Understanding the complexity of health

Abstract

The complexity of health cannot be adequately explained with a representation that only captures one level of abstraction of the ecosystem. Multiple levels of abstraction are needed to comprehend the full range of forces that affect the health of a population. These levels are outlined and run the gamut from cells to society, ranging from aberrations of immune system signalling to society's values and norms. Computational modelling of this multilevel system can enable both understanding and managing the complexity of health. This is illustrated in contexts that include Washington, DC, and New York City.

Applicability of Artificial Societies to Evaluate Health Care Policies

“…To be applicable for policymakers, the representation of various policies using policy levers with clear alternatives needs to be better understood as well-to really make the system immersive and interactive to become the "flight simulator for policymakers" discussed by Rouse. 6 Despite these known needs for improvement, the importance that social networks and social determinants of individuals have on opioid use outcomes became clear even in this early stage, contributing to the call for action by Squazzoni et al 8 Methods as discussed by Diallo et al 20 and Davis et al 25 help to better integrate research results of computational social science into agent-based approaches, allowing us to evaluate a multitude of different views.…”

Computational Decision Support for the COVID-19 Healthcare Coalition

“…However, as stated in the previous sections, countering COVID-19 is a multi-value, multi-criteria decision problem, where multiple criteria are influencing several, often conflicting values. As Rouse points out in [11], decision makers must be able to immerse into the complex problem space and have controls at hand easy enough to understand quickly but also powerful enough to evaluate their various options. The COVID-19 Healthcare Coalition Decision Support Dashboard (C19HCC DSD), shown in the next figure, was developed to support these ideas.…”

“…A special challenge was the design decision options for the decision makers. As observed in [11] as well, offering too many decision parameters to choose from can easily become a distraction. Instead, having a small set of welldesigned options prepared by the decision maker's staff is likely to lead to better acceptance and easier use.…”

“…The dashboard as such could be interpreted as the "command center" for the decision maker, providing for situational awareness as well as allow for the evaluation of different courses of action. This ambitious goal was not reached at the time this paper is written, but the full integration of solutions as described in by Rouse in [11] should be considered for future designs to be better prepared for challenges like the COVID-19 pandemic in the future. Finally, we observed the danger of epistemological and hermeneutical challenges as well [12].…”

“…A special challenge was the design decision options for the decision makers. As observed by Rouse, 11 offering too many decision parameters to choose from can easily become a distraction.…”