Laws, causation and dynamics at different levels

Butterfield, Jeremy

doi:10.1098/rsfs.2011.0052

Cited by 39 publications

(34 citation statements)

References 34 publications

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“…First, the simulated examples are such that the macro supervenes strictly upon the micro: once the micro is defined, all macro levels are fixed. Specifically, no extra causal ingredients are added at the macro level, such as rules that apply to the macro only (24). Furthermore, the mapping of micro into macro elements is such that the identity of micro elements is lost; otherwise, the macro level would have access to micro-level information that could offset its reduced repertoire size.…”

Section: Discussionmentioning

confidence: 99%

Quantifying causal emergence shows that macro can beat micro

Hoel

Albantakis

Tononi

2013

Proc. Natl. Acad. Sci. U.S.A.

240

307

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Causal interactions within complex systems can be analyzed at multiple spatial and temporal scales. For example, the brain can be analyzed at the level of neurons, neuronal groups, and areas, over tens, hundreds, or thousands of milliseconds. It is widely assumed that, once a micro level is fixed, macro levels are fixed too, a relation called supervenience. It is also assumed that, although macro descriptions may be convenient, only the micro level is causally complete, because it includes every detail, thus leaving no room for causation at the macro level. However, this assumption can only be evaluated under a proper measure of causation. Here, we use a measure [effective information (EI)] that depends on both the effectiveness of a system's mechanisms and the size of its state space: EI is higher the more the mechanisms constrain the system's possible past and future states. By measuring EI at micro and macro levels in simple systems whose micro mechanisms are fixed, we show that for certain causal architectures EI can peak at a macro level in space and/or time. This happens when coarse-grained macro mechanisms are more effective (more deterministic and/or less degenerate) than the underlying micro mechanisms, to an extent that overcomes the smaller state space. Thus, although the macro level supervenes upon the micro, it can supersede it causally, leading to genuine causal emergence-the gain in EI when moving from a micro to a macro level of analysis.I n science, it is usually assumed that, the better one can characterize the detailed causal mechanisms of a complex system, the more one can understand how the system works. At times, it may be convenient to resort to a "macro"-level description, either because not all of the "micro"-level data are available, or because a rough model may suffice for one's purposes. However, a complete understanding of how a system functions, and the ability to predict its behavior precisely, would seem to require the full knowledge of causal interactions at the micro level. For example, the brain can be characterized at a macro scale of brain regions and pathways, a meso scale of local populations of neurons such as minicolumns and their connectivity, and a micro scale of neurons and their synapses (1). With the goal of a complete mechanistic understanding of the brain, ambitious programs have been launched with the aim of modeling its micro scale (2).The reductionist approach common in science has been successful not only in practice, but has also been supported by strong theoretical arguments. The chief argument starts from the intuitive notion that, when the properties of micro-level physical mechanisms of a system are fixed, so are the properties of all its macro levels-a relation called "supervenience" (3). In turn, this relation is usually taken to imply that the micro mechanisms do all of the causal work, i.e., the micro level is causally complete. This leaves no room for any causal contribution at the macro level; otherwise, there would be "multiple causation" (4). This ...

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Section: Discussionmentioning

confidence: 99%

Quantifying causal emergence shows that macro can beat micro

Hoel

Albantakis

Tononi

2013

Proc. Natl. Acad. Sci. U.S.A.

240

307

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“…The construction of statistical neural states is strikingly analogous to what leads Butterfield [30] to the notion of 'meshing dynamics'. In his terminology, L-dynamics and M-dynamics mesh if coarse graining and time evolution commute.…”

Section: Theoretical Approachmentioning

confidence: 94%

“…G(m) lead to the same result. Compare the commutativity of coarse graining and time evolution in Butterfield [30].…”

Section: Appendix a Generating Partitions And Topological Equivalencementioning

confidence: 99%

Identifying mental states from neural states under mental constraints

Atmanspacher

2011

Interface Focus.

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This article emphasizes how the recently proposed interlevel relation of contextual emergence for scientific descriptions combines 'bottom-up' and 'top-down' kinds of influence. As emergent behaviour arises from features pertaining to lower level descriptions, there is a clear bottom-up component. But, in general, this is not sufficient to formulate interlevel relations stringently. Higher level contextual constraints are needed to equip the lower level description with those details appropriate for the desired higher level description to emerge. These contextual constraints yield some kind of 'downward confinement', a term that avoids the sometimes misleading notion of 'downward causation'. This will be illustrated for the example of relations between (lower level) neural states and (higher level) mental states.

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“…for example, [38][39][40]). As a logical category, an emergent property can be defined as a property that is "novel and robust relative to some natural comparison class" [41]. Less rigorously stated, the term "emergence is broadly used to assign certain properties to features we observe in nature that have certain dependence on more basic phenomena (and/or elements), but are in some way independent from them and ultimately cannot be reduced to those other basic interactions between the basic elements" [42].…”

Section: Definition 1 Symbolic Information Is Information That Is Rementioning

confidence: 99%

Structural and Symbolic Information in the Context of the General Theory of Information

Burgin

Feistel

2017

Information

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Abstract:The general theory of information, which includes syntactic, semantic, pragmatic, and many other special theories of information, provides theoretical and practical tools for discerning a very large diversity of different kinds, types, and classes of information. Some of these kinds, types, and classes are more important and some are less important. Two basic classes are formed by structural and symbolic information. While structural information is intrinsically imbedded in the structure of the corresponding object or domain, symbolic information is represented by symbols, the meaning of which is subject to arbitrary conventions between people. As a result, symbolic information exists only in the context of life, including technical and theoretical constructs created by humans. Structural information is related to any objects, systems, and processes regardless of the existence or presence of life. In this paper, properties of structural and symbolic information are explored in the formal framework of the general theory of information developed by Burgin because this theory offers more powerful instruments for this inquiry. Structural information is further differentiated into inherent, descriptive, and constructive types. Properties of correctness and uniqueness of these types are investigated. In addition, predictive power of symbolic information accumulated in the course of natural evolution is considered. The phenomenon of ritualization is described as a general transition process from structural to symbolic information.

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Laws, causation and dynamics at different levels

Cited by 39 publications

References 34 publications

Quantifying causal emergence shows that macro can beat micro

Quantifying causal emergence shows that macro can beat micro

Identifying mental states from neural states under mental constraints

Structural and Symbolic Information in the Context of the General Theory of Information

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