![]() ![]() ![]() Ultimately, the ability to see systems “as a whole” provides a framework for understanding complexity and change, testing levers for policies that would result in sustainable progress (Cavana and Mares 2004 Homer and Hirsch 2006 Senge 1990). These steps are selectively repeated until the model is able to generate useful insights while meeting certain requirements, such as its realism, robustness, and flexibility (Homer and Hirsch 2006). Once computerized, these models offer ways of systematically testing policies and scenarios in ways that answer both “what if” and “why” (Homer and Hirsch 2006).įurther, since modelling is iterative, the process relies on repeated attempts of scope selection, hypothesis generation, realistic causal diagramming, quantification, reliability testing, and policy analysis. Systems thinking and causal loop diagramming allows researchers to move from conceptual understanding of unidimensional problems to a completed systems model containing scores of such equations, each with their appropriate numerical inputs. Today, a system dynamics model consists of an interlocking set of differential and algebraic equations developed from a broad spectrum of relevant measured and experiential data (Cavana and Mares 2004). This has notably allowed researchers without quantitative backgrounds to participate in structural formation of the model. As a result of its conceptual intuition, the system dynamics paradigm originally came to be interdisciplinary in nature (Sterman 2001). In particular, it requires moving away from isolated events and causes and toward the organization of the system as a set of interacting parts (Kirkwood 1998). ![]() System dynamics was originally meant to invoke systems thinking by endogenizing relevant variables and mathematically connecting causally linked variables (Richardson 2011). 1.2 System DynamicsĪ system can be characterized as a group of multiple components that interact with each other. These complexities, coupled with the still growing landscape of global health data generation, interpretation and use, require a systematic approach that has the potential to facilitate decision-making that aligns our long-term best interests with those of the system as a whole (Sterman 2001). As a result, a supportive health data ecosystem requires at least the following five elements: (1) collaboration and coordination, (2) public acceptance and engagement with health data, (3) data protection regulation and models of data access and use, (4) data quality, interoperability, and other technical considerations, and (5) workforce capacity (Marjanovic et al. In its broadest sense, health data refers to any type of data that provides use for improved research and innovation, as well as healthcare related decision making (Marjanovic et al. ![]() One example of such a data-rich ecosystem is global health, where the potential of data holds promise across all the building blocks of health systems. Indeed, problems plaguing data-rich ecosystems require understanding how the whole system will react to a seemingly inconsequential modification in one part of the system (Sterman 2001). These systems give rise to dynamic complexity because the system is: (1) constantly changing, (2) tightly coupled, (3) governed by feedback, (4) nonlinear, (5) history-dependent, (6) self-organizing, (7) adaptive, (8) characterized by trade-offs, (9) counterintuitive, and (10) policy resistant (Sterman 2001: 12). 1.1 Data-Rich Ecosystemsĭata-rich ecosystems are defined as “technological and social arrangements underpinning the environments in which is generated, analysed, shared and used” (Marjanovic et al. We will introduce, first, what we mean by data-rich ecosystems second, the terminology of system dynamics and third, a few applications of system dynamics in data-rich ecosystems. This section will proceed in three parts. 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). 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. 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). ![]()
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