A new look at optimal growth under undertainty

Article Abstract:

A study presents an stochastic modeling of economic growth under uncertainty. The model demonstrates, in particular, a technology where key assumptions of monotonicity and concavity are obtained by assuming that output increases with input at a decreasing rate. Variables of capital stock, consumption, time, expectation, utility function, transition probability mapping current investment level and discount factor are taken into account. Stochastic modelling in economics should go beyond the deterministic framework of modelling.

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Efficient representation of state spaces for some dynamic models

Article Abstract:

A study investigated the use of data structures and coding mechanisms to efficiently portray the complex state spaces required for solving a number of discrete state dynamic programming issues. Efficient representations for two state spaces were developed and were shown to be useful in analyzing industry models of multi-product differentiated products companies, macroeconomic real business cycle models with heterogeneous agents and Bayesian learning models with noisy signals.

Statistical decision, Statistical decision theory, Vector spaces, Vectors (Mathematics)

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Adaptive control in the presence of time-varying parameters

Article Abstract:

A study analyzes the effects of time-varying parameters on the choice of control in an adaptive control framework. Active Learning, Passive Learning and Certainty Equivalence procedures are used to obtain controls and are carried out in the Monte Carlo experiment. The three procedures have demonstrated that stochastic models are efficient when there is a short time period. Among the three, the Active Learning (DUAL) method generates controls having the lowest variance.

Adaptive control

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