Modeling
Modeling is a formal approach to a mapping exercise. It can take many forms. Indeed, if broadly defined 'modeling' is virtually synonymous with 'mapping.' We provide here a link to, and brief overview of, a very useful resource on modeling broadly defined hosted on the website of our colleagues in Integration and Implementation Sciences.
Badham, J. (2010). A compendium of modeling techniques. Integration Insights #12, May. Full text online at: http://i2s.anu.edu.au/sites/default/files/resources/integration-insight_12.pdf (859KB PDF). It is noted that every model necessarily abstracts from reality. The interdisciplinarian is thus encouraged to employ multiple modeling techniques.
“This Integration Insight provides a brief overview of the most popular modeling techniques used to analyse complex real-world problems, as well as some less popular but highly relevant techniques. The modeling methods are divided into three categories, with each encompassing a number of methods:
Qualitative Aggregate Models (Soft Systems Methodology, Concept Maps and Mind Mapping, Scenario Planning, Causal (Loop) Diagrams), [Note that most of these refer to ‘mapping’ as described above rather than more formal modeling.]
Quantitative Aggregate Models (Function fitting and Regression, Bayesian Nets, System of differential equations / Dynamical systems, System Dynamics, Evolutionary Algorithms) and
Individual Oriented Models (Cellular Automata, Microsimulation, Agent Based Models, Discrete Event Simulation, Social Network Analysis).”
Each technique is broadly described with example uses, key attributes, and reference material.
Ideally a formal model would accurately capture the strength of each relationship in the system. In practice, some of these are hard to quantify in principle, and reliable data will exist for only a subset of them. Sensitivity analysis can be employed to show how the system works under different assumptions, but will often not generate robust results. Researchers should resist the temptation to ignore relationships for which little information exists, or make simplifying assumptions and then forget these when discussing the results of the analysis (which might be driven by the simplifying assumptions.
Just as is the case with different methods themselves, different modeling techniques have different strengths and weaknesses, and thus using multiple models is advantageous. In particular some models may emphasize different relationships from others.
Bergmann, Matthias , Thomas Jahn, Tobias Knobloch, Wolfgang Krohn, Christian Pohl, Engelbert Schramm (2012) Methods for Transdisciplinary Research: A Primer for Practice. Berlin: Campus. Stress the critical importance of models to transdisciplinarity. Need models to comprehend complex problems. There are various types of models. These differ importantly on whether they strive for generality or specificity (there is more detail in the latter). Models can be graphical, physical, mathematical, etc. The key to all is deciding which variables and relationships to stress. They suggest proceeding if desired toward increasingly formal models, perhaps to one capable of computer simulation. They have strategies devoted to forecasting and simulations.
Their first modeling strategy involves each discipline identifying its key variables and relationships; then group members agree on the type of model and how to put these elements together.
Laura Schmitt Olabisi, Stuart Blythe, Arika Ligmann-Zielinska, and Sandra Marquartt-Pyatt, "Modelling as a tool for cross-disciplinary communication in solving environmental problems," in Michael O’Rourke et al, Enhancing Communication and Collaboration in Interdisciplinary Research, Sage, July, 2013,271-90, discuss how computer modelling can aid interdisciplinary collaboration. They stress that there are best practices for doing so. They identify best practices not just for modelling but for team participation, institutional training, and communication with stakeholders.