Structuring ontologies from natural language for collaborative scenario modeling in agri-food systems

Prospective studies require discussing and collaborating with the stakeholders to create scenarios of the possible evolution of the studied value-chain. However, stakeholders do not always use the same words when referring to one idea. Constructing an ontology and homogenizing vocabularies is thus crucial to identify key variables, which serve in the construction of the needed scenarios. Nevertheless, it is a very complex and time-consuming task. In this paper we present the method we used to manually build ontologies adapted to the needs of two complementary system-analysis models (namely the "Godet" and the "MyChoice" models), starting from interviews of the agri-food system's stakeholders. The objective of the paper is to explore whether and how prospective studies may have to gain from complementing the methodologies used (here Godet) with formal approaches from other disciplines, such as knowledge engineering (here MyChoice), which is usually not the case currently.

How Individuals With Low Back Pain Conceptualize Their Condition: A Collaborative Modeling Approach

Low back pain (LBP) is complex. This study aimed to use collaborative modeling to evaluate conceptual models that individuals with LBP have of their condition, and to compare these models with those of researchers/clinicians. Twenty-eight individuals with LBP were facilitated to generate mental models, using "fuzzy cognitive maps," that represented conceptualization of their own LBP and LBP "in general." "Components" (ie, causes, outcomes and treatments) related to pain, disability and quality of life were proposed, along with the weighted "Connections" between Components. Components were classified into thematic categories. Weighting of Connections were summed for each Component to judge relative importance. Individual models were aggregated into a metamodel. When considering their own condition, participants' models included 19(SD = 6) Components and 43(18) Connections with greatest weight on "Biomechanical" components. When considering LBP in general, models changed slightly. Patient models contrasted the more complex models of researchers/clinicians (25(7) Components; 77(42) Connections), with most weight on "Psychological" components. This study provides unique insight into how individuals with LBP consider their condition, which is largely biomedical and narrower than clinician/researcher perspectives. Findings highlight challenges for changing public perception of LBP, and provide a method with potential utility to understand how individuals conceptualize their condition. PERSPECTIVE: Collaborative modeling was used to understand how individuals with low back pain conceptualize their own condition, the condition in general, and compare this with models of expert researchers/clinicians. Data revealed issues in how individuals with back pain conceptualize their condition, and the method's potential utility for clinical evaluation of patients.

Purpose, processes, partnerships, and products: four Ps to advance participatory socio-environmental modeling

Including stakeholders in environmental model building and analysis is an increasingly popular approach to understanding ecological change. This is because stakeholders often hold valuable knowledge about socio-environmental dynamics and collaborative forms of modeling produce important boundary objects used to collectively reason about environmental problems. Although the number of participatory modeling (PM) case studies and the number of researchers adopting these approaches has grown in recent years, the lack of standardized reporting and limited reproducibility have prevented PM's establishment and advancement as a cohesive field of study. We suggest a four-dimensional framework (4P) that includes reporting on dimensions of (1) the Purpose for selecting a PM approach (the why); (2) the Process by which the public was involved in model building or evaluation (the how); (3) the Partnerships formed (the who); and (4) the Products that resulted from these efforts (the what). We highlight four case studies that use common PM software-based approaches (fuzzy cognitive mapping, agent-based modeling, system dynamics, and participatory geospatial modeling) to understand human-environment interactions and the consequences of ecological changes, including bushmeat hunting in Tanzania and Cameroon, agricultural production and deforestation in Zambia, and groundwater management in India. We demonstrate how standardizing communication about PM case studies can lead to innovation and new insights about model-based reasoning in support of ecological policy development. We suggest that our 4P framework and reporting approach provides a way for new hypotheses to be identified and tested in the growing field of PM.