Power emergentism and the collapse problem

Strong emergentism is the position that certain higher-level properties display a kind of metaphysical autonomy from the lower-level properties in which they are grounded. The prospect of collapse is a problem for strong emergentism. According to those who press the collapse problem any purportedly strongly emergent feature inheres in the emergence base, and so is not genuinely autonomous from that base. Recently, Umut Baysan and Jessica Wilson have argued that power emergentism avoids the collapse problem. In this paper, I challenge the claim that power emergentism avoids the collapse problem, and argue for explanatory emergentism in its place.

Emergent Causal Laws and Physical Laws

Contrasting accounts of physicalism and strong emergentism face two problems. According to the neutrality problem, contrasting supervenience-based formulations of these positions cannot be neutral with respect to certain unrelated metaphysical commitments. According to the collapse problem, emergent properties can be accounted for using an appropriately expansive physical ontology, rendering strong emergentism metaphysically suspect. I argue that both these problems can be solved with a principled distinction between emergent causal laws and physical laws. I propose such a distinction based on a finite discontinuity in the behavior of fundamental physical constituents as a function of complexity.

Buzzsaws and blueprints: Commentary

The review article by Sabbagh & Gelman (S & G) on The emergence of language (EL) mentions several criticisms of strong emergentism, the view that language emerges through an interaction between domain-general learning mechanisms and the environment, without crediting the organism with innate knowledge of domain-specific rules, a view that successful connectionist modelling is taken to support. One criticism of this view and the support for it that connectionist modelling putatively provides has been made frequently, and is noted by S & G: it is arguable that connectionist simulations work only because the input to the network in effect contains a representation of the knowledge that the net seeks to acquire. I think it is worth adding to this another criticism that to my mind is a fundamental one, but which has not featured so strongly in critiques of connectionism. A primary goal of modern linguistics has been to account not merely for what patterns we do see in human languages, but for those that we do not. The concept of Universal Grammar is precisely a set of limitations on what constitutes a possible human language. The kind of example used in teaching Linguistics 101 is the fact that patterns of grammaticality are structurally, not linearly, determined: in English we form a yes–no question by inverting the subject NP and auxiliary verb, not by inverting the first and second words of the equivalent declarative sentence, or the first and fifth words, or any number of conceivable non-structural operations. Could a connectionist mechanism learn such non-structural operations? Perhaps I have asked the wrong people, but when I have queried researchers doing connectionist modelling, the answer appears to be ‘yes’. If that's the case, then connectionist mechanisms as currently developed do not constitute an explanatory model of human language abilities: they are too powerful.