Systems & Complexity

When multiple cognitive biases or forces act in the same direction simultaneously, the combined effect isn't additive — it's multiplicative. Recognising these confluences is essential for predicting extreme outcomes.

Beyond resilience — some systems actually gain from disorder, volatility, and stress, growing stronger from the very shocks that destroy the fragile.

Complex system-level properties that arise from simple interactions between individual components but cannot be predicted from those components alone.

Circular causal chains where the output of a system feeds back as input — either amplifying change (reinforcing) or stabilizing the system (balancing).

A complex system that works is invariably found to have evolved from a simple system that worked — you cannot design complexity from scratch.

Places within a complex system where a small shift produces large changes — Meadows 12 leverage points from shallow parameters to deep paradigms.

Building redundancy, slack, and buffers into any system so it can absorb unexpected shocks — systems optimised for efficiency are structurally fragile.

The capacity of a system to absorb disturbance and reorganise while maintaining essential function — not bouncing back to the same state, but adapting forward.

Interventions produce unintended downstream consequences that often dominate the intended first-order outcome — the consequences of consequences.

Every system is composed of stocks (accumulations) and flows (rates of change) — most people fixate on flows when stocks determine system behaviour.

Every system has exactly one constraint at any given time — optimising anything other than the constraint is an illusion of progress.

Force multipliers that allow disproportionate output from a given input — labor, capital, and code/media as the three forms of leverage.