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Is it possible to effectively address complex problems when there are multiple and often conflicting interests, as well as multiple interacting causalities, within a constantly changing and complex environment? The analysis of such problems often results in an endless list of often contradictory factors and provides a picture with no linear causality and no overall coherent meaning, too random to help explain the complex interactions that led to the problem. Understanding not only the characteristics of organisations with their multiple interacting issues and causalities, but their co-evolutionary dynamics is the key here. This chapter provides detailed advice on how to use the complexity perspective in real life examples showing how the two parts of the EMK methodology were used in a challenging context. The first part was the identification of the multi-dimensional problem space and the co-evolutionary dynamics between the multiple dimensions, which provided a starting point for decision-making. The second part acknowledged that complex problems do not have single solutions, but need a broader enabling environment, capable of addressing the challenge over time as it changes and evolves.

Human services planners and evaluators require an increasing high level of flexibility and adaptability to remain effective in measuring the effectiveness of social interventions. Understanding the logic and assessing the impact behind the intervention can be difficult because commonly-used evaluative tools are based primarily on linear methods that assume that a set amount of input, throughput, and output will result in a set outcome. This chapter takes a complexity science approach and facilitates the use of agent-based modelling (ABM). It provides the requisite background for evaluators and researchers to frame their efforts as complex adaptive systems. These systems have several components that include agents having options, boundaries, self-organising behaviour, different options from which to choose, feedback to adapt, and an emergent behaviour. Complexity is viewed as a mathematical field where the relations between inputs and are better understood through simulations. Both qualitative and quantitative aspects of complexity are addressed through two applications of ABM that consider related social policy issues.

Complexity science has been described as an amalgam of ‘models, methods, and metaphors’ for understanding dynamic systems. Methods most commonly associated with complexity are computational simulations. Although these have contributed greatly to organization, they represent just one category of complexity methods. A main goal of this chapter is to introduce what the author considers to be the 15 sciences of complexity, organized into three main paradigms or approaches: computational agent-based modelling; natural sciences and idiographic analogies; and, narrative and multi-method studies. The chapter presents a set of complexity methods and models that may be much broader than the norm. Researchers can use these to help identify the appropriate complexity methods to use to answer a specific research question. The value of this is underlined by many scholars who argue that the choice of a research method should be based on the kind of question being asked, rather than the method most familiar to the researcher.

This chapter looks at the use of visual representations of organisational strategy in combination with facilitated dialogue (’Visual Dialogue’) as a complexity-inspired tool for culture change and organisational development. The process creates spaces for employees and leaders to come together to make sense of what is happening, what needs to change, and what actions are required. At the operational level, the process helps shift the way people talk about change and, as a result, enables the change process to become more meaningful, engaging and effective. We describe some of the cultural challenges of turning strategy into action and show how we have used the Visual Dialogue process as an Organisational Development intervention to address some of the key aspects of these challenges. Finally, we describe the component parts of Visual Dialogue and how each contributes to creating such enabling environments and supporting emergent change.

This chapter discusses the complex analogy between biological evolution and technological innovation, focusing in particular on the novel construct modular exaptation. After carefully defining exaptation – a biological concept whose technological analogue is useful in innovation studies – the chapter explores its epistemological bases, arguing that the etiological concept of function – a biological tenet – is valid also in the technological domain. The complex analogy extends to biological and technological functional modules, providing the main building block on which modular exaptation can be founded. Establishing a complex analogy enables the description of the two domains via the same relational structure. In turns this allows the transferability of knowledge from the base domain to the target domain, and vice-versa. The complex analogy can therefore be considered a methodological tool for understanding complex systems in general and technological innovation in particular, as discussed in the final section of the chapter.

A methodology implies a purpose which here is to reveal and understand what patterns and structures exist in social systems and how, why and when they occur. In the natural sciences, we can perform repeatable experiments that allow us to find robust general laws by induction and make predictions about specific behaviour by deduction. In social systems, however, agents inhabiting a situation are really in co-evolution with each other and their environment, hence constantly changing over time. This makes induction for general laws much harder and predictions on the basis of deduction questionable. Complexity Science provides a ‘scientific’ basis for evolutionary, qualitative changes, revealing the impossibility of guaranteed prediction. We use several examples to show how complexity and evolution involve changing systems of changing elements – both qualitative and quantitative. Our interpretive frameworks (models) do not make predictions about the world but about themselves thus making, through reflexivity, evasive action more likely.

The socio-economic scenario of a country reflects its social, economic, political, ideological, ethical, cultural, or communicative habits, making its proper analysis for different countries quite challenging. Complexity science has provided some new methods and tools for dealing with this challenge. Country-level Gross Domestic Product (GDP) and population are the two most important issues in the socio-economic context. In order to show the effectiveness of different nonlinear tools in analysing socio-economic data, the authors implemented three popular nonlinear tools: recurrence rate, mean conditional recurrence (MCR) and complex networks (CN) to analyse country level GDP and population data to validate the derived results with the standard conclusions based on general theories of economics. recurrence rate is used to show how two non-identical systems get synchronized through their phase spaces. MCR detects the driver and response system in synchronized states and CN reflects the overall scenarios of the complex systems by its various statistical measures.