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Dynamic models of evolving systems
Author(s) -
Allen Peter M.
Publication year - 1988
Publication title -
system dynamics review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.491
H-Index - 57
eISSN - 1099-1727
pISSN - 0883-7066
DOI - 10.1002/sdr.4260040107
Subject(s) - computer science , set (abstract data type) , process (computing) , basis (linear algebra) , system dynamics , reduction (mathematics) , complex system , artificial intelligence , mathematics , programming language , operating system , geometry
The basis of systems modeling has been the Newtonian paradigm that states that the behavior of a system can be understood and anticipated by identifying its components and the causal links between them. This assumption leads to a set of deterministic differential or difference equations that governs the behavior of the system. However, such description is achieved by classifying elements into categories and supposing that only the most probable events in fact occur. But real systems evolve, that is, they add and subtract mechanisms, components, and interactions over time; the deterministic model does not reflect this. Clearly, evolution must therefore result from what has been removed in the reduction process. If we are to understand evolving systems better, to anticipate structural changes, and to explore the real impacts of decisions, we must study the effects on system dynamics of nonaverage behavior. We can then gain new insights into the nature of evolutionary processes and build better models that include the adaptive responses from within the system.