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Philippe J. Giabbanelli
Researcher at Furman University
Publications - 103
Citations - 1618
Philippe J. Giabbanelli is an academic researcher from Furman University. The author has contributed to research in topics: Computer science & Fuzzy cognitive map. The author has an hindex of 19, co-authored 73 publications receiving 1194 citations. Previous affiliations of Philippe J. Giabbanelli include Medical Research Council & University of Cambridge.
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Tools and methods in participatory modeling: Selecting the right tool for the job
Alexey Voinov,Alexey Voinov,Karen E. Jenni,Steven Gray,Nagesh Kolagani,Pierre D. Glynn,Pierre Bommel,Christina Prell,Moira Zellner,Michael Paolisso,Rebecca Jordan,Eleanor J. Sterling,Laura Schmitt Olabisi,Philippe J. Giabbanelli,Zhanli Sun,Christophe Le Page,Sondoss Elsawah,Todd K. BenDor,Klaus Hubacek,Klaus Hubacek,Klaus Hubacek,Bethany K. Laursen,Antoine J. Jetter,Laura Basco-Carrera,Laura Basco-Carrera,Alison Singer,Laura C. Young,Jessica Brunacini,Alex Smajgl +28 more
TL;DR: Putting more thought into the method selection process and choosing the most appropriate method for the project can produce better results, according to expert opinion and a survey of modelers engaged in participatory processes.
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A fuzzy cognitive map of the psychosocial determinants of obesity
TL;DR: This work proposes the first fuzzy cognitive map for the diagnosis of obesity based on psychosocial features and shows that small descriptions of patients' cases can be used for diagnosis.
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The nature and level of learner–learner interaction in a chemistry massive open online course (MOOC)
Andrew A. Tawfik,Todd D. Reeves,Amy E. Stich,Anila Gill,Chenda Hong,Joseph McDade,Venkata Sai Pillutla,Xiaoshu Zhou,Philippe J. Giabbanelli +8 more
TL;DR: Investigating the nature and level of learner–learner interaction within a popular Chemistry MOOC from Coursera suggests that learner-learner interactions were limited to lower phases of the IAM framework, changed over time, and was heavily dependent on a few highly-engaged learners.
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Impact of Different Policies on Unhealthy Dietary Behaviors in an Urban Adult Population: An Agent-Based Simulation Model
TL;DR: Interventions emphasizing healthy eating norms may be more effective than directly targeting food prices or regulating local food outlets and agent-based modeling may be a useful tool for testing the population-level effects of various policies within complex systems.
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Combining fuzzy cognitive maps with agent-based modeling: Frameworks and pitfalls of a powerful hybrid modeling approach to understand human-environment interactions
TL;DR: Two ways in which FCMs, which can be quickly developed using participatory modeling tools and use them to create a virtual population of agents with sophisticated decision-making processes are detailed, and key questions that modelers need to be mindful of are highlighted.