Ecosystem Services: On Idealization and Understanding Complexity

Our ability to scientifically know complex systems exceeds our ability to individually understand complexity. While the limits of knowledge within ecosystem services research can be expanded by added model or valuation complexity, a delicate balance between representation of complex systems and transparency for understanding suggests caution should be taken when designing applied ecosystem service research. An expanded view of processes of scientific knowledge production in ecosystem services research, particularly the function of idealizations, can refocus conversations about standards for ecosystem service research to consider tradeoffs between tractability for decision-support, representation of the systems at hand, and explanatory power for plotting out rough courses of action. True understanding involves recognizing idealization and being able to map scientific depiction back onto to the real world, suggesting a critical challenge moving forward is to be aware of and explicit about the limits of knowing through the lens of ecosystem services, and to seek transparent and tractable assessment methods that provide targeted understanding of the necessary information for particular decision contexts.