TABLE 1.

Attributes of a sampling of complex systems science approaches

	System Dynamics	Agent-Based Modeling	Social Network Analysis	Discrete-Event Simulation	Accimap
Prototypical problem motivating its use	Learning and action around persistent or policy resistant dynamically complex problems with inter-connected stakeholder responses	Learning about the relationship between micro-level rules and macro-level outcomes. Examining mechanisms of emergent outcomes.	Describing the nature of connections within a network, identifying the implications of structure on outcomes, identifying key nodes/connections	Estimating the impact of system redesign (e.g., system capacity and work processes) on consumer queueing, cost, and other performance outcomes	Identifying the cause(s) of an accident by studying the system in terms of the many potential decisions, events, and interactions that led to the accident. Contributory factors are grouped across six hierarchical levels.
Illustrative example	Comparison of policies designed to increase bicycle commuting in a car-centric city. Compared “what-if” policy scenarios on several outcomes, including injuries, physical activity, air pollution, and fuel costs. (Macmillan et al., 2014)⁴⁵	Construction of a virtual transport system to explore the mechanisms underpinning the “safety in numbers effect,” including assumptions related to bicycle density (Thompson et al., 2015)⁵¹	Description and analysis of a range of road users’ situational awareness in challenging traffic environments, such as at intersections. Network structure of situational awareness concepts were compared across road user types (Salmon et al., 2013)⁵⁵	Examination of emergency department operations as it relates to average treatment times and outcomes for patients. Simulated and compared different triage approaches on patient outcomes (Connelly & Bair, 2008)⁵⁴	Elucidation of decisions and actions of actors who share responsibility for young driver road safety, moving beyond a purely driver-centric view. Contributing factors at the following levels: government policy; regularly bodies and associations; local area government, planning, and budgeting; technical and operational management; physical processes and actor activities; and equipment and surroundings were identified, as well as key interactions between them (Parker et al. 2015)⁶⁰
Theoretical underpinnings	Engineering and management (Control Theory)	Social and behavioral sciences	Sociology	Operations and systems engineering	Risk management
Qualitative, quantitative or mixed approach	Mixed	Quantitative	Mixed	Quantitative	Qualitative
Core components/ structures of models	Stocks, flows, feedback loops, delays	Agents, state charts, rules of behavior and adaptation	Nodes and edges	Arrival, queueing, and service processes (rules, capacity, times)	Socio-technical levels and connections/ interactions