A Game-Theoretic Analysis of Market Selection Strategies for Competing Double Auction Marketplaces

In this paper, we propose a novel general framework for analysing competing double auction markets that vie for traders, who then need to choose which market to go to. Based on this framework, we analyse the competition between two markets in detail. Specifically, we game-theoretically analyse the equilibrium behaviour of traders' market selection strategies and adopt evolutionary game theory to investigate how traders dynamically change their strategies, and thus, which equilibrium, if any, can be reached. In so doing, we show that it is unlikely for these competing markets to coexist. Eventually, all traders will always converge to locating themselves at one of the markets. Somewhat surprisingly, we find that sometimes all traders converge to the market that charges higher fees. Thus we further analyse this phenomenon, and specifically determine the factors that affect such migration

Automated Bidding in Computing Service Markets. Strategies, Architectures, Protocols

This dissertation contributes to the research on Computational Mechanism Design by providing novel theoretical and software models - a novel bidding strategy called Q-Strategy, which automates bidding processes in imperfect information markets, a software framework for realizing agents and bidding strategies called BidGenerator and a communication protocol called MX/CS, for expressing and exchanging economic and technical information in a market-based scheduling system

Using Transfer Learning in Network Markets

Mechanism design is the sub-field of microeconomics and game theory, which considers agents have their own private information and are self-interested and tries to design systems that can produce desirable outcomes. In recent years, with the development of internet and electronic markets, mechanism design has become an important research field in computer science. This work has largely focused on single markets. In the real world, individual markets tend to connect to other markets and form a big “network market”, where each market occupies a node in the network and connections between markets reflect constraints on traders in the markets. So, it is interesting to find out how the structure of connected network markets impacts the performance of the resulting network markets and how we can optimize performance by varying the things that one could control in a network market. In this dissertation, I aim to find out whether we can apply transfer learning to other machine learning techniques like reinforcement learning in the design of network markets to help optimize the performance of the network markets. I applied transfer learning on both machine learning trading strategies and machine learning strategies for selecting which market to trade in. I found that, in most cases, by applying transfer learning to machine learning trading strategies or machine learning market selection strategies, we can improve the performance of the network market significantly