Abstract
Mukono-Wakiso. Both one-day sessions commenced with an exercise at farm level that was followed by an analysis at regional scale. The participants first listed important phenomena and processes relevant to problems encountered in the case study area, and subsequently identified interrelations and the strengths of these interactions. This joint effort and the visualization of the result in a cognitive map helped the platform members to improve their understanding on how to proceed in the platform process. Sensitivity analysis with the FCM-software (FuzzyDANCES) further supports systems understanding and can be used to improve the fuzzy cognitive map. We think that FCM can be applied from the start onwards in the Innovation Platform process, with regular
revisiting the cognitive maps to monitor changes in understanding of the problems at stake. At the same time, targeted research could support the subjective base of fuzzy cognitive maps with more objective information.
Apart from improving the function for FCM in innovation platforms, we try to involve FCM in methodologies that support the scaling out of interventions. Parallel to this, we look how FCM can fit in a portfolio of narratives and quantitative (model) approaches targeted at systems improvement
and development.
| Original language | English |
|---|---|
| Pages | 55 |
| Publication status | Published - 2015 |
| Event | International Conference on Integrated Systems Research - IITA, Ibadan, Nigeria Duration: 3 Mar 2015 → 6 Mar 2015 |
Conference
| Conference | International Conference on Integrated Systems Research |
|---|---|
| Country | Nigeria |
| City | Ibadan |
| Period | 3/03/15 → 6/03/15 |
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Fuzzy Cognitive Mapping for Innovation Platforms and Research in Development. / Paas, W.H.; Muchunguzi, P.; Sole, A. ; Lamers, D.; van Asten, P.; Tenywa, M.; Groot, J.C.J.
2015. 55 Abstract from International Conference on Integrated Systems Research, Ibadan, Nigeria.Research output: Contribution to conference › Abstract › Academic
TY - CONF
T1 - Fuzzy Cognitive Mapping for Innovation Platforms and Research in Development
AU - Paas, W.H.
AU - Muchunguzi, P.
AU - Sole, A.
AU - Lamers, D.
AU - van Asten, P.
AU - Tenywa, M.
AU - Groot, J.C.J.
PY - 2015
Y1 - 2015
N2 - Fuzzy Cognitive Mapping (FCM) combines mind maps with fuzzy logic to assess the (causal) relations among phenomena and processes. The strengths of these relationships are used to assess the impact of changes on the system as described in the mind map. FCM features a quick and low cost semi-quantitative systems analysis at different scales that visualizes the complex reality around interventions. Moreover, FCM can strengthen participation and ownership in platform processes. Consequently, converging to promising integrated scenarios at an early phase in the platform process could be achieved by using FCM. In addition, the inclusion of feedback loops in FCM enhances the learning process of stakeholders regarding (integrated) systems thinking. FCM was tested in two Humidtropics innovation platforms in Uganda: Kiboga-Kyankwanzi andMukono-Wakiso. Both one-day sessions commenced with an exercise at farm level that was followed by an analysis at regional scale. The participants first listed important phenomena and processes relevant to problems encountered in the case study area, and subsequently identified interrelations and the strengths of these interactions. This joint effort and the visualization of the result in a cognitive map helped the platform members to improve their understanding on how to proceed in the platform process. Sensitivity analysis with the FCM-software (FuzzyDANCES) further supports systems understanding and can be used to improve the fuzzy cognitive map. We think that FCM can be applied from the start onwards in the Innovation Platform process, with regularrevisiting the cognitive maps to monitor changes in understanding of the problems at stake. At the same time, targeted research could support the subjective base of fuzzy cognitive maps with more objective information.Apart from improving the function for FCM in innovation platforms, we try to involve FCM in methodologies that support the scaling out of interventions. Parallel to this, we look how FCM can fit in a portfolio of narratives and quantitative (model) approaches targeted at systems improvementand development.
AB - Fuzzy Cognitive Mapping (FCM) combines mind maps with fuzzy logic to assess the (causal) relations among phenomena and processes. The strengths of these relationships are used to assess the impact of changes on the system as described in the mind map. FCM features a quick and low cost semi-quantitative systems analysis at different scales that visualizes the complex reality around interventions. Moreover, FCM can strengthen participation and ownership in platform processes. Consequently, converging to promising integrated scenarios at an early phase in the platform process could be achieved by using FCM. In addition, the inclusion of feedback loops in FCM enhances the learning process of stakeholders regarding (integrated) systems thinking. FCM was tested in two Humidtropics innovation platforms in Uganda: Kiboga-Kyankwanzi andMukono-Wakiso. Both one-day sessions commenced with an exercise at farm level that was followed by an analysis at regional scale. The participants first listed important phenomena and processes relevant to problems encountered in the case study area, and subsequently identified interrelations and the strengths of these interactions. This joint effort and the visualization of the result in a cognitive map helped the platform members to improve their understanding on how to proceed in the platform process. Sensitivity analysis with the FCM-software (FuzzyDANCES) further supports systems understanding and can be used to improve the fuzzy cognitive map. We think that FCM can be applied from the start onwards in the Innovation Platform process, with regularrevisiting the cognitive maps to monitor changes in understanding of the problems at stake. At the same time, targeted research could support the subjective base of fuzzy cognitive maps with more objective information.Apart from improving the function for FCM in innovation platforms, we try to involve FCM in methodologies that support the scaling out of interventions. Parallel to this, we look how FCM can fit in a portfolio of narratives and quantitative (model) approaches targeted at systems improvementand development.
M3 - Abstract
SP - 55
ER -