Exploring the science of complexitySystems & complexity

Exploring the science of complexity series (part 23): Conclusions – How does complexity science differ from existing ways of understanding and interpreting international development and humanitarian problems?

This article is part 23 of a series of articles featuring the ODI Working Paper Exploring the science of complexity: Ideas and implications for development and humanitarian efforts.

The value of these useful, challenging fictions of complexity science is that they provide a new way of looking at aid problems. Specifically, the science of complexity stands in contrast with the two standard methods of scientific enquiry1 – induction and deduction (as mentioned in the section on adaptive agents). In one of the two previous ODI working papers on complexity theory, Michael Warner2 argues that complexity theory steers a middle ground between these two approaches. It is important to note that these approaches have provided scientists and thinkers with opposing paradigms since classical times right up to the present day, e.g. the contrast between the hard rock of Aristotle and the swirling mysticism of Plato, echoed in the differences between the approach of neoclassical economics compared with that of cultural anthropology.

Specifically, deduction works from the general to the specific. Starting with a theory, we narrow down to specific hypotheses that can be tested through the collection of observations – namely, data. This leads to testing the hypotheses using specific empirical data, thereby confirming the original theories. The deductive approach results in interventions which are designed using a detailed understanding and deconstruction of the parts of a system and how they fit together. The problem with applying such methods to international aid interventions is that they are at odds with the emerging understanding that the range of interconnections and interdependencies are too numerous to predict a specific outcome from a particular intervention. Complex systems cannot be usefully deconstructed into their casual components. The degree of complexity presented by many human systems means that the more each system is deconstructed, the more unknowns are introduced. In terms of the ability to design predictable interventions, applying deductive approaches to complex systems soon leads to diminishing returns.

By contrast, ‘inductionist’ approaches move from the specific to the general. They focus on observations of the world and try to detect patterns or regularities, thereby forming some tentative hypotheses and finally some general theories. Such approaches result in the development of generic rules for successful interventions, providing a model that can be scaled up and replicated. The problem here is that methods of induction tend to overlook the behavioural, experiential and experimental nature of complex systems. These characteristics mean that it cannot be correct to assume that ‘good practices’ of intervention which work in one setting are applicable to organisations in other systems – good practices may play out in very different ways in different settings.

The theories of complexity science challenge both of these ways of thinking about real world problems. Using concepts relating to the nature of complex systems, the nature of change, and the behaviour of intelligent actors within these, complexity theory provides a basis for guiding thinking in a way that encompasses both approaches and their limitations. By aiding understanding of the mechanisms through which unpredictable, unknowable and emergent change happens, complexity science enables a reinterpretation of existing systems and the problems faced within them. The value of complexity science – at its most effective – is to generate ideas and insights that help to see complex problems in a more realistic and holistic manner, thereby supporting more useful intuitions and actions.

Next part (part 24): Conclusions – What kinds of phenomena can complexity science help us better understand?

Article source: Ramalingam, B., Jones, H., Reba, T., & Young, J. (2008). Exploring the science of complexity: Ideas and implications for development and humanitarian efforts (Vol. 285). London: ODI. (https://www.odi.org/publications/583-exploring-science-complexity-ideas-and-implications-development-and-humanitarian-efforts). Republished under CC BY-NC-ND 4.0 in accordance with the Terms and conditions of the ODI website.

Header image source: qimono on PixabayPublic Domain.


  1. Axelrod, R. (1997). ‘Advancing the Art of Simulation in the Social Sciences’, Complexity, 3(2): 16–22.
  2. Warner, M. (2001). Complex Problems … Negotiated Solutions: The Practical Applications of Chaos and Complexity Theory to Community-based Natural Resource Management, London: ODI.
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Ben Ramalingam and Harry Jones with Toussaint Reba and John Young

Authors of the Overseas Development Institute (ODI) Working Paper "Exploring the science of complexity: Ideas and implications for development and humanitarian efforts".

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