nep-cmp New Economics Papers
on Computational Economics
Issue of 2007‒09‒16
five papers chosen by
Stan Miles
Thompson Rivers University

  1. Microsimulation Models.;An Integrated Approach with Real Data By Francesco MARCHIONNE
  2. From simple growth to numerical simulations: a primer in dynamic programming By Gianluca Femminis
  3. A heuristic solution framework for the resource constrained multi-project scheduling problem with sequence-dependent transfer times By Doreen Krüger; Armin Scholl
  4. Kriging Models That Are Robust With Respect to Simulation Errors By Siem, A.Y.D.; Hertog, D. den
  5. The Parameter Set in an Adaptive Control Monte Carlo Experiment: Some Considerations By Marco P. Tucci; David A. Kendrick; Hans M. Amman

  1. By: Francesco MARCHIONNE (Universita' Politecnica delle Marche, Dipartimento di Economia)
    Abstract: Due to the increasing the calculus power of computers, a growing number of economic;phenomena are being studied through microsimulation. However, this methodology is;not updated, and the basic structure of these models is till tied to outdated technologies;where economic hypothesis was used to simplify computational complexity as the;calculus power of computers was not strong enough. In this paper, I suggest an;innovative approach to the microsimulation. From a theoretical point of view, it should;be more efficient than the traditional approach. The paper is divided in three section. In;the first section, I explain this innovative approach and show its operating differences;respect to the traditional approach. In the second section, I explore the difficulties of its;concrete implementation through a case study and recommend some technical solutions;in order to overcome them. In the last section, I summarize the main results.
    Date: 2007–08
  2. By: Gianluca Femminis (DISCE, Università Cattolica)
    Abstract: These notes provide an intuitive introduction to dynamic programming. The first two Sections present the standard deterministic Ramsey model using the Lagrangian approach. These can be skipped by whom is already acquainted with this framework. Section 3 shows how to solve the well understood Ramsey model by means of a Bellman equation, while Section 4 shows how to "guess" the solution (when this is possible). Section 5 is devoted to applications of the envelope theorem. Section 6 provides a "paper and pencil" introduction to the numerical techniques used in dynamic programming, and can be skipped by the uninterested reader. Sections 7 to 9 are devoted to stochastic modelling, and to stochastic Bellman equations. Section 10 extends the discussion of numerical techniques. An Appendix provides details about the Matlab routines used to solve the examples.
    Keywords: Dynamic programming, Bellman equation, Optimal growth, Numerical techniques.
    JEL: C61 O41 C63
    Date: 2007–07
  3. By: Doreen Krüger (Friedrich-Schiller-Universität Jena Fakultät für Wirtschaftswissenschaften Lehrstuhl für Betriebswirtschaftliche Entscheidungsanalyse); Armin Scholl (Friedrich-Schiller-Universität Jena Fakultät für Wirtschaftswissenschaften Lehrstuhl für Betriebswirtschaftliche Entscheidungsanalyse)
    Abstract: We consider the problem of scheduling multiple projects subject to joint resource constraints. All approaches proposed in the literature so far are based on the assumption that resources can be transferred from one project to the other without any expense in time or cost. In many realworld settings this assumption is not realistic. For example, cranes have to be transported to another location and reinstalled there. In order to consider this additional aspect, we generalise the multi-project scheduling problem by additionally including transfer times which represent transportation, installation, adjustment, (re-) learning and other setup activities necessary when a resource is removed from one project and reassigned to another (or from one job to another within the same project). In this paper, we define the modified multi-project scheduling problem with transfer times (called RCMPSPTT), formulate it as an integer linear programme, propose heuristic solution procedures and present results of comprehensive computational experiments.
    Keywords: project scheduling, combinatorial optimisation, mathematical model, transfer times
    Date: 2007–09–10
  4. By: Siem, A.Y.D.; Hertog, D. den (Tilburg University, Center for Economic Research)
    Abstract: In the field of the Design and Analysis of Computer Experiments (DACE) meta-models are used to approximate time-consuming simulations. These simulations often contain simulation-model errors in the output variables. In the construction of meta-models, these errors are often ignored. Simulation-model errors may be magnified by the meta-model. Therefore, in this paper, we study the construction of Kriging models that are robust with respect to simulation-model errors. We introduce a robustness criterion, to quantify the robustness of a Kriging model. Based on this robustness criterion, two new methods to find robust Kriging models are introduced. We illustrate these methods with the approximation of the Six-hump camel back function and a real life example. Furthermore, we validate the two methods by simulating artificial perturbations. Finally, we consider the influence of the Design of Computer Experiments (DoCE) on the robustness of Kriging models.
    Keywords: Kriging;robustness;simulation-model error
    JEL: C60
    Date: 2007
  5. By: Marco P. Tucci; David A. Kendrick; Hans M. Amman
    Abstract: Comparisons of various methods for solving stochastic control economic models can be done with Monte Carlo methods. These methods have been applied to simple one-state, one-control quadraticlinear tracking models; however, large outliers may occur in a substantial number of the Monte Carlo runs when certain parameter sets are used in these models. This paper tracks the source of these outliers to two sources: (1) the use of a zero for the penalty weights on the control variables and (2) the generation of nearzero initial estimate of the control parameter in the systems equations by the Monte Carlo routine. This result leads to an understanding of why both the unsophisticated Optimal Feedback (Certainty Equivalence) and the sophisticated Dual methods do poorly in some Monte Carlo comparisons relative to the moderately sophisticated Expected Optimal Feedback method.
    Keywords: Adaptive control, Monte Carlo experiment, uncertain parameters, outliers.
    JEL: C63 E61
    Date: 2007–07

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