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The role of systems analysis in co-learning. Walter Rossing
The role of systems analysis in co-learning. Walter Rossing
The role of systems analysis in co-learning. Walter Rossing
The role of systems analysis in co-learning. Walter Rossing
The role of systems analysis in co-learning. Walter Rossing
The role of systems analysis in co-learning. Walter Rossing
The role of systems analysis in co-learning. Walter Rossing
The role of systems analysis in co-learning. Walter Rossing
The role of systems analysis in co-learning. Walter Rossing
The role of systems analysis in co-learning. Walter Rossing
The role of systems analysis in co-learning. Walter Rossing
The role of systems analysis in co-learning. Walter Rossing
The role of systems analysis in co-learning. Walter Rossing
The role of systems analysis in co-learning. Walter Rossing
The role of systems analysis in co-learning. Walter Rossing
The role of systems analysis in co-learning. Walter Rossing
The role of systems analysis in co-learning. Walter Rossing
The role of systems analysis in co-learning. Walter Rossing
The role of systems analysis in co-learning. Walter Rossing
The role of systems analysis in co-learning. Walter Rossing
The role of systems analysis in co-learning. Walter Rossing
The role of systems analysis in co-learning. Walter Rossing
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The role of systems analysis in co-learning. Walter Rossing

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A presentation from the WCCA 2011 event held in Brisbane, Australia.

A presentation from the WCCA 2011 event held in Brisbane, Australia.

Published in: Education, Technology
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  • 1. The role of systems analysis in co-learning Walter Rossing Wageningen Centre for Agro-ecology and Systems Analysis (WaCASA), Wageningen University
  • 2. Take home messages Systems analysis offers varied career opportunities Model to create diversity, not to find the answer Projects with impact start from vague deliverables
  • 3. Outline of presentation Learning and systems research cycles Different types of problems and systems research Co-learning and boundary work Knowledge for different uses Effective co-learning strategies Challenges for systems science
  • 4. Learning Learning is the process whereby knowledge is created through the transformation of experience: the learning cycle (Kolb 1984, Prentice Hall)  Abstract versus concrete  Active versus reflective
  • 5. The learning cycle Action: Implementing a ‘bright idea’ Plan: Observation: Which Find out improvements? consequences Analysis: What are implications? Kolb 1984, Prentice Hall
  • 6. The learning cycle, supported by the research cycle Action: Design / select: Implementing a Describe: Which? ‘bright idea’ what? Plan: Observation: Which Find out improvements? consequences Analysis: Explore: What are Explain: what if? implications? why? Models to support the reflective phases Kolb 1984, Prentice Hall Giller et al. 2008, Ecol. & Soc.
  • 7. Learning to make decisions: Four types of problems.. Far from certainty on required Moderately Unstructured and structured problems problems available (goals) knowledge Moderately Structured structured problems problems (means) Close to certainty Close to agreement on Far from agreement norms and values at stake Hisschemöller & Hoppe 2001, Policy Studies Review Annual
  • 8. ... and the role of scienceFar fromcertainty on Moderately structured Unstructured problemsrequired problems (goals)and ‘Messy’ problems Science as problemavailable Science as analyst or recognizer advocateknowledge Moderately structured Structured problems problems (means) Science as problemClose to Science as mediator solvercertainty Close to agreement on Far from agreement norms and values at stake R. Hoppe 2007, WUR-CSIRO Workshop
  • 9. Co-learning as a way to deal with ‘messy’ problems A process in which several agents simultaneously try to adapt to each others behaviour so as to produce desirable global system properties Co-learning is stimulated by boundary work:  Social practices to mediate between knowledge and action  Not fixed, negotiated and re-negotiated  Often implicit or ambiguous  Multiple arrangements per institute and per person possible
  • 10. Five boundary arrangements in land use modelling 3 cases 1 case 1 case No preferred arrangement in 4 cases the literature Sterk et al. 2009, Land Use Pol.
  • 11. Outline of presentation Learning and systems research cycles Different types of problems and systems research Co-learning and boundary work Knowledge for different uses Effective co-learning strategies Challenges for systems science
  • 12. Outline of presentation Learning and systems research cycles Different types of problems and systems research Co-learning and boundary work Knowledge for different uses Effective co-learning strategies Challenges for systems science
  • 13. Knowledge: different uses, different requirements Technically Relevant to Fair, unbiased, adequate in the decision respectful of all handling of or policy? stakeholders? evidence? Credibility Saliency Legitimacy Enlightenment: no clear user Decision support: single user Negotiation support: multiple users Adapted from Clark et al. 2011, PNAS
  • 14. Knowledge: different uses, different requirements Technically Relevant to Fair, unbiased, adequate in the decision respectful of all handling of or policy? stakeholders? evidence? Credibility Saliency Legitimacy Enlightenment: no clear user *** Decision support: single user *** *** Negotiation support: multiple users *** *** *** Deviation from - ++ +++ science tradition Adapted from Clark et al. 2011, PNAS
  • 15. Effective co-learning strategies Meaningful participation during agenda setting and research Arrangements for accountability Production of boundary objects, adaptable and robust to different viewpoints Carberry et al. 2002 McCown 2002 Sterk et al. 2009 Land Use Pol Clark et al. 2011 PNAS
  • 16. Participation in agenda setting and research Goal definition • Credible: ⋎ Formulation of a case-specific perception of sustainability • Salient: ? • Legitimate: ?? Indicator set Integrative models System definition Evaluation of the state Express system performance Definition of actual or aspired: dimensions and in terms of indicator set potential agro-ecosystems thresholds Sustainability assessment Rossing et al. 2007, AGEE
  • 17. Arrange for accountability E.g. EU project PURE: IPM for pesticide reduction Innovation system Suppliers Retail Extension Process NGOs facilitation: keeping up the ambition throughReflexive monitoring among involvement Research on bothcase study leaders and production and innovationmonitors system
  • 18. Model outputs as boundary objectsCalculated solutions Scenario studies Optimization Pareto based explorationin white Objective 2 Objective 1 Area of possible solutions Groot et al. 2009, JEM
  • 19. Model outputs as boundary objects Pareto based explorationObjective 2 Objective 1 Groot et al. 2010, EJA Groot & Rossing, 2011, MEE
  • 20. Challenges for systems science from boundary work  Requirements on knowledge  Credibility: science business-as-usual  Saliency: specificity versus generality  Legitimacy: research versus social embedding  Requirements on organization of research (projects!)  Accommodating multiple disciplines, stakeholders, levels of analysis  Providing governance to balance the above  Focus on research products that stimulate co-learning
  • 21. Take home messages From hermitic A focus on boundary scientist to objects stimulates political activist co-learning Systems analysis offers varied career opportunities Model to create diversity, not to find the answer Projects with impact start from vague deliverables Adaptive (self-reflexive) project management is indispensable (and a research topic in itself!)
  • 22. Thank you for your attention!© Wageningen UR

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