Springer Plan Prediction: Which Policy is Preferred by Which People?

This book develops an innovative system, in the form of an "app", that harnesses the power of the internet to predict which sorts of people will prefer which policy in ANY planning situation. It chronicles the accumulated research wisdom behind the system’s reasoning, along with several less successful approaches to policy making that have been found wanting in the past – including the myth, usually peddled by strategic planners, that it is possible to find a "best" plan which optimally satisfies everybody. The book lays out an entirely new kind of Planning Support System (PSS). It will facilitate decision-making that is far more community-sensitive than previously, and it will drastically improve the performance of anyone who needs to plan within socially-sensitive contexts – which is all of us. A standout feature of the system is its commitment to “scientific rigour”, as shown by its predicted plan scores always being graphically presented within error margins so that true statistical significance is instantly observable. Moreover, the probabilities that its predictions are correct are always shown – a refreshing change from most, if not all other Decision Support Systems (DSS) that simply expect users to accept their outputs on faith alone. From the Back Cover This book develops an innovative system, in the form of an "app", that harnesses the power of the internet to predict which sorts of people will prefer which policy in ANY planning situation. It chronicles the accumulated research wisdom behind the system’s reasoning, along with several less successful approaches to policy making that have been found wanting in the past – including the myth, usually peddled by strategic planners, that it is possible to find a "best" plan which optimally satisfies everybody. The book lays out an entirely new kind of Planning Support System (PSS). It will facilitate decision-making that is far more community-sensitive than previously, and it will drastically improve the performance of anyone who needs to plan within socially-sensitive contexts – which is all of us.A standout feature of the system is its commitment to “scientific rigour”, as shown by its predicted plan scores always being graphically presented within error margins so that true statistical significance is instantly observable. Moreover, the probabilities that its predictions are correct are always shown  – a refreshing change from most, if not all other Decision Support Systems (DSS) that simply expect users to accept their outputs on faith alone. About the Author Ray Wyatt worked as a strategic planner in local and regional government, consultancy, industry, market research and commerce. He then became an academic at Melbourne University, Australia, where he wrote planning-support software, consulted for government agencies and taught analytical planning techniques and GIS science for exactly 30 years. Consequently, he has published across a wide array of fields including applied artificial intelligence, strategic planning, decision aiding, spatial optimization, graphic communication, urban modelling, transportation, facilities management, and professional practice. Ray’s formal qualifications are a PhD from the University of Reading, UK, a masters degree and a diploma from the University of Melbourne, Australia, and an honours degree from the University of Queensland, Australia. He has taught university courses in Australia, England, Wales, USA, Italy and Brazil, written 143 papers (71 refereed), produced about 10 consultant reports, built 4 serious software packages, conducted 17 community workshops, presented 40 conference papers and delivered 52 other addresses around the world. He jointly founded and edited two refereed journals - Urban Policy & Research and Applied GIS; served on several other editorial boards and regularly reviewed papers for about 19 other journals. Ray is also a past Chairman of the Internati