Proximate and Ultimate Causes
In The You On AI Encyclopedia
The distinction emerged from Mayr's recognition that biology differs categorically from physics. Physics operates with one kind of causation: a hydrogen atom in the Andromeda galaxy obeys the same laws as a hydrogen atom in Cambridge. There is no historical contingency. The physicist never asks why this particular hydrogen atom behaves this way, because the answer is always the same. Biology is different. A biologist asking why the Arctic fox has white fur cannot answer by citing the physics of pigmentation. The physics explains how the fur is white — the absence of melanin, the scattering of light. It does not explain why the fur is white, which requires the specific evolutionary history of a species selected for camouflage in snow across thousands of generations.
Mayr's framework applies to the AI discourse with force its author could not have anticipated. The question that dominates the current conversation — Is Claude intelligent? — is a proximate question wearing the clothing of an ultimate one. The person asking usually wants to know whether the system reasons, understands, processes information in ways that resemble cognition. These are proximate questions about mechanism, investigable through interpretability research and behavioral testing. But the word intelligent smuggles in a vast evolutionary history — the history of a trait that evolved in a specific lineage, under specific selection pressures, over a specific span of time.
The conflation is not accidental. It is seductive because the proximate similarity is genuine — both humans and language models produce coherent text. But the ultimate causes differ entirely. The human was selected for survival in a competitive ecology across millions of years. The machine was engineered to minimize a loss function on a training dataset across months. Treating proximate similarity as evidence of ultimate equivalence is precisely the error Mayr spent his career identifying.
The practical consequence is methodological. Questions like does the system understand? or is the system conscious? cannot be answered by examining behavior alone, because behavior is the proximate manifestation, and identical behaviors can have radically different ultimate causes. The Arctic fox and a white-painted decoy fox produce the same proximate observation. Only the ultimate cause — selection versus paint — predicts behavior under novel conditions.
Origin
Mayr developed the distinction during the 1950s, as the Modern Synthesis consolidated and the intellectual ambitions of molecular biology began pressing outward from their proper domain. Physicists — Erwin Schrödinger among them — had begun writing about biology as if it were applied physics, and the reductionist program threatened to absorb evolutionary biology into mechanism. Mayr's 1961 paper was, in part, a defense of biology's explanatory autonomy.
The distinction built on earlier work by ethologist Niko Tinbergen, whose four questions (mechanism, ontogeny, adaptive value, phylogeny) offered a more elaborate version of the same insight. Mayr's formulation proved more portable, precisely because its binary simplicity forced the reader to recognize that every biological question is secretly two questions, and answering one is not answering the other.
Key Ideas
Two questions, not one. Every biological trait admits both a proximate explanation (how it works) and an ultimate explanation (why it exists). Neither reduces to the other.
Physics has only one causation. Non-living systems lack history; their present state is fully specified by universal laws operating on current conditions. Living systems have histories that universal laws alone cannot derive.
Ultimate causes are narrative. The explanation for why a species has a given trait is a story — a specific, unrepeatable sequence of selection events, environmental encounters, and contingent accidents.
Smuggled ultimates. When the AI discourse asks whether a machine is intelligent, the word carries an ultimate commitment that the proximate investigation cannot discharge. The question is malformed until the two levels are separated.
Same behavior, different cause. Proximate similarity does not entail ultimate equivalence. The real fox and the painted decoy both register as white; only one will molt in spring.
Debates & Critiques
Some philosophers of biology — including Samir Okasha and Kim Sterelny — have argued that Mayr's binary is too coarse, and that Tinbergen's four-question framework better captures the range of legitimate biological explanation. Others have defended Mayr's formulation as precisely the right level of abstraction for preventing category errors. The debate continues, but the core insight — that biological phenomena require explanations that physics alone cannot provide — has survived five decades of scrutiny and now underwrites virtually every serious philosophical engagement with artificial intelligence.
Further Reading
- Ernst Mayr, Cause and Effect in Biology (Science, 1961)
- Ernst Mayr, What Makes Biology Unique? (Cambridge University Press, 2004)
- Niko Tinbergen, On Aims and Methods of Ethology (Zeitschrift für Tierpsychologie, 1963)
- Samir Okasha, Agents and Goals in Evolution (Oxford University Press, 2018)
- Kim Sterelny and Paul Griffiths, Sex and Death: An Introduction to Philosophy of Biology (University of Chicago Press, 1999)