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Moving from Overwhelming to Actionable Complexity in Population Health Policy: Can ALife help?

Moving from Overwhelming to Actionable Complexity in
Population Health Policy: Can ALife help?
Alexandra Penn
University of Surrey
Centre for Research in Social Simulation (CRESS)
Centre for Evaluation of Complexity Across the Nexus (CECAN)
Keywords: Societal impact, ALife and society, population health, complexity-appropriate policy design, policy modeling.
1 Understanding Population Health as a Complex Adaptive System
In this issue, we highlight a call to arms to the Artificial Life community to join the effort to address
complex, whole-systems problems in health care. Many problems that society wishes to address in
population health are clearly problems of managing complex adaptive systems. They involve making
interventions in systems with multiple interacting causal connections, which span domains from
physiological to economic. Additionally, of course, the individuals whose health we ultimately wish
to improve adapt and change their behavior in response to medical or policy interventions.
But how do we do it? In complex adaptive systems, as the following article reminds us, individual-
level interventions are often not the most effective. Rather we might wish to change the conditions or
structures of interaction in which individual behaviors play out. Taking a whole-systems view of the
problem can allow us to identify higher-level levers that may allow us to reach broader swathes of the
population simultaneously. We might be able to change what emerges or is stable in the system, with-
out relying on the limited agency of individuals in a constrained and tangled context. Systems-based
and complexity methods can help decision makers to understand their system at a level that makes
such interventions possible to envisage and design.
2 From Overwhelming to Actionable Complexity: Avoiding the Horrendogram
However, as Silverman rightly points out, producing effective and actionable policies [1] should
be the goal that we aim to assist. Complexity approaches, however, run the risk of exposing
policy makers to overwhelming complexitysituations in which revealing the reality of the situation
paralyzes action. A case in point is the obesity systems map commissioned by the Foresight program
of the UK Government Office for Science in 2007 [2]. This qualitative causal map attempted to
© 2018 Massachusetts Institute of Technology Artificial Life 24: 218219 (2018) doi:10.1162/artl_e_00265
systemically represent the full range of factors that affect individual obesity and their causal inter-
connections. The systems map produced had 108 factors organized in ten domains. Of these
factors, only a very few concern individual physiology or calorie intake and energy expenditure.
The rest span a large range of domains and phenomena such as social psychology, economics,
the food production industry, transport infrastructure, urban design, and more, including long-term
social changes such as more women in the workplace, the move away from manual labour, longer
working hours, and a decrease in the social acceptability of smoking. It neatly shows the systemic
nature of the health problem and the need for diverse and innovative policy interventions above the
individual level.
The map should be a powerful tool for enhancing understanding and communicating complexity
and offers a means to identify levers, feedback loops, and causal cascades that could help to design
complexity-appropriate policy interventions that exploit system structure. Interestingly, however, it is
commonly referred to by policy professionals as the horrendogramand seems to have made peo-
ple feel powerless in the face of the realities of the issues complexity.
This perhaps highlights, more than anything else, that without guidance, models that demonstrate
complexity may simply be intimidating. Without a domain expert to facilitate model exploration and
translation, one has no idea how to start to unpick the complexity that has suddenly been revealed,
let alone how to go about acting on it. Working in policy or public domains therefore requires much
more than building models. It requires us to be on hand throughout a process of transforming over-
whelming complexity into actionable complexity.
This ultimately requires capacity building on complexity-appropriate methods and approaches
within policy-making contexts themselves. For now, it entails a commitment to stay with the model-
ing and interpretation process as long as it is supported, a willingness to work in a way that can meet
policy makersneeds, and the provision of clear methods for stakeholders to investigate and interact
with models. We must be clear, and illustrate with action and insights, that complex adaptive systems
approaches are not silver bullets, but will pay dividends if committed to over time.
3 What Can Artificial Life Offer?
Simulations in population health fall outside of what might be considered the traditional concerns of
the artificial life community; however, the skills that ALife researchers might bring to the domain are
clearly urgently required and, as Silverman reports, are being actively sought. It is heartening to find
that genuine recognition of societal challenges as complex adaptive systems problems is spreading,
despite the many entrenched interests and institutional constraints that lead to reductive thinking.
For many decision makers, however, that nagging feeling that things need to be done differently
continues to grow and grow. We must continue to build the groundwork, communicate, and actively
seek out opportunities to make a difference. The appetite for change in policy design and evaluation
is real, and the more support we can offer, the greater will be the chance that a groundswell of
complex adaptive systems thinking can bring real change.
1. Silverman, E. (2018). Bringing ALife and complex systems science to population health research. Artificial
Life,24(3), 220223.
2. Vandenbroeck, P., Goossens, J., & Clemens, M. (2007). Foresight tackling obesities: Future choicesObesity system
atlas. Government Office for Science. Available at
uploads/system/uploads/attachment_data/file/295153/07-1177-obesity-system-atlas.pdf (accessed June
A. Penn Societal Impact of Artificial Life: Editorial
Artificial Life Volume 24, Number 3 219
... However, map construction and analysis can be highly effective at generating novel insights and new questions, helping participants to visualise, think through and interrogate the implications of their system context and generate new more complexity-informed questions. It allows the exploration of aspects of system complexity, which are relevant and usable in context, a so-called 'actionable complexity' approach [32]. ...
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In a drive to achieve net zero emissions, U.K. transport decarbonisation policies are predominantly focussed on measures to promote the uptake and use of electric vehicles (EVs). This is reflected in the COP26 Transport Declaration signed by 38 national governments, alongside city region governments, vehicle manufacturers and investors. However, emerging evidence suggests that EVs present multiple challenges for air quality, mobility and health, including risks from non-exhaust emissions (NEEs) and increasing reliance on vehicles for short trips. Understanding the interconnected links between electric mobility, human health and the environment, including synergies and trade-offs, requires a whole systems approach to transport policymaking. In the present paper, we describe the use of Participatory Systems Mapping (PSM) in which a diverse group of stakeholders collaboratively constructed a causal model of the U.K. surface transport system through a series of interactive online workshops. We present the map and its analysis, with our findings illustrating how unintended consequences of EV-focussed transport policies may have an impact on air quality, human health and important social functions of the transport system. We conclude by considering how online participatory causal modelling techniques could be effectively integrated with empirical metrics to facilitate effective policy design and appraisal in the transport sector.
... Additionally, of course, the individuals whose health we ultimately wish to improve adapt and change their behavior in response to medical or policy interventions." [64] Several of these points were echoed by Silverman in justifying the use of systemsbased simulation for population health research [65]. Modeling changes in the heterogeneous health behaviors of individuals often uses the simulation technique of Agent-Based Modeling, and has been done in obesity research on multiple occasions [66][67][68][69][70]. ...
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Models are predominantly developed using either quantitative data (e.g., for structured equation models) or qualitative data obtained through questionnaires designed by researchers (e.g., for fuzzy cognitive maps). The wide availability of social media data and advances in natural language processing raise the possibility of developing models from qualitative data naturally produced by users. This is of particular interest for public health surveillance and policymaking, as social media provide the opinions of constituents. In this paper, we contrast a model produced by social media with one produced via expert reports. We use the same process to derive a model in each case, thus focusing our analysis on the impact of source selection. We found that three expert reports were sufficient to touch on more aspects of a complex problem (measured by the number of relationships) than several million tweets. Consequently, developing a model exclusively from social media may lead to oversimplifying a problem. This may be avoided by complementing social media with expert reports. Alternatively, future research should explore whether a much larger volume of tweets would be needed, which also calls for improvements in scalable methods to transform qualitative data into models.
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This chapter introduces Causal Loop Diagrams. We explore what exactly Causal Loop Diagrams are, describe how you can use them, take a step back to consider common issues and ‘tricks of the trade’, as well as present a brief history of the development of the method. This chapter can be viewed as a companion to Chap. 10.1007/978-3-031-01919-7_8 on System Dynamics; these two methods are closely related. Causal Loop Diagrams emerged from Systems Dynamics practice, and though it is a systems mapping method in its own right now, it is still often used as a stepping-stone to the development of System Dynamics models.
Despite tremendous advancements in population health in recent history, human society currently faces significant challenges from wicked health problems. These are problems where the causal mechanisms at play are obscured and difficult to address, and consequently they have defied efforts to develop effective interventions and policy solutions using traditional population health methods. Systems-based perspectives are vital to the development of effective policy solutions to seemingly intractable health problems like obesity and population aging. ALife in particular is well placed to bring interdisciplinary modeling and simulation approaches to bear on these challenges. This article summarizes the current status of systems-based approaches in population health, and outlines the opportunities that are available for ALife to make a significant contribution to these critical issues.
Foresight tackling obesities: Future choices-Obesity system atlas. Government Office for Science
  • P Vandenbroeck
  • J Goossens
  • M Clemens
Vandenbroeck, P., Goossens, J., & Clemens, M. (2007). Foresight tackling obesities: Future choices-Obesity system atlas. Government Office for Science. Available at uploads/system/uploads/attachment_data/file/295153/07-1177-obesity-system-atlas.pdf (accessed June 2018).