Bringing ALife and Complex Systems Science to Population

Bringing ALife and Complex Systems Science to Population
Health Research

Eric Silverman
University of Glasgow

MRC/CSO Social and Public Health Sciences Unit
eric.silverman@glasgow.ac.uk

Abstract 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.

Keywords: Population health, ALife and society, agent-based modeling, societal impact

1 The Population Health Challenges Facing 21st-Century Society

Few would dispute that population health research has achieved significant strides in recent human
history. The human life span has lengthened repeatedly beyond the upper bounds thought possible
by health scientists and demographers. Communicable diseases are far less of a scourge in many
countries than they used to be, thanks to huge advances in immunization and sanitation. In recent
decades, health problems due to noncommunicable diseases—tobacco-related illness, for example—
have been tackled through the deployment of health policy interventions that target behavior and
lifestyle across society.

However, major population health challenges still remain: the rapid graying of populations, lead-
ing to concerns around healthy aging and social care demand; widespread increases in obesity, caus-
ing increased rates of heart disease and diabetes; and inequalities in health, a strikingly persistent
problem even in societies that go to great effort to reduce inequity (e.g., Scandinavian countries).
These problems all share a common trait—these are issues driven by complex, interacting mecha-
nisms, not by the simple causal mechanisms that population health and epidemiologic methods are
designed to address.

Artificial Life 24: 220–223 (2018) doi:10.1162/artl_a_00264

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E. Silverman

Bringing ALife and Complex Systems Science to Population Health Research

In population health, these are called wicked problems, where causal links between risk factors and
outcomes are not well defined, and unearthing the mechanisms that drive these processes is difficult
[6]. As efforts to address these wicked challenges with traditional methods have floundered and
failed to produce effective and actionable policies, population health scientists have increasingly
turned toward the conceptual frameworks of complex systems science and simulation methods
to find a way forward.

2 Complex Systems and ALife for Health

The hunger for systems-based perspectives in population health has culminated recently in a call to
arms from Rutter et al. published in The Lancet, one of the most-cited public health journals, which
concluded that “reshaping public health research, policy, and practice to incorporate complex sys-
tems approaches will be essential for improving population health and reducing health inequalities”
[5, p. 2].

As heartening as that sounds, Rutter et al. also outline the challenges faced by complex systems
approaches: the bias of publishers in the field toward individual-level health interventions; wariness
of funders about new approaches; and policymakersʼ desire for easily actionable policy recommen-
dations. Population health researchers remain skeptical of the benefits of complex systems ap-
proaches to health systems; Naimi, for example, contends that epidemiological methods can cope
with producing causal effect estimates even in the presence of nonlinearity, so simulation models are
an unnecessary addition to the population health toolbox [3].

3 The Case for Modeling and Simulation

Naimiʼs criticisms do hold some weight, in that standard epidemiological models are perhaps more
efficient tools when seeking causal effect estimates relating to a health challenge where the causal
mechanisms are more visible. But the presence of these wicked problems demonstrates that sys-
temic understanding is necessary to make progress on some of the biggest challenges facing pop-
ulation health. As Rutter et al. describe, developing policy that addresses these challenges
“requires more than single interventions, such as traffic light food labeling or exercise on pre-
scription, many of which require high levels of individual agency, have low reach and impact,
and tend to widen health inequalities” [5, p. 1]. Policy tools have the potential to reach far greater
numbers of people, and to address population health problems through innovative means, such as
environmental change interventions or social programs. Yet without an in-depth understanding of
the diverse and interacting causes that drive these problems, those policy solutions can lead to
flawed outcomes.

Alcohol misuse research provides an instructive example here. A variety of programs in American
universities have tried to reduce alcohol misuse amongst students, as it causes numerous deaths each
year and can lead to profound social and academic consequences. Normative reeducation programs
are a common approach that shows students that the real alcohol consumption levels amongst their
peers are lower than they might assume, in order to encourage moderation in alcohol consumption.
However, evaluations of these programs “find no meaningful differences at schools with social
norms programs” and suggest they have “the lowest level of effectiveness.” Further, these programs
can actually lead to “boomerang effects wherein those exposed to normative reeducation programs
actually increase their alcohol consumption” [1, p. 356]. Ultimately these programs fail to take
account of the multiple interacting factors that drive college drinking, focusing instead on addressing
a single element of this complex web of causes. Apostolopoulos et al. propose that viewing alcohol
misuse through a complex systems lens rather than a linear causal one “can better capture the
dynamic complexity of these problems and facilitate substantive advances in both basic and applied
knowledge” [1, p. 360].

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Bringing ALife and Complex Systems Science to Population Health Research

So while Naimi may be correct in thinking that systems-based simulation approaches may not be
the most expedient answer to less wicked problems, this critique ignores the capacity for our
methods to explain and describe more complex issues, and in the process help build more effective
policy. Systems-based approaches to population health allow for the design and evaluation of health
interventions that address the complexities of these issues, rather than attempting to simplify them
out or focus on singular aspects. Simulation models can demonstrate the multi-layered interactions
between individual agents and their physical, social, and even emotional environments, allowing
stakeholders to test out policies that have the potential to affect broad swathes of the population
[7]. Using simulation enables us to develop a more comprehensive picture of these complex health
issues, and in turn avoid interventions and policy prescriptions that can lead to unintended adverse
consequences.

4 Benefits for ALife

From what we have seen so far, the outlook for a systems-based approach to population health
seems positive, but we still face an uphill battle. While some clearly accept the power of systems-
journal editors, and re-
based approaches and simulation modeling in particular, funders,
searchers themselves remain biased toward statistical studies of linear causal models. Anyone
in our community who sought to join the fight against wicked health issues could face a dif-
ficult journey, punctuated by rejections of their ideas and approaches—even amongst their own
peers, some of whom may bemoan stretching the boundaries of ALife beyond its perceived
remit.

I propose however that artificial life is particularly well suited to take on this challenge. ALife
is thoroughly steeped in the language and conceptual frameworks of complex systems, and
many of our approaches and models incorporate interacting layers of agents, environments,
and processes. Terms that are slowly entering the population health discourse include founda-
tional ALife concepts like emergence and adaptive behavior. In any interdisciplinary endeavor
there is the danger of reinventing the wheel, as core concepts in other disciplines are rediscov-
ered, renamed, and reapplied without the benefit of years of advancement that has already oc-
curred elsewhere; ALife is in a position to help steer and motivate these embryonic extensions
into complex systems viewpoints and methods, providing the benefit of our experience and
knowledge.

In turn, these changes in approach can lead to profound shifts in the nature of intervention
development and evaluation. Currently the population health landscape is shaped largely by reduc-
tionist statistical approaches, carving complicated problems into manageable chunks that can be
turned into actionable policy solutions. As we have seen, however, these linear approaches to com-
plex, nonlinear problems can produce policies that are ineffective or even counterproductive. By
demonstrating the benefits of modeling populations as multi-layered complex adaptive systems,
we can start changing the lens through which society is viewed by policymakers, and in the process
develop more effective solutions.

More pragmatically, applying ALife methods to population health can have significant benefits in
turn for our own efforts to bring models to data. Models of complex adaptive systems, particularly
agent-based models, can be difficult to parameterize and analyze. Population health is a profoundly
empirically focused discipline, and any systems-based approach will need to address these challenges
in order to make headway with researchers and policymakers in the area. This challenge will help us
develop more robust methods for mapping complex population data to our models, which will pay
dividends in other disciplinary interfaces. As an example, take the ODD protocol for documenting
agent-based models, first conceived in 2006 in ecological modeling circles, then later expanded to
ODD+D for social models and recently ODD+2D for data-rich social simulations; as the method
spread, new norms and techniques developed that have helped ABM become a more trusted tool
across numerous disciplines [2].

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5 Building a Movement

Research groups and projects in population health incorporating complex systems methodologies
are growing by the day, and opportunities to get involved are ever more plentiful. Simulation model-
ing is gaining acceptance, through high-profile projects like CASCADE, a five-year project at the
University of Sheffield using agent-based modeling to study alcohol use and misuse. Here at the
Complexity in Health Improvement Programme in the MRC/CSO Social and Public Health
Sciences Unit at the University of Glasgow we have hired four complex systems scientists in the
last eight months, have been granted capital funding for significant expansion of our scientific com-
puting resources, and are building a UK-wide network of simulation modelers in population health
spanning multiple universities. New research groups focused on complex systems approaches to
health are appearing with greater regularity, such as the Complexity and Computational Population
Health Research Group at Texas A&M University, while established groups in computational health
sciences are beginning to expand further into population health. Increasing interest in the use of
machine learning to work with big data in health is also likely to drive engagement with computa-
tional methods more generally.

On the whole, the field seems to be on the cusp of a significant shift toward systems-based
approaches, and as these efforts expand and funders begin taking note, this is an ideal time for
the ALife community to get involved. Along with Penn [4], I would also suggest that since we have
the ability to help these research efforts develop, we also have a responsibility to do so. The wicked
health problems, such as obesity, opioid addiction, and inequalities in health, all cause incalculable
damage to life and society. As consensus grows that new approaches are needed, we should step up
and do what we can to help develop smarter policy to address these issues.

Given recent trends in population health research, and widespread calls for a new approach to
major health challenges, the artificial life community has an opportunity to make a significant con-
tribution. Health scientists may contend our methods are complex, difficult to analyze, and poorly
suited for causal effect estimates—and we can concede these points happily. What we offer instead
are the tools to answer new questions, to model and evaluate policy in ways epidemiological
methods cannot, and to address challenges that require an understanding of complexity, emergence,
and adaptive behavior. If we reach across this disciplinary divide, we can help transform population
health into a vibrant model-based science, and in the process demonstrate the societal impact of our
ideas and methods.

References
1. Apostolopoulos, Y., Lemke, M. K., Barry, A. E., & Lich, K. H. (2018). Moving alcohol prevention research

forward—Part I: Introducing a complex systems paradigm. Addiction, 113, 353–362.

2. Laatabi, A., Marilleau, N., Nguyen-Huu, T., Hbid, H., & Ait Babram, M. (2018). ODD+2D: An ODD
based protocol for mapping data to empirical ABMs. Journal of Artificial Societies and Social Simulation, 21(2), 9,
http://jasss.soc.surrey.ac.uk/21/2/9.html.

3. Naimi, A. I. (2016). Integrating complex systems thinking into epidemiologic research. Epidemiology,

27(6), 843–847.

4. Penn, A. (2018). Report on the ISAL Special Session on ALife and Society, ALife XV, Cancún, Mexico,

2016. Artificial Life, 24(1), 80–84.

5. Rutter, H., Savona, S., Glonti, K., Bibby, J., Cummins, S., Finegood, D. T., Greaves, F., Harper, L., Hawe,
P., Moore, L., Petticrew, M., Rehfuess, E., Shiell, A., Thomas, J., & White, M. (2017). The need for a
complex systems model of evidence for public health. The Lancet, 390(10112), 2602–2604.

6. Signal, L. N., Walton, M. D., Ni Mhurchu, C., Maddison, R., Bowers, S. G., Carter, K. N., Gorton, D.,
Heta, C., Lanumata, T. S., McKerchar, C. W., OʼDea, D., & Pearce, J. (2013). Tackling ‘wicked’ health
promotion problems: A New Zealand case study. Health Promotion International, 28(1), 84–94.

7. Silverman, E., Charles, F., Porteous, J., Wood, I., & Ells, L. (2017). Agent-based virtual urban environments
for population health applications. Available at http://eprints.gla.ac.uk/156270/ (accessed June 2018).

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