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on Computational Economics |
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Issue of 2014‒01‒17
eight papers chosen by |
| By: | Peters, M.; Ketter, W. |
| Abstract: | Information systems have revolutionized the provisioning of decision-relevant information, and decision support tools have improved human decisions in many domains. Autonomous decision- making, on the other hand, remains hampered by systems’ inability to faithfully capture human preferences. We present a computational preference model that learns unobtrusively from lim- ited data by pooling observations across like-minded users. Our model quantifies the certainty of its own predictions as input to autonomous decision-making tasks, and it infers probabilistic segments based on user choices in the process. We evaluate our model on real-world preference data collected on a commercial crowdsourcing platform, and we find that it outperforms both individual and population-level estimates in terms of predictive accuracy and the informative- ness of its certainty estimates. Our work takes an important step toward systems that act autonomously on their users’ behalf. |
| Keywords: | assistive technologies, autonomous decision-making, multi-task learning, preferences, software agents |
| JEL: | C63 L15 O32 |
| Date: | 2013–05–22 |
| URL: | http://d.repec.org/n?u=RePEc:ems:eureri:40144&r=cmp |
| By: | Ketter, W.; Collins, J.; Reddy, P.; Weerdt, M.M. |
| Abstract: | This is the specification for the Power Trading Agent Competition for 2013 (Power TAC 2013). 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 do not model location-marginal pricing. Customer models include households and a variety of commercial and industrial entities, many of which have production capacity (such as solar panels or wind turbines) as well as electric vehicles. 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 and for real-time balancing of supply and demand. The balancing process is 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: | TAC, autonomous agents, electronic commerce, energy, policy guidance, portfolio management, power, preferences, sustainability, trading agent competition |
| JEL: | C63 O32 Q56 R4 |
| Date: | 2013–05–22 |
| URL: | http://d.repec.org/n?u=RePEc:ems:eureri:40138&r=cmp |
| By: | Giovanni Luca Lo Magno (Università di Palermo) |
| Abstract: | Maxima is a free and open-source computer algebra system (CAS), namely, software that can perform symbolic computations such as solving equations, determining derivatives of functions, obtaining Taylor series, and manipulating algebraic expressions. In this presentation, I discuss the Maxima Bridge System (MBS), a collection of software that allows Stata to interface with Maxima to use it as an engine for symbolic computation, transfer data from Stata to Maxima, and retrieve results from Maxima. The cooperation between Stata and Maxima provides the user with an environment for statistical analysis in which the power of symbolic computation can be easily used together with all the facilities supplied by Stata. In this environment, the statistician can employ symbolic computation algorithms, when convenient, to manage the complexity of algebra and calculus while using numerical computation when speed matters. |
| Date: | 2013–11–01 |
| URL: | http://d.repec.org/n?u=RePEc:boc:isug13:07&r=cmp |
| By: | HAYASHIYAMA Yasuhisa; NAKAJIMA Kazunori; SAKAMOTO Naoki; ABE Masahiro |
| Abstract: | After the Great East Japan Earthquake of 2011, government investment in the disaster stricken areas was provided for reconstruction of capital stock damaged, and dynamic analysis is needed to reveal the long-term effects of investment. On the other hand, government investment for reconstruction results in increased aggregate demand, gross income, and gross production in the disaster areas, and has spillover effects on the other prefectures except for the disaster areas, even if the amount of damaged capital stock is given. The purpose of this study is to measure the economic impacts and regional spillover effects of investment in the disaster areas for reconstruction, by focusing on the impacts of investment in the disaster areas for reconstruction on the demand-side effects and by using a multi-regional computable general equilibrium model (MRCGE) that consists of 47 prefectures and 20 industrial sectors. |
| Date: | 2014–01 |
| URL: | http://d.repec.org/n?u=RePEc:eti:rdpsjp:14004&r=cmp |
| By: | Mark Tucker; J. Mark Bull |
| Abstract: | The underlying stochastic nature of the requirements for the Solvency II regulations has introduced significant challenges if the required calculations are to be performed correctly, without resorting to excessive approximations, within practical timescales. It is generally acknowledged by actuaries within UK life offices that it is currently impossible to correctly fulfill the requirements imposed by Solvency II using existing computational techniques based on commercially available valuation packages. Our work has already shown that it is possible to perform profitability calculations at a far higher rate than is achievable using commercial packages. One of the key factors in achieving these gains is to calculate reserves using recurrence relations that scale linearly with the number of time steps. Here, we present a general vector recurrence relation which can be used for a wide range of non-unit linked policies that are covered by Solvency II; such contracts include annuities, term assurances, and endowments. Our results suggest that by using an optimised parallel implementation of this algorithm, on an affordable hardware platform, it is possible to perform the "brute force" approach to demonstrating solvency in a realistic timescale (of the order of a few hours). |
| Date: | 2014–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1401.1757&r=cmp |
| By: | Antonio Boggia; Fabrizio Luciani; Gianluca Massei; Luisa Paolotti; Lucia Rocchi; Tommaso Sediari |
| Abstract: | Climate change is a current matter that is viewed as controversial by the general public, within scientific communities and by governments. Among the production sectors, agriculture is the most significantly influenced by the effect of climate change. This study is aimed at understanding and measuring the changes that occur in agricultural systems due to climate, and at determining how climate change can affect both agricultural productivity and the environmental impacts of agricultural systems, by focusing a case study on the Trasimeno Lake (region of Umbria, Italy). The analysis was performed using the CropSyst software package. CropSyst is a multi-year, multicrop, daily time step crop growth simulation model with the possibility of connection with GIS (Geographic Information System) software. The specific typology of climate change that was simulated was the doubling of the amount of CO2 in the atmosphere during a time interval of 100 years (from year 2000 to 2100), according to the hypothesis of the CCM3 model (Govindasamy et al., 2003). The results of our simulation have shown an increase of plant productivity (connected with yield and biomass production) and an invariable situation concerning the indicators of stress by nitrogen, water and temperature. With reference to the environmental situation, our results have shown a general increase of nitrogen lisciviation, as a consequence of the increase of mineralisation, with potential effects on the soil fertility loss, and a decrease of runoff and deep percolation. The erosion indicator did not vary in a significant way. Our CropSyst - GIS system developed for this study has been useful both for the evaluation of the existing data, and for the simulation of future data. |
| Date: | 2013–12–02 |
| URL: | http://d.repec.org/n?u=RePEc:pia:wpaper:125/2013&r=cmp |
| By: | Li-Xin Wang |
| Abstract: | In this paper we use fuzzy systems theory to convert the technical trading rules commonly used by stock practitioners into excess demand functions which are then used to drive the price dynamics. The technical trading rules are recorded in natural languages where fuzzy words and vague expressions abound. In Part I of this paper, we will show the details of how to transform the technical trading heuristics into nonlinear dynamic equations. First, we define fuzzy sets to represent the fuzzy terms in the technical trading rules; second, we translate each technical trading heuristic into a group of fuzzy IF-THEN rules; third, we combine the fuzzy IF-THEN rules in a group into a fuzzy system; and finally, the linear combination of these fuzzy systems is used as the excess demand function in the price dynamic equation. We transform a wide variety of technical trading rules into fuzzy systems, including moving average rules, support and resistance rules, trend line rules, big buyer, big seller and manipulator rules, band and stop rules, and volume and relative strength rules. Simulation results show that the price dynamics driven by these technical trading rules are complex and chaotic, and some common phenomena in real stock prices such as jumps, trending and self-fulfilling appear naturally. |
| Date: | 2014–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1401.1888&r=cmp |
| By: | Matt Zahynacz |
| Abstract: | British Columbia’s natural gas industry is currently facing competitive pressures from other gas-producing jurisdictions in North America. The emergence of shale gas developments has resulted in natural gas prices falling dramatically. Nonetheless, British Columbia is positioned to take advantage of growing markets in Asia that have considerably higher prices than in North America through the export of liquefied natural gas (LNG) in carrier ships. This paper aims to assess the economic viability of an LNG industry in British Columbia by analyzing world LNG prices and trade, market development, and costs through a Monte Carlo risk assessment. |
| Keywords: | LNG trade, natural gas as coal replacement, Monte Carlo simulation, shale gas |
| JEL: | Q37 Q41 Q42 Q48 |
| Date: | 2013–04 |
| URL: | http://d.repec.org/n?u=RePEc:rep:wpaper:2013-03&r=cmp |