Supervisory control of a class of discrete event processes
SIAM Journal on Control and Optimization
Principles and applications of continual computation
Artificial Intelligence - special issue on computational tradeoffs under bounded resources
Theories of the Information Society (International Library of Sociology)
Theories of the Information Society (International Library of Sociology)
Redundance-free description of partitioned complex systems
Mathematical and Computer Modelling: An International Journal
A foresight support system to manage knowledge on information society evolution
SocInfo'12 Proceedings of the 4th international conference on Social Informatics
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This paper presents the theoretical foundations of an intelligent on-line modelling tool capable of processing heterogeneous information on complex techno-economical systems. Its main functionality is to investigate, elicit, and apply rules and principles that govern the development processes of technologies and related markets. Specifically, we will focus on applications of the tool to model the evolution of information technology (IT). We will distinguish several relevant subsystems of the system under study, which describe the demographic, education, global economic trends, as well as specific market factors that determine the demand for and use of IT. The group modelling techniques are implemented in the new tool to enable the collaborative and distributed model building with intelligent verification of entries called ‘model wiki'. Based on the information elicited from experts, gathered from the web and professional databases, a discrete-time control model of technological evolution emerges, coupled with a controlled discrete-event system. The latter processes qualitative information and models the influence of external events and trends on the discrete-time control system parameters. We propose novel uncertainty handling techniques capable of processing and combining different types of uncertain information, coming i.a. from Delphi research and forecasts. The quantitative information is dynamically updated by autonomous webcrawlers, following an adaptive intelligent strategy. The resulting model can be used to simulate long-term future trends and scenarios. Its ultimate goal is to perform an optimization process and derive recommendations for decision makers, for example when selecting IT investment strategies in an innovative enterprise.