Learning through Computer Model Improvisations

It has been convincingly argued that computer simulation modeling differs from traditional science. If we understand simulation modeling as a new way of doing science, the manner in which scientists learn about the world through models must also be considered differently. This article examines how researchers learn about environmental processes through computer simulation modeling. Suggesting a conceptual framework anchored in a performative philosophical approach, we examine two modeling projects undertaken by research teams in England, both aiming to inform flood risk management. One of the modeling teams operated in the research wing of a consultancy firm, the other were university scientists taking part in an interdisciplinary project experimenting with public engagement. We found that in the first context the use of standardized software was critical to the process of improvisation, the obstacles emerging in the process concerned data and were resolved through exploiting affordances for generating, organizing, and combining scientific information in new ways. In the second context, an environmental competency group, obstacles were related to the computer program and affordances emerged in the combination of experience-based knowledge with the scientists’ skill enabling a reconfiguration of the mathematical structure of the model, allowing the group to learn about local flooding.

[1]  S. Funtowicz,et al.  Science for the PostNormal Age , 2001 .

[2]  Dusya Vera,et al.  Theatrical Improvisation: Lessons for Organizations , 2004 .

[3]  Stuart N. Lane,et al.  Virtual Engineering: Computer Simulation Modelling for Flood Risk Management in England , 2011 .

[4]  Sylvain Néelz,et al.  Desktop review of 2D hydraulic modelling packages , 2009 .

[5]  François Bousquet,et al.  Modelling with stakeholders , 2010, Environ. Model. Softw..

[6]  M. Michael Reconnecting Culture, Technology and Nature: From Society to Heterogeneity , 2000 .

[7]  P. Gummett The Social Construction of Technological Systems: New Directions in the Sociology and History of Technology , 1988 .

[8]  Stuart N. Lane,et al.  Knowledge-theoretic models in hydrology , 2010 .

[9]  Tim Morris,et al.  Organisation and expertise: An exploration of knowledge bases and the management of accounting , 1998 .

[10]  M. Leinung The right tool for the job. , 2014, Endocrine practice : official journal of the American College of Endocrinology and the American Association of Clinical Endocrinologists.

[11]  W. Bijker The social construction of bakelite: toward a theory of invention , 1987 .

[12]  A. Montuori The Complexity of Improvisation and the Improvisation of Complexity: Social Science, Art and Creativity , 2003 .

[13]  S. Whatmore Mapping knowledge controversies: science, democracy and the redistribution of expertise , 2009 .

[14]  K. Bickerstaff,et al.  The Right Tool for the Job? Modeling, Spatial Relationships, and Styles of Scientific Practice in the UK Foot and Mouth Crisis , 2004 .

[15]  Margaret Morrison,et al.  Models as Mediating Instruments , 1999 .

[16]  Mikaela Sundberg,et al.  The Everyday World of Simulation Modeling , 2009 .

[17]  Rachel Prentice,et al.  Drilling Surgeons , 2007 .

[18]  P. N. Edwards A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming , 2010 .

[19]  Gabriele Gramelsberger Paul N. Edwards, A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming , 2012 .

[20]  Stuart N. Lane,et al.  Representation of landscape hydrological connectivity using a topographically driven surface flow index , 2009 .

[21]  Michaela Mueller Acting in An Uncertain World: An Essay on Technical Democracy , 2011 .

[22]  References , 1971 .

[23]  Martina Merz,et al.  Locating the Dry Lab on the Lab Map , 2006 .

[24]  Petter Grytten Almklov,et al.  Between and beyond data: How analogue field experience informs the interpretation of remote data sources in petroleum reservoir geology , 2011 .

[25]  I. Stengers,et al.  The Invention Of Modern Science , 2000 .

[26]  Ergon House,et al.  DEFRA / Environment Agency Flood and Coastal Defence R&D Programme , 2003 .

[27]  Ken Kamoche,et al.  Minimal Structures: From Jazz Improvisation to Product Innovation , 2001 .

[28]  Spencer R. Weart,et al.  The development of general circulation models of climate , 2010 .

[29]  Tarja Knuuttila,et al.  From Representation to Production: Parsers and Parsing in Language Technology , 2006 .

[30]  F. Al-Shamali,et al.  Author Biographies. , 2015, Journal of social work in disability & rehabilitation.

[31]  Eric Winsberg,et al.  Sanctioning Models: The Epistemology of Simulation , 1999, Science in Context.

[32]  Catharina Landström,et al.  Flood apprentices: an exercise in making things public , 2011 .

[33]  Stuart N. Lane,et al.  Coproducing Flood Risk Knowledge: Redistributing Expertise in Critical ‘Participatory Modelling’ , 2011 .

[34]  A. Hommels,et al.  Studying Obduracy in the City: Toward a Productive Fusion between Technology Studies and Urban Studies , 2005 .

[35]  John Turnpenny,et al.  Where Now for Post-Normal Science?: A Critical Review of its Development, Definitions, and Uses , 2011 .

[36]  David Gooding,et al.  Mapping Experiment as a Learning Process: How the First Electromagnetic Motor Was Invented , 1990 .