Abstract
Weak emergence is the view that a system’s macro properties can be explained by its micro properties but only in an especially complicated way. This paper explains a version of weak emergence based on the notion of explanatory incompressibility and “crawling the causal web.” Then it examines three reasons why weak emergence might be thought to be just in the mind. The first reason is based on contrasting mere epistemological emergence with a form of ontological emergence that involves irreducible downward causation. The second reason is based on the idea that attributions of emergence are always a reflection of our ignorance of non-emergent explanations. The third reason is based on the charge that complex explanations are anthropocentric. Rather than being just in the mind, weak emergence is seen to involve a distinctive kind of complex, macro-pattern in the mind-independent objective micro-causal structure that exists in nature. The paper ends by addressing two further questions. One concerns whether weak emergence applies only or mainly to computer simulations and computational systems. The other concerns the respect in which weak emergence is dynamic rather than static.
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Some who claim that weak emergence is just in the mind view this in a positive light (e.g., Newmann 1996 and McIntyre 1998), on the grounds that if weak emergence is just in the mind, then it surely exists and is real, and it has no objectionable metaphysical baggage. In this paper I am arguing that weak emergence is not just in the mind; it is real and objective in nature. Specifically, it is a certain kind of complex micro-causal network that has unpredictable macro effects. In addition, this kind of weak emergence has no metaphysical baggage. Each instance of weak emergence is entirely consistent with any reasonable form of naturalism.
The different variants of weak emergence in the literature include those by Wimsatt (1986, 1997, 2000), Rueger (2000a, b), Boogerd et al. (2005), and Bedau (1997, 2003). Different conceptions of weak emergence focus on different kinds of explanatory complexity, but all agree that weak emergence involves some distinctive kind of explanatory complexity.
See, e.g., Simon (1996).
Unfortunately, the phrase “weak emergence” is used in different ways by different philosophers. For example, what I call “weak emergence” differs from what is called “weak emergence” by Stephan (2006) or by K. Balog (personal communication), and it does not apply to typical Braitenberg’s vehicles (an example of weak emergence from A. Beckerman, personal communication).
See Bedau (1997, 2003). I should note that my earlier use of the word “derivation” to define weak emergence does not imply any connection with, or dependence on, a Hempelian Deductive-Nomological account of explanation, which is famous for its central role for derivations (Hempel 1965). Rather, I am content with a much more loose and informal notion of explanation. Nevertheless, explanation and deduction still use and rely on various derivations.
The explanation can apply to indeterministic systems by including complete information about the system’s inderministic micro-state changes in the information from which the explanation is sought. System boundary conditions are handled in a similar fashion. See Bedau (1997). My notion of incompressible explanation is closely connected with Chaitin’s notion of random sequence (Chaitin 1975, 1988) and Wolfram’s subsequent notion of incompressible computation (Wolfram 1985, 2002), as well as the notion of a dynamical system that must be simulated to discover its generic properties (Crutchfield et al. 1986).
E.g., Bedau (1997) reviews the supple adaptation to the edge of disorder that emerges from Packard’s Bugs model.
See, also, Bedau (2003, p. 163).
In this paper I will not take a stand on how to measure amounts of weak emergence. The most precise and explicit formal definition of amounts of weak emergence known to me is due to Paul Hovda (2008). Hovda defines the amount of simulation effort needed to derive something. This formalism could be interpreted as the amount of effort required for something’s generative explanation.
It should be noted that in many cases we still do not know how to explain some natural regularities or patterns we seem to see in nature. One good example is the arrow of complexity in the evolution of life on Earth (Bedau, forthcoming).
The computer-generated proof of the four colors theorem is one specific kind of example of a proof that one could work through in principle but not in practice.
See, e.g., Stanley (1971).
Bedau (2003) elaborates this claim.
I might mention that the indirectness itself of these definitions of weak emergence does not make the definitions dubious. Indirect definitions can still be perfectly useful and accurate.
By “naturalistic epistemic agent” I mean one with no magical abilities, such as an infinite amount of storage space.
See also Humphreys’s (2007a) discussion of computational emergence.
Different kinds of computational systems have been called “simulations” so I should clarify what I mean. The simulations I have in mind are those that crawl the causal web (recall above) and generate global properties out of myriad local interactions. In addition to cellular automata, so-called “agent-based” models are good examples of simulations that crawl the causal web. They explicitly describe how local causal processes unfold over time, and global properties are merely certain kinds of aggregations of local properties.
A tangential issue arises here: How can we tell if a computer model corresponds to reality, especially if the model is much simpler than the natural system being studied? This complex issue is beyond the scope of this footnote, but I would like to mention one point—that some computer simulations aim to explain only certain very general and robust global patterns and regularities in the behavior of certain complex systems. They do not attempt to explain the system’s detailed behavior. Further, sometimes a complex system’s robust global patterns and regularities are due to relatively simple and abstract features of the system; many of the details about the system do not materially affect its robust global behavior. In these cases, a very simple and abstract model can adequately explain the system’s robust behavior.
One example might be Shelling’s famous simple models of social phenomena such as segregated neighborhoods (Schelling 1968). These models abstract away from almost all the details of actual social neighborhoods. But they preserve certain key property—such as each agent’s awareness of the social class of its local neighbours and itself, and its preference for local neighbors of the same social class—and the models explain how global segregation can result merely from those simple facts. Furthermore, you can empirically test whether people actually do know the social classes of their immediate neighborhoods, so even simple models can be empirically grounded.
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Acknowledgement
I am grateful for helpful comments from Kellyn Bardeen, Paul Humphreys, and Philippe Huneman.
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Bedau, M.A. Is Weak Emergence Just in the Mind?. Minds & Machines 18, 443–459 (2008). https://doi.org/10.1007/s11023-008-9122-6
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DOI: https://doi.org/10.1007/s11023-008-9122-6