An adaptive agent model for analysing co-evolution of management and policies in a complex rangeland system

Janssen, Marco A.; Walker, Brian; Langridge, J.; Abel, Nick

doi:10.1016/s0304-3800(00)00256-8

Cited by 138 publications

(58 citation statements)

References 22 publications

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“…The behavior of the whole system depends on these interactions between agents, which can be represented in a model. MAS are used to set up spatial models, which integrate social and ecological dimensions (Bousquet 1994, Barreteau and Bousquet 2000, Janssen et al 2000, Kohler and Gumerman 2000. The aim of the modeling experiment was not to represent the whole system, but to build and test theories.…”

Section: Introductionmentioning

confidence: 99%

Adapting Science to Adaptive Managers: Spidergrams, Belief Models, and Multi-agent Systems Modeling

Lynam¹,

Bousquet²,

Page³

et al. 2002

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Section: Introductionmentioning

confidence: 99%

Adapting Science to Adaptive Managers: Spidergrams, Belief Models, and Multi-agent Systems Modeling

Lynam¹,

Bousquet²,

Page³

et al. 2002

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“…Recent research is focused on anticipating regime shifts (Biggs et al, 2009;Scheffer et al, 2012), optimal management of systems with regime shifts (Brock and Starrett, 2003;Horan et al, 2011;Polasky et al, 2011), or studying adaptive management in relation to threshold dynamics, using either multi-agent based models for studying emergent properties (Janssen and Carpenter, 1999;Janssen et al, 2000) or laboratory experiments with human subjects (Lindahl et al, 2012). However, the role of threshold dynamics for the LBD processes itself, is in dire need of further exploration.…”

Section: Thresholds Dynamicsmentioning

confidence: 99%

Modeling experiential learning: The challenges posed by threshold dynamics for sustainable renewable resource management

Lindkvist

Norberg

2014

Ecological Economics

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This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues.Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. Adaptive management incorporates learning-by-doing (LBD) in order to capture learning and knowledge generation processes, crucial for sustainable resource use in the presence of uncertainty and environmental change. By contrast, an optimization approach to management identifies the most efficient exploitation strategy by postulating an absolute understanding of the resource dynamics and its inherent uncertainties. Here, we study the potential and limitations of LBD in achieving optimal management by undertaking an analysis using a simple growth model as a benchmark for evaluating the performance of an agent equipped with a 'state-of-the-art' learning algorithm. The agent possesses no a priori knowledge about the resource dynamics, and learns management solely by resource interaction. We show that for a logistic growth function the agent can achieve 90% efficiency compared to the optimal control solution, whereas when a threshold (tipping point) is introduced, efficiency drops to 65%. Thus, our study supports the effectiveness of the LBD approach. However, when a threshold is introduced efficiency decreases as experimentation may cause resource collapse. Further, the study proposes that: an appropriate amount of experimentation, high valuation of future stocks (discounting) and, a modest rate of adapting to new knowledge, will likely enhance the effectiveness of LBD as a management strategy.

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“…Based on the above description of the New South Wales rangelands and focusing on pastoralists and wool production, Janssen et al (2000) developed an agent-based model of a rangeland system. The details of the model specification can be found in that publication.…”

Section: Agent-based and Optimization Models Of The Rangeland Systemmentioning

confidence: 99%

“…The model of Janssen et al (2000) provides a starting point for simulation models that assess suitable action rules of managers and regulators across different levels of spatial and temporal scales. However, the approach is limited by not allowing real-time learning or the introduction of novel management strategies and combinations.…”

Section: Agent-based and Optimization Models Of The Rangeland Systemmentioning

confidence: 99%

Rangelands, pastoralists and governments: interlinked systems of people and nature

Walker¹,

Janssen

2002

Phil. Trans. R. Soc. Lond. B

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We analyse commercially operated rangelands as coupled systems of people and nature. The biophysical components include: (i) the reduction and recovery of potential primary production, reflected as changes in grass production per unit of rainfall; (ii) changes in woody plants dependent on the grazing and fire regimes; and (iii) livestock and wool dynamics influenced by season, condition of the rangeland and numbers of wild and feral animals. The social components include the managers, who vary with regard to a range of cognitive abilities and lifestyle choices, and the regulators who vary in regard to policy goals.We compare agent-based and optimization models of a rangeland system. The agent-based model leads to recognition that policies select for certain management practices by creating a template that governs the trajectories of the behaviour of individuals, learning, and overall system dynamics. Conservative regulations reduce short-term loss in production but also restrict learning. A free-market environment leads to severe degradation but the surviving pastoralists perform well under subsequent variable conditions. The challenge for policy makers is to balance the needs for learning and for preventing excessive degradation. A genetic algorithm model optimizing for net discounted income and based on a population of management solutions (stocking rate, how much to suppress fire, etc.) indicates that robust solutions lead to a loss of about 40% compared with solutions where the sequence of rainfall was known in advance: this is a similar figure to that obtained from the agent-based model.We conclude that, on the basis of Levin's three criteria, rangelands with their livestock and human managers do constitute complex adaptive systems. If this is so, then command-and-control approaches to rangeland policy and management are bound to fail.

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An adaptive agent model for analysing co-evolution of management and policies in a complex rangeland system

Cited by 138 publications

References 22 publications

Adapting Science to Adaptive Managers: Spidergrams, Belief Models, and Multi-agent Systems Modeling

Adapting Science to Adaptive Managers: Spidergrams, Belief Models, and Multi-agent Systems Modeling

Modeling experiential learning: The challenges posed by threshold dynamics for sustainable renewable resource management

Rangelands, pastoralists and governments: interlinked systems of people and nature

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