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Causal Loop Diagrams Software ((LINK)) Free

However, I think one must be very cautious about simulating causal loop diagrams directly. A causal loop diagram is fundamentally underspecified, which is why no method of automated conversion of CLDs to models has been successful.

Causal Loop Diagrams Software Free

What I really recommend is that someone rework the software to simulate multiple loops one at a time. That would actually make for better storytelling, without risk of spurious results. You could even enhance it to generate an English narrative of the outcome, step by step.

Kumu is a simple and powerful platform for creating causal loop diagrams, stakeholder landscapes, power maps, and more. Kumu lets you unfold a diagram step-by-step, including additional data and narrative for each of the elements, connections, and loops in your map. Below is an example of a causal loop diagram built by the Hewlett Foundation to better understand the dynamics driving the US democratic system. Click on the image to be taken to the full, interactive map.

Insight Maker lets you express your thoughts using rich pictures and causal loop diagrams. It then lets you turn these diagrams into powerful simulation models. All this happens right in your browser, for free.

Within a qualitative case study, a schema was developed that incorporated HP goals, actions and strategies with WHO building blocks (leadership and governance, financing, workforce, services and information). The case was a multisectoral health system bounded in terms of geographical and governance structures and a history of support for HP. A detailed analysis of 20 state government strategic documents and interviews with 53 stakeholders from multiple sectors were completed. Based upon key findings and dominants themes, causal pathways and feedback loops were established. Finally, a causal loop diagram was created to visualise the complex array of feedback loops in the multisectoral health system that influenced HP policy and practice.

Creating a causal loop diagram enabled visualisation of the emergent properties of the case health system. It also highlighted specific leverage points at which HP policy and practice can be improved. This paper demonstrates the critical importance of leveraging leadership and governance for HP and adds urgency to the need for increased and strong advocacy efforts targeting all levels of government in multisectoral health systems.

When a causal link demonstrated a reciprocal relationship, a feedback loop was created. Each feedback loop was assessed in terms of its polarity (positive polarity signifying a reinforcing relationship and negative polarity signifying a balancing relationship) thus establishing whether the loop was a facilitating or inhibiting factor for HP policy and practice [50].

All feedback loops were then assembled into a causal loop diagram to create a visual model [51]. Vensim PLE software was used to create word-and-arrow diagrams, feedback loops and the causal loop diagram. In the interest of providing a more reader-friendly diagram, facilitating (happy face) or inhibiting (sad face) influences on HP policy and practice in the case health system were used (i.e. the polarity of each feedback loop is not labelled).

First, an overview of the HP policy and practice context followed by key findings from the document analysis and interviews are presented. The next section interweaves reporting on dominant themes and the feedback mechanisms identified. Finally, the causal loop diagram portraying all feedback mechanisms in play in the case study health system with respect to HP policy and practice is described.

Table 5 provides a list of key findings and illustrates, through check marks, if they were found in document review and/or interview data. Dominant themes are those where key findings were found in both document review and interview data (two check marks). In the following section, dominant themes are reported and feedback mechanisms identified. All feedback mechanisms are illustrated in one causal loop diagram (Fig. 2) and dominant themes are indicated through bold font. A detailed explanation of each feedback mechanism can be found in Additional file 1 (Description of causal links and feedback mechanisms).

Our use of a causal loop diagram enabled us to identify the complex interplay of factors that affect HP and explain why the case study health system no longer supported HP. We found a complex picture with numerous interactions and feedback mechanisms represented in the causal loop diagram. The approach used helped us understand the patterns in system behaviour. Doing this makes it possible to identify potential opportunities to disrupt or slow down vicious feedback mechanisms and/or amplify those that are virtuous cycles. The majority of feedback loops in the causal loop diagram were vicious cycles that would need to be disrupted or changed for HP to thrive in the case study heath system. Changing even one feedback loop could change the emergent order of the system because system behaviour is a product of how the parts fit together and not how they act separately. Thus, feedback mechanisms can be seen as leverage points to strengthen systems [59] and this section highlights potential implications and links to other literature.

Creating causal loop diagrams in conjunction with group model building processes with stakeholders is called for in the literature [78]. Time and resource constraints did not permit this step. Although the research team undertook extensive discussion and achieved consensus on the causal loop diagram, facilitating a group model building process would have been preferable to not only gain their perspectives but to engage in discussion about implications, priority leverage points and actions to strengthen HP in the case health system. Thus, future research could build upon this research and use participatory systemic inquiry methods [79].

Leadership and governance for HP were found to be central factors that influenced HP policy and practice confirming findings from other jurisdictions around the world [62]. This study demonstrates its critical importance and adds urgency to the need for increased and strong advocacy for HP. The application of a complex systems approach to HP policy and practice addressed a gap in the literature. Our new methods have made visible the complex web of factors that influenced HP in an Australian multisectoral health system. Our approach was pioneering in that we combined health system building blocks and feedback mechanisms as leverage points [59]. Our causal loop diagram offered a picture of the broad array of interdependent facilitating and inhibiting factors that can be targeted to improve HP policy and practice.

The lack of global data flow in healthcare systems negatively impacts decision-making both locally and globally. This Chapter aims to introduce global health specialists to causal loop diagrams (CLDs) and system dynamics models to help them better frame, examine, and understand complex issues characteristic to data-rich ecosystems. As machine and statistical learning tools become popular among data scientists and researchers, they can help us understand how various data sources and variables interact with each other mechanistically. These complementary approaches go a step beyond machine and statistical learning tools to represent causality between variables affecting data-driven ecosystems and decision-making.

System dynamics is a fundamentally interdisciplinary field of study that helps us understand complex systems and the sources of policy resistance in that system to be able to guide effective change (Sterman 2001). Within system dynamics, causal loop diagrams are the main analytical tools that assist in the identification and visualization of key variables and the connections between them. The related systems modelling methodology of system dynamics involves computer simulation models that are fundamentally unique to each problem setting (Homer and Hirsch 2006: 452).

This section will give a more thorough introduction to the terminologies, concepts, equations, and tools utilized in system dynamics. The first part will discuss CLDs and, with the use of a classic example, their role in visualizing the relationships that govern complex systems. Then, we will introduce and describe how stock and flow diagrams quantitatively build upon the qualitative relationships mapped out in CLDs. Finally, we briefly discuss the software utilized to simulate and test multiple scenarios for a given system dynamics model.

Stock & flow diagram of the Lotka-Volterra model. Assuming arbitrary initial values and constants, the theoretical results of the Lotka-Volterra system were generated using the software Vensim to demonstrate that the stock and flow diagrams is identical to the mathematical formulations. The top right represents the phase space between wolf and sheep populations. The bottom right diagram represent the time series of wolf and sheep populations

Causal loop diagrams developed by groups capture a shared understanding of complex problems and provide a visual tool to guide interventions. This paper explores the application of network analytic methods as a new way to gain quantitative insight into the structure of an obesity causal loop diagram to inform intervention design. Identification of the structural features of causal loop diagrams is likely to provide new insights into the emergent properties of complex systems and analysing central drivers has the potential to identify leverage points. The results found the structure of the obesity causal loop diagram to resemble commonly observed empirical networks known for efficient spread of information. Known drivers of obesity were found to be the most central variables along with others unique to obesity prevention in the community. While causal loop diagrams are often specific to single communities, the analytic methods provide means to contrast and compare multiple causal loop diagrams for complex problems.


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