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

Lynam, Timothy; Bousquet, François; Page, Christophe Le; D’Aquino, Patrick; Barreteau, Olivier; Chinembiri, F.; Mombeshora, B.G.

doi:10.5751/es-00320-050224

Cited by 74 publications

(36 citation statements)

References 12 publications

(5 reference statements)

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“…The tools evaluated here are all flexible and can be applied adaptively to co-learning or comanagement approaches (Lynam et al 2002). However, when a completely open research agenda is initiated in a co-management context, it is hard to predict where the process will lead.…”

Section: Key Lessons From the Fieldmentioning

confidence: 99%

A Review of Tools for Incorporating Community Knowledge, Preferences, and Values into Decision Making in Natural Resources Management

Lynam¹,

Jong²,

Sheil³

et al. 2007

E&S

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417

277

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ABSTRACT. We survey and evaluate selected participatory tools that have been proven effective in natural resources management and research during our extensive experience with forest communities. We first establish a framework for our analysis by identifying a set of criteria for evaluating each tool. Next we provide a brief description of each tool, followed by an evaluation and comparison of the strengths and weaknesses of all the tools examined and how well they can be adapted to diverse contexts. We also provide suggestions for avoiding common pitfalls. Our findings suggest that most tools are flexible enough to be adapted to a range of applications, and that results are more robust when tools are used in concert. Practitioners should not be disturbed when results are contradictory or unexpected; initial surprises can lead to unexpected discoveries. Given the complexity of natural resources and their management, picking the right tool does not guarantee that the data desired will be produced, but selecting the wrong tool does make success less likely. The tools assessed are Bayesian belief networks and system dynamic modeling tools, discourse-based valuation, the 4Rs framework, participatory mapping, scoring or the Pebble Distribution Method, future scenarios, spidergrams, Venn diagrams, and Who Counts Matrices.

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Section: Key Lessons From the Fieldmentioning

confidence: 99%

A Review of Tools for Incorporating Community Knowledge, Preferences, and Values into Decision Making in Natural Resources Management

Lynam¹,

Jong²,

Sheil³

et al. 2007

E&S

Self Cite

417

277

View full text Add to dashboard Cite

show abstract

“…Consequently, the process produced an increased understanding (both social and ecological) among stakeholders. As pointed out in Lynam et al (2002), models can provide a commonly agreed representation of a problem. They are an image of the common understanding that, although imperfect, can be changed and improved with time.…”

Section: Resultsmentioning

confidence: 99%

“…Their conceptual approach is similar to the process that has taken place within the USPP as it developed the DSS model in response to water-management challenges in the San Pedro Basin. Similarly, Lynam et al (2002) present two case studies where models were developed and used with stakeholder participation to tackle common resourcemanagement problems. Discussions were facilitated with the use of different participatory tools (spidergrams, spatial mapping, belief models, and multi-agent system models) so that stakeholders would find a common understanding, define goals and priorities, and propose potential alternative solutions to address problems.…”

Section: From Theory To Practice: System-specific Resiliencementioning

confidence: 99%

Increasing Social–Ecological Resilience by Placing Science at the Decision Table: the Role of the San Pedro Basin (Arizona) Decision-Support System Model

Serrat-Capdevila¹,

Browning-Aiken²,

Lansey³

et al. 2009

E&S

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“…It facilitates effective communication with stakeholders, while promoting a focus on key variables and relationships of the system, rather than being bogged down in the details. The Bayesian network technique has increasingly been applied to land and water management problems [Cain, 2001;Lynam et al, 2002;Robertson and Wang, 2004;Varis and Kuikka, 1999].…”

Section: Bayesian Network As a Technique For System Representationmentioning

confidence: 99%

A Bayesian network approach to knowledge integration and representation of farm irrigation: 1. Model development

Wang

McAllister²,

Abuzar³

et al. 2009

Water Resources Research

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[1] Irrigation is important to many agricultural businesses but also has implications for catchment health. A considerable body of knowledge exists on how irrigation management affects farm business and catchment health. However, this knowledge is fragmentary; is available in many forms such as qualitative and quantitative; is dispersed in scientific literature, technical reports, and the minds of individuals; and is of varying degrees of certainty. Bayesian networks allow the integration of dispersed knowledge into quantitative systems models. This study describes the development, validation, and application of a Bayesian network model of farm irrigation in the Shepparton Irrigation Region of northern Victoria, Australia. In this first paper we describe the process used to integrate a range of sources of knowledge to develop a model of farm irrigation. We describe the principal model components and summarize the reaction to the model and its development process by local stakeholders. Subsequent papers in this series describe model validation and the application of the model to assess the regional impact of historical and future management intervention.

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

Cited by 74 publications

References 12 publications

A Review of Tools for Incorporating Community Knowledge, Preferences, and Values into Decision Making in Natural Resources Management

A Review of Tools for Incorporating Community Knowledge, Preferences, and Values into Decision Making in Natural Resources Management

Increasing Social–Ecological Resilience by Placing Science at the Decision Table: the Role of the San Pedro Basin (Arizona) Decision-Support System Model

A Bayesian network approach to knowledge integration and representation of farm irrigation: 1. Model development

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

Cited by 74 publications

References 12 publications

A Review of Tools for Incorporating Community Knowledge, Preferences, and Values into Decision Making in Natural Resources Management

A Review of Tools for Incorporating Community Knowledge, Preferences, and Values into Decision Making in Natural Resources Management

Increasing Social&#8211;Ecological Resilience by Placing Science at the Decision Table: the Role of the San Pedro Basin (Arizona) Decision-Support System Model

A Bayesian network approach to knowledge integration and representation of farm irrigation: 1. Model development

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Increasing Social–Ecological Resilience by Placing Science at the Decision Table: the Role of the San Pedro Basin (Arizona) Decision-Support System Model