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.
Elvira Silva, Spiro E. Stefanou, and Alfons Oude Lansink
- Published in print:
- 2021
- Published Online:
- April 2021
- ISBN:
- 9780190919474
- eISBN:
- 9780197537176
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780190919474.003.0005
- Subject:
- Economics and Finance, Microeconomics
This chapter discusses three concepts of the directional distance function in the presence of internal adjustment costs, designated as adjustment cost directional distance functions. These functions ...
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This chapter discusses three concepts of the directional distance function in the presence of internal adjustment costs, designated as adjustment cost directional distance functions. These functions are the building blocks of technical inefficiency measures. Duality between an adjustment cost directional distance function and an indirect optimal value function allows the construction of economic measures of inefficiency. Duality is established between the adjustment cost directional input function and the optimal current value function of the intertemporal cost minimization problem. From this dual relation, a dynamic cost inefficiency measure is derived and decomposed into technical inefficiency and allocative inefficiency. Similarly, dynamic input-output measures of inefficiency are derived from the adjustment cost directional technology distance function and duality between this function and the current profit function.Less
This chapter discusses three concepts of the directional distance function in the presence of internal adjustment costs, designated as adjustment cost directional distance functions. These functions are the building blocks of technical inefficiency measures. Duality between an adjustment cost directional distance function and an indirect optimal value function allows the construction of economic measures of inefficiency. Duality is established between the adjustment cost directional input function and the optimal current value function of the intertemporal cost minimization problem. From this dual relation, a dynamic cost inefficiency measure is derived and decomposed into technical inefficiency and allocative inefficiency. Similarly, dynamic input-output measures of inefficiency are derived from the adjustment cost directional technology distance function and duality between this function and the current profit function.
Elvira Silva, Spiro E. Stefanou, and Alfons Oude Lansink
- Published in print:
- 2021
- Published Online:
- April 2021
- ISBN:
- 9780190919474
- eISBN:
- 9780197537176
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780190919474.003.0006
- Subject:
- Economics and Finance, Microeconomics
The concept of efficiency involves measuring the current practice against the maximum potential for a given technology. This chapter addresses the intertemporal measurement of economic growth and ...
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The concept of efficiency involves measuring the current practice against the maximum potential for a given technology. This chapter addresses the intertemporal measurement of economic growth and performance at the firm level, the dynamic generalizations of the economies of scale and scope, and capacity utilization in the context of the directional distance technology dynamic adjustment. These concepts serve as the foundation to characterizing the Luenberger total productivity indicator and its change at the firm level. The decomposition of the Luenberger total productivity change indicator is developed into the contribution of technical inefficiency change, scale inefficiency change, and technical change. Each of these components is impacted by different policy remedies.Less
The concept of efficiency involves measuring the current practice against the maximum potential for a given technology. This chapter addresses the intertemporal measurement of economic growth and performance at the firm level, the dynamic generalizations of the economies of scale and scope, and capacity utilization in the context of the directional distance technology dynamic adjustment. These concepts serve as the foundation to characterizing the Luenberger total productivity indicator and its change at the firm level. The decomposition of the Luenberger total productivity change indicator is developed into the contribution of technical inefficiency change, scale inefficiency change, and technical change. Each of these components is impacted by different policy remedies.
Elvira Silva, Spiro E. Stefanou, and Alfons Oude Lansink
- Published in print:
- 2021
- Published Online:
- April 2021
- ISBN:
- 9780190919474
- eISBN:
- 9780197537176
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780190919474.003.0007
- Subject:
- Economics and Finance, Microeconomics
Econometric approaches provide another avenue to implementing the frameworks and concepts of dynamic efficiency and productivity measurement. This chapter addresses both structural and reduced-form ...
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Econometric approaches provide another avenue to implementing the frameworks and concepts of dynamic efficiency and productivity measurement. This chapter addresses both structural and reduced-form econometric approaches to estimating the dynamic directional distance function directly as well as to estimating the cost function that accommodates technical inefficiency. An application to a farm-level panel data set is presented that estimates the decomposition of dynamic cost inefficiency into technical and allocative inefficiency measures presented in Chapter 4 and then determines the components of primal and dual Luenberger total factor productivity change based on the elaboration of these concepts in Chapter 5. In addition to the discussion of empirical issues, this chapter provides an empirical illustration using micro-level data.Less
Econometric approaches provide another avenue to implementing the frameworks and concepts of dynamic efficiency and productivity measurement. This chapter addresses both structural and reduced-form econometric approaches to estimating the dynamic directional distance function directly as well as to estimating the cost function that accommodates technical inefficiency. An application to a farm-level panel data set is presented that estimates the decomposition of dynamic cost inefficiency into technical and allocative inefficiency measures presented in Chapter 4 and then determines the components of primal and dual Luenberger total factor productivity change based on the elaboration of these concepts in Chapter 5. In addition to the discussion of empirical issues, this chapter provides an empirical illustration using micro-level data.
Elvira Silva, Spiro E. Stefanou, and Alfons Oude Lansink
- Published in print:
- 2021
- Published Online:
- April 2021
- ISBN:
- 9780190919474
- eISBN:
- 9780197537176
- Item type:
- book
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/oso/9780190919474.001.0001
- Subject:
- Economics and Finance, Microeconomics
The book takes on a systematic treatment of dynamic decision making and performance measurement. The analytical foundations of the dynamic production technology are introduced and developed in detail ...
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The book takes on a systematic treatment of dynamic decision making and performance measurement. The analytical foundations of the dynamic production technology are introduced and developed in detail for several primal representations of the technology with an emphasis on dynamic directional distance functions. Dynamic cost minimization and dynamic profit maximization are developed for primal and dual representations of the dynamic technology. A dynamic production environment can be characterized as one where current production decisions impact future production possibilities. Consequently, the dynamic perspective of production relationships necessarily involves the close interplay between stock and flow elements in the transformation process and how current decisions impact the changes in future stocks. Stock elements in the production transformation process can involve physical elements that can be effectively employed in the transformation process, which can include the stock of technical knowledge and expertise available to the decision maker during the decision period. The dynamic generalization of concepts measuring the production structure (e.g., economies of scale, economies of scope, capacity utilization) and performance (e.g., allocative, scale and technical inefficiency, productivity) are developed from primal and dual perspectives. As an important source of productivity growth, production efficiency analysis is the subject of countless studies. Yet, theoretical and empirical studies focusing on production efficiency have ignored typically the time interdependence of production decisions and the adjustment paths of the firm over time. The empirical implementation of these production and performance measures is developed at length for both nonparametric and econometric approaches.Less
The book takes on a systematic treatment of dynamic decision making and performance measurement. The analytical foundations of the dynamic production technology are introduced and developed in detail for several primal representations of the technology with an emphasis on dynamic directional distance functions. Dynamic cost minimization and dynamic profit maximization are developed for primal and dual representations of the dynamic technology. A dynamic production environment can be characterized as one where current production decisions impact future production possibilities. Consequently, the dynamic perspective of production relationships necessarily involves the close interplay between stock and flow elements in the transformation process and how current decisions impact the changes in future stocks. Stock elements in the production transformation process can involve physical elements that can be effectively employed in the transformation process, which can include the stock of technical knowledge and expertise available to the decision maker during the decision period. The dynamic generalization of concepts measuring the production structure (e.g., economies of scale, economies of scope, capacity utilization) and performance (e.g., allocative, scale and technical inefficiency, productivity) are developed from primal and dual perspectives. As an important source of productivity growth, production efficiency analysis is the subject of countless studies. Yet, theoretical and empirical studies focusing on production efficiency have ignored typically the time interdependence of production decisions and the adjustment paths of the firm over time. The empirical implementation of these production and performance measures is developed at length for both nonparametric and econometric approaches.
