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SOCIETAL IMPACT OF ARTIFICIAL LIFE
Moving from Overwhelming to Actionable Complexity in
Population Health Policy: Can ALife help?
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  should
be the goal that we aim to assist. Complexity approaches, however, run the risk of exposing
policy makers to overwhelming complexity—situations 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 . This qualitative causal map attempted to
© 2018 Massachusetts Institute of Technology Artificial Life 24: 218–219 (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
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 horrendogram”and seems to have made peo-
ple feel powerless in the face of the realities of the issue’s 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 makers’needs, 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
2. Vandenbroeck, P., Goossens, J., & Clemens, M. (2007). Foresight tackling obesities: Future choices—Obesity system
atlas. Government Office for Science. Available at https://assets.publishing.service.gov.uk/government/
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