Project aim. Reflecting on the level of detail/complexity in models to be able to advance understanding of social ecological systems.
Approach. Combining theory (on cooperation) and case knowledge (Balinese irrigation and rice farming) with agent-based modelling to demonstrate the need and implication of introducing more complexity in social-ecological models.
Why? Theories often say everything and at the same time nothing about particular cases. Case knowledge is rich, detailed, but not necessarily generalisable. There is a need for integrating knowledge and making use of the strength of both theories and case towards context-sensitive theories, i.e. a generalised understanding that can be used to draw implications for cases with similar characteristics.
Context. Social-ecological systems research | Natural resource management | common pool resources | cooperation. Social-ecological systems  represents lens that represents challenges related to environmental change as a complex adaptive system, i.e. the social and ecological system are embedded, interconnected and are continuously changing. Understanding the complexity of challenges is considered key in being able to strengthen the capacity to deal unexpected events and crises and identifying sustainable ways for humans to live within the Earth’s boundaries.

More info. conference paper, paper (in prep), software on openABM

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