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Innovation Diffusion Processes Prof. Renato Guseo
When approaching statistics we have all met the notion of “population” with
reference to some of its relevant properties and global relationships such as
means, percentages, probabilities and other functional relationships considered
within given distributive contexts simple or multiple. One of the main
features of the statistical meaning of population is its characterization as a
“set” to which a vector of observable variables is associated. Many other
elements are latent or residual. However, we observe that individual data, that
is the level of relevant variables, may be partly due to a system of
interpersonal relationships that connect between them the population
components. Individuals’ change may be strictly due to the concrete possibility
to communicate, through various
languages, information that is relevant for many others. The specialized
diffusion of human languages and the
dual characterization of populations’
genes have been widely studied and confirmed (see for instance
CavalliSforza, 1996). The existence of languagebased
networks is an important feature of social systems and has much to do with collective learning. An interesting
insight on this topic has been offered by the physicist Marchetti (1980): no
abstract formulations but simple ideas to be read without prejudices. The
absorption of a new idea, a discovery, a new technology by social systems, that
cannot be considered as simple statistical populations, is based on the concept
of innovation. Innovation implies a
change in terms of production and consumption systems: while invention and
discovery represent the potential change, innovation is the realization of such
a change.
Complex Systems and Aggregate Differential approaches
Innovation Diffusion, Quantitative Marketing and Energy Sources
Complex Systems and Aggregate Differential Approaches: Dualism (trailer)
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Aggregate and AgentBased Models for the Diffusion of Innovations, PhD Thesis, Scuola di Dottorato di Ricerca in "Economia e Management", ciclo XX, Università di Padova. GUIDOLIN, M., MORTARINO, C. (2010). Crosscountry diffusion of photovoltaic systems: modelling choices and forecasts for national adoption patterns, Technological Forecasting and Social Change, 77(2), 279296, http://dx.doi.org/10.1016/j.techfore.2009.07.003 · GUSEO, R. (2004). Interventi strategici e aspetti competitivi nel ciclo di vita di innovazioni; Strategic Interventions and Competitive Aspects in Innovation Life Cycle ; Working Paper Series, N. 11, Department of Statistical Sciences, University of Padua, Italy. · GUSEO, R. (2007). How much Natural Gas is there? Depletion Risks and Supply Security Modelling. (submitted) · GUSEO, R. (2011). Worldwide Cheap and Heavy Oil Productions: A LongTerm Energy Model. Energy Policy, 39(9), 55725577, doi: 10.1016/j.enpol.2011.04.060 · GUSEO, R., CAPUZZO, S. (2006). A quando l'ultima stilla? Le serie storiche delle estrazioni alla base di nuovi scenari di previsione sul picco del petrolio, Ambiente Risorse Salute , n. 108 Marzo/Aprile, 614. (download) · GUSEO, R., DALLA VALLE, A. (2005). Oil and Gas Depletion: Diffusion Models and Forecasting under Strategic Intervention, Statistical Methods and Applications, vol. 14, 3, 375387 doi: 10.1007/s1026000501186. · GUSEO, R., DALLA VALLE, A., GUIDOLIN, M. (2007). World Oil Depletion Models: Price Effects Compared with Strategic or Technological Interventions; Technological Forecasting and Social Change, 74(4), 452  469 doi: 10.1016/j.techfore.2006.01.004 · GUSEO, R., GUIDOLIN, M. (2006). Cellular Automata and Riccati Equation Models for Diffusion of Innovations. (Short Paper), Atti della XLIII Riunione Scientifica della Società Italiana di Statistica, Torino 1416/6/2006, Vol. Sessioni Spontanee, 103106, CLEUP, Padova. · GUSEO, R., GUIDOLIN, M. (2007a). Cellular Automata with Network Incubation Period versus Perturbed Riccati Equation Models in Information Technology Innovation Diffusion. S.Co.2007, Fifth Conference, Complex Models and Computational Intensive Methods for Estimation and Prediction, Venice 68 september 2007, P. Mantovan, A. Pastore, S. Tonellato, (Eds.) Book of Short Papers, 27277. · GUSEO, R., GUIDOLIN, M. (2007b). A Class of Automata Networks for Diffusion of Innovations Driven by Riccati Equations. Working Paper Series, N. 6, April 2007, Department of Statistical Sciences, University of Padua, Italy. · GUSEO, R., GUIDOLIN, M. (2008). Cellular Automata and Riccati Equation Models for Diffusion of Innovations. Statistical Methods and Applications, 17(3), 291  308 doi: 10.1007/s1026000700593. · GUSEO,R., GUIDOLIN, M. (2009a). Modelling a Dynamic Market Potential: A Class of Automata Networks for Diffusion of Innovations. 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Department of Statistical Sciences  University of Padova
