Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-22T05:13:57.688Z Has data issue: false hasContentIssue false

A Computational Modeling Strategy for Levels

Published online by Cambridge University Press:  01 January 2022

Abstract

Rather than taking the ontological fundamentality of an ideal microphysics as a starting point, this article sketches an approach to the problem of levels that swaps assumptions about ontology for assumptions about inquiry. These assumptions can be implemented formally via computational modeling techniques that will be described below. It is argued that these models offer a way to save some of our prominent commonsense intuitions concerning levels. This strategy offers a way of exploring the individuation of higher level properties in a systematic and formally constrained manner.

Type
Computational Emergence and Its Applications
Copyright
Copyright © The Philosophy of Science Association

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Berkowitz, S. (1982), An Introduction to Structural Analysis: The Network Approach to Social Research. Toronto: Butterworth.Google Scholar
Cartwright, N. (1983), How the Laws of Physics Lie. Oxford: Clarendon.CrossRefGoogle Scholar
Cartwright, N. (1999), The Dappled World: A Study of the Boundaries of Science. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Freeman, L. (2004), The Development of Social Network Analysis: A Study in the Sociology of Science. Vancouver: Empirical.Google Scholar
Louçã, J., Symons, J., Rodrigues, D., and Morais, A. (2007), “Pattern-Oriented Analysis of Communication Flow: The Case Study of Cicada barbara lusitanica”, paper given at the 21st European Conference on Modelling and Simulation—ECMS 2007, Prague.CrossRefGoogle Scholar
Newman, M. (2000), “Small Worlds: The Structure of Social Networks”, Santa Fe Institute working paper, http://www.santafe.edu/research/publications/wplist/1999.Google Scholar
Putnam, Hilary, and Oppenheim, Paul (1958), “The Unity of Science as a Working Hypothesis”, in Feigl, Herbert, Maxwell, Grover, and Scriven, Max (eds.), Minnesota Studies in the Philosophy of Science 2. Minneapolis: University of Minnesota Press, 336.Google Scholar
Rheingold, H. (2002), Smart Mobs: The Next Social Revolution. Cambridge: Perseus.Google Scholar
Scott, J. (2000), Social Network Analysis: A Handbook. 2nd ed. Newberry Park, CA: Sage.Google Scholar
Spivey, J. M. (2006), The Z Notation: A Reference Manual. Hertfordshire: Prentice Hall.Google Scholar
Symons, J., Louçã, J., Rodrigues, D., and Morais, A. (2007), “Detecting Emergence in the Interplay of Networks”, in Trajkovski, Goran P. and Collins, Samuel G. (eds.), Emergent Agents and Socialities: Social and Organizational Aspects of Intelligence. Association for the Advancement of Artificial Intelligence Technical Report FS-07-04. Menlo Park, CA: AAAI Press, 8693.Google Scholar
Wasserman, S., and Faust, K. (1994), Social Networks Analysis: Methods and Applications. Cambridge: Cambridge University Press.CrossRefGoogle Scholar