Systems theory

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Organisational systems can be simple, complicated or complex. Best you know which one yours is. Differentiate between the type of system and how to manage that system. So:

System	System characteristics	Management techniques
Simple system	Static process. Requires a series of steps. No interaction. Little if/then logic. Fixable (and best handled) by algorithm.	Manageable by unskilled application of algorithm. Suitable for machine production.
Complicated system	Bounded interactive process: Involves interaction with an autonomous/uncontrollable agent but boundaries are fixed, and rules of engagement are static fully specified, known to all participants. All relevant information is available to all participants.	Manageable by skilled application of algorithm. Suitable for autonomous operation by subject matter expert.
Complex system	Unbounded, interactive process. Involves interaction with automonous agents without boundaries, without pre-agreed rules, and where information is limited and asymmetric. Rules, boundaries and each participant’s objectives are dynamic and change interactively. Impossible to predict.	Requires expertise, experience, autonomy, imaginative adaptability, heuristics, and the ability to make, adjust and reject provisional conclusions as information changes.

Simple systems

Simple systems: simple systems are situations where essentially inanimate objects interact with each other in ways that are fully understood. Lego is a simple system. So is a cake recipe, or a bungee jump. The components of a simple system don’t fight back. Simple systems are therefore predictable. They can only go wrong if components fail or you don’t follow instructions. In either case they fail in predictable ways. As such, simple systems are suitable for checklists, recipes etc, where algorithms can overcome the hubris that will surely rain down on the heads of those who treat simple processes as trivial. Disinfecting your instruments before performing heart surgery, for example, is a simple step to take, but not a trivial one.

Examples

Common simple systems and what happens when they go wrong:

• A cake recipe: cake doesn’t rise.
• (theoretically) A negotiation playbook, with comprehensive escalation procedures (it’s really a disguised complicated system)

Complicated systems

Complicated systems require interaction with autonomous agents whose specific behaviour is beyond the observer’s control, and might be intended to defeat the observer’s objective, but whose range of behaviour is deterministic, rule-bound and known and can be predicted in advance, and where the observer’s observing behaviour does not itself interfere with the essential equilibrium of the system.

You know you have a complicated system when it cleaves to a comprehensive set of axioms and rules, and thus it is a matter of making sure that the proper models are being used for the situation at hand. Chess and Alpha Go are complicated, but not complex, systems. So are most sports. You can “force-solve” them, at least in theory.

Complicated systems benefit from skilled management and some expertise to operate: a good chess player will do better than a poor one, and clearly a skilled, fit footballer can execute a plan better than a wheezy novice — but in the right hands and given good instructions even a mediocre player can usually manage without catastrophe. While success will be partly a function of user’s skill and expertise, a bad player with a good plan may defeat a skilled player with a bad one.

Given enough processing power, complicated systems are predictable, determinative and calculable. They’re tame, not wicked problems.

Examples

Common complicated systems and what happens when they go wrong:

• Music performance: flying rotten cabbages; no encore.
• Chess, Go, Poker, Bridge: other guy wins.

Complex systems

Complex systems present as “wicked problems”. They are dynamic, unbounded, incomplete, contradictory and constantly changing. They comprise an indefinite set of subcomponents that interact with each other and the environment in unexpected, non-linear ways. They are thus unpredictable, chaotic and “insoluble” — no algorithm can predict how they will behave in all circumstances. Probabilistic models may work passably well most of the time, but the times where statistical models fail may be exactly the times you really wish they didn’t, as Long Term Capital Management would tell you. Complex systems may comprise many other simple, complicated and indeed complex systems, but their interaction with each other will be a whole other thing. So while you may manage the simple and complicated sub-systems effectively with algorithms, checklists, and playbooks — and may manage tthe system on normal times, you remain at risk to “tail events” in abnormal circumstances. You cannot eliminate this risk: accidents in complex systems are inevitable — hence “normal”, in Charles Perrow’s argot. However well you manage a complex system it remains innately unpredictable.

Examples

Common complex systems and what happens when they go wrong:

• Nuclear power plant: Chernobyl, Three Mile Island and Fukushima
• The environment (and any naturally selecting ecosystem, really): Covid-19; global warming
• Air traffic control system: Name your air crash but two classics are Air New Zealand’s Mount Erebus disaster and ValuJet 592 This is a fantastic article about the latter.
• Financial markets: Take your pick: LTCM, Enron, Global Financial Crisis
• The world wide web:

See also

• Complexity