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Edited by Eran Vigoda-Gadot and Dana R. Vashdi
A Guide to Success through Failure
In this introductory chapter, the aims and ambitions of the book are set out and explained.
Questions, Methods and Choices
Edited by Catherine Walshe and Sarah Brearley
Edited by B. Guy Peters and Guillaume Fontaine
Edited by Nicolina Montesano Montessori, Michael Farrelly and Jane Mulderrig
Professor Eve Mitleton-Kelly
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.
Professor Michael E. Wolf-Branigin, Dr William G. Kennedy, Dr Emily S. Ihara and Dr Catherine J. Tompkins
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.