基于Agent的报价学习对碳排放权拍卖的影响
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摘要
针对碳排放权序贯拍卖中竞标主体的报价学习行为对拍卖结果的影响,引入自适应性报价模型并改进PSO(particle swarm optimization)智能算法来模拟控排企业报价动态变化的学习机制,并构建基于多Agent的拍卖仿真模型,实验分析不同拍卖情景下,报价学习行为对碳排放权拍卖出清价格、年均履约水平以及拍卖效率的影响.实验结果表明:报价学习行为可以显著提高企业年均履约水平,但是在歧视价格拍卖以及统一价格拍卖(市场供小于求时)下,会显著降低碳排放权出清价格和拍卖效率;歧视价格拍卖的出清价格和拍卖效率都高于统一价格拍卖,但从公平性以及降低企业成本的角度出发,统一价格拍卖可以减少拍卖对没有学习行为的竞标主体的不公平性以及避免报价学习行为对拍卖效率的影响.
This paper introduces a self-adaptive pricing model and improved particle swarm optimization(PSO) algorithm to simulate the learning mechanism of the dynamic change of bidding prices to examine the impacts of bidders' learning bidding strategies on carbon allowance sequential auction. A multi-agent auction simulation model is constructed to study the impacts of bidders' learning behaviors on carbon allowance clearing prices, annual performance level, and auction efficiency under different experimental situations. The results show that the bidding learning behaviors can significantly increase the average annual compliance level of bidding firms but reduce the clearing prices and auction efficiencies under discriminatory-price auction and uniform-price auction(when market supply is less than demand). Meanwhile, discriminatory-price auctions have higher clearing prices and auction efficiency than uniform-price auctions. However, in view of fairness and reducing corporate costs, uniform-price auctions reduce unfairness for firms who do not have bidding learning behaviors, and avoid the influence of bidding learning behaviors on auction efficiency.
This paper introduces a self-adaptive pricing model and improved particle swarm optimization(PSO) algorithm to simulate the learning mechanism of the dynamic change of bidding prices to examine the impacts of bidders' learning bidding strategies on carbon allowance sequential auction. A multi-agent auction simulation model is constructed to study the impacts of bidders' learning behaviors on carbon allowance clearing prices, annual performance level, and auction efficiency under different experimental situations. The results show that the bidding learning behaviors can significantly increase the average annual compliance level of bidding firms but reduce the clearing prices and auction efficiencies under discriminatory-price auction and uniform-price auction(when market supply is less than demand). Meanwhile, discriminatory-price auctions have higher clearing prices and auction efficiency than uniform-price auctions. However, in view of fairness and reducing corporate costs, uniform-price auctions reduce unfairness for firms who do not have bidding learning behaviors, and avoid the influence of bidding learning behaviors on auction efficiency.
引文
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[19] Erev I, Roth A E. Predicting how people play games:Reinforcement learning in experimental games with unique, mixed strategy equilibria. American Economic Review, 1998, 88(4):848–881.
[20]郑君君,王向民,朱德胜,等.考虑学习速度的小世界网络上排污权拍卖策略演化.中国管理科学, 2017(3):76–84.Zheng J J, Wang X M, Zhu D S, et al. Evolution of strategies in emissions permits auction on small-world networks with learning speed. Chinese Journal of Management Science, 2017(3):76–84.(in Chinese)
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[22] Tang L, Wu J, Yu L, et al. Carbon emissions trading scheme exploration in China:A multi-agent-based model. Energy Policy, 2015,81:152–169.
[23] Tang L, Wu J, Yu L, et al. Carbon allowance auction design of China’s emissions trading scheme:A multi-agent-based approach.Energy Policy, 2017, 102:30–40.
[24] Damianov D S, Becker J G. Auctions with variable supply:Uniform price versus discriminatory. European Economic Review, 2010,54(4):571–593.
[25] Gode D K, Sunder S. Allocative efficiency of markets with zero-intelligence traders:Market as a partial substitute for individual rationality. Journal of Political Economy, 1993, 101(1):119–137.
[26] Farnia F, Frayret J M, Lebel L, et al. Multiple-round timber auction design and simulation. International Journal of Production Economics, 2013, 146(1):129–141.
[27] Kennedy J, Eberhart R. Particle swarm optimization//Proceedings of 1995 IEEE International Conference on Neural Networks,Perth, 2011:1942–1948.
[28] Shi X H, Liang Y C, Lee H P, et al. An improved GA and a novel PSO-GA-based hybrid algorithm. Information Processing Letters,2005, 93(5):255–261.
[29] Zhu X B. Strategic learning in multiple equilibria for double bargaining mechanism by PSO. Applied Mechanics&Materials, 2014,571–572:258–261.
[30]郑君君,张平,饶从军,等.基于Swarm的股权拍卖机制设计与仿真研究.系统工程理论与实践, 2014, 34(4):883–891.Zheng J J, Zhang P, Rao C J, et al. Research on mechanism design and simulation of equity auction based on Swarm. Systems Engineering:Theory&Practice, 2014, 34(4):883–891.(in Chinese)
[2] Newell R G, Pizer W A, Raimi D. Carbon markets 15 years after Kyoto:Lessons learned, new challenges. Journal of Economic Perspectives, 2013, 27(1):123–146.
[3]徐静,郭松睿,任庆忠.基于混合能源的企业碳减排决策研究.系统工程学报, 2017, 32(6):732–737.Xu J, Guo S R, Ren Q Z. Enterprises carbon emission reduction strategy based on mixed energy. Journal of Systems Engineering,2017, 32(6):732–737.(in Chinese)
[4] Anderson B, Maria C D. Abatement and allocation in the pilot phase of the EUETS. Environmental&Resource Economics, 2011,48(1):83–103.
[5] Parry I W H, Iii R C W, Goulder L H. When can carbon abatement policies increase welfare:The fundamental role of distorted factor markets. Journal of Environmental Economics&Management, 1999, 37(1):52–84.
[6] Rao C, Li P. Multi-stage sequential uniform price auction mechanism for divisible goods. Expert Systems with Applications, 2013,40(15):6105–6114.
[7] Wolfstetter E. Auctions:An introduction. Journal of Economic Surveys, 1996, 10(4):367–420.
[8]胡二琴,赵勇,陈莹,等.序贯拍卖中报价排序信息披露的研究.系统工程学报, 2016, 31(3):317–327.Hu E Q, Zhao Y, Chen Y. Study of bids’ ordinal ranks information disclosure in sequential auctions. Journal of Systems Engineering,2016, 31(3):317–327.(in Chinese)
[9]韩晓虎.序贯拍卖理论若干问题研究.北京:对外经济贸易大学, 2016.Han X H, Study on Some Issues in Sequential Auction Theory. Beijing:University of International Business and Economics, 2016.(in Chinese)
[10]陈欣,刘明,刘延.碳交易价格的驱动因素与结构性断点:基于中国七个碳交易试点的实证研究.经济问题, 2016(11):29–35.Chen X, Liu M, Liu Y, et al. Price drivers and structural breaks in China’s carbon prices:Based on seven carbon trading pilots. On Economic Problems, 2016(11):29–35.(in Chinese)
[11] Feng J, Chatterjee K. Simultaneous vs. sequential sales:Bidder competition and supply uncertainty. Decision Support Systems,2010, 49(3):251–260.
[12]王素凤,杨善林.考虑保留价影响报价策略的碳排放权拍卖模型.管理工程学报, 2016, 30(2):181–187.Wang S F, Yang S L. Carbon emission rights auction considering the influence of reserve price on bidding strategies. Journal of Industrial Engineering&Engineering Management, 2016, 30(2):181–187.(in Chinese)
[13] Jiang M X, Yang D X, Chen Z Y, et al. Market power in auction and efficiency in emission permits allocation. Journal of Environmental Management, 2016, 183(Pt 3):576.
[14] Wei Y M. Auction design for the allocation of carbon emission allowances:Uniform or discriminatory price. International Journal of Energy&Environment, 2010, 1(3):533–546.
[15] Liu Z, Yan J, Shi Y, et al. Multi-agent based experimental analysis on bidding mechanism in electricity auction markets. International Journal of Electrical Power&Energy Systems, 2012, 43(1):696–702.
[16] Vickrey W. Counterspeculation, Auctions and competitive sealed tenders. Journal of Finance, 1961, 16(1):8–37.
[17] Criesmer J H, Levitan R E, Shubikt M. Toward a study of bidding processes, Part IV:Games with unknown costs. Naval Research Logistics, 1967, 14(4):415–433.
[18] Cong R G, Wei Y M. Experimental comparison of impact of auction format on carbon allowance market. Renewable&Sustainable Energy Reviews, 2012, 16(6):4148–4156.
[19] Erev I, Roth A E. Predicting how people play games:Reinforcement learning in experimental games with unique, mixed strategy equilibria. American Economic Review, 1998, 88(4):848–881.
[20]郑君君,王向民,朱德胜,等.考虑学习速度的小世界网络上排污权拍卖策略演化.中国管理科学, 2017(3):76–84.Zheng J J, Wang X M, Zhu D S, et al. Evolution of strategies in emissions permits auction on small-world networks with learning speed. Chinese Journal of Management Science, 2017(3):76–84.(in Chinese)
[21]郑君君,李润发,李诚志,等.基于迭代组合拍卖的排污权交易影响因素研究.技术经济, 2018(1):76–83.Zheng J J, Li R F, Li C Z, et al. Research on factors influencing emission trading based on iterative combined auction. Technology Economics, 2018(1):76–83.(in Chinese)
[22] Tang L, Wu J, Yu L, et al. Carbon emissions trading scheme exploration in China:A multi-agent-based model. Energy Policy, 2015,81:152–169.
[23] Tang L, Wu J, Yu L, et al. Carbon allowance auction design of China’s emissions trading scheme:A multi-agent-based approach.Energy Policy, 2017, 102:30–40.
[24] Damianov D S, Becker J G. Auctions with variable supply:Uniform price versus discriminatory. European Economic Review, 2010,54(4):571–593.
[25] Gode D K, Sunder S. Allocative efficiency of markets with zero-intelligence traders:Market as a partial substitute for individual rationality. Journal of Political Economy, 1993, 101(1):119–137.
[26] Farnia F, Frayret J M, Lebel L, et al. Multiple-round timber auction design and simulation. International Journal of Production Economics, 2013, 146(1):129–141.
[27] Kennedy J, Eberhart R. Particle swarm optimization//Proceedings of 1995 IEEE International Conference on Neural Networks,Perth, 2011:1942–1948.
[28] Shi X H, Liang Y C, Lee H P, et al. An improved GA and a novel PSO-GA-based hybrid algorithm. Information Processing Letters,2005, 93(5):255–261.
[29] Zhu X B. Strategic learning in multiple equilibria for double bargaining mechanism by PSO. Applied Mechanics&Materials, 2014,571–572:258–261.
[30]郑君君,张平,饶从军,等.基于Swarm的股权拍卖机制设计与仿真研究.系统工程理论与实践, 2014, 34(4):883–891.Zheng J J, Zhang P, Rao C J, et al. Research on mechanism design and simulation of equity auction based on Swarm. Systems Engineering:Theory&Practice, 2014, 34(4):883–891.(in Chinese)