasonably good cook to cook dinner. They will most likely use a certain amount of assumptions (you two are the two people eating, it will be one course, dietary preferences and allergies are known, what they have in the fridge) to arrive at an answer for what they are going to do to fulfil what they agreed to do, likely a spontaneous outcome of mentally going through options and satisficing until a result was found.

Now, insist the meal be three courses. The answer will take longer to arrive at, and will now include acquiring additional information and external planning. Additionally add that you invited 2-5 friends, but all of them are flaky and may or may not come. Unless you have an extremely patient friend, it is at this point that you will likely face a demand for additional constraints to make the process of arriving at a workable and possible course of actions feasible.

What’s Selection Force/Selectivity? (“Selektionskraft”)

Luhmann dubs the compound force that reduces the infinite amount of choice in the environment to an actionable amount the “selection force” (or Selektionskraft), as in: It’s the strength of the process of selection (of the possible actions you could take) that can vary, and so the amount of complexity you can deal with inside the system also varies.

A constraint on your actions increases the selection force, because a whole dimension of complexity now has a fixed value. The space of possible solutions just got smaller.

This is separated out from the notion of “complexity”, because the complexity in the environment does not vanish when the systems becomes more sophisticated, it just shifts. As a system becomes more complex to address more complex problems, its problems also shift: From being overwhelmed by the environment and not being able to cope with the demands placed upon it from the outside, to straining under its own complexity and size.

Complexity can’t be reduced globally, but you can choose where it goes. Complex problems demand complex systems to address them, complex systems demand high levels of coordination and resources. A complex system made to solve a complex problem directs its selection force to solving the problem, and puts the complexity outside of that path. Aviation achieves safety in highly complex domains by shifting complexity to verification, maintenance, construction and training, allowing for the complex problem of operations to be solved well and extensively.

This is the point where Luhmann’s model goes from an interesting framing of complexity and systems to a model capable of generating new insights: Much of all human behaviour manipulates selection force in some form or shape, but most of the time, interpersonal social dynamics are the chief way that is done.

Luhmann generalises this to the very specific and not-at-all overloaded term Influence. Remember that in this model, all complexity and all selection force is viewed from the point of a single actor. You can aggregate this to the system level, but practically, the thresholds for what is actionable and what is unactionable amounts of complexity happen on the actor level.

What’s Influence?

Influence is therefore also seen on the actor level, as communication between two people. One person can make sensible decisions over more dimensions of input if another person processes some of them, makes partial or complete selections, and then passes that on. The first person can then choose to either accept or discard the selection performed by someone else. If they accept the decisions made, Luhmann terms that to be accepting the influence of someone else.

This happens in a great many situations and circumstances. Most straight-forward, your boss tells you to research something, you come back with constraints. This is first your boss influencing you (by constraining your next action), then you influencing your boss (by choosing what constraints to report on, how to present them, etc).

Specifically, this happens all the time and is impossible to avoid. The explanation and definition chosen by Luhmann is deliberately a very nuts-and-bolts framing, applicable to basically all situations that involves at least one person that has encountered another person at some point in their life.

The generalisation of influence is the ability to have an expectation of someone influencing you, that you can expect someone to perform selection for you. With this expectation, single actors in complex systems become able to collectively solve complex problems. When you go to work, you expect, in some sense, that your boss is going to point you in a certain direction for what you will do. How this fits into the larger picture is your boss’ job to worry about, you rely on his influence on you to do your job to the expectations that have been communicated to you.

Why Is This Model Useful?

By framing it around dimensions of input to a system and interpersonal influence, actions are seen inside the system around the influence they create, i.e., for whom they perform selection. The amorphous social phenomenon known as power7 is also a generalisation of influence, people with more power in a system have others in the system accept their decisions and selections more often. Computers can be said to have influence on actors by framing events, omitting or including information, causing the actor to choose one action over another.

This definition of complexity also provides a quite simple answer to the “I could build that in a weekend!”8 phenomenon programmers are prone to experiencing, namely: Those companies often choose to accept substantially more dimensions of input than those saying that can see, in choosing to accept that complexity into the system, it now has to be accounted for and actions have to be differentiated.

In general, it allows us to rephrase the question of “Why would they do that?” to “Who are they accepting, or not accepting, decisions from, and for what reason?” that accepts and builds on top of the local rationality principle9, while also centreing the social dynamics.

Looking for where and how selection of possibilities is performed, where possibilities are eliminated, and where and how that is propagated to other people shines light on the underlying social processes in complex systems.

  1. Luhmann, N. (2012, written est. 1960). Macht Im System. Suhrkamp. 

  2. Okay, three. He also created and used an impressive version of the Zettelkasten, though at this point I consider it to be a distraction from his much more interesting sociology work. Contrary to now-popular belief, he didn’t do anything close to inventing it (see history of the term here.), but he was the subject of Ahrens’ book How To Take Smart Notes

  3. This is a not inconsiderable part of why he remained relatively unknown in the English-speaking world, his prose is extremely concise and very hard to translate without losing meaning. He was an incredibly prolific author with 70 books and more than 400 articles, and (a small) part of how he was that prolific is not using a ton of words to make his points. 

  4. For all the fun that comes with that loose definition of a system, see also Bowker, G., & Star, S. L. (2000). Sorting Things Out: Classification and Its Consequences. Universal: MIT Press. 

  5. This leads to a further Luhmann idea, the one of differentiation: Separating/recognising different objects as different in what they are, how they behave, and so on is the first step to treating them differently. Complexity in the world increases whenever someone differentiates one thing from another thing in some dimension, because now that difference has to be accounted for in behaviour. 

  6. A subsequent application of the Law Of Stretched Systems points towards systems always sitting right on the cusp of being paralysed by complexity, and any increases in complexity-management being instantly eaten up by additional complexity being added to the system. 

  7. The actual subject of Macht Im System (literally translated, “Power In The System”) for which this model is used as underpinning. More posts to come on that topic, nuts and bolts for now. 

  8. The subject of a rather good essay by Dan Luu, about programmers wondering why some tech companies with simple products employ so many programmers. 

  9. A longer definition with some actionable citations can be found here, but it can be abbreviated to something as short as “people make decisions based on what they know and can see, which is not and can’t be the whole of the system, leading them to make decisions that can seem nonsensical when seeing the whole of the system in hindsight”.