New Horizons in Modeling and Simulation for Social Epidemiology and Public Health. Daniel Kim

2 Rationale for New Modeling and Simulation Tools : Agent‐Based Modeling and Microsimulation

       Daniel Kim1,2 and Ross A. Hammond3,4,5

       1 Bouvé College of Health Sciences, Northeastern University, Boston, MA, USA

       2 School of Public Policy and Urban Affairs, Northeastern University, Boston, MA, USA

       3 Center on Social Dynamics & Policy, The Brookings Institution, Washington, DC, USA

       4 Brown School, Washington University in St. Louis, St. Louis, MO, USA

       5 The Santa Fe Institute, Santa Fe, NM, USA

2.1 Advantages of Systems Science Approaches over Conventional Approaches

      The real world is made up of a series of complex systems. As we have seen in Chapter 1, health and disease are products of causal factors operating through multiple pathways at multiple levels. Such complex systems are not simply linear—they are characterized by causal feedback loops and complex interactions between actors at multiple levels and are inherently dynamic. Traditional multivariable models adopt a more reductionist approach and lack the ability to capture such features. In general, they implement static or discretely longitudinal analyses, do not incorporate potential nonlinearities such as feedback loops, and do not capture behavioral responses of individuals (Luke and Stamatakis 2012). By contrast, systems science approaches were explicitly developed to account for such features.

      Although variation in the relationship between exposures and outcomes that is “exogenous” or “as if random” is the primary objective of advanced methods used to strengthen causal inference, the real world is filled with endogeneity. Endogenous factors are those found within the same system, meaning that they may bias the association between an exposure and an outcome. Notably, systems science approaches do not regard the endogeneity of the real world as nuisances; rather, through a more holistic approach, they model the presence of such complex pathways and mechanisms to better understand them (Luke and Stamatakis 2012).

Systems science approaches represent innovative sets of tools that can model and simulate the real world with enough complexity to be useful. Yet importantly, like their traditional model cousins, they reflect simplified versions of reality. Ideally, systems models retain enough of the salient characteristics of complexity to enhance our understanding of the problem under study, without being so complex themselves that they are opaque and as impenetrable to our understanding as reality itself. Moreover, systems science approaches enable virtual conduct of experiments that are often not feasible, whether due to cost, ethical reasons, or the simple fact that there is no way to explore the impact of an intervention (e.g. policy) and also go back in time and intervene differently to compare outcomes. With simulation models, it is straightforward to compare a wide array of hypothetical scenarios in silico. For further exposition of the virtues of modeling, see Epstein (2008) and Mabry et al. (2010).

2.2 Specific Advantages of Agent‐Based Modeling and Microsimulation Modeling

      Agent‐Based Modeling

Agent‐based modeling (ABM) offers four specific advantages for public health research. First, because each actor in the system under study can be explicitly represented, no aggregation or statistical summary is required in treatment of either individual characteristics or outcomes. As a result, ABM is a powerful tool for considering heterogeneity—whether in biology, cognition, demography, or context. This is especially important for topics such as health disparities (Kaplan et al. 2017). Second, ABM offers an effective way to consider adaptation—processes of learning, evolution, or bidirectional interaction between individuals over time. This means that not only can we consider short‐run impacts of policies or interventions, but we can also explore potential impacts over very long time horizons. Topics such as obesity, antibiotic resistance, and developmental origins of health and disease often benefit from such considerations. Third, ABM is able to incorporate very sophisticated representations of structure and space, including social network data, physical space data from geographic information systems (GISs) or light detection and ranging (LIDAR), and biological space (e.g. physiology). Rather than either assuming away spatial elements or reducing them to summary statistics (for example, zip code‐level density of retailers), agent‐based models can carry a full accounting of spatial exposure and interaction throughout the dynamic simulation. Recent efforts to consider “precision prevention” in communities (Gillman and Hammond 2016; Economos and Hammond 2017) and retailer‐oriented tobacco control policies (Luke et al. 2017) leverage this facility of ABM. Finally, ABM is well suited for multilevel modeling. Each individual agent can contain detailed representations of “below‐the‐skin” processes such as energy balance, cognition, decision‐making, or disease progression; at the same time, the agents can interact with each other, with physical environments, and with population‐level signals (Hammond 2009; Hammond and Ornstein 2014). Although arguably essential to full understanding of many chronic disease challenges, crossing the “skin barrier” remains rare in social epidemiology.

      Microsimulation Models

      Microsimulation models (MSM) enable simulations of policies on samples of economic agents (individual, households, and firms) at the individual level (Bourguignon and Spadaro 2006). These simulations allow for the projection of the consequences of modifying economic conditions for each individual agent in the sample. Through such projections, we can estimate the overall aggregate impacts of a policy as well as the distributional consequences of the policy in terms of “winners”