Complex systems

Managing complexity with human learning systems

By Toby Lowe

toby-lowe
Toby Lowe (biography)

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.

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Can foresight and complexity play together?

By James E. Burke

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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.

What is foresight?

Foresight starts from a place of humility—we cannot predict the future—and an acceptance of ambiguity.

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Three complexity principles for convergence research

By Gemma Jiang

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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.

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Fifteen characteristics of complex social systems

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.

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Accountability and adapting to surprises

By Patricia Hirl Longstaff

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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.

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Foundations of a translational health sciences doctoral program

By Gaetano R. Lotrecchiano and Paige L. McDonald

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Gaetano R. Lotrecchiano (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).

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

By Jan Kwakkel

jan-kwakkel
Jan Kwakkel (biography)

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

  1. Intrinsic limits to predictability in complex systems.
  2. A variety of stakeholders with different perspectives on what the system is and what problem needs to be solved.
  3. 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.

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Scaling up amidst complexity

By Ann Larson

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Ann Larson (biography)

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.

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Complexity and agent-based modelling

By Richard Taylor and John Forrester

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Richard Taylor (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.

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Modeling as empowerment

By Laura Schmitt Olabisi

schmitt-olabisi
Laura Schmitt Olabisi (biography)

Who can make systems change? The challenges of complexity are intensely felt by those who are trying to make strategic interventions in coupled human-environmental systems in order to fulfill personal, societal, or institutional goals. The activists, leaders, and decision-makers I work with often feel overwhelmed by trying to deal with multiple problems at once, with limited time, resources, and attention. We need tools to help leaders cut through the complexity so that they can identify the most effective strategies to make change.

This is where participatory system dynamics modelers like myself come in.

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