On Quantifying the Effects of Formal and Final Causes in Ecosystem Development
In physics and in traditional biology it has sufficed until now to describe phenomena as the results of purely material or efficient causes. However, a growing number of biologists and philosophers think that a satisfactory description of biological development must also include reference to what Aristotle labeled formal and final (or teleonomic, sensu Mayr) causation. A measure called the network ascendency has been defined to track the changes in the system that result from positive feedback acting as an endogenous formal cause of system development. In turn, positive feedback appears to exert a selection pressure reminiscent of teleonomic final cause upon each of its constituent elements.
Associating development with the increase of network ascendency permits the modelling of final cause using the powerful modern tools of mathematical optimization. Cheung and Goldman have developed an algorithm to find a reconfiguration of any given starting network that locally optimizes its network ascendency. Typically, the optimal configuration, as demonstrated by two simple examples, is a “one-tree“, that is, a single, directed cycle adjoined to a tree. Studying the differences between the observed and the optimal networks yields insights into the particular constraints acting on the system and reveals the most efficient pathways through the network.
KeywordsPositive Feedback Network Ascendency Optimal Configuration Average Mutual Information Ecosystem Development
Unable to display preview. Download preview PDF.
- Boulding, K. E. 1978. Ecodynamics: A New Theory of Societal Evolution. Sage Publications, Beverly Hills, California. 368 p.Google Scholar
- Cheung, A. K-T. 1985a. Network Optimization in Ecosystem Development. Doctoral Dissertation, Department of Mathematical Sciences, The Johns Hopkins University, Baltimore, Maryland, 163 p.Google Scholar
- Cheung, A. K-T. 1985b: ECONET: Algorithms for network optimization in ecosystem development analysis. Technical Report No. 423, Department of Mathematical Sciences, The Johns Hopkins university, Baltimore, Maryland. 63 p.Google Scholar
- Cousins, S. H. 1985. The trophic continuum in marine ecosystems: structure and equations for a predictive model. pp76–93. IN: R. E. Ulanowicz and T. Platt (eds.) Ecosystem Theory for Biological Oceanography. Canadian Bulletin of Fisheries and Aquatic Sciences. 213.Google Scholar
- Kennington, J. L. and R. V. Helgason. 1980. Algorithms for Network Programming. John Wiley and Sons, New York. 291 p.Google Scholar
- McEliece, R. J. 1977. The Theory of Information and Coding. Addison-Wesley, Reading, Massachusetts. 302 p.Google Scholar
- McLuhan, M. 1973. Understanding Media: The Extensions of Man. Mentor Books, New York.Google Scholar
- Platt, T. and K. H. Mann and R. E. Ulanowicz. 1981. Mathematical Models in Biological Oceanography. Unesco Press, Paris. 157 p.Google Scholar
- Richardson G. P. 1984. The evolution of the feedback concept in American social science. Ph.D. thesis. Massachusetts Institute of Technology, Cambridge, Massachusetts. p.Google Scholar
- Rosen, R. 1985. Information and complexity. pp221–233. IN: R. E. Ulanowicz and T. Platt (eds.) Ecosystem Theory for Biological Oceanography. Canadian Bulletin of Fisheries and Aquatic Sciences. 213 p.Google Scholar
- Ulanowicz, R. E. 1986. Growth and Development: Ecosystems Phenomenology. Springer-Verlag, New York. 203p.Google Scholar
- Wills, G. 1978. Inventing America. Doubleday, Garden City, New York. 398 p.Google Scholar