The epistemological predicament associated with purposive quantitative analysis

Abstract This paper discusses the epistemological predicament associated with the formal modeling of the behavior of complex adaptive systems. This is a class of systems which: (i) express functions and structures on multiple levels and scales; and (ii) become “something different” in time, because of evolution. The paper addresses four points. (#1) The pre-analytical definition of “what is observed and how” is essential in determining any quantitative output of mathematical models. Scientists have to learn how to acknowledge and to deal better with the fact that the observer always affects what is observed when defining the descriptive domain. This influence of the observer occurs even before there is interaction with the observed in the process of gathering empirical data. (#2) The peculiar human ability to share a commensurate experience involves the concept of semiotic identity. The generation of knowledge is possible only because of the co-existence of a semiotic reality and physical systems. (#3) The special organization of living systems depends on their ability to establish and maintain a semiotic coupling between functional and structural types. This coupling is associated with the concept of holon and explains why it is impossible to formalize in substantive terms organizations recognized as holons. Holons can only be handled in semiotic terms. (#4) A strong semiotic identity entails an uncontested selection of an appropriate sampling procedure for validating the choice of the formal identity used in the model. On the contrary, a weak semiotic identity entails a tautology in the modeling relation. The formal identity used to represent the semiotic identity in the model has also to be used to decide about the relative sampling used for validation. The distinction between strong and weak semiotic identities places a limit on the power of modeling. A sound modeling relation requires strong semiotic identities, whereas the typical issues associated with science for governance imply perceptions and representations based on weak semiotic identities.

[1]  Robert Rosen,et al.  Essays on Life Itself , 1999 .

[2]  Kozo Mayumi,et al.  The Origins of Ecological Economics: The Bioeconomics of Georgescu-Roegen , 2001 .

[3]  Arthur Koestler,et al.  Janus: A Summing Up , 1978 .

[4]  S. Morgenthaler.  Robustness in Statistics , 2001 .

[5]  Ramón Margalef.  Perspectives in Ecological Theory , 1968 .

[6]  Kalevi Kull,et al.  On semiosis, Umwelt, and semiosphere , 1998 .

[7]  S. Salthe.  Evolving Hierarchical Systems: Their Structure and Representation , 1985 .

[8]  Dale A. Quattrochi,et al.  Thermal Remote Sensing in Land Surface Processing , 2004 .

[9]  R Revelle,et al.  Energy Use in Rural India , 1976, Science.

[10]  Roydon Andrew Fraser,et al.  Exergy analysis of ecosystems: establishing a role for thermal remote sensing , 2004 .

[11]  R. Rosen.  THE REPRESENTATION OF BIOLOGICAL SYSTEMS FROM THE STANDPOINT OF THE THEORY OF CATEGORIES , 1958 .

[12]  C. T. de Wit,et al.  Energy production and use in Dutch agriculture , 1974 .

[13]  Richard C. Fluck,et al.  Net Energy Sequestered in Agricultural Labor , 1981 .

[14]  G. Box.  Robustness in the Strategy of Scientific Model Building. , 1979 .

[15]  R. H. Dowdy.  Energy, Agriculture and Waste Management , 1977 .

[16]  Mario Giampietro,et al.  Sustainability and technological development in agriculture , 1994 .

[17]  R. Ulanowicz.  Ecology, the ascendent perspective , 1997 .

[18]  Safa N. Hamad,et al.  Energy Inputs to Irrigation , 1975 .

[19]  S. Gliessman,et al.  Multi‐Scale Integrated Analysis of Agroecosystems , 2006 .

[20]  Robert Rosen,et al.  COMPLEXITY AS A SYSTEM PROPERTY , 1977 .

[21]  HERBERT A. SIMON,et al.  The Architecture of Complexity , 1991 .

[22]  R. Rosen.  Life Itself: A Comprehensive Inquiry Into the Nature, Origin, and Fabrication of Life , 1991 .

[23]  Tang,et al.  Self-Organized Criticality: An Explanation of 1/f Noise , 2011 .

[24]  Cutler J. Cleveland,et al.  Encyclopedia of Energy , 2004 .

[25]  Ilya Prigogine,et al.  From Being To Becoming , 1980 .

[26]  Mario Giampietro,et al.  Complex Systems and Energy , 2004 .

[27]  Niels Ole Finnemann,et al.  Downward Causation: Minds, Bodies and Matter , 2001 .

[28]  T. Shallice.  What ghost in the machine? , 1992, Nature.

[29]  T. Allen,et al.  Toward a Unified Ecology. , 1994 .

[30]  Thomas B. Starr,et al.  Hierarchy: Perspectives for Ecological Complexity , 1982 .

[31]  Howard T. Odum,et al.  Environmental Accounting: Emergy and Environmental Decision Making , 1995 .

[32]  Arthur Koestler,et al.  Beyond Atomism and Holism—the Concept of the Holon , 2015 .

[33]  Grégoire Nicolis,et al.  Self-Organization in nonequilibrium systems , 1977 .

[34]  H. Maturana,et al.  Autopoiesis and Cognition , 1980 .

[35]  A. R. Johnson,et al.  A hierarchical framework for the analysis of scale , 1989, Landscape Ecology.

[36]  M. Norman.  Energy inputs and outputs of subsistence cropping systems in the tropics , 1978 .

[37]  Mario Giampietro,et al.  Comments on “The Energetic Metabolism of the European Union and the United States” by Haberl and Colleagues: Theoretical and Practical Considerations on the Meaning and Usefulness of Traditional Energy Analysis , 2006 .

[38]  H. Maturana,et al.  Autopoiesis and Cognition : The Realization of the Living (Boston Studies in the Philosophy of Scie , 1980 .

[39]  M. Giampietro,et al.  The epistemological challenge of self-modifying systems: Governance and sustainability in the post-normal science era , 2006 .

[40]  K. Bailey.  Social Entropy Theory , 1990 .

[41]  B. Mandelbrot.  How Long Is the Coast of Britain? Statistical Self-Similarity and Fractional Dimension , 1967, Science.