Michio Morishima
- Published in print:
- 1969
- Published Online:
- November 2003
- ISBN:
- 9780198281641
- eISBN:
- 9780191596667
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/0198281641.003.0012
- Subject:
- Economics and Finance, Development, Growth, and Environmental
Various dynamic utility functions are proposed for optimum economic growth. The different sections of the chapter discuss: maximization of satisfaction over time; whether dynamic utility should be ...
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Various dynamic utility functions are proposed for optimum economic growth. The different sections of the chapter discuss: maximization of satisfaction over time; whether dynamic utility should be cardinal; the postulated properties of dynamic preferences—separability, consistence, and stationariness; and the invariability of the internal structure of the dynamic utility function with respect to monotonic transformation of the index. All this discussion is based on the assumption that the marginal rate of substitution between any two contemporary goods is independent of consumption in any other period. Finally, an alternative proposal is made based on the fact that the marginal rate of substitution between any two contemporary goods is not independent of consumption in any other period.Less
Various dynamic utility functions are proposed for optimum economic growth. The different sections of the chapter discuss: maximization of satisfaction over time; whether dynamic utility should be cardinal; the postulated properties of dynamic preferences—separability, consistence, and stationariness; and the invariability of the internal structure of the dynamic utility function with respect to monotonic transformation of the index. All this discussion is based on the assumption that the marginal rate of substitution between any two contemporary goods is independent of consumption in any other period. Finally, an alternative proposal is made based on the fact that the marginal rate of substitution between any two contemporary goods is not independent of consumption in any other period.
Kenji Doya, Shin Ishii, Alexandre Pouget, and Rajesh P.N. Rao (eds)
- Published in print:
- 2006
- Published Online:
- August 2013
- ISBN:
- 9780262042383
- eISBN:
- 9780262294188
- Item type:
- book
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262042383.001.0001
- Subject:
- Neuroscience, Disorders of the Nervous System
A Bayesian approach can contribute to an understanding of the brain on multiple levels, by giving normative predictions about how an ideal sensory system should combine prior knowledge and ...
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A Bayesian approach can contribute to an understanding of the brain on multiple levels, by giving normative predictions about how an ideal sensory system should combine prior knowledge and observation, by providing mechanistic interpretation of the dynamic functioning of the brain circuit, and by suggesting optimal ways of deciphering experimental data. This book brings together contributions from both experimental and theoretical neuroscientists that examine the brain mechanisms of perception, decision making, and motor control according to the concepts of Bayesian estimation. After an overview of the mathematical concepts, including Bayes theorem, that are basic to understanding the approaches discussed, contributors discuss how Bayesian concepts can be used for interpretation of such neurobiological data as neural spikes and functional brain imaging. Next, they examine the modeling of sensory processing, including the neural coding of information about the outside world, and finally, they explore dynamic processes for proper behaviors, including the mathematics of the speed and accuracy of perceptual decisions and neural models of belief propagation.Less
A Bayesian approach can contribute to an understanding of the brain on multiple levels, by giving normative predictions about how an ideal sensory system should combine prior knowledge and observation, by providing mechanistic interpretation of the dynamic functioning of the brain circuit, and by suggesting optimal ways of deciphering experimental data. This book brings together contributions from both experimental and theoretical neuroscientists that examine the brain mechanisms of perception, decision making, and motor control according to the concepts of Bayesian estimation. After an overview of the mathematical concepts, including Bayes theorem, that are basic to understanding the approaches discussed, contributors discuss how Bayesian concepts can be used for interpretation of such neurobiological data as neural spikes and functional brain imaging. Next, they examine the modeling of sensory processing, including the neural coding of information about the outside world, and finally, they explore dynamic processes for proper behaviors, including the mathematics of the speed and accuracy of perceptual decisions and neural models of belief propagation.
Jorge Gonçalves and Sean Warnick
- Published in print:
- 2009
- Published Online:
- August 2013
- ISBN:
- 9780262013345
- eISBN:
- 9780262258906
- Item type:
- chapter
- Publisher:
- The MIT Press
- DOI:
- 10.7551/mitpress/9780262013345.003.0013
- Subject:
- Biology, Biomathematics / Statistics and Data Analysis / Complexity Studies
This chapter introduces the biochemical network, highlighting its role in managing the complexity of biochemical systems and its link to system dynamics. It describes network reconstruction and its ...
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This chapter introduces the biochemical network, highlighting its role in managing the complexity of biochemical systems and its link to system dynamics. It describes network reconstruction and its fundamental difficulties and limitations and the effect of noise and nonlinearities in a biochemical system. It addresses how network reconstruction can be attained using two common types of experiments based on gene silencing and overexpression, and discusses trade-offs between steady-state and time-series data. This chapter shows that the use of dynamic structure functions revealed the dynamic properties of structurally perturbed systems, enabling convenient analysis of modified structures.Less
This chapter introduces the biochemical network, highlighting its role in managing the complexity of biochemical systems and its link to system dynamics. It describes network reconstruction and its fundamental difficulties and limitations and the effect of noise and nonlinearities in a biochemical system. It addresses how network reconstruction can be attained using two common types of experiments based on gene silencing and overexpression, and discusses trade-offs between steady-state and time-series data. This chapter shows that the use of dynamic structure functions revealed the dynamic properties of structurally perturbed systems, enabling convenient analysis of modified structures.
