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stochastic rangeland use under capital constraints

AMERICAN JOURNAL OF AGRICULTURAL ECONOMICS 2007年第3期89卷 805-817页

作者： Kobayashi, Mimako Howitt, Richard E. Jarvis, Lovell S. Laca, Emilio A. Univ Calif Davis CADMS Sch Vet Med Davis CA 95616 USA Univ Calif Davis Dept Agr & Resource Econ Davis CA 95616 USA Univ Calif Davis Dept Plant Sci Davis CA 95616 USA

The stocking density on Kazakhstan's extensive rangelands is well below traditional levels. To analyze dynamic flock performance, we develop a stochastic dynamic programming model for livestock systems with stochastic forage production. The model contains continuous five state and 12 control variables, allowing improved characterization of the biophysical relationships and economic tradeoffs inherent in such systems. Most Kazakhstan herders have restricted access to capital. The model indicates that the cost of capital strongly affects flock size and productivity. We conclude that capital constraints are important to explaining the current low stocking density. Improving capital markets in rural areas warrants policy attention.

关键词： extensive livestock production Kazakhstan stochastic dynamic programming transition economies

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Bayes' learning of unknown parameters

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JOURNAL OF DIFFERENCE EQUATIONS AND APPLICATIONS 2007年第2-3期13卷 121-133页

作者： Dechert, W. D. O'Donnell, S. I. Brock, W. A. Univ Wisconsin Dept Econ Madison WI 53706 USA Baylor Coll Med Childrens Nutr Res Ctr Houston TX 77030 USA

We present both analytical and numerical results for a model where the stochastic dynamical system is not fully known. We implement an optimal control solution for the problem that incorporates Bayes' learning of an unknown parameter in the model. The computational solution is for a model of phosphorus in a lake and we show that in that context full learning takes place. The model includes a Skiba-like point, and although the long run level of phosphorus in the lake is sensitive to initial conditions, learning is not.

关键词： Bayesian learning Skiba pollution stochastic dynamic programming

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Optimal Capacity Planning in stochastic Loss Networks with Time-Varying Workloads

Optimal Capacity Planning in Stochastic Loss Networks with T...

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International Conference on Measurement and Modeling of Computer Systems

作者： Bhadra, Sandeep Lu, Yingdong Squillante, Mark S. Univ Texas Austin Dept Elect & Comp Engn Austin TX 78712 USA

ISBN: (纸本)1595936394

We consider a capacity planning optimization problem in a general theoretical framework that extends the classical Erlang loss model and related stochastic loss networks to support tune-varying workloads. The time horizon consists of a sequence of coarse time intervals, each of which involves a stochastic loss network under a fixed multi-class workload that can change in a general manner from one interval to the next. The optimization problem consists of determining the capacities for each time interval that maximize a utility function over the entire time horizon, finite or infinite, where rewards gained from servicing customers are offset by penalties associated with deploying capacities in an interval and with changing capacities among intervals. We derive a state-dependent optimal policy within the context of a particular limiting regime of the optimization problem, and we prove this solution to be asymptotically optimal. Then, under fairly mild conditions, we prove that a similar structural property holds for the optimal solution of the original stochastic optimization problem, and we show how the optimal capacities comprising this solution can be efficiently computed.

关键词： stochastic loss networks stochastic dynamic programming Asymptotic optimality Erlang loss formula Erlang fixed-point approximation Capacity planning Time-varying workloads

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Design of optimal, robust energy management strategy for a parallel HEV

Design of optimal, robust energy management strategy for a p...

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IEEE International Conference on Robotics and Biomimetics (ROBIO 2007)

作者： Li, Weimin Xu, Guoqing Tong, Hang Xu, Yangsheng Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Chinese Univ Hong Kong Dept MAE Sha Tin Peoples R China Tongji Univ Dept Elect Engn Shanghai 200331 Peoples R China CUHK CAS Shenzen Inst Adv Integrat Technol Shenzhen 518067 Peoples R China

ISBN: (纸本)9781424417612

A novel approach based on stochastic dynamic programming(SDP) is proposed to develop optimal, robust energy control strategies for a parallel hybrid electric vehicle (HEV). Unlike other approaches that take a specific drive cycle as prior deterministic information, we model the transport mission as a stochastic process based on collected data. The problem of dynamic energy management in HEV system is formulated as an infinite-horizon SDP optimization problem and solved using an efficient "policy iteration" algorithm. Our approach guarantees an optimization of vehicle performance and is robust to the accuracy of available information. In addition, the resulting energy management strategy is suitable for real-time implementation. Hence, it is more efficient and more useful in practice. Experimental results demonstrate the effectiveness of our approach compared to a rule-based algorithms.

关键词： hybrid electric vehicle stochastic dynamic programming energy management strategy

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Modelling water blending - sensitivity of optimal policies

Modelling water blending - sensitivity of optimal policies

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International Congress on Modelling and Simulation (MODSIM07)

作者： Webby, Brian Green, David Metcalfe, Andrew Univ Adelaide Sch Math Sci Fac Engn Comp & Math Sci Adelaide SA 5005 Australia

ISBN: (纸本)9780975840047

Throughout the world there is increased demand on freshwater resources. Such resources are limited in quantity and erratic in availability but are renewed over time through the water cycle. Planning for water management then has temporal scales and stochastic variation to consider. There may also be several sources of water suitable for non-potable purposes. Supplying these by supplementing higher quality water with sources of differing quality and availability is the blending problem studied in this paper. Consider a supplier in a non-potable use water market. The supplier obtains water from a range of sources and delivers it to a number of users. One such application, on which our model is loosely based, is the integrated water resource management project of the northern region of metropolitan Adelaide. Here the major potential sources are the stormwater harvesting and aquifer storage project of the City of Salisbury, recycled water from Bolivar water treatment plant and Adelaide's reticulated, potable water supply. Potential major users of the water (sinks) are a wool processing plant, a residential grey water network and council parks and gardens (Figure 1). [GRAPHICS] This is an optimisation problem of blending water from various sources to meet quantity and quality requirements of the sinks, with the objective of maximising expected profit from undertaking supply. The supplier undertakes to supply a guaranteed quantity of water to each sink in each time period and charges a premium for this water. There are further, preferred demands which are delivered at the supplier's discretion and for which a lesser cost applies, both amounts to meet salinity conditions. Profit is maximised if lowest cost sources are used to supply the firm demand and, perhaps, the preferred demand. The monetary value of the water resources is assessed by linear programming (LP) and by integer linear programming (ILP). The problem is solved over sequential time periods, that is, it is sol

关键词： Integer programming linear programming stochastic dynamic programming CVaR

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The optimal asset allocation of the main types of pension funds: a unified framework

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GENEVA RISK AND INSURANCE REVIEW 2007年第2期32卷 113-128页

作者： Romaniuk, Katarzyna Univ Paris 01 PRISM UFR 06 F-75005 Paris France

The existing literature deals with the optimal investment strategy of defined benefit (DB) or defined contribution (DC) pension plans. This article's objective is to compare the optimal policies of different types of pension plans. This is done by first defining an original framework, which is based on the distinction between the nature of the guarantee-which can be internal or external-offered by or to a pension fund. This framework allows to establish links between optimization programs of DC, DB and targeted money purchase schemes. The case of an internal guarantee appears as a standard portfolio insurer's problem. The second kind of guarantee, not analyzed in the literature yet with regard to the resulting optimal policy, is characterized by the existence of an option in the final wealth definition. Four funds are present in the internal guarantee optimal allocation: the speculative component, the preference independent guarantee- and contribution-hedge terms and the preference dependent state variable-hedge fund. The external guarantee program, solved with an original method using the principles of standard options theory, yields an optimal policy incorporating the delta of the option embodied in the final wealth definition. The conclusion is that the resulting optimal portfolio policy becomes riskier.

关键词： pension funds defined benefit defined contribution asset allocation internal and external guarantees stochastic dynamic programming options theory

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Optimal Portfolio Management in a CIR Framework

Optimal Portfolio Management in a CIR Framework

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3rd International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM 2007)

作者： Wan, Shuping Jiangxi Univ Finance & Econ Coll Informat Technol Nanchang 330013 Peoples R China

ISBN: (纸本)9781424413119

The optimal portfolio problem for a bank account, single risky stock and a rolling horizon bond is developed. The stochastic short-term interest rate with the Cox-Ingersoll-Ross (CIR) dynamics affects the prices of the stock and rolling horizon bond. The investment objective is maximizing expected CRRA utility of terminal wealth. The problem has been solved by the stochastic dynamic programming principle and the completion of squares technique. The closed-form optimal trading strategy is obtained. A numerical example illustrating the results is presented.

关键词： portfolio rolling horizon bond Riccati equation stochastic dynamic programming

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Asset pricing with dynamic programming

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Computational Economics 2007年第3-4期29卷 233-265页

作者： Grüne, Lars Semmler, Willi Mathematisches Institut Fakultät für Mathematik und Physik Universität Bayreuth 95440 Bayreuth Germany Schwartz Center for Economic Policy Analysis New School New York NY United States Center for Empirical Macroeconomics Bielefeld University Bielefeld Germany

The study of asset price characteristics of stochastic growth models such as the risk-free interest rate, equity premium, and the Sharpe-ratio has been limited by the lack of global and accurate methods to solve dynamic optimization models. In this paper, a stochastic version of a dynamic programming method with adaptive grid scheme is applied to compute the asset price characteristics of a stochastic growth model. The stochastic growth model is of the type as developed by [Brock and Mirman (1972), Journal of Economic Theory, 4, 479-513 and Brock (1979), Part I: The growth model (pp. 165-190). New York: Academic Press;The economies of information and uncertainty (pp. 165-192). Chicago: University of Chicago Press. (1982). It has become the baseline model in the stochastic dynamic general equilibrium literature. In a first step, in order to test our procedure, it is applied to this basic stochastic growth model for which the optimal consumption and asset prices can analytically be computed. Since, as shown, our method produces only negligible errors, as compared to the analytical solution, in a second step, we apply it to more elaborate stochastic growth models with adjustment costs and habit formation. In the latter model preferences are not time separable and past consumption acts as a constraint on current consumption. This model gives rise to an additional state variable. We here too apply our stochastic version of a dynamic programming method with adaptive grid scheme to compute the above mentioned asset price characteristics. We show that our method is very suitable to be used as solution technique for such models with more complicated decision structure. © Springer Science+Business Media, LLC 2006.

关键词： Adaptive grid Asset pricing stochastic dynamic programming stochastic growth models

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The use of stochastic dynamic programming in optimal landscape reconstruction for metapopulations

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ECOLOGICAL APPLICATIONS 2003年第2期13卷 543-555页

作者： Westphal, MI Pickett, M Getz, WM Possingham, HP Univ Queensland Ctr Ecol Dept Zool & Entomol St Lucia Qld 4072 Australia Univ Calif Berkeley Dept Environm Sci Policy & Management Berkeley CA 94720 USA Conservat Council S Australia Adelaide SA Australia Univ Queensland Dept Math St Lucia Qld 4067 Australia

A decision theory framework can be a powerful technique to derive optimal management decisions for endangered species. We built a spatially realistic stochastic metapopulation model for the Mount Lofty Ranges Southern Emu-wren (Stipiturus malachurus intermedius), a critically endangered Australian bird. Using diserete-time Markov,chains to describe the dynamics of a metapopulation and stochastic dynamic programming (SDP) to find optimal solutions, we evaluated the following different management decisions: enlarging existing patches, linking patches via corridors, and creating a new patch. This is the first application of SDP to optimal landscape reconstruction and one of the few times that landscape reconstruction dynamics have been integrated with population dynamics. SDP is a powerful tool that has advantages over standard Monte Carlo simulation methods because it can give the exact optimal strategy for every landscape configuration (combination of patch areas and presence of corridors) and pattern of metapopulation occupancy, as well as a trajectory of strategies. It is useful when a sequence of management actions can be performed over a given time horizon, as is the case for many endangered species recovery programs, where only fixed amounts of resources are available in each time step. However, it is generally limited by computational constraints to rather small networks of patches. The model shows that optimal metapopulation, management decisions depend greatly on the current state of the metapopulation,. and there is no strategy that is universally the best. The extinction probability over 30 yr for the optimal state-dependent management actions is 50-80% better than no management, whereas the best fixed state-independent sets of strategies are only 30% better than no management. This highlights the advantages of using a decision theory tool to investigate conservation strategies for metapopulations. It is clear from these results that the sequence of managem

关键词： Australia conservation decision theory metapopulation optimal landscape reconstruction Southern Emu-wren Stipiturus malachurus intermedius stochastic dynamic programming threatened species

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NONLINEAR OPTIMAL CONTROL OF A SEQUENTIAL INSPECTION OPERATION

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IFAC Proceedings Volumes 2007年第12期40卷 804-809页

作者： M. Pachter P.R. Chandler D. Swaroop AFIT/ENG 2950 Hobson Way Bldg. 640Wright-Patterson AFB OH 45433-7765 AFRL/VACA 2210 8th Street WPAFB OH 45433-7531

A simplified inspection scenario is considered where a Micro Air Vehicle with limited endurance is tasked with search and classification in a multi-target environment and where false, that is, clutter, targets are present. The sequential inspection operation, which includes a human operator for classification, is modelled, and a nonlinear discrete-time stochastic control problem is formulated. An analytic, closed-form, optimal control law is derived.

关键词： Nonlinear Optimal Control Sequential Inspection stochastic dynamic programming

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