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Understanding Collective Decision Making
A Fitness Landscape Model Approach
Synopsis
Collective decision making seems a straightforward matter: people come together and decide. But why is it that today's winners can turn into tomorrow's losers? Why can't you always get what you want? How does the interaction between the decision makers influence the outcome? And are opportunists better off than stubborn decision makers?
This book takes a refreshing look at collective decision making by using models of evolutionary biology and naturalistic decision making to analyse real-world cases. These cases include the rise and fall of the Dutch high-speed railway project and the unexpected effects of introducing public-private partnerships to connect the new Thai national airport to Bangkok. Gerrits and Marks successfully guide the reader towards an in-depth understanding through rich empirical research and uncover the beautiful complexity of collective decision making.
Understanding Collective Decision Making will be of great interest to academics working in public administration, political science and evolutionary theory. Public managers will also find this book helpful to understand why and how collective decisions are formed.
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What Reviewers Are Saying
'For a while, complexity theory holds the promise of a new paradigm for the planning sciences. This book is a major step forward in that endeavour. It provides a unique blend of analytical reasoning and in-depth case studies within an original and coherent framework.' --Koen Frenken, Utrecht University, the Netherlands'Brilliant! Grounded in the concept of fitness landscapes - as well as the latest advances in case-based modeling and critical realism, and also visual and social complexity - this book is a significant breakthrough in the study of collective decision making, particularly as it unfolds across conceptual time/space. To do so, Gerrits and Marks appeal to the readers' intuition, helping them navigate two fictional worlds (clock and cloud) to arrive at four different real-world empirical examples, which are wonderfully illustrated. There is also a web-based software package (un-code.org), which the authors developed and which readers can now use to employ these new tools. An absolute must-read!' --Brian Castellani, Kent State University, US