Why efficient systems become fragile: the conditions under which robust organization emerges
An evolutionary, agent-based model with physical constraints. It asks whether competition under fluctuation and scarcity keeps the fastest organizations or the most robust ones, and which institutional rules decide the outcome. Results are provisional.
Project facts
| Programme | Systemic transitions |
|---|---|
| Status | Provisional results What the status labels mean |
| Methods | Agent-based and complex-systems modelling |
| Scale | Theory |
| Geography | Abstract; not calibrated to any economy |
The question
In systems facing fluctuation and scarcity, does selection keep the fastest, most power-maximizing organizations or the most robust ones? And which institutional arrangements decide the outcome?
What the figures leave out
Efficiency counts redundancy, buffers, slowness and diversity as costs. In living systems they are what keeps an organism viable, as the biologist Olivier Hamant has argued. This project asks when competition removes them and when it keeps them.
Approach
Agents compete and evolve under explicit thermodynamic constraints. Hypotheses are written down before each round of experiments, and the model is tested on stress scenarios held out from its design. Robustness is measured with tools from viability theory. Failed predictions are reported with the rest.
Scope
Where it stands
Provisional results, described qualitatively and not quoted as findings. The model has run several rounds of experiments and has not been peer reviewed. The next round, on property and collective-choice rules, is designed and not yet run.
The decision it informs
Which safety margins survive competition, and which need rules, pooling or public provision. Whether rules triggered by a shock protect better than permanent restrictions.
INSTRUMENTS · Robustness roadmaps for public and private decision makers.
What the work required
- Agent-based and evolutionary modelling
- Thermodynamic and ecological economics (the maximum power principle)
- Turning biological robustness and viability theory into measurable criteria
- Research governance: pre-registration, published failures, full provenance
How it connects
Builds on
None yet
Working on robustness, resilience or the economics of safety margins?
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