Complexity and Ecosystem Management: The Theory and Practice of Multi-agent SystemsMarco Janssen The quality of ecosystems is affected by the actions of different stakeholders who use them in a variety of ways. In order to understand this complex relationship between humans and nature, it is vital to understand the complexity of the interacting agent |
Contents
| 1 | |
Methods and Concepts | 11 |
The transition from local to global dynamics a proposed framework for agentbased thinking in social ecological systems | 13 |
Changing the rules of the game lessons from immunology and linguistics for selforganization of institutions | 35 |
Futures predictions and other foolishness | 48 |
Validation and verification of multi agent systems | 63 |
Using artificial agents to understand laboratory experiments of commonpool resources with real agents | 75 |
Implications of spatial heterogeneity of grazing pressure on the resilience of rangelands | 103 |
Adjustment costs of agrienvironmental policy switchings an agentbased analysis of the German region Hohenlohe | 127 |
Agentbased simulation of organic farming conversion in Allier département | 158 |
Scientific measurements and villagers knowledge an integrative multiagent model from the semiarid areas of Zimbabwe | 188 |
Simulating landcover change in SouthCentral Indiana an agentbased model of deforestation and afforestation | 218 |
Multiagent systems and role games collective learning processes for ecosystem management | 248 |
Institutional change for sustainable land use a participatory approach from Australia | 286 |
| 314 | |
| 343 | |
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Common terms and phrases
actions adaptive agents aggregated agricultural allows analysis approach assumed average behavior cells chapter characteristics communication compared complex considered costs crop decision defined depend described developed discussion distribution dynamics economic ecosystem effect emergent empirical environment evolution example expected experiments exploration factor farmers farms field Figure forest function future given heterogeneity households human important increase individual influence initial institutions interactions interest investment land landscape learning less mean measure methods multi-agent systems natural objectives observed organic outcomes parameters particular patterns performance period players population possible prediction present problems production region represent resource response returns role game rules scenarios sheep shows simulation slope social spatial specific stakeholders strategy structure Table theory types understanding utility validation values variables yields
Popular passages
Page 317 - Bian, L. (1997) Multiscale nature of spatial data in scaling up environmental models, in DA Quattrochi and MF Goodchild (eds) Scale in Remote Sensing and GIS, Raton Lewis Publishers, Boca Raton, FL, 13-26.
Page 327 - WD and CW Ramm, 1987. Correct Formulation of the Kappa Coefficient of Agreement.