Systems Science

Overview

A system is set of things that are interconnected in ways that result in the generation of identifiable behavioral patterns over time. Systems Science is an interdisciplinary field that studies the complexity of systems in nature, social or any other scientific field. Some of the systems science methodologies include systems dynamics modeling, agent-based modeling, microsimulation, and Big Data techniques. Systems science thinking can help researchers to understand the factors influencing the distribution and determinants of health and disease in populations, by providing information on the broad picture on how individuals and other components of populations are interconnected in society in scenarios in which researchers cannot controlled the environment. Therefore, system science thinking can help researchers to find answers on how interventions may generate change in disease outcomes given the system multilevel factors present in populations.

Description

Sometimes we encounter complex public health problems that cannot be analyzed adequately using traditional epidemiologic models. The relationships among variables may change over time and involve feedback loops, there may be long delays between causes and effects, and change in one part of the system may yield unintended consequences in another. Grounded in the core principles of engineering, system dynamics was developed to identify the connections between changing system structure and system behavior. System dynamics models aim to designate a structure that can replicate the key characteristics of a dynamic problem independent of exogenous factors. A system dynamics approach emphasizes a continuous view of complex social, managerial, economic, or ecological systems. A system in this framework is characterized by interdependence of units, mutual interaction, information feedback systems, and circular causality.

to the insanely complex (this map was commissioned by the government of Tony Blair as part of a comprehensive plan to combat obesity in the UK; see Vandenbroeck et al, below, for a full description of how the systems model was created):

Central to system dynamics modeling are stock and flow relationships. Stocks are entities that accumulate or lessen over time; these are the sources of the system’s disequilibrium or changing behavior. Flows are the rates of change in stocks.

Creating a system dynamics model is an iterative process that involves collaboration among multiple stakeholders. In addition to refining the systems map, participants provide information about reasonable levels of stocks and rates of flow necessary to create computer simulation models that describe how the system works. The simulation models are calibrated using experimental data and may be tested to see if they can reproduce historical patterns before they are used to make predictions. When a model has been deemed reliable, it is initialized into a “steady state,” or a state of dynamic equilibrium, involving a given number of stocks and rates of flow based on known data. Different scenarios are then simulated by varying the stocks and flows.

System dynamics modeling may be used when component stocks and flows are identifiable and measurable, when feedback loops are both operative and known, and especially when variation is central to the research hypothesis. It is particularly suited to tackling public health issues in which inputs are drawn from across various disciplines, such as health policy, chronic disease management, health care capacity, and “syndemics”–the existence of overlapping syndromes or epidemics within populations.

Readings

Textbooks & Chapters

Systems Dynamics Review. Chichester, UK. Wiley-Blackwell, Ltd.
Methodological

Randers J, ed. Elements of the System Dynamics Method. 1st ed. Cambridge MA: MIT Press. 1980.

Richardson GP. Feedback Thought in Social Science and Systems Theory. 1st ed. Philadelphia, PA: University of Pennsylvania Press. 1991.
Applications

De Savigny D and Adam T, eds. Systems Thinking for Health Systems Strengthening. Geneva: World Health Organization. 2009.

Vandenbroeck P, Goossens J, Clemens M. Tackling Obesities: Future Choices–Building the Obesity System Map. UK: Government Office for Science. 2007. (www.foresight.gov.uk)

How to Draw a Systems Map

DePinho H. Steps for Creating a Systems Map
Kirkwood CW. System Dynamics Methods: A Quick Introduction (1998), Chapter 1.

Methodological Articles

Ackermann F, Andersen DF, Eden C Richardson GP. Using a group decision support system to add value to group model building. System Dynamics Review. 2010: 26(4); 335-346.

Barlas Y. Model validation in system dynamics. Presented at 1994 International System Dynamics Conference; 1994. Stirling, Scotland.

Homer JB. Structure, data, and compelling conclusions: Notes from the field. System Dynamics Review. 1997; 13(4): 293-309.

Homer JB, Hirsch GB. System Dynamics Modeling for Public Health: Background and Opportunities. Am J Public Health. 2006; 96(3): 452-458.

Leischow SJ, Milstein B. Systems Thinking and Modeling for Public Health Practice. Am J Public Health. 2006; 96(3): 403-5.

Ness RB, Koopman JS, Roberts MS. Causal System Modeling in Chronic Disease Epidemiology: A Proposal. Annals of Epidemiology. 2007; 17(7): 564-8.

Richardson, GP and Andersen DF. Systems Thinking, Mapping, and Modeling for Group Decision and Negotiation. In: Eden C and Kilgour DN, eds, Handbook for Group Decision and Negotiation. Dordrecht: Spring; 2010: 313-324.

Dangerfield B, Fang Y, Roberts C. Model-based scenarios for the epidemiology of HIV/AIDS: The consequence of highly active antiretroviral therapy. System Dynamics Review. 2001; 17(2): 119-150.

Garedew, E., M. Sandewall, and U. Soderberg, A dynamic simulation model of land-use, population, and rural livelihoods in the Central Rift Valley of Ethiopia. Environ Manage, 2012. 49(1): p. 151-62.

Hirsch G, Homer J, Evans E, Zielinaki A. A System Dynamics Model for Planning Cardiovascular Disease Interventions. Am J Public Health. 2010; 100(4): 616-622.

Milstein B, Homer J, Hirsch G. Analyzing National Health Reform Strategies with a Dynamic Simulation Model. Am J Public Health. 2010; 100(5): 811-819.

Panjeti, V.G. and L.A. Real, Mathematical models for rabies. Adv Virus Res, 2011. 79: 377-95.

Qi, C. and N.B. Chang, System dynamics modeling for municipal water demand estimation in an urban region under uncertain economic impacts. J Environ Manage, 2011. 92(6): 1628-41.

Thompson KM, Tebbens RJD, Using system dynamics to develop policies that matter: Global management of poliomyelitis and beyond. System Dynamics Review. 2008; 24(4):433-449.

Wanduku, D. and G.S. Ladde, A two-scale network dynamic model for human mobility process.Math Biosci, 2011. 229(1): 1-15.