Towards a Credible Forecasting Process for Sustainable User Innovation

The document presents a review of the challenges that arise when forecasting techniques are applied to predict the evolution of sustainable user innovation. It also provides an augmented list of variables that may be used in the process of envisioning the future of lifestyles in Europe. Forecasting any kind of individual and social behaviour requires assembling several elements from different disciplines: from mere technical methodological challenges (choice of model) to substantial theoretical discussions (prediction of outcomes); from data gathering strategies (combination of sources and their reliability) to measurement (Choice of variables used to represent the relevant ideas); from establishing the rules of micro-behaviour of individuals to using well established models for individual interactions. This document is a review of measurement indicators in sustainability models, focusing on available models for forecasting the future of natural environments, ecosystems, climate change, urban mobility and demographic patterns. In the context of a large European research project there is a need to perform systematic forecasts of several Dimensions of individual lifestyles Therefore, the purpose is to provide the building blocks in which a systematic and credible forecasting of user innovation in Europe in 2030/2050 can be built.

[1]  S. Seuring,et al.  Core issues in sustainable supply chain management – a Delphi study , 2008 .

[2]  D. Meadows,et al.  The Limits to Growth , 1972 .

[3]  Colin Hunter,et al.  Assessing the environmental impact of tourism development: the use of the Delphi technique , 1989 .

[4]  Daniel A. Kaufmann,et al.  The Worldwide Governance Indicators: Methodology and Analytical Issues , 2010 .

[5]  Corruption Perceptions Index 2012 - Statistical Assessment , 2012 .

[6]  Dawn Nafus,et al.  This One Does Not Go Up to 11: The Quantified Self Movement as an Alternative Big Data Practice , 2014 .

[7]  Helder Coelho,et al.  Interdisciplinary Applications of Agent-Based Social Simulation and Modeling , 2014 .

[8]  J. Landeta Current validity of the Delphi method in social sciences , 2006 .

[9]  Saint John Walker Big Data: A Revolution That Will Transform How We Live, Work, and Think , 2014 .

[10]  J. Jordana,et al.  The emergence of regulatory regionalism: transnational networks and the diffusion of regulatory agencies within regions , 2015 .

[11]  Andreas Pyka,et al.  Innovation Networks - A Simulation Approach , 2001, J. Artif. Soc. Soc. Simul..

[12]  Enrique Kremers,et al.  Agent based modeling of energy networks , 2014 .

[13]  P. Allen,et al.  Modelling sustainable energy futures for the UK , 2014 .

[14]  L. Greene EHPnet: United Nations Framework Convention on Climate Change , 2000, Environmental Health Perspectives.

[15]  Eric Gossett,et al.  Big Data: A Revolution That Will Transform How We Live, Work, and Think , 2015 .

[16]  Nien Fan Zhang,et al.  Forecasting and time series analysis , 1976 .

[17]  L. Codispoti The limits to growth , 1997, Nature.

[18]  Erik Brynjolfsson,et al.  Big data: the management revolution. , 2012, Harvard business review.

[19]  Michael J. Shaw,et al.  Simulation of Order Fulfillment in Divergent Assembly Supply Chains , 1998, J. Artif. Soc. Soc. Simul..

[20]  Murray Turoff,et al.  The Delphi Method: Techniques and Applications , 1976 .

[21]  Anthony J. Bagnall,et al.  A multiagent model of the UK market in electricity generation , 2005, IEEE Transactions on Evolutionary Computation.

[22]  Scott E. Page,et al.  Agent-Based Models , 2014, Encyclopedia of GIS.

[23]  J. Manyika Big data: The next frontier for innovation, competition, and productivity , 2011 .

[24]  M. Adler,et al.  Gazing into the oracle : the Delphi method and its application to social policy and public health , 1996 .

[25]  Ian Miles,et al.  Foresight tools for participative policy-making in inter-governmental processes in developing countries: Lessons learned from the eLAC Policy Priorities Delphi , 2009 .

[26]  F. Chapin,et al.  A safe operating space for humanity , 2009, Nature.

[27]  Suzanne D. Pawlowski,et al.  The Delphi method as a research tool: an example, design considerations and applications , 2004, Inf. Manag..

[28]  Melanie Swan,et al.  The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery , 2013, Big Data.

[29]  Andrew Harvey,et al.  Forecasting, Structural Time Series Models and the Kalman Filter , 1990 .

[30]  L. Pazvakawambwa,et al.  Forecasting methods and applications. , 2013 .

[31]  Viktor Mayer-Schnberger,et al.  Big Data: A Revolution That Will Transform How We Live, Work, and Think , 2013 .

[32]  Thomas C. Schelling,et al.  Dynamic models of segregation , 1971 .