Management of water resource systems in the presence of uncertainties by nonlinear approximation techniques and deterministic sampling

Two methods of approximate solution are developed for T-stage stochastic optimal control (SOC) problems, aimed at obtaining finite-horizon management policies for water resource systems. The presence of uncertainties, such as river and rain inflows, is considered. Both approaches are based on the use of families of nonlinear functions, called “one-hidden-layer networks” (OHL networks), made up of linear combinations of simple basis functions containing parameters to be optimized. The first method exploits OHL networks to obtain an accurate approximation of the cost-to-go functions in the dynamic programming procedure for SOC problems. The approximation capabilities of OHL networks are combined with the properties of deterministic sampling techniques aimed at obtaining uniform samplings of high-dimensional domains. In the second method, admissible solutions to SOC problems are constrained to take on the form of OHL networks, whose parameters are determined in such a way to minimize the cost functional associated with SOC problems. Exploiting these tools, the two methods are able to cope with the so-called “curse of dimensionality,” which strongly limits the applicability of existing techniques to high-dimensional water resources management in the presence of uncertainties. The theoretical bases of the two approaches are investigated. Simulation results show that the proposed methods are effective for water resource systems of high dimension.     

Stochastic modelling of reservoir operations

The nature of hydrologic parameters in reservoir management models is uncertain. In mathematical programming models the uncertainties are dealt with either indirectly (sensitivity analysis of a deterministic model) or directly by applying a chance-constrained type of formulation or some of the stochastic programming techniques (LP and DP based models). Various approaches are reviewed in the paper. Moran's theory of storage is an alternative stochastic modelling approach to mathematical programming techniques. The basis of the approach and its application is presented. Reliability programming is a stochastic technique based on the chance-constrained approach, where the reliabilities of the chance constraints are considered as extra decision variables in the model. The problem of random event treatment in the reservoir management model formulation using reliability programming is addressed in this paper.     

Metastability in stochastic dynamics of disordered mean-field models

We study a class of Markov chains that describe reversible stochastic dynamics of a large class of disordered mean field models at low temperatures. Our main purpose is to give a precise relation between the metastable time scales in the problem to the properties of the rate functions of the corresponding Gibbs measures. We derive the analog of the Wentzell-Freidlin theory in this case, showing that any transition can be decomposed, with probability exponentially close to one, into a deterministic sequence of “admissible transitions”. For these admissible transitions we give upper and lower bounds on the expected transition times that differ only by a constant factor. The distributions of the rescaled transition times are shown to converge to the exponential distribution. We exemplify our results in the context of the random field Curie-Weiss model. Received: 26 November 1998 / Revised version: 21 March 2000 / Published online: 14 December 2000     

An Oligopolistic Investment Model of the Finnish Electricity Market

The investment problem faced by producers in deregulated electricity markets contains high uncertainties about the future. It can also be seen as a game, as only a small number of large players act in the market. A dynamic stochastic oligopoly model to describe the production and investment in such a situation is developed and applied to the Finnish electricity market. The demand growth rate is modeled as a stochastic variable. The strategies of the firms consist of investments and production levels for base and peak load periods. The firms have nuclear, hydro and thermal capacities, but are only allowed to invest in new thermal capacity. Using a so-called sample-path adapted open-loop information structure, the model contributes to the understanding of the dynamics of production, investment and market power in a medium time horizon. The solution method uses recent developments in variational inequality and mixed complementarity problem formulations.     

Ergodicity in infinite Hamiltonian systems with conservative noise

Summary. We study the stationary measures of an infinite Hamiltonian system of interacting particles in ℝ ³ subject to a stochastic local perturbation conserving energy and momentum. We prove that the translation invariant measures that are stationary for the deterministic Hamiltonian dynamics, reversible for the stochastic dynamics, and with finite entropy density, are convex combination of “Gibbs” states. This result implies hydrodynamic behavior for the systems under consideration. Received: 17 December 1994/In revised form: 12 April 1996     

A meta-model for multiobjective routing in MPLS networks

MPLS (Multiprotocol Label Switching) enables the utilisation of explicit routes and other advanced routing mechanisms in multiservice packet networks, capable of dealing with multiple and heterogeneous QoS (Quality of Service) parameters. Firstly the paper presents a discussion of conceptual and methodological issues raised by multiobjective routing optimisation models for MPLS networks. The major contribution is the proposal of a multiobjective routing optimisation framework for MPLS networks. The major features of this modelling framework are: the formulation of a three-level hierarchical routing optimisation problem including network and service performance objectives, the inclusion of fairness objectives in the different levels of optimisation and a two-level stochastic representation of the traffic in the network (traffic flow and packet stream levels). A variant of the general model for two classes of traffic flows, QoS traffic and Best Effort traffic, is also presented. Finally a stochastic teletraffic modelling approach, underlying the optimisation model, is fully described. Work partially supported by programme POSI of the III EC programme cosponsored by FEDER and national funds.     

An extended Gauss–Seidel method for a class of multi-valued complementarity problems

The complementarity problem is one of the basic topics in nonlinear analysis; however, the methods for solving complementarity problems are usually developed for problems with single-valued mappings. In this paper we examine a class of complementarity problems with multi-valued mappings and propose an extension of the Gauss–Seidel algorithm for finding its solution. Its convergence is proved under off-diagonal antitonicity assumptions. Applications to Walrasian type equilibrium problems and to nonlinear input–output problems are also given. In this work, the authors were supported by Brescia University grant PRIN - 2006: “Oligopolistic models and order monotonicity properties”, the third author was also supported by the joint RFBR–NNSF grant, project No. 07-01-92101.     

Stochastic programming: Potential hazards when random variables reflect market interaction

There are two types of random phenomena modeled in stochastic programs. One type is what we may term “external” or “natural” random variables, such as temperature or the roll of a dice. But in many other cases, random variables are used to reflect the behavior of other market participants. This is the case for such as price and demand of a product. Using simple game theoretic models, we demonstrate that stochastic programming may not be appropriate in these cases, as there may be no feasible way to replace the decisions of others by a random variable, and arrive at the correct decision. Hence, this simple note is a warning against certain types of stochastic programming models. Stochastic programming is unproblematic in pure forms of monopoly and perfect competition, and also with respect to external random phenomena. But if market power is involved, such as in oligopolies, the modeling may not be appropriate.     

The semantics of knowledge attributions

The basic idea of conversational contextualism is that knowledge attributions are context sensitive in that a given knowledge attribution may be true if made in one context but false if made in another, owing to differences in the attributors’ conversational contexts. Moreover, the context sensitivity involved is traced back to the context sensitivity of the word “know,” which, in turn, is commonly modelled on the case either of genuine indexicals such as “I” or “here” or of comparative adjectives such as “tall” or “rich.” But contextualism faces various problems. I argue that in order to solve these problems we need to look for another account of the context sensitivity involved in knowledge attributions and I sketch an alternative proposal.     

Multicriteria optimisation and simulation: an industrial application

This paper deals with multicriteria discrete-continuous problems of scheduling nonpreemptable jobs. The need for reusability and modularity leads us to build a “generic” optimisation and simulation framework, while the need to quickly generate good compromises between conflicting objectives requires the implementation of multicriteria scheduling models. This paper describes the practical possibilities of three hybrid models within this framework. The validation of the framework is presented in terms of its application to a real, highly constrained, discrete-continuous problem. The optimisation model is based on the hybridisation of a classical hill-climber meta-heuristic with the Promethee II multicriteria method.     

Suitable-portfolio investors,nondominated frontier sensitivity,and the effect of multiple objectives on standard portfolio selection

In standard portfolio theory, an investor is typically taken as having one stochastic objective, to maximize the random variable of portfolio return. But in this paper, we focus on investors whose purpose is to build, more broadly, a “suitable portfolio” taking additional concerns into account. Such investors would have additional stochastic and deterministic objectives that might include liquidity, dividends, number of securities in a portfolio, social responsibility, and so forth. To accommodate such investors, we develop a multiple criteria portfolio selection formulation, corroborate its appropriateness by examining the sensitivity of the nondominated frontier to various factors, and observe the conversion of the nondominated frontier to a nondominated surface. Furthermore, multiple criteria enable us to provide an explanation as to why the “market portfolio,” so often found deep below the nondominated frontier, is roughly where one would expect it to be with multiple criteria. After commenting on solvability issues, the paper concludes with the idea that what is the “modern portfolio theory” of today might well be interpreted as a projection onto two-space of a real multiple criteria portfolio selection problem from higher dimensional space. M. Hirschberger: Research conducted while a Visiting Scholar at the Department of Banking and Finance, Terry College of Business, University of Georgia, October 2003–March 2004.     

An analytical study of the sensitivity of a discrete stochastic model for sales dynamics

We consider a discrete model for sales dynamics in the case of a stochastic model of the market. The model includes “fast” and “slow” components of the market situation described by a stochastic process of “white noise” type and the correlated stochastic process. By using an integral representation of the main characteristics of the Kalman filter, we obtain expressions for stochastic parameters of additional errors of the estimate that arise in the case where the characteristics of noises are inexact. We make an asymptotical analysis of these expressions and give recommendations for the price-forming strategy in the case of uncertainty of the market situation. Bibliography: 2 titles. Translated fromObchyslyuval'na ta Prykladna Matematyka, No. 81, 1997, pp. 110–116.     

Stochastic load modelling for electric energy distribution applications

Summary The problem of electric load modelling for low aggregation levels is addressed in this paper. The objective is to obtain good “demand” and “response” behaviour models of any group of loads in an electric energy distribution system for any of the functional applications that are beeing considered in the framework of the Distribution Management Systems, aimed to improve the energy efficiency, reliability and quality of the system. A brief critical revision of the methodologies used for that purpose is in the paper, and the advantages of using approaches where physical knowledge about the load characteristics is used, are stated and demonstrated.     

Simplified estimation of multivariate duration models with unobserved heterogeneity

A simple structure is suggested for modelling unobserved heterogeneity in multivariate duration models which avoids the “curse of dimensionality” and numerical integration of the likelihood function.     

Point localization and spectral theory for symmetric random operators

In a Hilbert space context, we propose a rather general notion of “random operators” which allows for taking stochastic limits. After establishing a connection with measurable fields of closed operators, we may speak of a spectral theory for symmetric random operators. Received: 18 December 2008     

Dual effect free stochastic controls

In stochastic optimal control, a key issue is the fact that “solutions” are searched for in terms of “closed-loop control laws” over available information and, as a consequence, a major potential difficulty is the fact that present control may affect future available information. This is known as the “dual effect” of control. Our main result consists in characterizing the maximal set of closed-loop control laws containing open-loop ones and for which the information provided by observations closed with such a feedback remains fixed. We give more specific results in the two following cases: multi-agent systems and discrete time stochastic input-output systems with dynamic information structure.     

The Deregulated Electricity Market Viewed as a Bilevel Programming Problem

In this paper, we present a bilevel programming formulation of a deregulated electricity market. By examining the electricity market in this format, we achieve two things. First, the relation of the deregulated electricity market to general economic models that can be formulated as bilevel programming problems (e.g. Stackelberg leader-follower games and principal-agency models) becomes clear. Secondly, it provides an explanation of the reason why the so-called “folk theorems” can be proven to be false for electricity networks. The interpretation of the deregulated electricity market as a bilevel program also indicates the magnitude of the error that can be made if the electricity market model studied does not take into account the physical constraints of the electric grid, or oversimplifies the electricity network to a radial network.     

Multidimensional random subshifts of finite type and their large deviations

Summary I introduce random multidimensional subshifts of finite type which generalize models of spin-glasses and establish the “almost sure” large deviations bounds for Gibbs measures there. The paper is sequel to [EKW] where the corresponding results were obtained for deterministic multidimensional subshifts of finite type. Partially supported by US-Israel BSF     

Interaction dynamics of two reinforcement learners

The paper investigates a stochastic model where two agents (persons, companies, institutions, states, software agents or other) learn interactive behavior in a series of alternating moves. Each agent is assumed to perform “stimulus-response-consequence” learning, as studied in psychology. In the presented model, the response of one agent to the other agent's move is both the stimulus for the other agent's next move and part of the consequence for the other agent's previous move. After deriving general properties of the model, especially concerning convergence to limit cycles, we concentrate on an asymptotic case where the learning rate tends to zero (“slow learning”). In this case, the dynamics can be described by a system of deterministic differential equations. For reward structures derived from [2×2] bimatrix games, fixed points are determined, and for the special case of the prisoner's dilemma, the dynamics is analyzed in more detail on the assumptions that both agents start with the same or with different reaction probabilities.     

On the reduction to a complete class in multiple decision problems (2)

Summary As a criterion for the reduction to a complete class of decision rule in case where actions, samples and states are finite in number, “regret-relief ratio” criterion and “incremental loss-gain ratio” criterion were introduced in 2-state of nature case [2]. In this paper, “generalized regret-relief ratio” criterion ink-state of nature case is introduced as an extension of “regret-relief ratio” criterion and its usefulness is shown with an example. The Institute of Statistical Mathematics