Suboptimal Policies for Stochastic $$N$$ N -Stage Optimization: Accuracy Analysis and a Case Study from Optimal Consumption

Mauro Gaggero (), Giorgio Gnecco () and Marcello Sanguineti ()
Additional contact information
Mauro Gaggero: National Research Council of Italy
Giorgio Gnecco: University of Genoa
Marcello Sanguineti: University of Genoa

A chapter in Models and Methods in Economics and Management Science, 2014, pp 27-50 from Springer

Abstract: Abstract Dynamic Programming formally solves stochastic optimization problems with an objective that is additive over a finite number of stages. However, it provides closed-form solutions only in particular cases. In general, one has to resort to approximate methodologies. In this chapter, suboptimal solutions are searched for by approximating the decision policies via linear combinations of Gaussian and sigmoidal functions containing adjustable parameters, to be optimized together with the coefficients of the combinations. These approximation schemes correspond to Gaussian radial-basis-function networks and sigmoidal feedforward neural networks, respectively. The accuracies of the suboptimal solutions are investigated by estimating the error propagation through the stages. As a case study, we address a multidimensional problem of optimal consumption under uncertainty, modeled as a stochastic optimization task with an objective that is additive over a finite number of stages. In the classical one-dimensional context, a consumer aims at maximizing over a given time horizon the discounted expected value of consumption of a good, where the expectation is taken with respect to a stochastic interest rate. The consumer has an initial wealth and at each time period earns an income, modeled as an exogenous input. We consider a multidimensional framework, in which there are $$d > 1$$ d > 1 consumers that aim at maximizing a social utility function. First we provide conditions that allow one to apply our estimates to such a problem; then we present a numerical analysis.

Keywords: Optimal Consumption; Sigmoidal Feedforward Neural Network (SFNNs); Stochastic Interest Rates; Social Utility Function; Sanguineti (search for similar items in EconPapers)
Date: 2014
References: Add references at CitEc
Citations: View citations in EconPapers (1)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-319-00669-7_3

Ordering information: This item can be ordered from
http://www.springer.com/9783319006697

DOI: 10.1007/978-3-319-00669-7_3

Access Statistics for this chapter

More chapters in International Series in Operations Research & Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().