Mapping Mental Models Through an Improved Method for Identifying Causal Structures in Qualitative Data

Qualitative data are commonly used in the development of system dynamicsmodels, but methods for systematically identifying causal structures in qualita-tive data have not been widely established. This article presents a modifiedprocess for identifying causal structures (e.g., feedback loops) that are commu-nicated implicitly or explicitly and utilizes software to make coding, tracking,and model rendering more efficient. This approach draws from existingmethods, system dynamics best practice, and qualitative data analysis tech-niques. Steps of this method are presented along with a description of causalstructures for an audience new to system dynamics. The method is applied to aset of interviews describing mental models of clinical practice transformationfrom an implementation study of screening and treatment for unhealthy alco-hol use in primary care. This approach has the potential to increase rigour andtransparency in the use of qualitative data for model building and to broadenthe user base for causal-loop diagramming

Robustness of Model Predictions under Extension

Often, mathematical models of the real world are simplified representations of complex systems. A caveat to using models for analysis is that predicted causal effects and conditional independences may not be robust under model extensions, and therefore applicability of such models is limited. In this work, we consider conditions under which qualitative model predictions are preserved when two models are combined. We show how to use the technique of causal ordering to efficiently assess the robustness of qualitative model predictions and characterize a large class of model extensions that preserve these predictions. For dynamical systems at equilibrium, we demonstrate how novel insights help to select appropriate model extensions and to reason about the presence of feedback loops. We apply our ideas to a viral infection model with immune responses.Comment: Accepted for oral presentation at the Causal Discovery & Causality-Inspired Machine Learning Workshop at Neural Information Processing Systems, 202

Causal Loop Analysis of coastal geomorphological systems

As geomorphologists embrace ever more sophisticated theoretical frameworks that shift from simple notions of evolution towards single steady equilibria to recognise the possibility of multiple response pathways and outcomes, morphodynamic modellers are facing the problem of how to keep track of an ever-greater number of system feedbacks. Within coastal geomorphology, capturing these feedbacks is critically important, especially as the focus of activity shifts from reductionist models founded on sediment transport fundamentals to more synthesist ones intended to resolve emergent behaviours at decadal to centennial scales. This paper addresses the challenge of mapping the feedback structure of processes controlling geomorphic system behaviour with reference to illustrative applications of Causal Loop Analysis at two study cases: (1) the erosion–accretion behaviour of graded (mixed) sediment beds, and (2) the local alongshore sediment fluxes of sand-rich shorelines. These case study examples are chosen on account of their central role in the quantitative modelling of geomorphological futures and as they illustrate different types of causation. Causal loop diagrams, a form of directed graph, are used to distil the feedback structure to reveal, in advance of more quantitative modelling, multi-response pathways and multiple outcomes. In the case of graded sediment bed, up to three different outcomes (no response, and two disequilibrium states) can be derived from a simple qualitative stability analysis. For the sand-rich local shoreline behaviour case, two fundamentally different responses of the shoreline (diffusive and anti-diffusive), triggered by small changes of the shoreline cross-shore position, can be inferred purely through analysis of the causal pathways. Explicit depiction of feedback-structure diagrams is beneficial when developing numerical models to explore coastal morphological futures. By explicitly mapping the feedbacks included and neglected within a model, the modeller can readily assess if critical feedback loops are included

Causal Loop Analysis of coastal geomorphological systems

As geomorphologists embrace ever more sophisticated theoretical frameworks that shift from simple notions of evolution towards single steady equilibria to recognise the possibility of multiple response pathways and outcomes, morphodynamic modellers are facing the problem of how to keep track of an ever-greater number of system feedbacks. Within coastal geomorphology, capturing these feedbacks is critically important, especially as the focus of activity shifts from reductionist models founded on sediment transport fundamentals to more synthesist ones intended to resolve emergent behaviours at decadal to centennial scales. This paper addresses the challenge of mapping the feedback structure of processes controlling geomorphic system behaviour with reference to illustrative applications of Causal Loop Analysis at two study cases: (1) the erosion-accretion behaviour of graded (mixed) sediment beds, and (2) the local alongshore sediment fluxes of sand-rich shorelines. These case study examples are chosen on account of their central role in the quantitative modelling of geomorphological futures and as they illustrate different types of causation. Causal loop diagrams, a form of directed graph, are used to distil the feedback structure to reveal, in advance of more quantitative modelling, multi-response pathways and multiple outcomes. In the case of graded sediment bed, up to three different outcomes (no response, and two disequilibrium states) can be derived from a simple qualitative stability analysis. For the sand-rich local shoreline behaviour case, two fundamentally different responses of the shoreline (diffusive and anti-diffusive), triggered by small changes of the shoreline cross-shore position, can be inferred purely through analysis of the causal pathways. Explicit depiction of feedback-structure diagrams is beneficial when developing numerical models to explore coastal morphological futures. By explicitly mapping the feedbacks included and neglected within a model, the modeller can readily assess if critical feedback loops are included

Protocol for a mixed methods realist evaluation of a health service user feedback system in Bangladesh

Introduction: Responsiveness to service users’ views is a widely-recognised objective of health systems. A key component of responsive health systems is effective interaction between users and service providers. Despite a growing literature on patient feedback from high-income settings, less is known about effectiveness of such systems in low and middle income countries. Methodology and analysis: This paper disseminates the protocol for an 18-month ‘RESPOND’ project that aims to evaluate the system of collecting and responding to user feedback in Bangladesh. This mixed-method study uses a Realist Evaluation approach to examine user feedback systems at two Upazila Health Complexes in Comilla district of Bangladesh, and comprises three steps: i) initial theory development; ii) theory validation; and iii) theory refinement and development of lessons learned. The project also utilises: i) Process evaluation to understand causal mechanisms and contexts of implementation; ii) Statistical analysis of patient feedback to clarify the nature of issues reported; iii) Social science methods to illuminate feedback processes and user and provider experiences; and iv) Health policy and systems research to clarify issues related to integration of feedback systems with quality assurance and human resource management. During data analysis, qualitative and quantitative findings will be integrated throughout to help achieve study objectives. Analysis of qualitative and quantitative data will be done using a convergent mixed methods model, involving continuous triangulation of multiple datasets to facilitate greater understanding of the context of user feedback systems including the links with relevant policies, practices and programmes. Ethics and dissemination: Ethics approvals were obtained from the University of Leeds and the Bangladesh Medical Research Council. All data collected for this study will be anonymised and identifying characteristics of respondents will not appear in a final manuscript or reports. The study findings will be presented at scientific conferences and published in peer-reviewed journals

Going Beyond Functionings to Capabilities : an Econometric Model to Explain and Estimate Capabilities

Any attempt to operationalise the capability approach necessitates an adequate framework for the measurement of the abstract unobservable multidimensional concept that the term human development stands for. One such attempt is the latent variable approach including principal components, factor analysis and MIMIC models. The first two models provide estimates of the latent variables but are silent on the factors influencing these variables (capabilities in our context). MIMIC models represent a step further in this direction as they include exogenous “causal” variables for the latent factors but the effects go only in one direction i.e. from the “causes” to the latent variables. We argue that some of these causal factors not only influence human development but they are also influenced by it and that unless this feedback mechanism is taken into account we do not have a complete picture of this complex phenomenon. In this paper we present a theoretical framework incorporating the above aspects into a coherent system of causes, effects and interactions, leading to an econometric model which represents a generalisation of existing latent variable models. Estimating the model will enable us to explain the level of capabilities, say how they can be best improved, test our theoretical hypotheses and derive estimators that reflect the actual capabilities rather than just the functionings.human development, capability approach, latent variables, qualitative response, simultaneous equations.

The uses of qualitative data in multimethodology:Developing causal loop diagrams during the coding process

In this research note we describe a method for exploring the creation of causal loop diagrams (CLDs) from the coding trees developed through a grounded theory approach and using computer aided qualitative data analysis software (CAQDAS). The theoretical background to the approach is multimethodology, in line with Minger’s description of paradigm crossing and is appropriately situated within the Appreciate and Analyse phases of PSM intervention. The practical use of this method has been explored and three case studies are presented from the domains of organisational change and entrepreneurial studies. The value of this method is twofold; (i) it has the potential to improve dynamic sensibility in the process of qualitative data analysis, and (ii) it can provide a more rigorous approach to developing CLDs in the formation stage of system dynamics modelling. We propose that the further development of this method requires its implementation within CAQDAS packages so that CLD creation, as a precursor to full system dynamics modelling, is contemporaneous with coding and consistent with a bridging strategy of paradigm crossing

Long-Term Functionality of Rural Water Services in Developing Countries: A System Dynamics Approach to Understanding the Dynamic Interaction of Causal Factors

Research has shown that sustainability of rural water infrastructure in developing countries is largely affected by the dynamic and systemic interactions of technical, social, financial, institutional, and environmental factors that can lead to premature water system failure. This research employs systems dynamic modeling, which uses feedback mechanisms to understand how these factors interact dynamically to influence long-term rural water system functionality. To do this, the research first identified and aggregated key factors from literature, then asked water sector experts to indicate the polarity and strength between factors through Delphi and cross impact survey questionnaires, and finally used system dynamics modeling to identify and prioritize feedback mechanisms. The resulting model identified 101 feedback mechanisms that were dominated primarily by three and four-factor loops that contained some combination of the factors: Water System Functionality, Community, Financial, Government, Management, and Technology. These feedback mechanisms were then scored and prioritized, with the most dominant feedback mechanism identified as Water System Functionality – Community – Finance – Management. This research offers insight into the dynamic interaction of factors impacting sustainability of rural water infrastructure through the identification of these feedback mechanisms and makes a compelling case for future research to longitudinally investigate the interaction of these factors in various contexts

IT Project Management from a Systems Thinking Perspective: A Position Paper

We proposes a Systems Thinking approach to the study of IT project management and show how this approach helps project managers in controlling their projects. To illustrate our proposal, we present an example model of the dynamics of IT out-sourcing projects. The example model explains these dynamics in terms of feedback loops consisting of causal relations re-ported in the literature. The model provides insight in how coordination, trust, information exchange and possibilities for op-portunistic behaviour influence each other and together influence delivery quality, which in turn influences trust. The integra-tion of these insights provided by applying the Systems Thinking perspective helps project managers to reason about how their choices influence project outcome. The Systems Thinking perspective can serve as an additional tool in the academic study of IT project management. Applying the Systems Thinking perspective also calls for additional research in which this perspective is itself the object of study