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on Computational Economics |
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Issue of 2009‒09‒19
five papers chosen by |
| By: | Ian White (MRC Biostatistics Unit, Cambridge University) |
| Abstract: | Simulation studies are a powerful tool, but their analysis is not always done well; in particular, Monte Carlo standard errors are often not reported. I present a Stata program, - simsum-, which can output a range of summaries, including bias, precision of one method relative to another, percentage difference between model-based and empirical standard error, power and coverage. Monte Carlo standard errors are computed for all these quantities, using exact or approximate formulae. |
| Date: | 2009–09–16 |
| URL: | http://d.repec.org/n?u=RePEc:boc:usug09:08&r=cmp |
| By: | Leonidas, Spiliopoulos |
| Abstract: | This paper addresses the question of whether neural networks (NNs), a realistic cognitive model of human information processing, can learn to backward induce in a two-stage game with a unique subgame-perfect Nash equilibrium. The NNs were found to predict the Nash equilibrium approximately 70% of the time in new games. Similarly to humans, the neural network agents are also found to suffer from subgame and truncation inconsistency, supporting the contention that they are appropriate models of general learning in humans. The agents were found to behave in a bounded rational manner as a result of the endogenous emergence of decision heuristics. In particular a very simple heuristic socialmax, that chooses the cell with the highest social payoff explains their behavior approximately 60% of the time, whereas the ownmax heuristic that simply chooses the cell with the maximum payoff for that agent fares worse explaining behavior roughly 38%, albeit still significantly better than chance. These two heuristics were found to be ecologically valid for the backward induction problem as they predicted the Nash equilibrium in 67% and 50% of the games respectively. Compared to various standard classification algorithms, the NNs were found to be only slightly more accurate than standard discriminant analyses. However, the latter do not model the dynamic learning process and have an ad hoc postulated functional form. In contrast, a NN agent’s behavior evolves with experience and is capable of taking on any functional form according to the universal approximation theorem. |
| Keywords: | Agent based computational economics; Backward induction; Learning models; Behavioral game theory; Simulations; Complex adaptive systems; Artificial intelligence; Neural networks |
| JEL: | C45 C7 C73 |
| Date: | 2009–09–12 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:17267&r=cmp |
| By: | Kretschmer, Bettina; Peterson, Sonja; Ignaciuk, Adriana |
| Abstract: | Biofuels and other forms of bioenergy have received increased attention in recent times: They have partly been acclaimed as an instrument to contribute to rural development, energy security and to fight global warming but have been increasingly come under attack for their potential to contribute to rising food prices. It has thus become clear that bioenergy cannot be evaluated independently of the rest of the economy and that national and international feedback effects are important. In this paper we describe how the CGE model DART is extended to include firstgeneration biofuel production technologies. DART can now be used to assess the efficiency of combined climate and bioenergy policies. As a first example the effects of a 10% biofuel target in the EU are analyzed. |
| Keywords: | biofuels, CGE model, EU climate policy, Environmental Economics and Policy, Resource /Energy Economics and Policy, |
| Date: | 2009–10 |
| URL: | http://d.repec.org/n?u=RePEc:ags:gaae09:53253&r=cmp |
| By: | Johannes Schmidt (Doctoral School Sustainable Development (dokNE), University of Natural Resources and Applied Life Sciences, Vienna); Sylvain Leduc (International Institute for Applied Systems Analysis, Schlossplatz 1, A-2361 Laxenburg, Austria); Erik Dotzauer (Mälardalen University, Box 883, SE-72123 Västerås, Sweden); Georg Kindermann (International Institute for Applied Systems Analysis, Schlossplatz 1, A-2361 Laxenburg, Austria); Erwin Schmid (Institute for Sustainable Economic Development, Department of Economics and Social Sciences, University of Natural Resources and Applied Life Sciences, Vienna) |
| Abstract: | Austria aims at increasing its share of renewable energy production by 11% until 2020. Combined Heat and Power (CHP) plants fired by forest wood can significantly contribute to attaining this target. However, the spatial distribution of biomass supply and of heat demand limits the potentials of CHP production. This paper assesses CHP potentials using a mixed integer programming model that optimizes locations of bioenergy plants. Investment costs of district heating infrastructure are modeled as a function of heat demand densities, which can differ substantially. Gasification of biomass in a combined cycle process is assumed as production technology. Some model parameters have a broad range according to a literature review. Monte-Carlo simulations have therefore been performed to account for model parameter uncertainty in our analysis. Optimal locations of plants are clustered around big cities in the East of Austria. At current power prices, biomass based CHP production allows producing around 3% of Austria’s total current energy demand. Yet, the heat utilization decreases when CHP production increases due to limited heat demand that is suitable for district heating. |
| Keywords: | Combined Heat and Power, District Heating, Bioenergy, Biomass, Mixed Integer Programming, Monte-Carlo Simulation |
| JEL: | C61 C63 |
| Date: | 2009–06 |
| URL: | http://d.repec.org/n?u=RePEc:sed:wpaper:432009&r=cmp |
| By: | Brenton, Paul; Saborowski, Christian; Staritz, Cornelia; von Uexkull, Erik |
| Abstract: | TRIST is a simple, easy to use tool to assess the adjustment implications of trade reform. It improves on existing tools. First, it is an improvement in terms of accuracy because projections are based on revenues actually collected at the tariff line level rather than simply applying statutory rates. Second, it is transparent and open; runs in Excel, with formulas and calculation steps visible to the user; and is open-source and users are free to change, extend, or improve according to their needs. Third, TRIST has greater policy relevance because it projects the impact of tariff reform on total fiscal revenue (including VAT and excise) and results are broken down to the product level so that sensitive products or sectors can be identified. And fourth, the tool is flexible and can incorporate tariff liberalization scenarios involving any group of trading partners and any schedules of products. This paper describes the TRIST tool and provides a range of examples that demonstrate the insights that the tool can provide to policy makers on the adjustment impacts of reducing tariffs. |
| Keywords: | Trade Policy,Free Trade,Debt Markets,International Trade and Trade Rules,Economic Theory&Research |
| Date: | 2009–09–01 |
| URL: | http://d.repec.org/n?u=RePEc:wbk:wbrwps:5045&r=cmp |