Strengthening Weak Emergence
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Strengthening Weak Emergence Nora Berenstain1 Received: 28 April 2019 / Accepted: 14 August 2020 © Springer Nature B.V. 2020
Abstract I offer an improved version of Bedau’s influential (Philos Perspect 11:375–99, 1997) account of weak emergence in light of insights from information theory. Bedau analyzes weak emergence in terms of the non-derivability of a system’s macrostates from its microstates except by simulation. However, non-derivability alone does not guarantee that a system’s macrostates are weakly emergent. Rather, it is non-derivability plus the algorithmic compressibility of the system’s macrostates that makes them weakly emergent. I argue that the resulting information-theoretic picture provides a metaphysical account of weak emergence rather than a merely epistemic one.
1 Introduction Weak emergence provides a potentially useful conceptual framework to make sense of chaotic phenomena and systems whose behaviors can be modeled with chaos theory. Chaos theory, as a branch of mathematics, models dynamical systems whose behaviors demonstrate sensitive dependence on initial conditions. While the behavior of chaotic systems often appears random, it is actually determined by underlying rules and patterns. A chaotic complex system is thus usually understood as a nonlinear system whose path through phase space appears random while depending on deterministic rules or laws. Some examples of systems that may demonstrate chaotic behavior are stock markets (Hsieh 1991), the movement of krill herds (Saremi et al. 2014), weather patterns such as turbulence, cardiac arrhythmias (Oestreicher 2007), the motion of a magnetic pendulum over a plane containing two or more magnets, and the fractal development of landscape features such as rivers and clouds (Lovejoy 1982). Chaos theory is an interdisciplinary area of study, as systems demonstrating chaotic properties span a remarkably wide disciplinary range. The concept of weak emergence is useful for making sense of chaotic phenomena because it aims to capture the relation between the behaviors of complex systems * Nora Berenstain [email protected] 1
Department of Philosophy, University of Tennessee, 801 McClung Tower, Knoxville, TN 37916, USA
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and the foundational physical states from which they arise. It originated as a notion that could tie biological systems to processes of computation and the limits of computability. Bedau (2013) suggests that the discipline of synthetic biology, for instance, can be understood as the practice of engineering weak emergence and that this explains why synthesis is so important for predicting life’s emergent properties. Bedau (2003) has also argued that weak emergence illuminates the notions of complexity invoked in biology and psychology and that it sheds light on the methodologies of modeling approaches such as a neural networks and agent-based models. The models in complexity science are so bound up with presumptions of emergence that Bedau suggests that, “one could fairly call the whole ent
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