nep-cmp New Economics Papers
on Computational Economics
Issue of 2010‒03‒13
eleven papers chosen by
Stan Miles
Thompson Rivers University

  1. Improved Bid Prices for Choice-Based Network Revenue Management By Joern Meissner; Arne Strauss
  2. Agent-based Simulation of Cooperative Innovation By Flavio Lenz-Cesar; Almas Heshmati
  3. Simulation and Prosecution of a Cartel with Endogenous Cartel Formation By Johannes Paha
  4. Order acceptance and scheduling in a single-machine environment: exact and heuristic algorithms. By Talla Nobibon, Fabrice; Herbots, Jada; Leus, Roel
  5. The Macroeconomic Impact of Skilled Emigration from South Africa: A CGE Analysis By Heinrich R Bohlman
  6. Exploring the bullwhip effect by means of spreadsheet simulation. By Boute, Robert; Lambrecht, Marc
  7. A Theory of Liberal Churches By Michael D. Makowsky
  8. Should Governments Minimize Debt Service Cost and Risk? By Massimo Bernaschi; Alessandro Missale; Davide Vergni
  9. Switching Rates and the Asymptotic Behavior of Herding Models By Albrecht Irle; Jonas Kauschke; Thomas Lux; Mishael Milakovic
  10. Cohort postponement and period measures By Joshua R. Goldstein; Thomas Cassidy
  11. A case of using formal concept analysis in combination with emergent self organizing maps for detecting domestic violence. By Poelmans, Jonas; Elzinga, Paul; Viaene, Stijn; Dedene, Guido

  1. By: Joern Meissner (Department of Management Science, Lancaster University Management School); Arne Strauss (Department of Management Science, Lancaster University Management School)
    Abstract: In many implemented network revenue management systems, a bid price control is being used. In this form of control, bid prices are attached to resources, and a product is offered if the revenue derived from it exceeds the sum of the bid prices of its consumed resources. This approach is appealing because once bid prices have been determined, it is fairly simple to derive the products that should be offered. Yet it is still unknown how well a bid price control actually performs. Recently, considerable progress has been made with network revenue management by incorporating customer purchase behavior via discrete choice models. However, the majority of authors have presented control policies for the booking process that are expressed in terms of which combination of products to offer at a given point in time and given resource inventories. The recommended combination of products as identified by these policies might not be representable through bid price control. If demand were independent from available product alternatives, an optimal choice of bid prices is to use the marginal value of capacity for each resource in the network. But under dependent demand, this is not necessarily the case. In fact, it seems that these bid prices are typically not restrictive enough and result in buy-down effects. We propose (1) a simple and fast heuristic that iteratively improves on an initial guess for the bid price vector; this first guess could be, for example, dynamic estimates of the marginal value of capacity. Moreover, (2) we demonstrate that using these dynamic marginal capacity values directly as bid prices can lead to significant revenue loss as compared to using our heuristic. Finally, (3) we investigate numerically how much revenue performance is lost due to the confinement of product combinations that can be represented by a bid price. Our heuristic is not restricted to a particular choice model and can be combined with any method that provides estimates of the marginal values of capacity. In our numerical experiments, we test the heuristic on some popular networks examples taken from peer literature. We use a multinomial logit choice model which allows customers from different segments to have products in common that they are considering purchasing. In most instances, our heuristic policy results in significant revenue gains over some currently available alternatives at low computational cost.
    Keywords: revenue management, network, bid prices, choice model
    JEL: C61
    Date: 2010–01
  2. By: Flavio Lenz-Cesar; Almas Heshmati (TEMEP, School of Industrial and Management Engineering College of Engineering, Seoul National University)
    Abstract: This paper introduces an agent-based simulation model representing the dynamic processes of cooperative R&D in the manufacturing sector of South Korea. Firms¡¯ behaviors were defined according to empirical findings on a dataset from the internationally standardized Korean Innovation Survey in 2005. Simulation algorithms and parameters were defined based on the determinants on firms¡¯ likelihood to participate in cooperation with other firms when conducting innovation activities. The calibration process was conducted to the point where artificially generated scenarios were equivalent to the one observed in the real world. The aim of this simulation game was to create a basic implementation that could be extended to test different policies strategies in order to observe sector responses (including cross-sector spillovers) when promoting cooperative innovation.
    Keywords: Collaborative R&D, Agent-base simulation, Korean innovation survey
    JEL: C15 D21 D85
    Date: 2010–01
  3. By: Johannes Paha (Justus-Liebig-University Gießen)
    Abstract: In many cases, collusive agreements are formed by asymmetric firms and include only a subset of the firms active in the cartelized industry. This paper endogenizes the process of cartel formation in a numeric simulation model where firms differ in marginal costs and production technologies. The paper models the incentive to collude in a differentiated products Bertrand-oligopoly. Cartels are the outcomes of a dynamic formation game in mixed strategies. I find that the Nash-equilibrium of this complex game can be obtained efficiently by a Differential Evolution stochastic optimization algorithm. It turns out that large firms have a higher probability to collude than small firms. Since firms' characteristics evolve over time, the simulation is used to generate data of costs, prices, output-quantities, and profits. This data forms the basis for an evaluation of empirical methods used in the detection of cartels.
    Keywords: Collusion, Cartel Detection, Cartel Formation, Differential Evolution, Heuristic Optimization, Industry Simulation
    JEL: C51 C69 C72 D43 L12 L13 L40
    Date: 2010
  4. By: Talla Nobibon, Fabrice; Herbots, Jada; Leus, Roel
    Abstract: In this paper, we develop exact and heuristic algorithms for the order acceptance and scheduling problem in a single-machine environment. We consider the case where a pool consisting of firm planned orders as well as potential orders is available from which an over-demanded company can select. The capacity available for processing the accepted orders is limited and orders are characterized by known processing times, delivery dates, revenues and the weight representing a penalty per unit-time delay beyond the delivery date promised to the customer. We prove the non-approximability of the problem and give two linear formulations that we solve with CPLEX. We devise two exact branch-and-bound procedures able to solve problem instances of practical dimensions. For the solution of large instances, we propose six heuristics. We provide a comparison and comments on the efficiency and quality of the results obtained using both the exact and heuristic algorithms, including the solution of the linear formulations using CPLEX.
    Keywords: Order acceptance; Scheduling; Single machine; Branch-and-bound; Heuristics; Firm planned orders;
    Date: 2009–02
  5. By: Heinrich R Bohlman
    Date: 2010
  6. By: Boute, Robert; Lambrecht, Marc
    Abstract: One of the main supply chain deficiencies is the bullwhip effect: demand fluctuations increase as one moves up the supply chain from retailer to manufacturer. The Beer Distribution Game is widely known for illustrating these supply chain dynamics in class. In this paper we present a spreadsheet application, exploring the two key causes of the bullwhip effect: demand forecasting and the type of ordering policy. We restrict our attention to a single product two-echelon system and illustrate how tuning the parameters of the replenishment policy induces or reduces the bullwhip effect. We also demonstrate how bullwhip reduction (dampening the order variability) may have an adverse impact on inventory holdings and/or customer service. As such, the spreadsheets can be used as an educational tool to gain a clear insight into the use of inventory control policies and forecasting in relation to the bullwhip effect and customer service.
    Keywords: Bullwhip effect; Replenishment rules; Forecasting techniques; Spreadsheet simulation; Beer distribution game;
    Date: 2009–09
  7. By: Michael D. Makowsky (Department of Economics, Towson University)
    Abstract: There is a counterintuitive gap in the club theory of religion. While it elegantly accounts for the notable success of strict sectarian religious groups in recruiting members and maintaining commitment, it exhibits less satisfactory properties when used to account for groups requiring neither extreme nor zero sacrifice. Such corner solutions, compared to the moderate middle, are rarely observed empirically. Within the original representative agent model, moderate groups are everywhere and always a suboptimal choice for rational, utility maximizing agents. In this paper, we extend the original model to operate within a multi-agent computational context, with heterogeneous agents occupying coordinates in a two dimensional lattice, making repeated decisions over time. Our model offers the possibility of successful moderate groups, including outcomes wherein the population is dominated by moderate groups. The viability of moderate groups is a result of heterogeneous agent wages. Lower wage agents offer greater time contributions, but lesser financial contributions to groups. Higher sacrifice rates incentive greater contributions from members, but reduce private productivity and screen out other potential members with greater financial resources. Moderate groups succeed by offering an optimal balance of these countervailing forces.
    Keywords: Club Theory of Religion, Liberal, Moderate, Multi-Agent Computational Model, Sacrifice, Heterogeneous Agents.
    JEL: C63 Z12 D71
    Date: 2010–02
  8. By: Massimo Bernaschi (Istituto Applicazioni del Calcolo ``M. Picone'', CNR); Alessandro Missale (University of Milan); Davide Vergni (Istituto Applicazioni del Calcolo ``M. Picone'', CNR)
    Abstract: Simulation-based cost-risk analysis of the interest expenditure is increasingly used for policy evaluation of public debt strategies by governments around the world. This paper is a first attempt to empirically evaluate this approach by comparing its implications for the maturity structure of public debt with those derived from the optimal taxation theory of debt management. To this end, we simulate the time path of the distribution of the interest expenditure for stylized portfolios of different maturities using simple stochastic models of the evolution of the term structure of interest rates, and examine the performance of such portfolios with standard cost-risk indicators. We find that: i) the ranking of debt portfolios by expenditure risk may depend on the length of the simulation period; to obtain the same policy conclusions as the optimal taxation theory, the time horizon must extend up to the redemption date of the longest maturity bond issued over the simulation period; ii) in sharp contrast with optimal taxation theory, a cost-risk trade off naturally emerges when a risk premium on long term bonds is considered, but this may not be sufficient to identify the optimal maturity structure. Our analysis points to the danger of assuming the cost-risk minimization of the interest expenditure as the main objective of debt management. A policy that either aims to minimize the interest expenditure over a too short horizon or does not consider that risk premiums may reflect a fair price for insurance may lead to sub-optimal debt strategies.
    Keywords: debt management, maturity structure, interest costs, interest rate risk, optimal taxation, simulation models, term structure,
    Date: 2009–12–15
  9. By: Albrecht Irle; Jonas Kauschke; Thomas Lux; Mishael Milakovic
    Abstract: Markov chains have experienced a surge of economic interest in the form of behavioral agent-based models that aim at explaining the statistical regularities of financial returns. We review some of the relevant mathematical facts and show how they apply to agent-based herding models, with the particular goal of establishing their asymptotic behavior because several studies have pointed out that the ability of such models to reproduce the stylized facts hinges crucially on the size of the agent population (typically denoted by n), a phenomenon that is also known as n-dependence. Our main finding is that n-(in)dependence traces back to both the topology and the velocity of information transmission among heterogeneous financial agents
    Keywords: Markov chains, agent-based finance, herding, N-dependence
    JEL: C10 D84 D85 G19
    Date: 2010–02
  10. By: Joshua R. Goldstein (Max Planck Institute for Demographic Research, Rostock, Germany); Thomas Cassidy (Max Planck Institute for Demographic Research, Rostock, Germany)
    Abstract: We introduce a new class of models in which demographic behavior such as fertility is postponed by differing amounts depending only on cohort membership. We show how this model fits into a general framework of period and cohort postponement that includes the existing models in the literature, notably those of Bongaarts and Feeney and Kohler and Philipov. The cohort-based model shows the effects of cohort shifts on period fertility measures and provides an accompanying tempo-adjusted measure of period total fertility in the absence of observed shifts. Simulation reveals that when postponement is governed by cohorts, the cohort-based indicator outperforms the Bongaarts and Feeney model that is now in widespread use. The cohort-based model is applied to fertility in several modern populations.
    JEL: J1 Z0
    Date: 2010–02
  11. By: Poelmans, Jonas; Elzinga, Paul; Viaene, Stijn; Dedene, Guido
    Abstract: In this paper, we propose a framework for iterative knowledge discovery from unstructured text using Formal Concept Analysis and Emergent Self Organizing Maps. We apply the framework to a real life case study using data from the Amsterdam-Amstelland police. The case zooms in on the problem of distilling concepts for domestic violence from the unstructured text in police reports. Our human-centered framework facilitates the exploration of the data and allows for an efficient incorporation of prior expert knowledge to steer the discovery process. This exploration resulted in the discovery of faulty case labellings, common classification errors made by police officers, confusing situations, missing values in police reports, etc. The framework was also used for iteratively expanding a domain-specific thesaurus. Furthermore, we showed how the presented method was used to develop a highly accurate and comprehensible classification model that automatically assigns a domestic or non-domestic violence label to police reports.
    Keywords: Formal concept analysis; Emergent self organizing map; Text mining; Actionable knowledge discovery; Domestic violence;
    Date: 2009–07

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