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A common strategy for simplifying complex systems involves partitioning them into subsystems whose behaviors are roughly independent of one another at shorter timescales. Dynamic causal models clarify how doing so reveals a system’s nonequilibrium causal relationships. Here I use these models to elucidate the idealizations and abstractions involved in representing a system at a timescale. The models reveal that key features of causal representations—such as which variables are exogenous—may vary with the timescale at which a system is considered. This has implications for debates regarding which systems can be represented causally.
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Stored only when both papers are in the corpus — 13 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
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