A Cognitive Model of an Epistemic Community: Mapping the Dynamics of Shallow Lake Ecosystems

We used fuzzy cognitive mapping (FCM) to develop a generic shallow lake ecosystem model by augmenting the individual cognitive maps drawn by 8 scientists working in the area of shallow lake ecology. We calculated graph theoretical indices of the individual cognitive maps and the collective cognitive map produced by augmentation. The graph theoretical indices revealed internal cycles showing non-linear dynamics in the shallow lake ecosystem. The ecological processes were organized democratically without a top-down hierarchical structure. The steady state condition of the generic model was a characteristic turbid shallow lake ecosystem since there were no dynamic environmental changes that could cause shifts between a turbid and a clearwater state, and the generic model indicated that only a dynamic disturbance regime could maintain the clearwater state. The model developed herein captured the empirical behavior of shallow lakes, and contained the basic model of the Alternative Stable States Theory. In addition, our model expanded the basic model by quantifying the relative effects of connections and by extending it. In our expanded model we ran 4 simulations: harvesting submerged plants, nutrient reduction, fish removal without nutrient reduction, and biomanipulation. Only biomanipulation, which included fish removal and nutrient reduction, had the potential to shift the turbid state into clearwater state. The structure and relationships in the generic model as well as the outcomes of the management simulations were supported by actual field studies in shallow lake ecosystems. Thus, fuzzy cognitive mapping methodology enabled us to understand the complex structure of shallow lake ecosystems as a whole and obtain a valid generic model based on tacit knowledge of experts in the field.Comment: 24 pages, 5 Figure

Policy compass: FCM-based policy impact evaluation using public open data

This paper presents how Fuzzy Cognitive Map (FCM) technique can be applied by a policy maker to support the policy impact evaluation using the example of Policy Compass, an EU research project. The practical usage example on interest rate policy shows the potential of FCMs as a policy impact modelling tool. Through the provision of a more intuitive and easier means of using open data based on FCM techniques, the Policy Compass project can play a critical role for both policy maker and lay public to evaluate the policy impact and prepare for future policy making

Identifying the Components and Interrelationships of Smart Cities in Indonesia: Supporting Policymaking via Fuzzy Cognitive Systems

Multiple Indonesian cities currently aim to qualify as “smart cities.” Previous research on defining smart cities (e.g., the implementation-oriented maturity model) tends to focus on components over interrelationships, is challenging to apply to a specific context such as Indonesia, and offers limited support for policy-relevant questions. In this paper, we propose to address these shortcomings to support policymakers in identifying concrete action plans in Indonesia specifically. Our approach clarifies interrelationships for the context of use and supports structural (e.g., what aspects of a “smart city” are impacted by an intervention?) as well as what-if policy questions. We started with a systems\u27 science approach to developing a cognitive map of the components and their interrelationships, as is increasingly done in participatory modeling and particularly in socio-ecological management. We transformed semi-structured interviews of 10 Indonesian experts into maps and assembled them to create the first comprehensive smart cities cognitive map for Indonesia, totaling 52 concepts and 98 relationships. While a cognitive map already provides support for decision-making (e.g., by identifying loops in the system), it is only conceptual and thus cannot form predictions. Consequently, we extended our cognitive map into a fuzzy cognitive map (FCM), whose inference abilities allow to examine the dynamic response of an indicator (e.g., “smart city”) in response to different interventions. As fuzzy cognitive maps include the strengths of interrelationships but not the notion of time, future research may refine our model using system dynamics. This refinement would support policymakers in investigating when to conduct and/or evaluate an intervention

A Framework for Personalized Content Recommendations to Support Informal Learning in Massively Diverse Information WIKIS

Personalization has proved to achieve better learning outcomes by adapting to specific learners’ needs, interests, and/or preferences. Traditionally, most personalized learning software systems focused on formal learning. However, learning personalization is not only desirable for formal learning, it is also required for informal learning, which is self-directed, does not follow a specified curriculum, and does not lead to formal qualifications. Wikis among other informal learning platforms are found to attract an increasing attention for informal learning, especially Wikipedia. The nature of wikis enables learners to freely navigate the learning environment and independently construct knowledge without being forced to follow a predefined learning path in accordance with the constructivist learning theory. Nevertheless, navigation on information wikis suffer from several limitations. To support informal learning on Wikipedia and similar environments, it is important to provide easy and fast access to relevant content. Recommendation systems (RSs) have long been used to effectively provide useful recommendations in different technology enhanced learning (TEL) contexts. However, the massive diversity of unstructured content as well as user base on such information oriented websites poses major challenges when designing recommendation models for similar environments. In addition to these challenges, evaluation of TEL recommender systems for informal learning is rather a challenging activity due to the inherent difficulty in measuring the impact of recommendations on informal learning with the absence of formal assessment and commonly used learning analytics. In this research, a personalized content recommendation framework (PCRF) for information wikis as well as an evaluation framework that can be used to evaluate the impact of personalized content recommendations on informal learning from wikis are proposed. The presented recommendation framework models learners’ interests by continuously extrapolating topical navigation graphs from learners’ free navigation and applying graph structural analysis algorithms to extract interesting topics for individual users. Then, it integrates learners’ interest models with fuzzy thesauri for personalized content recommendations. Our evaluation approach encompasses two main activities. First, the impact of personalized recommendations on informal learning is evaluated by assessing conceptual knowledge in users’ feedback. Second, web analytics data is analyzed to get an insight into users’ progress and focus throughout the test session. Our evaluation revealed that PCRF generates highly relevant recommendations that are adaptive to changes in user’s interest using the HARD model with rank-based mean average precision (MAP@k) scores ranging between 100% and 86.4%. In addition, evaluation of informal learning revealed that users who used Wikipedia with personalized support could achieve higher scores on conceptual knowledge assessment with average score of 14.9 compared to 10.0 for the students who used the encyclopedia without any recommendations. The analysis of web analytics data show that users who used Wikipedia with personalized recommendations visited larger number of relevant pages compared to the control group, 644 vs 226 respectively. In addition, they were also able to make use of a larger number of concepts and were able to make comparisons and state relations between concepts