The Cost of Rational Agency

The rational agency assumption limits systems to domains of application that have never been observed. Moreover, representing agents as being rational in the sense of maximising utility subject to some well specified constraints renders software systems virtually unscalable. These properties of the rational agency assumption are shown to be unnecessary in representations or analogies of markets. The demonstration starts with an analysis of how the rational agency assumption limits the applicability and scalability of the IBM information filetering economy. An unrestricted specification of the information filtering economy is developed from an analysis of the properties of markets as systems and the implementation of a model based on intelligent agents. This extended information filtering economy modelis used to test the analytical results on the scope for agents to act as intermediaries between human users and information sources

Gravity Effects on Information Filtering and Network Evolving

In this paper, based on the gravity principle of classical physics, we propose a tunable gravity-based model, which considers tag usage pattern to weigh both the mass and distance of network nodes. We then apply this model in solving the problems of information filtering and network evolving. Experimental results on two real-world data sets, \emph{Del.icio.us} and \emph{MovieLens}, show that it can not only enhance the algorithmic performance, but can also better characterize the properties of real networks. This work may shed some light on the in-depth understanding of the effect of gravity model