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AbstractWe present an agent-based computational model in which bounded rational firms and workers trade on fully decentralized markets for final goods and labor by means of random matching protocols. The model replicates several macroeconomic phenomena regularly observed in the data, with aggregate features emerging from the localized interactions of individual entities. The model is then used as a computational laboratory to run an experiment on the role of fiscal policy in increasing macroeconomic performance.
Grid computing has recently become an important paradigm for managing computationally demanding applications, composed of a collection of services. The dynamic discovery of services, and the selection of a particular service instance providing the best value out of the discovered alternatives, poses a complex multi-attribute n:m allocation decision problem, which is often solved using a centralized resource broker. To manage complexity, this article proposes a two-layer architecture for service discovery in such Application Layer Networks (ALN). The first layer consists of a service market in which complex services are translated to a set of basic services, which are distinguished by price and availability. The second layer provides an allocation of services to appropriate resources in order to enact the specified services. This framework comprises the foundations for a later comparison of centralized and decentralized market mechanisms for allocation of services and resources in ALNs and Grids.
In this paper we sketch some reflections on the pitfalls and inconsistencies of the research program—currently dominant among the profession—aimed at providing microfoundations to macroeconomics along a Walrasian perspective. We argue that such a methodological approach constitutes an unsatisfactory answer to a well-posed research question, and that alternative promising routes have been long mapped out but only recently explored. In particular, we discuss a recent agent-based, truly non-Walrasian macroeconomic model, and we use it to envisage new challenges for future research
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