2021/09/01 by Pankaj Mishra, Mishra, Pankaj, Ahmed Moustafa +4
Computer Science · Decision Sciences · #Auction Theory and Applications #Blockchain Technology Applications and Security #Computer Science and Game Theory (cs.GT) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Multiagent Systems (cs.MA) #Optimization and Search Problems #cs.GT #cs.LG #cs.MA
paper · pdf · doi:10.48550/arxiv.2109.00207
arxiv created 2021/09/01 · openalex publication_date 2021/09/01 · arxiv updated 2021/09/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In this work, we focus on resource allocation in a decentralised open market. In decentralised open markets consists of multiple vendors and multiple dynamically-arriving buyers, thus makes the market complex and dynamic. Because, in these markets, negotiations among vendors and buyers take place over multiple conflicting issues such as price, scalability, robustness, delay, etc. As a result, optimising the resource allocation in such open markets becomes directly dependent on two key decisions, which are; incorporating a different kind of buyers' preferences, and fairness based vendor elicitation strategy. Towards this end, in this work, we propose a three-step resource allocation approach that employs a reverse-auction paradigm. At the first step, priority label is attached to each bidding vendor based on the proposed priority mechanism. Then, at the second step, the preference score is calculated for all the different kinds of preferences of the buyers. Finally, at the third step, based on the priority label of the vendor and the preference score winner is determined. Finally, we compare the proposed approach with two state-of-the-art resource pricing and allocation strategies. The experimental results show that the proposed approach outperforms the other two resource allocation approaches in terms of the independent utilities of buyers and the overall utility of the open market.