The Surrey Energy Economics Centre (SEEC) consists of members of the School of Economics who work on energy economics, environmental economics and regulation. The School of Economics has a long-standing tradition of energy economics research from its early origins under the leadership of Professor Colin Robinson. This was consolidated in 1983 when the University established SEEC, with Colin as the Director; to study the economics of energy and energy markets. SEEC undertakes original energy economics research and since being established it has conducted research across the whole spectrum of energy economics, including the international oil market, North Sea oil & gas, UK & international coal, gas privatisation & regulation, electricity privatisation & regulation, measurement of efficiency in energy industries, energy & development, energy demand modelling & forecasting, and energy & the environment. SEEC research output includes SEEDS-Surrey Energy Economic Discussion paper Series and SEERS-Surrey Energy Economic Report Series (details at www.seec.surrey.ac.uk/Research/SEEDS.htm) as well as a range of other academic papers, books and monographs. SEEC also runs workshops and conferences that bring together academics and practitioners to explore and discuss the important energy issues of the day SEEC also attracts a large proportion of the School's PhD students and oversees the MSc in Energy Economics & Policy. Many students have successfully completed their MSc and/or PhD in energy economics and gone on to very interesting and rewarding careers, both in academia and the energy industry.

Agent-based computational economics (ACE) has been used for tackling major research questions in macroeconomics for at least two decades. This growing field positions itself as an alternative to dynamic stochastic general equilibrium (DSGE) models. In this paper, we provide a much needed review and synthesis of this literature and recent attempts to incorporate insights from ACE into DSGE models. We first review the arguments raised against DSGE in the macroeconomic ACE (macro ACE) literature, and then review existing macro ACE models, their explanatory power and empirical performance. We then turn to the literature on behavioural New Keynesian models that attempts to synthesize these two approaches to macroeconomic modelling by incorporating insights of ACE into DSGE modelling. Finally, we provide a thorough description of the internally rational New Keynesian model, and discuss how this promising line of research can progress.

PurposeThis paper aims to study the evolution of definitions of internet of things (IoT) through time, critically assess the knowledge these definitions contain and facilitate sensemaking by providing those unfamiliar with IoT with a theoretical definition and an extended framework.Design/methodology/approach164 articles published between 2005 and 2019 are collected using snowball sampling. Further, 100 unique definitions are identified in the sample. Definitions are examined using content analysis and applying a theoretical framework of five knowledge dimensions.FindingsIn declarative/relational dimensions of knowledge, increasing levels of agreement are observed in the sample. Sources of tautological reasoning are identified. In conditional and causal dimensions, definitions of IoT remain underdeveloped. In the former, potential limitations of IoT related to resource scarcity, privacy and security are overlooked. In the latter, three main loci of agreement are identified.Research limitations/implicationsThis study does not cover all published definitions of IoT. Some narratives may be omitted by our selection criteria and process.Practical implicationsThis study supports sensemaking of IoT. Main loci of agreement in definitions of IoT are identified. Avenues for further clarification and consensus are explored. A new framework that can facilitate further investigation and agreement is introduced.Originality/valueThis is, to the authors’ knowledge, the first study that examines the historical evolution of definitions of IoT vis-à-vis its technological features. This study introduces an updated framework to critically assess and compare definitions, identify ambiguities and resolve conflicts among different interpretations. The framework can be used to compare past and future definitions and help actors unfamiliar with IoT to make sense of it in a way to reduce adoption costs. It can also support researchers in studying early discussions of IoT.

Innovation and entrepreneurship are the most important catalysts of dynamism in market economies. While it is known that entrepreneurial activities are locally embedded, mutual effects of entrepreneurs and their local regional environment have not been adequately addressed in the existing literature. In this article, we use agent‐based simulation experiments to investigate the role of entrepreneurship in the emergence of regional industrial clusters. We present fundamental extensions to the Simulating Knowledge Dynamics in Innovation Networks model (Ahrweiler et al., Industry and Labor Dynamics: The Agent‐based Computational Economics Approach; World Scientific: Singapore, 2004; pp 284–96) by using a multilevel modeling approach. We analyze the effects of changing entrepreneurial character of regions on the development industrial clusters in two simultaneously simulated regions. We find that an increase in the entrepreneurship of one region has a negative effect on the other region due to competition for factors of production and innovative outputs. The major policy implication of this finding is the limitation it posits on regional innovation and development policies that aspire to support clusters in similar areas of industrial specialization. © 2014 Wiley Periodicals, Inc. Complexity 19: 14–29, 2014