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on Computational Economics |
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Issue of 2017‒07‒02
seven papers chosen by |
| By: | Michele Berardi; Jaqueson K Galimberti (KOF Swiss Economic Institute, ETH Zurich, Switzerland) |
| Abstract: | Under adaptive learning,recursive algorithms are proposed to represent how agents update their beliefs over time. For applied purposes these algorithms require initial estimates of agents perceived law of motion. Obtaining appropriate initial estimates can become prohibitive within the usual data availability restrictions of macroeconomics. To circumvent this issue we propose a new smoothing-based initialization routine that optimizes the use of a training sample of data to obtain initials consistent with the statistical properties of the learning algorithm. Our method is generically formulated to cover different specifications of the learning mechanism, such as the Least Squares and the Stochastic Gradient algorithms. Using simulations we show that our method is able to speed up the convergence of initial estimates in exchange for a higher computational cost. |
| Date: | 2017–01 |
| URL: | http://d.repec.org/n?u=RePEc:kof:wpskof:17-425&r=cmp |
| By: | Azamat Uzbekov (Seoul National University, Seoul, Korea); Jörn Altmann (Seoul National University, Seoul, Korea) |
| Abstract: | Although cloud computing technology gets increasingly sophisticated, a resource allocation method still has to be proposed that allows providers to take into consideration the preferences of their customers. The existing engineering-based and economics-based resource allocation methods do not take into account jointly the different objectives that engineers and marketing employees of a cloud provider company follow. This article addresses this issue by presenting the system architecture and, in particular, the business-preference-based scheduling algorithm that integrates the engineering aspects of resource allocation with the economics aspects of resource allocation. To show the workings of the new business-preference-based scheduling algorithm, which integrates a yield management method and a priority-based scheduling method, a simulation has been performed. The results obtained are compared with results from the First-Come-First-Serve scheduling algorithm. The comparison shows that the proposed scheduling algorithm achieves higher revenue than the engineering-based scheduling algorithm. |
| Keywords: | Cloud Computing, Resource Allocation, FCFS, Yield Management, Scheduling, Pricing, Economics-Based Resource Allocation, System Architecture. |
| JEL: | C61 C63 D24 D81 L86 M15 |
| Date: | 2016–12 |
| URL: | http://d.repec.org/n?u=RePEc:snv:dp2009:2016134&r=cmp |
| By: | Elodie Letort; Pierre Dupraz; Laurent Piet |
| Abstract: | Nitrate pollution remains a major problem in some parts of France, especially in the Brittany region, which is characterized by intensive livestock production systems. Although farmers must not exceed a regulatory limit of nitrogen contained in manure per hectare, many farmers in this region exceed this limit. Therefore, they must treat the excess of manure that they produce or export it to be spread in neighbouring farms and/or areas, inducing fierce competition in the land market. Another adaptation strategy consists of modifying production practices or the production system as a whole, i.e., changing the structure of the farm. In this paper, a spatial agent-based model (ABM) has been developed to assess policy options in the regulation of manure management practices. The objective is to highlight the potential effects of these policies on the farmland market and the structural changes that they induce. Our results show that the different policies, which result in similar environmental benefits, induce different changes in the land market and in agricultural structures. |
| Keywords: | Q15, C63, D22 |
| JEL: | Q15 C63 D22 |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:rae:wpaper:201701&r=cmp |
| By: | Netsanet Haile (Seoul National University, Seoul, Korea); Jörn Altmann (Seoul National University, Seoul, Korea) |
| Abstract: | Within a closed ecosystem, end-users cannot interoperate with other platforms or port their software and data easily without a cost for interface integration or data re-formatting. The customers of these closed software service platforms are locked-in. Potential customers, who are aware of this lock-in issue, are hesitant to adopt a closed software service platform, slowing down the wide deployment of the software service platform. This paper applies an economic perspective to investigate the value creation for providers and users at different levels of interoperability. For the analysis, a value creation model for software service platforms within a software service ecosystem has been developed. Simulations of the value creation model show that, even if investments in interoperability and portability are aimed at addressing user requirements, their impact also drives the providers’ profitability. Furthermore, emerging providers require investing more than market leading providers, as they have less power to set de facto standards. The simulation results also show that there is an optimal level of investments, with respect to profit and return on investments. Overall, from these results, platform providers cannot only obtain an understanding on how investments in interoperability and portability impact cost, enable cost-effective service integration, and create value, but also design new strategies for optimizing investments. |
| Keywords: | Cloud Computing, Software Service Platform, Interoperability, Portability, Value Creation Model, Computational Economics, Simulation, Value Analysis, Net Present Value, Return On Investments, Investment Assessment. |
| JEL: | C15 D24 D46 D85 E22 L86 O31 |
| Date: | 2017–05 |
| URL: | http://d.repec.org/n?u=RePEc:snv:dp2009:2017136&r=cmp |
| By: | Gordeev, Dmitry (Russian Presidential Academy of National Economy and Public Administration (RANEPA)); Kaukin, A.S. (Russian Presidential Academy of National Economy and Public Administration (RANEPA)); Ponomarev, Yuriy (Russian Presidential Academy of National Economy and Public Administration (RANEPA)) |
| Abstract: | The purpose of this work is to develop an oil market model of the Russian Federation that will yield qualitative and quantitative results, modeling various shocks arising in the oil market, and will allow a more balanced approach to the adoption of decisions aimed at further development of the oil and gas sector. |
| Date: | 2017–05 |
| URL: | http://d.repec.org/n?u=RePEc:rnp:wpaper:051738&r=cmp |
| By: | Fritz Schiltz (Leuven Economics of Education Research, University of Leuven, Belgium); Chiara Masci (Modelling and Scientific Computing, Department of Mathematics, Politecnico di Milano, Italy); Tommaso Agasisti (Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Italy); Daniel Horn (Institute of Economics, Centre for Economic and Regional Studies, Hungarian Academy of Sciences) |
| Abstract: | Educational systems can be characterized by a complex structure: students, classes and teachers, schools and principals, and providers of education. The added value of schools is likely influenced by all these levels and, especially, by interactions between them. We illustrate the ability of Machine Learning (ML) methods (Regression Trees, Random Forests and Boosting) to model this complex ‘education production function’ using Hungarian data. We find that, in contrast to ML methods, classical regression approaches fail to identify relevant nonlinear interactions such as the role of school principals to accommodate district size policies. We visualize nonlinear interaction effects in a way that can be easily interpreted. |
| Keywords: | machine learning, education production function, interaction effects, non-linear effects |
| JEL: | C5 C18 C49 I21 H75 |
| Date: | 2017–06 |
| URL: | http://d.repec.org/n?u=RePEc:has:bworkp:1704&r=cmp |
| By: | Leonardo Costa Ribeiro (Inmetro, Rio de Janeiro, Brazil); Leonardo Gomes de Deus (Cedeplar-UFMG, Belo Horizonte, Brazil); Pedro Mendes Loureiro (SOAS, London, UK); Eduardo da Motta e Albuquerque (Cedeplar-UFMG, Belo Horizonte, Brazil) |
| Abstract: | This article proposes a network model to replicate the behaviour of the profit rate in the long run. Specifically, it accounts for the results of an empirical investigation of the profit rate in the US, which show that it has fractal properties and its complexity changes over time. The starting point of the model is Marx’s insights on the interplay between the tendency of the rate to fall and its countertendencies. It combines these insights with the persistent generation of new commodities – inventions – and a specific set of new branches of production that triggers technological revolutions. A simulation running this network model successfully replicates historical features of the system. |
| Keywords: | Rate of profit; Technological revolutions; Marx; Complex systems; Metamorphoses of capitalism; Simulation models |
| JEL: | P16 O33 B51 |
| Date: | 2017–06 |
| URL: | http://d.repec.org/n?u=RePEc:cdp:texdis:td555&r=cmp |