Complex Systems, Modelling and Simulation

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S. Schweber, M. Watcher, Stud. Hist. Phil. Mod. Phys., Vol. 31, No. 4, pp. 583-609, 2000An introduction of a new Hacking-type revolution of computation and simulation. Explanation of Hacking-type revolutions and the current chabge in physics and chemistry.

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<p>* Martin Fisher School of Physics, Brandeis University, Waltham, MA 02254, U.S.A. (e-mail:schweber@brandeis.edu).R Collegium Helveticum, Semper-Sternwarte, ETH-Zentrum/STW, Schmelzbergstrasse 25, CH-8092 Zurich, Switzerland (e-mail: waechter@collegium.ethz.ch).Stud. Hist. Phil. Mod. Phys., Vol. 31, No. 4, pp. 583}609, 2000 2000 Published by Elsevier Science Ltd.Printed in Great Britain1355-2198/00 $ - see front matterComplex Systems, Modelling andSimulationSam Schweber*, Matthias WaKchterRSome mathematicians see analogies between theorems or theories, the very bestones see analogies between analogies.Stefan Banach (quoted in Ulam, 1976)1. IntroductionWe tend to think of the growth of scienti"c knowledge in terms of the Kuhnianmodel. But in his Structure Kuhn was speci"cally concerned with the dynamicsof disciplinary and subdisciplinary changes. There are also much larger revo-lutions than the disciplinary ones described by Kuhn, which we call Hacking-type revolutions. Ian Hacking has identi"ed their characteristics (Hacking, 1981,1992):(i) They transform a wide range of scienti"c practices and they are multi-disciplinary. In a Hacking-type revolution something happens in more thanone discipline; a multiplicity of scienti"c disciplines are transformed.(ii) New institutions are formed that epitomise the new directions.(iii) They are linked with substantial social change. After a Hacking-typerevolution there is a di!erent feel to the world, there is a marked change inthe texture of the world. And(iv) because they are &amp;big', there can be no complete, all-encompassing historyof a Hacking-type revolution.PII: S 1 3 5 5 - 2 1 9 8 ( 0 0 ) 0 0 0 3 0 - 7583 We would like to thank Prof. Libby Schweber for showing us a manuscript of the Introduction toher forthcoming book on the meanings and uses of statistics in France and Great Britain during thenineteenth century. Hacking's notion of a style of reasoning is there analysed critically and we havemade use of that analysis.That the scienti"c revolution of the seventeenth century satis"es all these criteriais clear. The Royal Society and the AcadeH mie des Sciences are some of the newinstitutions it created. Hacking pointed to the rise of the bourgeoisie as indica-tive of the substantial social change associated with that revolution. The numer-ous statistical societies founded in the 1830s are some of the new institutionsassociated with what Hacking called the probabilistic revolution. The avalancheof numbers gave a di!erent feel to the world: it had become quanti"ed andnumbers and statistics ruled it. It was Mr. Gradkin's world. Concomitantly, thepreviously dominant determinist Weltanschauung became replaced by a view ofthe world in which probability and chance played an ever increasing role. Theresult was the emergence of a new statistical style, constituted by a plethoraof abstract statistical entities and governed by autonomous statistical laws,which are &amp;used not only to predict phenomena but also to explain [them]'.Hacking (1981) called this new use of numbers the &amp;inferential' style of statisticalreasoning, and contrasted it with the more traditional descriptive style alreadyin place.The end of the nineteenth century witnessed another Hacking-type revolutionconnected with the microscopic modelling of matter and the establishment ofscience-based technologies, particularly those grounded in the new understand-ing of organic chemistry and of electricity. World War II was likewise respon-sible for a large scale transformation of science and scienti"c practices but weshall not call it a Hacking-type revolution*in order to emphasise the fact thatwe require a Hacking revolution to have a basic and central conceptual com-ponent: it must have associated with it a new style of scientixc reasoning. Styles ofreasoning are the constructs that specify what counts as scienti"c knowledge,and constitute the cognitive conditions of possibility for science (Hacking, 1992).They are made concrete and explicit through the speci"cation of ontological andexplanatory models. The emergence of a new style is associated with theintroduction of new types of sentences, entities and explanations. Hacking hasgiven the following as examples of styles of reasoning: the taxonomic, experi-mental, statistical and genetic or evolutionary styles of reasoning.The World War II transformation was brought about by the plethora of noveldevices and instruments produced by the wartime activities: oscilloscopes; fastelectronic circuitry; the myriad of new vacuum tubes; microwave generators(magnetrons, klystrons) and detectors; rockets; computers; nuclear reactors; newparticle detectors. Many of these devices had been introduced before the war butin a relatively primitive state and on an individual basis. These instruments dida!ect a wide variety of "elds. The scale on which these devices and instrumentsbecame available transformed the stage*but the conceptual foundations after584 Studies in History and Philosophy of Modern Physics` See Fortun and Schweber (1993) and Edwards (1996) for the genesis and the details of thisrevolution.` See for example Herman et al. (1973), Wilson (1986), Lawley (1987), Allen and Tildesley (1993),Galison (1996, 1998) and Ceperley (1999). For an insightful philosophical overview see Keller(2000b). See also the National Research Council 1999 report Strengthening the Linkages between theSciences and Mathematical Sciences (Washington DC: National Academy Press).the war were not altered by their introduction. World War II did initiatea scienti"c revolution in management science, risk assessment, military planningand consolidated an engineering approach that became known as systemsengineering. A new style of reasoning evolved during World War II in manage-ment science connected with operations research and game theory (and theassociated mathematical tools such as linear and non-linear programming). Inthe United States, the new institutions connected with this Hacking revolutionare exempli"ed by the Rand Corporation. The new style of reasoning restruc-tured companies like the Ford Motor Company under McNamara and trans-formed the U.S. Department of Defense when he and his whiz-kids came to thePentagon in 1960.`We are witnessing another Hacking-type revolution, in which the computer isthe central element*in the same sense that the steam engine was the centralelement in the "rst industrial revolution and that factories driven by steampower and steam locomotives and railroads transformed the economic andsocial landscape. That the computer has similarly generated a sweeping trans-formation of the social, material, economic and cultural context is evi-dent*think only of the transformation of the workplace and the novelroutinisations that the computer has introduced, of e-commerce, of the newclasses of professionals, etc. However, we shall not be concerned in this paperwith these broad and general features of the revolution. Rather we focus on oneof its components, which for lack of a better name we call the &amp;complex systemscomputer modelling and simulation' revolution, for complexity has become oneof our buzzwords and mathematical modelling and simulation on computersconstitute, we claim, its style of reasoning. Historically, the interaction betweenmathematics and the sciences has had many di!erent aspects. Two of these havebeen modelling and simulation. The formulation of a mathematical model ofa system may involve any branch of mathematics in its architecture, construc-tion, testing and evaluation. By simulation of a system is usually meant thearticulation of an existing mathematical model. The output of the simulationmay be graphical, numerical or analytical; its purpose is to better understand themodel and/or to assess its predictions (Abraham, 1991, p. 2). The facility withwhich computers can generate such outputs*in ever more complex prob-lems*is of course the reason why they have played a fundamental role in this&amp;revolution'. Dramatic advances in microelectronics made large mainframes andcheap, powerful desktop computing possible. Powerful graphics were one of theby-products of these advances, and graphics in turn generated a new kind ofintuition in mathematical modelling. Computers have transformed simulation.`585 Complex Systems, Modelling and SimulationAlthough they have not replaced classical analysis, numerical computation andgraphical representation have become the dominant methods of simulation.Computers may also be revolutionising mathematics by their use as heuristictools and by challenging the usual concept of what constitutes a proof(MacKenzie, 1996).Julian Schwinger once quipped that Feynman diagrams had brought quan-tum "eld theory, one of the most abstruse branches of theoretical physics, to themasses (Schwinger, 1983). Similarly desk top computing has revolutioniseddoing theory and the relationship between theory and experiment in many"elds. Indeed, computers have revolutionised modelling in the physical sciences.The advantage of being able to explore models rapidly and e$ciently using thetools of &amp;pencil and paper' mathematical analysis has been greatly reduced.A skilled modeller can now analyse the consequences of a given model oftenmuch more rapidly using computers than doing so by analytic means. Thecomputer has thus levelled the "eld of doing certain kinds of theoreticalmodelling. And the number of scientists using desktop computers for modellingpurposes is increasing dramatically. This quantitative increase is responsible fora qualitative change in the way science is being done in many "elds. ThusRohrlich already in 1990 suggested that computer simulation in physics entaileda qualitatively new and di!erent methodology [that lies] somewhere intermediatebetween traditional theoretical physical science and its empirical methods ofexperimentation and observation. In many cases, it involves a new syntax whichgradually replaces the old, and it involves theoretical model experimentation ina qualitatively new and interesting way. Scienti"c activity has thus reached a newmilestone somewhat comparable to the milestones that started the empiricalapproach (Galileo) and the deterministic mathematical approach to dynamics (theold syntax of Newton and Laplace) (Rohrlich, 1990, p. 507).An equally dramatic transformation is occurring in &amp;supercomputing'. JamesLanger, in a report to the U.S. National Academy of Sciences summarising the"ndings of a July 1998 National Conference on Advanced Scienti"c Computing,noted thatcomputing speeds and capacities have been growing exponentially for over twodecades. [But] it is only over the last several years that scienti"c computation hasreached the point where it is on a par with laboratory experiments and mathemat-ical theory as a tool for research in science and engineering. The computer literallyis providing a new window through which we can observe the natural world inexquisite detail.Similarly and just as dramatically, during the past few years the computer-basedInternet has emerged as a vehicle for communicating huge amounts of informationthroughout the world. [...] These advances in computing and communicationpoint to a structural transformation of the ways in which we gain understanding,make informed decisions, and innovate in modern society. A profound transforma-tion in the way research is carried out is taking place. Nor is the transformationover. Since it is likely that in the next decade &amp;terascale' computing systems, with586 Studies in History and Philosophy of Modern Physics" In 1996, in order to ensure the safety and reliability of the United States' nuclear arsenal and toadhere fully to the Comprehensive Test Ban Treaty, the U.S. Department of Energy established theAccelerated Strategic Computing Initiative (ASCI). The goal of ASCI is to simulate the results ofnew weapons designs, and to simulate the e!ects of aging on existing and new designs, all in theabsence of additional data from underground nuclear tests. Thus with funding from ASCI three newcomputer systems that can sustain more than 1 tera#ops have been installed at the Los Alamos,Sandia and Lawrence Livermore National Laboratory. By 2002 computer systems 10 times morepowerful are to be delivered to these laboratories, and by 2004 computers capable of 100 trillionsoperations per second will be available.` Thus in December 1999 the Human Genome Project &amp;celebrated' the completion of the mapping ofchromosome 22 by an international team located in Cambridge, England, St. Louis and OklahomaCity in the US and in Tokyo, Japan.' For a valuable introduction to the subject see Auyang (1998).` See for example the websites of the centres for the study of complex systems at the University ofMichigan (http://www.pscs.umich.edu/), Georgia Tech and Brandeis University.speeds and capacities approximately 1000 times larger than the present ones,will become widely available to the scienti"c and engineering community,major changes will be continue to be e!ected.'"' In addition, broad access to digitallibraries in which massive experimental data sets are stored, and access toweb-based connections to a wide variety of analytic tools will become the norm.This is already the case in high energy physics and with some parts of the genomeproject.'`'The ever growing complexity of the problems being addressed is nurturing andaccelerating the trend toward collaborative research. Success in solving the prob-lems being tackled not only requires investigators with di!erent backgrounds andtoolkits to interact with one another but also demands intensive collaborationwith computer scientists. But by virtue of the web and of the internet thesecollaborations no longer require that the members of the collaboration be togetherat the same place at the same time.The computer made the study of non-linear systems and the phenomena theyexhibit a practical possibility. In particular, the use of high resolution computergraphics made it possible to identify and explore ordered patterns in thesehighly irregular phenomena. The combining of &amp;numerical experiments' on thecomputer with mathematical analysis gave rise to...</p>

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