Complex systems – Integration and Implementation Insights

The essential conditions for, and characteristics of, complexity

May 5, 2026May 5, 2026

By Jean Boulton.

What are the underpinning necessities or conditions—the essential ingredients—that lead to and engender the qualities or characteristics of the complex world, especially its processual and emergent nature?

Three conditions for complexity: the essential ingredients

A watch or intricate machine is not complex. Nor is a saucer of water. So, when do we regard something as complex? What are the necessary conditions for complexity fully to be realised?

These are:

open boundaries
diversity
reflexive inter-relationships among constituents.

Let’s look at each of these in more detail.

Data variety and why it matters

October 8, 2025September 2, 2025

By Richard Berry.

What are the differing characteristics of data? Why are they important for systems to function effectively? What is requisite variety of data?

There are nine characteristics of data variety which agitate systems. These are volume, velocity, variety, veracity, validity, vulnerability, viscosity, vectors and virtualisation. Together, the ‘9Vs’ constitute a data requisite variety framework and are described below.

1. Volume

Description: The amounts of available data.

Example: Volume can vary widely from the results of small-scale research to the tsunami of digital material accessible through the internet. The latter can overwhelm both people and organisations.

How measuring impact gets in the way of real world change

December 12, 2023November 28, 2023

By Toby Lowe

Why is the idea that we can measure our impact to understand how well we are performing fundamentally flawed? Why is it impossible to “demonstrate your impact” in complex environments?

Although the idea of measuring impact is seductive, almost all useful social change is achieved as part of a complex system. In other words, your work is a small part of a much larger web of entangled and interdependent activity and social forces.

The systems map of the outcome of obesity, shown in the figure below, illustrates this perfectly – it shows all the factors contributing to people being obese (or not), and all the relationships between those factors.

This is the reality of trying to make impact in the world – your actions are part of a web of relationships – most of which are beyond your control, many of which are beyond your influence, quite a few of which will be completely invisible to you.

Managing complexity with human learning systems

March 3, 2026January 10, 2023

By Toby Lowe

How can those in public service – be they researchers, policy makers or workers in government agencies, private businesses managers, or voluntary and community organisation leaders – think more effectively about improving people’s lives, when they understand that each person’s life is a unique complex system?

A good starting point is understanding that real outcomes in people’s lives aren’t “delivered” by organisations (or by projects, partnerships or programmes, etc). Outcomes are created by the hundreds of different factors in the unique complex system that is each person’s life.

In other words, an outcome is the product of hundreds of different people, organisations, and factors in the world all coming together in a unique and ever-changing combination in a particular person’s life. Very little of what influences the outcome is under the control or influence of those who undertake public service.

Can foresight and complexity play together?

January 9, 2026February 16, 2021

By James E. Burke

author_james-burke — James E. Burke (biography)

What is foresight and how does it differ from prediction? What role can complexity play in foresight? Does Cynefin® offer a possible framework to begin integrating foresight and complexity?

In this blog post, I describe how:

Foresight identifies clues for the future and integrates them into forecasts
Complexity theory offers ways to understand how the future emerges
Cynefin® gives us a framework of domains that allows us to better understand trends and forecasts.

Three complexity principles for convergence research

January 9, 2026February 4, 2021

By Gemma Jiang

author_gemma-jiang — Gemma Jiang (biography)

How can principles adapted from complexity thinking be applied to convergence research? How can such principles help integrate knowledge, methods, and expertise from different disciplines to form novel frameworks that catalyze scientific discovery and innovation?

I present three principles from the complexity paradigm that are highly relevant to convergence research. I then describe three types of transformative containers that I have developed to create enabling conditions for applying complexity principles to convergence.

1. Ecosystem consciousness: An inversion of perspectives

Ecosystem consciousness is necessary because in complex systems the whole (ecosystem) is bigger than the sum of its parts; the wellbeing of the whole and the parts are interdependent and mutually reinforcing.

Fifteen characteristics of complex social systems

May 31, 2021March 17, 2020

By Hamilton Carvalho

Author - Hamilton Carvalho — Hamilton Carvalho (biography)

What is it about complex social systems that keeps reproducing old problems, as well as adding new ones? How can public policy move away from what I call the Mencken Syndrome (in reference to a quotation from American journalist Henry Mencken) – that is, continually proposing clear and simple solutions to complex social problems – that are also wrong!

With this goal in mind, I have compiled a list of fifteen major characteristics of complex social systems based on the system dynamics and complexity sciences literatures, as well as my own research.

Accountability and adapting to surprises

April 29, 2025August 27, 2019

By Patricia Hirl Longstaff

Image of Patricia Hirl Longstaff — Patricia Hirl Longstaff (biography)

We have all been there: something bad happens and somebody (maybe an innocent somebody) has their career ruined in order to prove that the problem has been fixed. When is blame appropriate? When is the blame game not only the wrong response, but damaging for long-term decision making?

In a complex and adapting world, errors and failure are not avoidable. The challenges decision-makers and organizations face are sometimes predictable but sometimes brand new. Adapting to surprises requires more flexibility, fewer unbreakable rules, more improvisation and deductive tinkering, and a lot more information about what’s going right and going wrong. But getting there is not easy because this challenges some very closely held assumptions about how the world works and our desire to control things.

Let’s not kid ourselves. Sometimes people do really dumb things that they should be blamed for. What we need is to be more discriminating about when finding blame and accountability is appropriate.

Foundations of a translational health sciences doctoral program

February 18, 2025April 10, 2018

By Gaetano R. Lotrecchiano and Paige L. McDonald

authors_gaetano-lotrecchiano_paige-mcdonald — 1. Gaetano R. Lotrecchiano (biography)
2. Paige L. McDonald (biography)

How can doctoral studies be developed to include innovation in practice and research, as well as systems and complexity thinking, along with transdisciplinarity? This blog post is based on our work introducing a PhD in Translational Health Sciences at George Washington University in the USA.

Innovation in Practice and Research

We suggest that innovation in practice and research is achieved by the integration of knowledge in three key foundational disciplines:

translational research
collaboration sciences
implementation science (Lotrecchiano et al., 2016).

Managing deep uncertainty: Exploratory modeling, adaptive plans and joint sense making

April 15, 2022August 15, 2017

By Jan Kwakkel

How can decision making on complex systems come to grips with irreducible, or deep, uncertainty? Such uncertainty has three sources:

Intrinsic limits to predictability in complex systems.
A variety of stakeholders with different perspectives on what the system is and what problem needs to be solved.
Complex systems are generally subject to dynamic change, and can never be completely understood.

Deep uncertainty means that the various parties to a decision do not know or cannot agree on how the system works, how likely various possible future states of the world are, and how important the various outcomes of interest are.

Scaling up amidst complexity

October 9, 2025June 27, 2017

By Ann Larson

How can new or under-utilized healthcare practices be expanded and institutionalized to achieve audacious and diverse global health outcomes, ranging from eliminating polio to reversing the rise in non-communicable diseases? How can complex adaptive systems with diverse components and actors interacting in multiple ways with each other and the external environment best be dealt with? What makes for an effective scale-up effort?

Four in-depth case studies of scale-up efforts were used to explore if there were different pathways to positively change a complex adaptive system.

Complexity and agent-based modelling

February 12, 2025March 7, 2017

By Richard Taylor and John Forrester

authors_richard-taylor_john-forrester_updated — 1. Richard Taylor (biography)
2. John Forrester (biography)

Policy problems are complex and – while sometimes simple solutions can work – complexity tools and complexity thinking have a major part to play in planning effective policy responses. What is ‘complexity’ and what does ‘complexity science’ do? How can agent-based modelling help address the complexity of environment and development policy issues?

Complexity

At the most obvious level, one can take complexity to mean all systems that are not simple, by which we mean that they can be influenced but not controlled. Complexity can be examined through complexity science and complex system models.