Optimization aims to hold a system in an optimal state

Standard practice across social-ecological systems is to attempt to achieve an “optimal state” of the system and hold it there.

An optimization approach aims to get a system into some particular “optimal state,” and then hold it there. That state, it is believed, will deliver maximum sustained benefit. It is sometimes recognized that the optimal state may vary under different conditions, and the approach is then to find the optimal path for the state of the system. This approach is sometimes referred to as a maximum sustainable yield or optimal sustainable yield paradigm.^[1]

To achieve this outcome, those who attempt to manage the system build…

…models that generally assume (among other unrecognized assumptions) that changes will be incremental and linear (cause-and-effect changes). These models mostly ignore the implications of what might be happening at higher scales and frequently fail to take full account of changes at lower scales.^[2]

The foundational conundrum regarding system optimization, however, is that Systems cannot be held in an optimal state.

Optimization does not work as a best-practice model because this is not how the world works. The systems we live in and depend on are usually configured and reconfigured by extreme events, not average conditions. … And, very importantly, while minor changes are often incremental and linear, the really significant ones are usually lurching and nonlinear.^[3]

Thus: Black Swan events are rare, impactful, and retrospectively predictable.

#systems #resilience

See also:

• Systems cannot be held in an optimal state

• Complex systems are characterized by VUCA

• Complex adaptive systems are unpredictable and non-linear

• Systems of nature tend to follow an adaptive cycle

• Black Swan events are rare, impactful, and retrospectively predictable