Complexity in Eight Days

What the notes do

The notes distinguish a change in a system’s state, a change in its dynamics, and a change in the effective description used to study it (pp. 1–2). They move from spaces, observables and dynamics through coarse-graining, hierarchy and renormalisation, emergence and non-equilibrium, information and prediction, adaptive systems, inadequate descriptions, and revising spaces. Applications include biological, financial and institutional systems.

Choosing a state space, observables, admissible interventions and evolution rules involves modelling judgement (pp. 3–4, §1.1). A coarse-grained description is closed under the stated condition π ∘ F = G ∘ π. Failure of that condition concerns the chosen projection; it does not establish that every microscopic description fails (p. 8, §§2.2–2.3). Whether a model is sufficient depends on its target, horizon and conditions (p. 15, §§4.2–4.3).

The notes distinguish predictive information loss, memory, computational limits and causal inadequacy, rather than treating ‘reductionism fails’ as a general theorem (p. 19, §6.1). Adaptive simulations can explore models, but are not empirical proof (pp. 17–19). Infrastructure interacts with other systems, and whose opportunities count is a question for analysis (p. 28, §8.4). The account of complexity depends partly on the question asked; interaction, feedback, collective organisation and adaptation can make component-wise reasoning insufficient (p. 29, §8.5).

Questions for reading

These are editorial prompts for comparison. They are not claims that the author draws the same conclusions as the linked traditions.

• For a particular prediction or intervention, which observables and time horizon make the model sufficient? What would show that the chosen projection has lost something needed?
• Who chooses the description, and what changes when the purpose or the people whose opportunities count are different?
• How does the account of emergence through coarse-graining compare with other uses of emergence? Which claim is mathematical, and which depends on empirical evidence?

The notes acknowledge modelling choices, purposes and whose opportunities count; they do not offer a sustained critique of boundary judgements. They do not define ‘system’ generally or survey systems thinking and cybernetics as traditions of inquiry.

A comparison with Ashby

In An Introduction to Cybernetics, Ashby examines when grouping states retains determinate transitions (§6/13, pp. 103–105), and distinguishes a system considered as a thing from the observer’s selected variables (§6/14, pp. 106–107). Comparing these passages with Amonya’s closure condition (p. 8, §§2.2–2.3) can clarify what each description preserves and who selects it. This is an editorial comparison, not a claim of influence or priority.

Explore the atlas

These are editorial reading routes, not evidence of influence or agreement.

• Modelling — Choices of description and conditions of model sufficiency (pp. 3–4, §1.1; p. 15, §§4.2–4.3). Editorial reading route, not a claim of influence.
• Boundary — What is included and whose opportunities count (pp. 3–4, §1.1; p. 28, §8.4). Editorial comparison, not a developed general theory of boundaries.
• Observer — Modelling judgements and the purposes of a description (pp. 3–4, §1.1; p. 15, §§4.2–4.3). Editorial comparison; the notes do not develop a cybernetic theory of the observer.
• Emergence — Coarse-graining, closure and limits of reduction (p. 8, §§2.2–2.3; p. 19, §6.1). Editorial reading route, not an asserted historical connection.
• Complexity — Complexity depends partly on the question asked (p. 29, §8.5). A source-specific account, not a universal definition.