Heterogeneous construction of scientific knowledge and practice: II. Diverse components in development of the simulation model

My analysis of the Kerang project begins with the modeling because that was the part that I, as a participant, observed most closely.  I refer to myself in the third person as “the modeler” to express some distance between my position and actions in 1978–79 and my subsequent interpretive role.

My analysis of the modeling begins from the observation that the development of the Kerang Farm Model (KFM) drew on many diverse “components.” (The reason for choosing this neutral term will emerge in due course.)  These components included: data on soil quality; expected crop yields; range of farm sizes; technical assumptions used in the linear program; the status of the different agents in the project; the geographical distance between the Institute and the Kerang region; the computer packages available; the terms of reference set by the Ministry; and so on.  Moreover, many of these components link the different domains of social action or “social worlds” (Clarke 1990, 1991; Fujimura 1988) of the various agents—from the modeler to the farmers.  I need to put some order into this heterogeneity of components and assess their relative importance to the knowledge-making.  My experience as the modeler allows me to unpack parts of the processes of model building here.

Figure 4.2  A schema of the diverse components involved in the production of the Kerang Farm Model (Symbols: PI, Principal investigator; M, modeler; AgEc, agricultural economist;  AgEx, agricultural extension officers.  See text for discussion.)

Consider the central technical feature of the KFM, the use of a linear program for economic analysis.  This required the assumption that farmers operate to maximize one objective: in the KFM, this objective was income.  Furthermore, the use of a linear program for policy formation assumed that if the optimal mix of farming activities according to the KFM were different from a farmers’ existing mix, the farmer would change accordingly and immediately.  Even though the economic future of the region obviously entailed the farmers’ participation, the study was not designed to investigate why and how farmers change, how directly and readily they respond to economic signs, or the extent to which any overriding economic rationality governed their actions.  Finally, the use of the linear program assumed that all the relevant activities could be well specified.  However, the potential economic and ecological benefits of novel long-term options, such as selective reforestation and organic soil restoration, were difficult, if not impossible, to estimate without knowing the outcome of measures that has not yet been implemented, such as experimental plots, publicity, education, advocacy, and subsided loans for tree planting.

The modeler questioned these technical assumptions.  He expressed interest in techniques that incorporated more than one objective, but the principal investigator could not envisage modeling an alternative objective to income.  In any case, software for multiobjective analysis was not available at the computer center used by the Institute.  The modeler designed the KFM to allow the effect over time of new investments to be examined, but when the project approached its deadline, this part of the model development was halted.  The modeler learned of a sociological study on the factors influencing Kerang farmers to change their practices.  This study had not, however, been released at that time and the principal investigator lent no institutional support to obtain advance access to it.  Finally, the modeler located informants with experience in reforestation and organic soil restoration, but was told that these and other issues were outside the economic specialization of the Institute and best left for others to deal with.

In affirming the technical assumptions in the KFM, the principal investigator invoked his senior and permanent position at the Institute, the terms of reference and deadlines that the Ministry had set, and the Institute’s specialization in quantitative economic research.  These assumptions, in turn, had several consequences for the research.  They eliminated certain issues from investigation, e.g., farmer’s views of their objectives.  They shaped the data that needed to be collected, e.g., there was no need to investigate how farmers change or build scenarios for the novel long-term options.  And they colored the relationships put into the model—e.g., the time course of investment became a secondary issue to the farmers’ income-optimizing activities.  The authority over the young researcher exercised here by the experienced principal investigator was not extraordinary.  Nevertheless, through such exchanges the principal investigator and the modeler were negotiating the different components of what would count as a representation of reality and a guide to policy formation.

Of course, there were parties other than the principal investigator and the modeler potentially involved in accepting or disputing the KFM.  The farmers might have objected to the way their behavior was modeled.  The KFM could also have been disputed by economists interested in multi-objective techniques; by sociologists interested in how people act, interact, and change; or by agricultural policymakers interested in using the study to help change the state of farming in the region.  None of these potential disputes proved significant at the time.  The farmers were separated from the formulation and operation of the KFM.  Conversely, the KFM was insulated from the farmers by several considerations: location—the modeling was performed in the city; the chain of personnel involved—modeler–agricultural economist–senior agricultural extension officers–local agricultural extension officers–farmers; and levels of abstraction and generalization.  No one in the Institute, the principal investigator in particular, had training in multi-objective economic analysis or ready access to suitable computer software.  There were no sociologists included in the project team or advisory committee.  The Ministry, through the range of options established in the terms of reference for the study, indicated that change would be initiated by government policy based on economic and engineering criteria.  The farmers were, in effect, to be instruments, more than co-participants, in determining the future of the region.  In short, the Ministry did not dispute the KFM as a representation of reality; neither were any farmers, economists, or sociologists in a position to do so.

Extracted from Taylor, Peter J. 2005. Unruly Complexity: Ecology, Interpretation, Engagement. U. Chicago Press.

References

Clarke, A. (1990). “A social worlds research adventure: The case of reproductive science,” in S. E. Cozzens and T. F. Gieryn (Eds.), Theories of Science in Society.  Bloominton: Indiana University Press, 15-42.

—— (1991). “Social worlds/arenas theory as organizational theory,” in D. R. Maines (Ed.), Social Organization and Social Process: Essays in Honor of Anselm Strauss.  New York: Aldine de Gruyter, 119-158.

Fujimura, J. (1988). “The molecular biology bandwagon in cancer research:  Where social worlds meet.” Social Problems 35: 261-283.

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