This article focuses on emergence in social systems. The author begins by proposing a new tool to explore the mechanisms of social emergence: multi agent–based computer simulation. He then draws on philosophy of mind to develop an account of social emergence that raises potential problems for the methodological individualism of both social mechanism and of multi agent simulation. He then draws on various complexity concepts to propose a set of criteria whereby one can determine whether a given social mechanism generates (...) emergent properties, in the sense that their explanation cannot be reduced to a mechanistic account of individuals and their interactions. This combined account helps to resolve the competing claims of methodological individualists and social realists. The author’s conclusion is that the scope of mechanistic explanation may be limited due to the extreme complexity of many social systems. (shrink)

This article focuses on emergence in social systems. The author begins by proposing a new tool to explore the mechanisms of social emergence: multi agent–based computer simulation. He then draws on philosophy of mind to develop an account of social emergence that raises potential problems for the methodological individualism of both social mechanism and of multi agent simulation. He then draws on various complexity concepts to propose a set of criteria whereby one can determine whether a given social mechanism generates (...) emergent properties, in the sense that their explanation cannot be reduced to a mechanistic account of individuals and their interactions. This combined account helps to resolve the competing claims of methodological individualists and social realists. The author’s conclusion is that the scope of mechanistic explanation may be limited due to the extreme complexity of many social systems. Key Words: emergence • mechanism • computer simulation • methodological individualism • social realism. (shrink)

Richard Levins’ distinction between aggregate, composed and evolved systems acquires new significance as we recognize the importance of mechanistic explanation. Criteria for aggregativity provide limiting cases for absence of organization, so through their failure, can provide rich detectors for organizational properties. I explore the use of failures of aggregativity for the analysis of mechanistic systems in diverse contexts. Aggregativity appears theoretically desireable, but we are easily fooled. It may be exaggerated through approximation, conditions of derivation, and extrapolating from some conditions (...) of decomposition illegtimately to others. Evolved systems particularly may require analyses under alternative complementary decompositions. Exploring these conditions helps us to better understand the strengths and limits of reductionistic methods. (shrink)

My paper draws on examples from molecular biology, the details of which I have developed elsewhere (Rheinberger 1992, 1993, 1995, 1997). Here, I can give only a brief outline of my argument. Reduction of complexity is a prerequisite for experimental research. To make sense of the universe of living beings, the modern biologist is bound to divide his world into fragments in which parameters can be defined, quantities measured, qualities identified. Such is the nature of any "experimental system." Ontic complexity (...) has to be reduced in order to make experimental research possible. The complexity of the research object, however, is epistemically retained in the rich context of an experimental landscape, where the eruption of "volcanic systems" can change the scenery dramatically as the result of particular, unprecedented findings. (shrink)

Arguments for or against a trend in the evolution of complexity are weakened by the lack of an unambiguous definition of complexity. Such definitions abound for both dynamical systems and biological organisms, but have drawbacks of either a conceptual or a practical nature. Physical complexity, a measure based on automata theory and information theory, is a simple and intuitive measure of the amount of information that an organism stores, in its genome, about the environment in which it evolves. It is (...) argued that physical complexity must increase in molecular evolution of asexual organisms in a single niche if the environment does not change, due to natural selection. It is possible that complexity decreases in co‐evolving systems as well as at high mutation rates, in sexual populations, and in time‐dependent landscapes. However, it is reasoned that these factors usually help, rather than hinder, the evolution of complexity, and that a theory of physical complexity for co‐evolving species will reveal an overall trend towards higher complexity in biological evolution. BioEssays 24:1085–1094, 2002. © 2002 Wiley‐Periodicals, Inc. (shrink)

This article explores the tension between standardization and flexibility in information infrastructure. Just like other large technical systems, the geographically dispersed yet highly interconnected II becomes increasingly resistant to change. Still, II design must anticipate and prepare for changes, even substantial ones, if infrastructure is to survive. An II contains a huge number of components that alternate between standardization and change throughout their lifetimes. These components are interdependent: when one is changed, others have to remain stable, and vice versa. The (...) article examines theoretical concepts for frammg these aspects of an II. The empirical underpinning of the article is a study of two existing embryonic manifestations of II. (shrink)

Organization in a tangled world -- Process views of organization -- Alfred North Whitehead on process -- Bruno Latour on relativizing the social, and the becoming of networks -- Niklas Luhmann on autopoiesis and recursiveness in social systems -- James March on decision processes and organization : a logic of streams -- Karl Weick on organizing and sensemaking -- A scheme for process based organizational analysis -- Some implications for organizational analysis.