An equilibrium analysis of trading across multiple double auction marketplaces using fictitious play

Shi, Bing; Gerding, Enrico H.; Jennings, Nicholas R.

doi:10.1016/j.elerap.2016.04.002

Cited by 5 publications

(6 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…But the original algorithm can only solve the complete information game, so Rabinovich et al proposed a generalized FP algorithm to analyze the continuous type of incomplete information game [30], but this generalized algorithm is only suitable for unilateral auctions. Shi et al made improvements on this basis to analyze the incomplete information game problem under double auctions [31]. For the first time, Schvartzman and Wellman combined empirical game theory with the Q-learning algorithm in reinforcement learning to analyze the optimal trading strategy of traders in the double auction market [32], but this algorithm is only suitable for a small and discrete space of bidding actions.…”

Section: Related Workmentioning

confidence: 99%

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

Shi

Song

2021

Journal of Sensors

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%

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

Shi

Song

2021

Journal of Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…McAfee's double auction is most widely used because of its economic robustness such as truthfulness, individual rationality and ex-post budget balance (Chen et al, 2014). However, most studies on it have focused on how to design strategy-proof DA mechanisms (Colini-Baldeschi et al, 2016;Nassiri-Mofakham et al, 2015;Samimi et al, 2016;Shi et al, 2016;Ma and Leung, 2007), while little research effort has been done concerning privacy and security issues in the auctions, leading to bid privacy disclosure largely. This would in turn lead to a series of severe consequences.…”

Section: The Problem Of Bid Privacy Disclosurementioning

confidence: 99%

“…Nassiri-Mofakham et al presented a new packaging model MACBID (Nassiri-Mofakham et al, 2015) in order to find the combination (and quantities) of the items and the total price which best satisfy the bidder's need. Bing et al investigated bidding strategies for automated traders that select marketplaces and submit offers across multiple double auction marketplaces (Shi et al, 2016). However, most of them considered only single-sided auctions, and the existing double auction mechanisms do not provide too much privacy guarantee.…”

Section: Related Workmentioning

confidence: 99%

“…With the rapid development of electronic commerce over the Internet, a large number of auction services exist on the Internet during the past a few years. Double auctions are adopted by many of current electronic exchanges for matching buyers and sellers (Shi et al, 2016). Other auctions are also widely employed as efficient mechanisms to allocate goods, resource, etc., to promote the ideality of competitive pricing and to maximize the commodity value.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Privacy-preserving Double Auction Mechanism Based on Homomorphic Encryption and Sorting Networks

Xu¹,

Chen²,

Zhong³

2019

Preprint

View full text Add to dashboard Cite

As an effective resource allocation approach, double auctions (DAs) have been extensively studied in electronic commerce. Most previous studies have focused on how to design strategy-proof DA mechanisms, while not much research effort has been done concerning privacy and security issues. However, security, especially privacy issues have become such a public concern that the European governments lay down the law to enforce the privacy guarantees recently. In this paper, to address the privacy issue in electronic auctions, we concentrate on how to design a privacy-preserving mechanism for double auctions by employing Goldwasser-Micali homomorphic encryption and sorting networks. We achieve provable privacy such that the auctions do not reveal any bid information except the auction results, resulting in a strict privacy guarantee. Moreover, to achieve practical system performance, we compare different sorting algorithms, and suggest using the faster ones. Experimental results show that different sorting algorithms may have great effect on the performance of our mechanism, and demonstrate the practicality of our protocol for real-world applications in electronic commerce.

show abstract

“…We assume all the agents involved (buyers and sellers) deploy scaling based strategies, and identify the Nash Equilibrium (NE) of the induced game. Researchers have used fictitious play-based convergence to equilibrium (e.g., (Shi et al 2010)) in double auctions. However, such strategies are not useful in PDAs when the agents need to place bids in real-time for new auctions.…”

Section: Introductionmentioning

confidence: 99%

Bidding in Smart Grid PDAs: Theory, Analysis and Strategy

Ghosh

Gujar

Paruchuri

et al. 2020

AAAI

View full text Add to dashboard Cite

Periodic Double Auctions (PDAs) are commonly used in the real world for trading, e.g. in stock markets to determine stock opening prices, and energy markets to trade energy in order to balance net demand in smart grids, involving trillions of dollars in the process. A bidder, participating in such PDAs, has to plan for bids in the current auction as well as for the future auctions, which highlights the necessity of good bidding strategies. In this paper, we perform an equilibrium analysis of single unit single-shot double auctions with a certain clearing price and payment rule, which we refer to as ACPR, and find it intractable to analyze as number of participating agents increase. We further derive the best response for a bidder with complete information in a single-shot double auction with ACPR. Leveraging the theory developed for single-shot double auction and taking the PowerTAC wholesale market PDA as our testbed, we proceed by modeling the PDA of PowerTAC as an MDP. We propose a novel bidding strategy, namely MDPLCPBS. We empirically show that MDPLCPBS follows the equilibrium strategy for double auctions that we previously analyze. In addition, we benchmark our strategy against the baseline and the state-of-the-art bidding strategies for the PowerTAC wholesale market PDAs, and show that MDPLCPBS outperforms most of them consistently.

show abstract

An equilibrium analysis of trading across multiple double auction marketplaces using fictitious play

Cited by 5 publications

References 39 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

Privacy-preserving Double Auction Mechanism Based on Homomorphic Encryption and Sorting Networks

Bidding in Smart Grid PDAs: Theory, Analysis and Strategy

Contact Info

Product

Resources

About