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An approach towards holism in science and engineering

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Abstract

This paper posits the desirability of a shift towards a holistic approach over reductionist approaches in the understanding of complex phenomena encountered in science and engineering. An argument based on set theory is used to analyze three examples that illustrate the shortcomings of the reductionist approach. Using these cases as motivational points, a holistic approach to understand complex phenomena is proposed, whereby the human brain acts as a template to do so. Recognizing the need to maintain the transparency of the analysis provided by reductionism, a promising computational approach is offered by which the brain is used as a template for understanding complex phenomena. Some of the details of implementing this approach are also addressed.

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References

  1. Ackoff, R., Emery F. (1972). On Purposeful Systems. Aldine-Atherton, USA.

    Google Scholar 

  2. Ackoff, R. (1973). Science in the systems age: beyond IE, OR, and MS. Operations Research. 21 (3): 661–671

    Article  MathSciNet  Google Scholar 

  3. Bears, M.F., Connors, B.W. & Paradiso, M.A. (2006). Neuroscience: Exploring the Brain. Lippincott Williams and Wilkins, USA

    Google Scholar 

  4. Boria, F., Stanford, B., Bowman, S. & Ifju, P. (2009). Evolutionary optimization of a morphing wing with wind-tunnel hardware in the loop. AIAA Journal, 47 (2): 399–409

    Article  Google Scholar 

  5. Braun, R. & Kroo, I. (1997). Development and application of the collaborative optimization architecture in a multidisciplinary design environment. In: Alexandrov, N.M., Hussaini, M.Y. (eds.), Multidisciplinary design and optimization: state of the art. SIAM, Philadelphia, USA

    Google Scholar 

  6. Carpenter, G. & Grossberg, S. (1988). The ART of adaptive pattern recognition by a self-organizing neural network. Computer, 21 (3): 77–87

    Article  Google Scholar 

  7. Cares, J. (2004). An Information Age Combat Model. Produced for the United States Office of the Secretary of Defense, USA

    Google Scholar 

  8. Checkland, P. (1981). Systems Thinking, Systems Practice. Wiley, USA

    Google Scholar 

  9. Descartes, R. (1637/2006). Discourse on the Method of Rightly Conducting the Reason and Seeking Truth in the Sciences. 1st World Publishing, USA

    Google Scholar 

  10. Dzeroski, S. (2003). Multi-relational data mining: an introduction. Newsletter of the ACM Special Interest Group on Knowledge Discovery and Data Mining, 5 (1): 1–16

    Google Scholar 

  11. Epstein, J. (2006). Generative Social Science: Studies in Agent-based Computational Modeling. Princeton University Press, Princeton, USA

    MATH  Google Scholar 

  12. Felleman, D.J. & Van Essen, D.C. (1991). Distributed hierarchical processing in the primate cerebral cortex. Cerebral Cortex, 1 (1): 1–47

    Article  Google Scholar 

  13. George, D. (2008). How the brain might work: a hierarchical and temporal model for learning and recognition. Ph.D. Dissertation, Stanford University, Stanford, USA

    Google Scholar 

  14. Gros, C. (2008). Complex and Adaptive Dynamical Systems: A Primer. Springer, Berlin, Germany

    Book  MATH  Google Scholar 

  15. Gurmani, A.P. & Lewis, K. (2008). Using bounded rationality to improve decentralized design. AIAA Journal, 46 (12): 99–137

    Google Scholar 

  16. Hawkins, J. & Blakeslee, S. (2004). On Intelligence. Time Books, New York, USA

    Google Scholar 

  17. Hawkins, J. & George, D. (2009). Towards a mathematical theory of cortical micro-circuits. PLoS Computational Biology, 5 (10): 1–26

    Google Scholar 

  18. Hayek, F. (1994). The theory of complex phenomena. In: Martin, M., McIntyre, L.C. (eds.), Readings in the philosophy of social science. MIT Press, USA

    Google Scholar 

  19. Holmes, P., Lumley, J.L. & Berkooz, G. (1996). Turbulence, Coherent Structures, Dynamical Systems, and Symmetry. Cambridge University Press, Cambridge, UK

    Book  MATH  Google Scholar 

  20. Ilachinski, A. (2004). Artificial War: Multi-agent Based Simulation of Combat. World Scientific Publishing Co., USA

    Book  Google Scholar 

  21. Jensen, D. (1999). Statistical challenges to inductive inference in linked data. Preliminary In: the Seventh International Workshop on Artificial Intelligence and Statistics

  22. Kohl, N. & Miikkulainen, R. (2009). Evolving neural networks for strategic decision-making problems. Neural Networks, 22: 326–337

    Article  Google Scholar 

  23. Kroo, I., Atlus, S., Braun, R., Gage, P. & Sobieski, I. (1994). Multidisciplinary optimization methods for aircraft preliminary design. AIAA Paper, 94–4325

  24. Nakamori, Y. (2003). Systems methodology and mathematical models for knowledge management. Journal of Systems Science and Systems Engineering, 12 (1): 49–72

    Article  Google Scholar 

  25. O’Hanlon, M. & Campbell, J. (2008). Iraq Index: Tracking Variables of Reconstruction and Security in Post-Saddam Iraq. The Brookings Institution

  26. Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, USA

    Google Scholar 

  27. Quartz, S. & Sejnowski, T.J. (1997). The neural basis of cognitive development: a constructivist manifesto. Behavioral and Brain Sciences, 20 (4): 537–596

    Google Scholar 

  28. Quiroga, R.Q., Reddy, L., Kreiman, G., Koch, C. & Fried, I. (2005). Invariant visual representation by single neurons in the human brain. Nature, 274: 1102–1107

    Article  Google Scholar 

  29. Rogallo, R.S. & Moin, P. (1984). Numerical simulation of turbulent flows. Annual Review of Fluid Mechanics, 16: 99–137

    Article  Google Scholar 

  30. Sherwin, J. (2010). A computational approach to situational awareness. Journal of Battlefield Technology, 13 (1): 1–7

    Google Scholar 

  31. Sheth, B.R., Sharma, J., Rao, S.C. & Sur, M. (1996). Orientation maps of subjective contours in visual cortex. Science, 274 (5295): 2110–2115

    Article  Google Scholar 

  32. Simon, H.J. (1981). The Sciences of the Artificial. MIT Press, USA

    Google Scholar 

  33. Tien, J.M. & Berg, D. (2003). A case for service systems engineering. Journal of Systems Science and Systems Engineering, 12 (1): 13–38

    Article  Google Scholar 

  34. United States Department of Defense (2003-2008). Report to Congress: Measuring Stability and Security in Iraq. Produced for the United States Congress, USA

    Google Scholar 

  35. Wiskott, L. & Sejnowski, T. (2002). Slow feature analysis: unsupervised learning of invariances. Neural Computation, 14 (4): 715–770.

    Article  MATH  Google Scholar 

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Authors and Affiliations

  1. Department of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA

    Jason Sherwin

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  1. Jason Sherwin
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Correspondence to Jason Sherwin.

Additional information

This work was sponsored by Prof. Dimitri Mavris and the Aerospace Systems Design Laboratory

Jason Sherwin graduated in May 2010 as a Ph.D. in Aerospace Engineering from Georgia Institute of Technology. Before earning his M.S. in Aerospace Engineering, he completed a B.A. in Physics (with honors) from the University of Chicago. In the fall of 2010, he will continue his work on engineering applications of machine learning as research faculty at Columbia University’s Department of Biomedical Engineering. In 2009, he was a Nunn Security Fellow in the Center for International Strategy and Policy at the Sam Nunn School of International Affairs. He is an internationally published author with a growing catalog of articles and journal publications. He looks forward to continuing work on machine learning approaches to difficult problems in science and engineering. In particular, the holistic perspective discussed in this work will remain the driving influence.

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Sherwin, J. An approach towards holism in science and engineering. J. Syst. Sci. Syst. Eng. 19, 285–305 (2010). https://doi.org/10.1007/s11518-010-5141-y

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