The Thesis
Human systems are becoming observable
Organisations are not the hierarchies drawn on a chart; they are systems defined by flows of information, patterns of decision-making, incentives and constraints, and delays between action and consequence.
These systems have historically been opaque. Today, they are less so.
Decisions, communications, transactions and workflows increasingly leave observable traces.
For the first time, large-scale human systems can be studied empirically rather than inferred indirectly.
A common structure
Despite their differences, large-scale human systems share a small number of defining features.
They are only partially observable. Decisions are made on delayed and distorted information. Incentives are locally rational but globally misaligned. Outcomes emerge from interactions, not from intent alone.
This applies to organisations trying to execute strategy, energy systems attempting to transition, and governments attempting to coordinate complex outcomes.
“The mechanics evolve, but the underlying dynamics persist.”
Current research directions
The Organisational Manifold
Can organisations be mapped into a common space that reveals their underlying shape? If organisations occupy a common space, can lessons from one transfer meaningfully to another?
AI as a Context Layer
What happens when intelligence becomes ambient and embedded throughout an organisation? Does intelligence begin to move from individuals and applications into the environment in which work takes place?
Organisational Shape
Can hidden organisational structure be inferred from observable behaviour? If so, can organisations be understood and measured in ways that conventional organisation charts and process models cannot capture?
These are research directions rather than conclusions. They are deliberately ambitious and may evolve, converge or prove to have been framed incorrectly as the work develops.
If a system becomes more observable, it becomes more amenable to modelling.
Better models create new possibilities for intervention.
Better interventions may allow systems to behave differently.
Understanding those possibilities, and their limits, is the work of Tillbourne.