The Experts below are selected from a list of 198 Experts worldwide ranked by ideXlab platform
Attilio Trezzini - One of the best experts on this subject based on the ideXlab platform.
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The Social Significance of Consumption and the Elasticity of Output to Demand in the Long Run: A Reply to Gualerzi
Review of Political Economy, 2017Co-Authors: Attilio TrezziniAbstract:Gualerzi's comment on Trezzini’s 2015 article (‘Growth without Normal Capacity Utilization and the Meaning of the Long-Run Saving Ratio.’) underestimates the role played by the long-run elasticity of output with respect to changes in aggregate demand in my analysis and in the demand-led processes of growth.
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Growth without Normal Capacity Utilization and the Meaning of the Long-Run Saving Ratio
Review of Political Economy, 2015Co-Authors: Attilio TrezziniAbstract:The ratio of saving to income over a long period is analyzed here in a theoretical context that takes account of the role of aggregate demand in the growth process, and in which it is not assumed that the economy must operate at a Normal rate of Capacity utilization in the long run. The very notion of the long-run saving rate is therefore redefined with respect to the one found in the literature where Normal utilization is assumed. We argue that the long-run saving ratio must be conceived as the result of the interaction of many different influences and can therefore be similar in radically different circumstances and different in similar circumstances with respect both to the incentive to accumulate and to the pattern of saving decisions.
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growth without Normal Capacity utilization
European Journal of The History of Economic Thought, 2003Co-Authors: Antonella Palumbo, Attilio TrezziniAbstract:Within the demand-led approach to growth, the long-period tendencies of quantities cannot be effectively studied through theoretical positions entailing Normal utilization of Capacity. Whether in the form of constant or of average Normal utilization, this assumption contradicts the supposed autonomy of aggregate demand. Analysis of the operation of the adjustment of Capacity to demand suggests that potentially offsetting forces make fully adjusted positions irrelevant. As quantities cannot be assumed to gravitate towards such positions, the relations between quantity variables determined on the Normal utilization hypothesis provide a poor guide to the analysis of reality.
C. Giraud-carrier - One of the best experts on this subject based on the ideXlab platform.
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High Capacity Neural Networks for Familiarity Discrimination
9th International Conference on Artificial Neural Networks: ICANN '99, 1999Co-Authors: R. Bogacz, M.w. Brown, C. Giraud-carrierAbstract:This paper presents two new novelty discrimination models for uncorrelated patterns based on neural modelling. The first model uses a single neuron with Hebbian learning and works well when the number of memorised patterns is less than 0.046N (N, the number of inputs). The second model is based on checking the value of the energy function of a Hopfield network. By sacrificing the ability to extract patterns - not needed for familiarity detection - an amazing jump from the Normal Capacity for retrieval of 0.145N to a Capacity for novelty discrimination of 0.023N2 is achieved. In addition, both models give some insight on the effect of deja vu, since there is always a very small probability of detecting novel patterns as familiar.
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High Capacity neural networks for familiarity discrimination
1999 Ninth International Conference on Artificial Neural Networks ICANN 99. (Conf. Publ. No. 470), 1999Co-Authors: R. Bogacz, M.w. Brown, C. Giraud-carrierAbstract:This paper presents two new novelty discrimination models for uncorrelated patterns based on neural modelling. The first model uses a single neuron with Hebbian learning and works well when the number of memorised patterns is less than 0.046 N (where N is the number of inputs). The second model is based on checking the value of the energy function of a Hopfield network. By sacrificing the ability to extract patterns, not needed for familiarity detection, an amazing jump from the Normal Capacity for retrieval of 0.145 N to a Capacity for novelty discrimination of 0.023 N/sup 2/ is achieved. In addition, both models give some insight on the effect of deja vu, since there is always a very small probability of detecting novel patterns as familiar.
D. Jeffery - One of the best experts on this subject based on the ideXlab platform.
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ITSC - Validation of Capacity reductions in traffic monitoring systems
2006 IEEE Intelligent Transportation Systems Conference, 2006Co-Authors: Pengjun Zheng, M. Mcdonald, D. JefferyAbstract:Both planned and unplanned events can reduce the capacities on roadways and result in congestion and delay. Currently, there are no complete and reliable sources which can be used by traffic operators to estimate Capacity reductions. On many occasions, operators have to estimate Capacity reductions using a large degree of subjectivity. This paper describes a methodology developed to validate Capacity reductions from data collected within the traffic monitoring system. Methods for the determination of Normal Capacity, short-term and long-term Capacity reductions are introduced. The proposed method has been successfully applied to derive Capacity reductions using operational data. The results obtained are encouraging. It is believed that the method could be implemented in many traffic control centres to improve Capacity reduction estimations
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Validation of Capacity reductions in traffic monitoring systems
2006 IEEE Intelligent Transportation Systems Conference, 2006Co-Authors: Pengjun Zheng, M. Mcdonald, D. JefferyAbstract:Both planned and unplanned events can reduce the capacities on roadways and result in congestion and delay. Currently, there are no complete and reliable sources which can be used by traffic operators to estimate Capacity reductions. On many occasions, operators have to estimate Capacity reductions using a large degree of subjectivity. This paper describes a methodology developed to validate Capacity reductions from data collected within the traffic monitoring system. Methods for the determination of Normal Capacity, short-term and long-term Capacity reductions are introduced. The proposed method has been successfully applied to derive Capacity reductions using operational data. The results obtained are encouraging. It is believed that the method could be implemented in many traffic control centres to improve Capacity reduction estimations
Davide Gualerzi - One of the best experts on this subject based on the ideXlab platform.
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Growth, Normal Capacity Utilization and the Long-Run Saving Ratio: A Comment
Review of Political Economy, 2017Co-Authors: Davide GualerziAbstract:In a recent paper Attilio Trezzini presents an explanation of the saving ratio that does not rely on Normal Capacity utilization positions. Trezzini instead focuses on the fluctuations of consumption and investment. But that very focus, I argue, requires a different kind of approach. Once the traditional theory of saving is discarded, the ‘indeterminacy’ of the saving ratio opens the way to an analysis of the evolution of consumption, and of how that evolution affects aggregate demand. The generation and evolution of autonomous demand are matters of obvious relevance to the classical Keynesian approach to the analysis of growth. The present comment takes James Duesenberry’s criticism of demand theory as the starting point for an examination of the evolving standard of consumption and autonomous (‘innovative’) investment, therefore addressing directly the investment–consumption relationship. There are of course a number of complicated questions involved and they have not yet been satisfactorily analysed. They are part of the necessary task of articulating a theory of consumption consistent with demand-led growth and forward-looking investment decisions.
R. Bogacz - One of the best experts on this subject based on the ideXlab platform.
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High Capacity Neural Networks for Familiarity Discrimination
9th International Conference on Artificial Neural Networks: ICANN '99, 1999Co-Authors: R. Bogacz, M.w. Brown, C. Giraud-carrierAbstract:This paper presents two new novelty discrimination models for uncorrelated patterns based on neural modelling. The first model uses a single neuron with Hebbian learning and works well when the number of memorised patterns is less than 0.046N (N, the number of inputs). The second model is based on checking the value of the energy function of a Hopfield network. By sacrificing the ability to extract patterns - not needed for familiarity detection - an amazing jump from the Normal Capacity for retrieval of 0.145N to a Capacity for novelty discrimination of 0.023N2 is achieved. In addition, both models give some insight on the effect of deja vu, since there is always a very small probability of detecting novel patterns as familiar.
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High Capacity neural networks for familiarity discrimination
1999 Ninth International Conference on Artificial Neural Networks ICANN 99. (Conf. Publ. No. 470), 1999Co-Authors: R. Bogacz, M.w. Brown, C. Giraud-carrierAbstract:This paper presents two new novelty discrimination models for uncorrelated patterns based on neural modelling. The first model uses a single neuron with Hebbian learning and works well when the number of memorised patterns is less than 0.046 N (where N is the number of inputs). The second model is based on checking the value of the energy function of a Hopfield network. By sacrificing the ability to extract patterns, not needed for familiarity detection, an amazing jump from the Normal Capacity for retrieval of 0.145 N to a Capacity for novelty discrimination of 0.023 N/sup 2/ is achieved. In addition, both models give some insight on the effect of deja vu, since there is always a very small probability of detecting novel patterns as familiar.
