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The emergence of coupled natural and human landscapes marked a transformative interval in the human past that set our species on the road to the urbanized, industrial world in which we live, and enabled technologies and social institutions responsible for human-natural couplings in other domains. The Mediterranean region has the world's longest history of coupled natural and human landscapes. While this has resulted in severe environmental degradation in some areas, the western Mediterranean has maintained productive rural landscapes and supported dense urban populations for millennia. In many respects, the western Mediterranean embodies fundamental questions of sustainability: How can the earth sustain a human population rapidly growing toward 10 billion and not only maintain human life but also quality of life? How can humanity successfully manage a continually changing landscape driven by interacting human and natural forces to make this goal possible? This project will contribute to answering such questions, applying a new form of 'experimental socio-ecology' made possible by recent advances in computation. The methodologies proposed here, along with parallel work of others in the NSF CNH and related programs, give social science a more central role in interdisciplinary research on the grand challenges (nearly all of which are human challenges) that we face today.

We are using an integrative modeling laboratory to simulate the feedbacks in which human decisions are affected by land-cover and terrain, vegetation is affected by land-use and landscape change, and the land surface and underlying soils/sediments are affected by land-cover and land-use. Experiments carried out in this laboratory will quantitatively assess the socio-ecological consequences of variations in 1) decision-making strategies that guide land-use and response to changing social and natural environments; 2) human population, and in the number, size, and location of communities; 3) agricultural land-use practice (e.g., farming vs. herding,shifting vs. intensive cultivation); and, 4) biophysical conditions (e.g., climate, land-cover, terrain) important for agriculture. Simulated geological, botanical, cultural data will be generated during model runs that can be statistically sampled and compared with empirical data collected from real-world study areas in order to evaluate the ability of modeling experiments to characterize interlinked social and natural processes.