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on Computational Economics |
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Issue of 2014‒03‒30
thirteen papers chosen by |
| By: | Vymetal, Dominik; Ježek, Filip |
| Abstract: | Business simulations are useful tools due to the fact that it eases management decision making. No doubt there are many processes which must be considered and simulated. Therefore, such business simulator is often composed of many processes and contains many agents and interrelations as well. Since the business simulator based on multi-agent system is characterized by many interrelations within, this article deals with a specific part of the business simulator only – a demand function and its modeling. The aim of this partial research is to suggest demand function which would be most suitable for the business simulation. In this paper a new approach for customer decision function in business process simulation was presented. The decision of the customer is based on Marshallian demand function and customer utility function using Cobb-Douglas preferences. The results obtained by means of the MAREA simulation environment proved that this approach yields correct simulation results. |
| Keywords: | business simulator, multi-agent system, demand function, MAREA |
| JEL: | C63 C88 D4 |
| Date: | 2014–03–22 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:54716&r=cmp |
| By: | Daniel Farhat (Department of Economics, University of Otago, New Zealand) |
| Abstract: | This study engineers a household sector where individuals process macroeconomic information to reproduce consumption spending patterns in New Zealand. To do this, heterogeneous artificial neural networks (ANNs) are trained to forecast changes in consumption. In contrast to existing literature, results suggest that there exists a trained ANN that significantly outperforms a linear econometric model at out-of-sample forecasting. To improve the accuracy of ANNs using only in-sample information, methods for combining private knowledge into social knowledge are explored. For one type of ANN, relying on an expert is beneficial. For most ANN structures, weighting an individual's forecast according to how frequently that individual's ANN is a top performer during in-sample training produces more accurate social forecasts. By focusing only on recent periods, considering the severity of an individual's errors in weighting their forecast is also beneficial. Possible avenues for incorporating ANN structures into artificial social simulation models of consumption are discussed. |
| Keywords: | Artificial neural networks, forecasting, aggregate consumption, social simulation |
| JEL: | C45 E17 E27 |
| Date: | 2014–03 |
| URL: | http://d.repec.org/n?u=RePEc:otg:wpaper:1404&r=cmp |
| By: | Nguyet Nguyen; Giray \"Okten |
| Abstract: | Generation of pseudorandom numbers from different probability distributions has been studied extensively in the Monte Carlo simulation literature. Two standard generation techniques are the acceptance-rejection and inverse transformation methods. An alternative approach to Monte Carlo simulation is the quasi-Monte Carlo method, which uses low-discrepancy sequences, instead of pseudorandom numbers, in simulation. Low-discrepancy sequences from different distributions can be obtained by the inverse transformation method, just like for pseudorandom numbers. In this paper, we will present an acceptance-rejection algorithm for low-discrepancy sequences. We will prove a convergence result, and present error bounds. We will then use this acceptance-rejection algorithm to develop quasi-Monte Carlo versions of some well known algorithms to generate beta and gamma distributions, and investigate the efficiency of these algorithms numerically. We will also consider the simulation of the variance gamma model, a model used in computational finance, where the generation of these probability distributions are needed. Our results show that the acceptance-rejection technique can result in significant improvements in computing time over the inverse transformation method in the context of low-discrepancy sequences. |
| Date: | 2014–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1403.5599&r=cmp |
| By: | Anna Klabunde |
| Abstract: | In this paper an agent-based model of endogenously evolving migrant networks is developed to identify the determinants of migration and return decisions. Individuals are connected by links, the strength of which declines over time and distance. Methodologically, this paper combines parameterization using data from the Mexican Migration Project with calibration. It is shown that expected earnings, an idiosyncratic home bias, network ties to other migrants, strength of links to the home country and age have a significant impact on circular migration patterns. The model can reproduce spatial patterns of migration as well as the distribution of number of trips of migrants. It is shown how it can also be used for computational experiments and policy analysis. |
| Keywords: | Circular migration; social networks; agent-based computational economics |
| JEL: | C63 F22 J61 |
| Date: | 2014–02 |
| URL: | http://d.repec.org/n?u=RePEc:rwi:repape:0471&r=cmp |
| By: | Gabriele Standardi (Fondazione Eni Enrico Mattei); Federico Perali (University of Verona, Department of Economic Sciences); Luca Pieroni (University of Perugia, Department of Economics, Finance and Statistics) |
| Abstract: | This paper aims at modeling a global CGE trade model for the EU15 subnational regions. This model is used to assess production reallocation across sectors in each EU15 region, assuming a scenario in which world tariff liberalization is implemented in the agricultural sector. The model is parsimonious in terms of data, focusing on unskilled and skilled labor as the source of heterogeneity across regions. A stylized model is built to interpret trade policy effects. Results show decreases in agricultural production in the EU15 of about 0.93%. All regions reduce agriculture but show different magnitudes in the relative changes of production. Large reallocation effects are observed between manufactures and services, some regions specializing in the former and others in the latter. In addition, the introduction of labor mobility within the EU15 and the EU27 causes strong amplification effects in manufactures and services. |
| Keywords: | CGE modeling; International trade; Agriculture |
| JEL: | F13 D58 Q17 |
| Date: | 2014–03 |
| URL: | http://d.repec.org/n?u=RePEc:fem:femwpa:2014.25&r=cmp |
| By: | Yingying Lu; David I. Stern |
| Abstract: | We explore how and by how much the values of elasticities of substitution affect estimates of the cost of emissions reduction policies in computable general equilibrium (CGE) models. We use G-Cubed, an intertemporal CGE model, to carry out a sensitivity and factor decomposition analysis. Average abatement cost rises non-linearly as elasticities are reduced. Changes in the substitution elasticities between capital, labor, energy, and materials have a greater impact on mitigation costs than do inter-fuel elasticities of substitution. The former has more effect on business as usual emissions and the latter on average abatement costs. As elasticities are reduced, business as usual emissions and GDP growth also decrease so that there is not much variation in the total costs of reaching a given target across the parameter space. Our results confirm that the cost of climate mitigation policy is at most a few percent of global GDP. |
| Keywords: | Elasticity of substitution, Mitigation policy, CGE models, G-Cubed, Sensitivity analysis, Decomposition analysis |
| JEL: | Q54 Q58 C68 |
| Date: | 2014–03 |
| URL: | http://d.repec.org/n?u=RePEc:een:camaaa:2014-28&r=cmp |
| By: | Hübler, Michael; Löschel, Andreas; Voigt, Sebastian |
| Abstract: | We assess recent Chinese climate policy proposals in a multi-region, multi-sector computable general equilibrium model with a Chinese carbon emissions trading scheme (ETS). When the emissions intensity per GDP in 2020 is required to be 45% lower than in 2005, the model simulations indicate that the climate policy- induced welfare loss in 2020, measured as the level of GDP and welfare in 2020 under climate policy relative to their level under business-as-usual (BAU) in the same year, is about 1%. The Chinese welfare loss in 2020 slightly increases in the Chinese rate of economic growth in 2020. When keeping the emissions target fixed at the 2020 level after 2020 in absolute terms, the welfare loss will reach about 2% in 2030. If China's annual economic growth rate is 0.5 percentage points higher (lower), the climate policy-induced welfare loss in 2030 will rise (decline) by about 0.5 percentage points. Full auctioning of carbon allowances results in very similar macroeconomic effects as free allocation, but full auctioning leads to higher reductions in output than free allocation for ETS sectors. Linking the Chinese to the European ETS and restricting the transfer volume to one third of the EU's reduction effort creates at best a small benefit for China, yet with smaller sectoral output reductions than auctioning. These results highlight the importance of designing the Chinese ETS wisely. -- |
| Keywords: | China,climate policy,ETS,linking,CGE |
| JEL: | C68 Q54 Q56 |
| Date: | 2014 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:zewdip:14020&r=cmp |
| By: | Ketter, W.; Collins, J.; Reddy, P.; Weerdt, M.M. |
| Abstract: | This is the specification for the Power Trading Agent Competition for 2014 (Power TAC 2014). Power TAC is a competitive simulation that models a “liberalized” retail electrical energy market, where competing business entities or “brokers” offer energy services to customers through tariff contracts, and must then serve those customers by trading in a wholesale market. Brokers are challenged to maximize their profits by buying and selling energy in the wholesale and retail markets, subject to fixed costs and constraints. Costs include fees for publication and withdrawal of tariffs, and distribution fees for transporting energy to their contracted customers. Costs are also incurred whenever there is an imbalance between a broker’s total contracted energy supply and demand within a given time slot. The simulation environment models a wholesale market, a regulated distribution utility, and a population of energy customers, situated in a real location on Earth during a specific period for which weather data is available. The wholesale market is a relatively simple call market, similar to many existing wholesale electric power markets, such as Nord Pool in Scandinavia or FERC markets in North America, but unlike the FERC markets we are modeling a single region, and therefore we model locational-marginal pricing through a simple manipulation of the wholesale supply curve. Customer models include households, electric vehicles, and a variety of commercial and industrial entities, many of which have production capacity such as solar panels or wind turbines. All have “real-time” metering to support allocation of their hourly supply and demand to their subscribed brokers, and all are approximate utility maximizers with respect to tariff selection, although the factors making up their utility functions may include aversion to change and complexity that can retard uptake of marginally better tariff offers. The distribution utility models the regulated natural monopoly that owns the regional distribution network, and is responsible for maintenance of its infrastructure. Real-time balancing of supply and demand is managed by a market-based mechanism that uses economic incentives to encourage brokers to achieve balance within their portfolios of tariff subscribers and wholesale market positions, in the face of stochastic customer behaviors and weather-dependent renewable energy sources. The broker with the highest bank balance at the end of the simulation wins. |
| Keywords: | autonomous agents, electronic commerce, energy, preferences, portfolio management, power, policy guidance, sustainability, trading agent competition |
| Date: | 2014–03–19 |
| URL: | http://d.repec.org/n?u=RePEc:ems:eureri:50740&r=cmp |
| By: | Hwang, In Chang |
| Abstract: | A recursive method for solving an integrated assessment model of climate and the economy is developed in this paper. The method approximates value function with a logarithmic basis function and searches for solutions on a set satisfying optimality conditions. These features make the method suitable for a highly nonlinear model with many state variables and various constraints, as usual in a climate-economy model. |
| Keywords: | Dynamic programming; recursive method; value function iteration; integrated assessment |
| JEL: | C61 C63 Q54 |
| Date: | 2014–03–25 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:54782&r=cmp |
| By: | Paolo Pellizzari (Department of Economics, University Of Venice Cà Foscari); Dan Ladley (Department of Economics Leicester University) |
| Abstract: | A trader's execution strategy has a large effect on his profits. Identifying an optimal strategy, however, is often frustrated by the complexity of market microstructure's. We analyse an order book based continuous double auction market under two different models of trader's behaviour. In the first case actions only depend on a linear combination of the best bid and ask. In the second model traders adopt the Markov perfect equilibrium strategies of the trading game. Both models are analytically intractable and so optimal strategies are identified by the use of numerical techniques. Using the Markov model we show that, beyond the best quotes, additional information has little effect on either the behaviour of traders or the dynamics of the market. The remarkable similarity of the results obtained by the linear model indicates that the optimal strategy may be reasonably approximated by a linear function. We conclude that whilst the order book market and strategy space of traders are potentially very large and complex, optimal strategies may be relatively simple and based on a minimal information set. |
| Keywords: | Continuous Double Auction, Order Book, Information, Optimal Trading |
| JEL: | D44 G10 C63 |
| Date: | 2014 |
| URL: | http://d.repec.org/n?u=RePEc:ven:wpaper:2014:05&r=cmp |
| By: | Takashi Kamihigashi; John Stachurski |
| Abstract: | In this paper we introduce a technique for perfect simulation from the stationary distribution of a standard model of industry dynamics. The method can be adapted to other, possibly non-monotone, regenerative processes found in industrial organization and other fields of economics. The algorithm we propose is a version of coupling from the past. It is straightforward to implement and exploits the regenerative property of the process in order to achieve rapid coupling. |
| Keywords: | Regeneration, simulation, coupling from the past, perfect sampling |
| Date: | 2014–02–25 |
| URL: | http://d.repec.org/n?u=RePEc:ipg:wpaper:2014-144&r=cmp |
| By: | Somayeh Koohborfardhaghighi (College of Engineering, Seoul National University); Jorn Altmann (College of Engineering, Seoul National University) |
| Abstract: | People-to-people interactions in the real world and in virtual environments (e.g., Facebook) can be represented through complex networks. Changes of the structural properties of these complex networks are caused through a variety of dynamic processes. While accepting the fact that variability in individual patterns of behavior (i.e., establishment of random or FOAF-type potential links) in social environments might lead to an increase or decrease in the structural properties of a complex network, in this paper, we focus on another factor that may contribute to such changes, namely the size of personal networks. Any personal network comes with the cost of maintaining individual connections. Despite the fact that technology has shrunk our world, there is also a limit to how many close friends one can keep and count on. It is a relatively small number. In this paper, we develop a multi-agent based model to capture, compare, and explain the structural changes within a growing social network (e.g., expanding the social relations beyond one's social circles). We aim to show that, in addition to various dynamic processes of human interactions, limitations on the size of personal networks can also lead to changes in the structural properties of networks (i.e., the average shortest-path length). Our simulation result shows that the famous small world theory of interconnectivity holds true or even can be shrunk, if people manage to utilize all their existing connections to reach other parties. In addition to this, it can clearly be observed that the network¡¯s average path length has a significantly smaller value, if the size of personal networks is set to larger values in our network growth model. Therefore, limitations on the size of personal networks in network growth models lead to an increase in the network¡¯s average path length. |
| Keywords: | Small-World Network, Complex Networks, Average Shortest Path Length, Size of Personal Networks, Network Growth Model. |
| JEL: | C02 C6 C15 D85 |
| Date: | 2014–01 |
| URL: | http://d.repec.org/n?u=RePEc:snv:dp2009:2014110&r=cmp |
| By: | Widad Guechtouli |
| Abstract: | The process of knowledge diffusion is complex. Knowledge is intangible and therefore is not easy to capitalize within an organization, or share between a set of individuals. The aim of this paper is to study the impact of two different structures of communication on both processes of knowledge transfer and individual learning, in the context of a community of practice. We will specifically compare two types of communication structures (through face-to- face interactions and through a forum) by using agent-based models. Results show that each structure has a different impact on individual learning and knowledge transfer. Though, communication through face-to-face interactions seems to make individuals learn slower than on a web forum. Conclusions are widely discussed. |
| Keywords: | knowledge, communication structure, communities of practice, agent-based models. |
| Date: | 2014–02–25 |
| URL: | http://d.repec.org/n?u=RePEc:ipg:wpaper:2014-177&r=cmp |