An assessment of strategies for choosing between competitive marketplaces

Miller, Tim; Niu, Jinzhong

doi:10.1016/j.elerap.2011.07.009

Cited by 15 publications

(7 citation statements)

References 11 publications

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“…Furthermore, there also exist some works analyzing the market pricing strategies in the competing environment with multiple double auction markets. Miller and Niu experimentally analyzed traders' market selection strategies in the competing marketplaces trading environment [35]. Cai et al analyzed the impact of different adaptive strategies on the trading strategy and its own earnings in the market competition environment [36].…”

Section: Related Workmentioning

confidence: 99%

Trading and Pricing Sensor Data in Competing Edge Servers with Double Auction Markets

Shi

Song

2021

Journal of Sensors

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With the development of the IoT (Internet of Things), sensors networks can bring a large amount of valuable data. In addition to be utilized in the local IoT applications, the data can also be traded in the connected edge servers. As an efficient resource allocation mechanism, the double auction has been widely used in the stock and futures markets and can be also applied in the data resource allocation in sensor networks. Currently, there usually exist multiple edge servers running double auctions competing with each other to attract data users (buyers) and producers (sellers). Therefore, the double auction market run on each edge server needs efficient mechanism to improve the allocation efficiency. Specifically, the pricing strategy of the double auction plays an important role on affecting traders’ profit, and thus, will affect the traders’ market choices and bidding strategies, which in turn affect the competition result of double auction markets. In addition, the traders’ trading strategies will also affect the market’s pricing strategy. Therefore, we need to analyze the double auction markets’ pricing strategy and traders’ trading strategies. Specifically, we use a deep reinforcement learning algorithm combined with mean field theory to solve this problem with a huge state and action space. For trading strategies, we use the Independent Parametrized Deep Q-Network (I-PDQN) algorithm combined with mean field theory to compute the Nash equilibrium strategies. We then compare it with the fictitious play (FP) algorithm. The experimental results show that the computation speed of I-PDQN algorithm is significantly faster than that of FP algorithm. For pricing strategies, the double auction markets will dynamically adjust the pricing strategy according to traders’ trading strategies. This is a sequential decision-making process involving multiple agents. Therefore, we model it as a Markov game. We adopt Multiagent Deep Deterministic Policy Gradient (MADDPG) algorithm to analyze the Nash equilibrium pricing strategies. The experimental results show that the MADDPG algorithm solves the problem faster than the FP algorithm.

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Section: Related Workmentioning

confidence: 99%

Trading and Pricing Sensor Data in Competing Edge Servers with Double Auction Markets

Shi

Song

2021

Journal of Sensors

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“…Although there is almost no research regarding this topic, the study of relationship between traders and marketplaces in online business provides insight to our work. Miller and Niu (2012) viewed the marketplace selection as an N-armed bandit problem, authors assessed four reinforcement algorithms by using the JCAT double auction simulation platform. The trader profit and global allocative efficiency were discussed by comparing with the random marketplace selection.…”

Section: Related Workmentioning

confidence: 99%

e-Commerce Game Model - Balancing Platform Service Charges with Vendor Profitability

Jianya

Weigang

et al. 2014

Proceedings of the 16th International Conference on Enterprise Information Systems

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One of the biggest challenges in e-commerce is to utilize data mining methods for the improvement of profitability for both platform hosts and e-commerce vendors. Taking Alibaba as an example, the more efficient method of operation is to collect hosting service fees from the vendors that use the platform. The platform defines a service fee value and the vendors can decide whether to accept or not. In this sense, it is necessary to create an analytical tool to improve and maximize the profitability of this partnership. This work proposes a dynamic in-cooperative E-Commerce Game Model (E-CGM). In E-CGM, the platform hosting company and the e-commerce vendors have their payoff functions calculated using backwards induction and their activities are simulated in a game where the goal is to achieve the biggest payoff. Taking into consideration various market conditions, E-CGM obtains the Nash equilibrium and calculates the value for which the service fee would yield the most profitable result. By comparing the data mining results obtained from a set of real data provided by Alibaba, E-CGM simulated the expected transaction volume based on a selected service fee. The results demonstrate that the proposed model using game theory is suitable for e-commerce studies and can help improve profitability for the partners of an online business model.

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“…There is a large body of work that uses the JCAT library [10] to explore competition between continuous double auction markets [11][12][13]. In a spirit similar to our work, they use simple learning algorithms such as Zero-Intelligence [14] or Zero-Intelligence-Plus [15] for both markets and traders, and analyse the allocation efficiency of double auction markets when they are competing against each other.…”

Section: Introductionmentioning

confidence: 99%

Fragmentation in trader preferences among multiple markets: market coexistence versus single market dominance

2021

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Technological advancement has led to an increase in the number and type of trading venues and a diversification of goods traded. These changes have re-emphasized the importance of understanding the effects of market competition: does proliferation of trading venues and increased competition lead to dominance of a single market or coexistence of multiple markets? In this paper, we address these questions in a stylized model of zero-intelligence traders who make repeated decisions at which of three available markets to trade. We analyse the model numerically and analytically and find that the traders’ decision parameters—memory length and how strongly decisions are based on past success—make the key difference between consolidated and fragmented steady states of the population of traders. All three markets coexist with equal shares of traders only when either learning is too weak and traders choose randomly, or when markets are identical. In the latter case, the population of traders fragments across the markets. With different markets, we note that market dominance is the more typical scenario. Overall we show that, contrary to previous research emphasizing the role of traders’ heterogeneity, market coexistence can emerge simply as a consequence of co-adaptation of an initially homogeneous population of traders.

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An assessment of strategies for choosing between competitive marketplaces

Cited by 15 publications

References 11 publications

Trading and Pricing Sensor Data in Competing Edge Servers with Double Auction Markets

Trading and Pricing Sensor Data in Competing Edge Servers with Double Auction Markets

e-Commerce Game Model - Balancing Platform Service Charges with Vendor Profitability

Fragmentation in trader preferences among multiple markets: market coexistence versus single market dominance

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