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Copyright © 2011 by the author(s). Published here under license by The Resilience Alliance.
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Van Noordwijk, M., B. Lusiana, G. Villamor, H. Purnomo, and S. Dewi. 2011. Feedback loops added to four conceptual models linking land change with driving forces and actors. Ecology and Society 16(1): r1. [online] URL: http://www.ecologyandsociety.org/vol16/iss1/resp1/
Response to Hersperger et al. 2010. “Linking Land Change with Driving Forces and Actors: Four Conceptual Models.” Feedback Loops Added to Four Conceptual Models Linking Land Change with Driving Forces and Actors
1World Agroforestry Centre (ICRAF), Bogor, Indonesia, 2Institute for Plant Production in the Tropics and Sub-Tropics, University of Hohenheim, Germany, 3Centre for Development Research (ZEF), Bonn, Germany, 4Centre for International Forestry Research (CIFOR), Bogor, Indonesia
Feedback loops that link the consequences of land change to agents and driving forces are essential in understanding the relevance of models. This aspect needs to be added to the four model types discussed by Hersperger et al. (2010)
Although we appreciate the efforts to develop a functional taxonomy of models of land use change, driving forces, and actors, we miss an important class: models with feedback from the consequences of land use change to actors, to driving forces, and/or both. Because the primary societal reason for a scientific analysis of changes in land cover is the consequences of land cover change on a wide range of stakeholder interests and the various ways stakeholders can try to modify land cover change in their favor, the utility of the conceptual models will depend strongly on the type of entry points the models provide for feedback (Fig. 1).
Four main types of feedback are:
Regarding the claim of Hersperger et al. that most current agent-based models consider only one type of agent, that may be true numerically, but the exceptions are important and point to a way forward. Typically, agent-based models capture the ‘heterogeneity’ of a group that would be considered to be homogenous or represented by an average in other models. Brown and Robinson (2006) referred to heterogeneity in two types, namely (1) variability, which reflects continuous variation in agent characteristics across entire populations or within single agent types, and (2) categorization, introducing multiple types or groups of individuals with similar or differentiated preferences. Accordingly, heterogeneity is represented through various agent characteristics, e.g., preferences on a number of different factors that are independent and uncorrelated, thus creating complex interactions. This method of categorization was applied in the LUDAS model, a multiagent system model applied in Vietnam, of Le et al. 2008 and in follow-up models that are currently in development. In fact, agent-based models can also apply to the drivers rather than to the actors, as is done in organization centered multiagent systems (Purnomo and Guizol 2006).
Current modeling efforts that take the driver-agent-land relationship as a subsystem of a dynamic feedback description (van Noordwijk 2001, Lusiana et al. 2010, Villamor et al. 2011) are challenged by the way models can be validated (Lusiana et al. 2011). However, important aspects that emerge from these efforts are that the degree to which models can be learning tools for multiple stakeholders and act as ‘boundary objects’ (Clark et al. 2010) is at least as important as their academic ‘validation’ as conventionally quantified.
The Hersperger et al. taxonomy does not really address the nature of multiple scale issues in overall system dynamics. Further work on the framework is needed before such categorization of models can help individual research projects, communication and generalizations beyond the individual project, as the paper claims.
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