contact us

Use the form on the right to contact us.

You can edit the text in this area, and change where the contact form on the right submits to, by entering edit mode using the modes on the bottom right.


123 Street Avenue, City Town, 99999

(123) 555-6789


You can set your address, phone number, email and site description in the settings tab.
Link to read me page with more information.

FHS and STEPS co-host workshop on complex adaptive systems


Future Health Systems is a research consortium working to improve access, affordability and quality of health services for the poor. We are a partnership of leading research institutes from across the globe working in a variety of contexts: in low-income countries (Bangladesh, Uganda), middle-income countries (China, India) and fragile states (Afghanistan) to build resilient health systems for the future. After a successful first five-year phase from 2006-2011 (see our success stories), we are entering a new six-year phase of research, funded mainly by UK aid.

Download four-page brochure (1.05 MB) >
Read more about us >

FHS and STEPS co-host workshop on complex adaptive systems

Future Health Systems

Health systems are seen as a complex adaptive systems (CAS), with multiple actors and relationships operating in difficult and changing contexts, with many points of intervention, and numerous intended and unintended consequences that can improve or damage people’s health. Although CAS frameworks are increasingly recognized as relevant to understanding health systems, health systems researchers have to date not taken advantage of CAS research methods to inform interventions that will be effective on a large scale and in sustainable ways.

In June 2014, Future Health Systems (FHS) and the STEPS Centre (Social, Technological and Environmental Pathways to Sustainability) co-hosted a workshop exploring CAS approaches to health systems strengthening in low- and middle-income countries (LMICs).  FHS and STEPS are particularly concerned with policies, programs, and individual level interventions promote and protect people’s health and wellbeing, particularly vulnerable and disadvantaged populations.

The workshop was designed mainly to build capacity among both consortia on specific methods for working with and understanding CAS (see the list below).

This page collects presentations from across the three days, videos and reactions and blogs about the event.

CAS Research Methods

Agent-based modeling (ABM)

ABMs are used to create a virtual representation of a complex system, modeling individual agents who interact with each other and the environment.  Although the interactions are based on simple, pre-defined rules, in a complex system these simulations allow for the identification of emergence and self-organization.

Network Analysis (or Social Network Analysis)

Network analysis uses graphical methods to demonstrate relations between objects.  Grounded in computer science, it has applications in social, biological and physical sciences.  Social network analysis involves application of network theory to social entities (e.g. people, groups, organizations), demonstrating nodes (individual actors within a network) and ties (the type of relationships) between the actors, and uses a range of tools for displaying the networks and analyzing the nature of the relationships.  

Scenario planning

This is a strategic planning method that uses a series of tools to identify and analyze possible future events and alternative possible outcomes. These can involve quantitative projections and/or qualitative judgments about alternatives.  The value lies more in learning from the planning process than the actual plans or scenarios.

Systems dynamics modeling

Not a single method, but an approach that uses a set of tools to understand the behavior of complex systems over time.  The methods focus on the concepts of stocks and flows and feedback loops.  They are designed to solve the problem of simultaneity (mutual causation) by being able to change variables over small periods of time while allowing for feedback and various interactions and delays.  The common tools include causal loop diagrams and stock and flow diagrams.

Causal loop diagrams (CLDs)

CLDs are a system dynamics tool that produces qualitative illustrations of mental models, focused on highlighting causality and feedback loops.  Feedback loops can be either reinforcing or balancing, and CLDs can help to explain the role of such loops within a given system.  CLDs are often developed in a participatory approach.  They drawings can be further developed by categorizing the types of variables and quantifying the relationships between variables to form a stock and flow diagram

Innovation (or change management) history

Innovation or change management history aims to generate knowledge about a system by compiling a systematic history of key events, intended and unintended outcomes and measures taken to address emergent issues. It involves in-depth interviews with as many key stakeholders as possible to build an understanding of the performance of the system from a number of different points of view

Participatory Impact Pathways Analysis (PIPA)

PIPA is a workshop-based approach that combines impact pathway logic models and network mapping through a process involving stakeholder engagement.  PIPA workshops aim to help participants to make their assumptions and underlying mental models about how projects run explicit and to reach consensus on how to achieve impact.

Process mapping

A set of tools, such as flow charts, to provide a pictorial representation of a sequence of actions and responses. Their use can be quite flexible, such as to make clear current processes, as a basis for identifying bottlenecks or inefficient steps, or to produce an ideal map of how they’d like them to be.

Stock and flow diagrams

 Stock and flow diagrams are quantitative system dynamics tools used for illustrating a system that can be used for model-based policy analysis in a simulated, dynamic environment.  Stock and flow diagrams explicitly incorporate feedback to understand complex system behavior and capture non-linear dynamics.

Systems archetypes

Systems archetypes are a number of generic structures that describe common behaviors between the parts of a system.  They provide templates to demonstrate different types of balancing and reinforcing feedback loops, which can be used by teams to come to a diagnosis about how a system is working, and particularly about how performance changes over time.

Video introductions

Full presentations

Blogs and commentary