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Emergence Made Ontological? Computational versus Combinatorial Approaches

Published online by Cambridge University Press:  01 January 2022

Philippe Huneman
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Abstract

I challenge the usual approach of defining emergence in terms of properties of wholes “emerging” upon properties of parts. This approach indeed fails to meet the requirement of nontriviality, since it renders a bunch of ordinary properties emergent; however, by defining emergence as the incompressibility of a simulation process, we have an objective meaning of emergence because the difference between the processes satisfying the incompressibility criterion and the other processes does not depend on our cognitive abilities. Finally, this definition fulfills the nontriviality and the scientific-adequacy requirements better than the combinatorial approach, emergence here being a predicate of processes rather than of properties.

Type
Computational Emergence and Its Applications
Information
Philosophy of Science , Volume 75 , Issue 5: Proceedings of the 2006 Biennial Meeting of the Philosophy of Science Association. Part II: Symposia Papers. Edited by Cristina Bicchieri and Jason Alexander , December 2008 , pp. 595 - 607
Copyright
Copyright © The Philosophy of Science Association

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Footnotes

‡

I warmly thank Paul Humphreys and John Sumons for their precious comments on an earlier version of this article.

References

Atay, F., and Jost, J. (2004), “On the Emergence of Complex Systems on the Basis of the Coordination of Complex Behaviours of Their Elements”, On the Emergence of Complex Systems on the Basis of the Coordination of Complex Behaviours of Their Elements 10 (1): 17–22..Google Scholar
Bar Yam, Y. (2004), “A Mathematical Theory of Strong Emergence Using Multiscale Variety”, A Mathematical Theory of Strong Emergence Using Multiscale Variety 9 (6): 15–24..Google Scholar
Bechtel, W., and Richardson, R. (1992), “Emergent Phenomena and Complex Systems”, in Beckermann, A., Flohr, H., and Kim, J. (eds.), Emergence or Reduction? Berlin: de Gruyter, 257–287.Google Scholar
Bedau, M. (1997), “Weak Emergence”, in Tomberlin, James (ed.), Philosophical Perspectives: Mind, Causation, and World, Vol. 11. Oxford: Blackwell, 375–399.Google Scholar
Burke, M., Furnier, G., and Prasad, K. (2006), “The Emergence of Local Norms in Networks”, The Emergence of Local Norms in Networks 11 (5): 65–83..Google Scholar
Buss, S., Papadimitriou, C., and Tsisiklis, J. (1992), “On the Predictability of Coupled Automata: An Allegory about Chaos”, On the Predictability of Coupled Automata: An Allegory about Chaos 5:525–539.Google Scholar
Chalmers, D. (2006), “Strong and Weak Emergence”, in Clayton, P. and Davies, P. (eds.), The Re-emergence of Emergence. Oxford: Oxford University Press, 244–256.Google Scholar
Crane, T. (2001), “The Significance of Emergence”, in Gillett, C. and Loewer, B. (eds.), Physicalism and Its Discontents. Cambridge: Cambridge University Press, 207–224.CrossRefGoogle Scholar
Crutchfield, J., and Hanson, J. (1993), “Turbulent Pattern Bases for Cellular Automata”, Turbulent Pattern Bases for Cellular Automata 69:279–301.Google Scholar
Crutchfield, J., and Shalizi, C. (2001), “Pattern Discovery and Computational Mechanics”, arXiv:cs/0001027v1.Google Scholar
Dennett, D. (1991), “Real Patterns”, Real Patterns 88 (1): 27–51..Google Scholar
Dessalles, J. L., and Phan, D. (2005), “Emergence in Multi-agent Systems: Cognitive Hierarchy, Detection, and Complexity Reduction”, in Mathieu, P., Beaufils, B., and Brandouy, O. (eds.), Artificial Economics. Lecture Notes in Economics and Mathematical Systems 564. Berlin and New York: Springer, 147–159.Google Scholar
Epstein, J. ([1999] 2007), “Agent-Based Computational Models and Generative Social Science”, in Generative Social Science: Studies in Agent-Based Computational Modeling. Princeton, NJ: Princeton University Press, 4–46.Google Scholar
Gilbert, N. (2002), “Varieties of Emergence”. Paper presented at the Social Agents: Ecology, Exchange, and Evolution Conference, Chicago (http://www.soc.surrey.ac.uk/staff/ngilbert/ngpub/paper148_NG.pdf).Google Scholar
Hanson, J., and Crutchfield, J. (1997), “Computational Mechanics of Cellular Automata: An Example”, Computational Mechanics of Cellular Automata: An Example 103:169–189.Google Scholar
Hordijk, W., Crutchfield, J., and Mitchell, M. (1996), “Embedded Particle Computation in Evolved Cellular Automata”, in Toffoli, T., Biafore, M., and Leao, J. (eds.), PhysComp'96. Cambridge, MA: New England Complex Systems Institute, 153–158.Google Scholar
Humphreys, P. (1997), “How Properties Emerge”, How Properties Emerge 64:53–70.Google Scholar
Humphreys, P. (2004), Extending Ourselves. New York: Oxford University Press.CrossRefGoogle Scholar
Humphreys, P. (2008), “Synchronic and Diachronic Emergence”, in Huneman and Humphreys 2008, 431–442.Google Scholar
Huneman, P. (2008), “Emergence and Adaptation”, in Huneman and Humphreys 2008, 493–520.Google Scholar
Huneman, P., and Humphreys, P., eds. (2008), “Special Section on Dynamic Emergence and Computation”, special issue, Minds and Machines, vol. 4.Google Scholar
Klee, R. (1984), “Microdetermnisms and Concepts of Emergence”, Microdetermnisms and Concepts of Emergence 51:44–63.Google Scholar
Nagel, K., and Rasmussen, K. (1994), “Traffic at the Edge of Chaos”, in Brooks, R. (ed.), Artificial Life IV. Cambridge, MA: MIT Press.Google Scholar
Newman, D. (1996), “Emergence and Strange Attractors”, Emergence and Strange Attractors 63:245–261Google Scholar
O’Connor, T. (1994), “Emergent Properties”, Emergent Properties 31:91–104.Google Scholar
Schelling, T. (1969), “Models of Segregation”, Models of Segregation 59 (2): 488–493..Google Scholar
Seager, W. (2005), “Emergence and Efficacy”, in Erneling, C. and Johnson, D. (eds.), The Mind as a Scientific Object between Brain and Culture. Oxford: Oxford University Press, 176–192.Google Scholar
Shalizi, C., Haslinger, R., Rouquier, J. B., Klinkner, C., and Moore, C. (2006), “Automatic Filters for the Detection of Coherent Structures in Spatiotemporal Systems,” ArXiv CG/0508001.CrossRefGoogle Scholar
Silberstein, M. (2002), “Reduction, Emergence and Explanation”, in Silberstein, M. and Machamer, P. (eds.), Blackwell Guide to the Philosophy of Science. Oxford: Blackwell, 80–107.Google Scholar
Silberstein, M., and McGeever, J. (1995), “The Search for Ontological Emergence”, The Search for Ontological Emergence 49:182–200.Google Scholar
Tassier, T. (2004), “A Model of Fads, Fashions and Group Formations”, A Model of Fads, Fashions and Group Formations 9 (5): 51–61.Google Scholar
Wilson, J. (forthcoming), “Non-reductive Physicalism and Degrees of Freedom”, British Journal for Philosophy of Science.Google Scholar
Wimsatt, W. (1997), “Aggregation: Reductive Heuristics for Finding Emergence”, Aggregation: Reductive Heuristics for Finding Emergence 64 (Proceedings): S372–S384.Google Scholar
Wimsatt, W. (2007), “Emergence as Non-aggregativity and the Biases of Reductionisms”, in Re-engineering Philosophy for Limited Beings: Piecewise Approximations to Reality. Cambridge, MA: Harvard University Press, 274.CrossRefGoogle Scholar
Wolfram, S. (1984), “Universality and Complexity in Cellular Automata”, Universality and Complexity in Cellular Automata 10:1–35.Google Scholar