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

Setting Fees in Competing Double Auction Marketplaces: An Equilibrium Analysis

In this paper, we analyse competing double auction marketplaces that vie for traders and need to set appropriate fees to make a profit. Specifically, we show how competing marketplaces should set their fees by analysing the equilibrium behaviour of two competing marketplaces. In doing so, we focus on two different types of market fees: registration fees charged to traders when they enter the marketplace, and profit fees charged to traders when they make transactions. In more detail, given the market fees, we first derive equations to calculate the marketplaces' expected profits. Then we analyse the equilibrium charging behaviour of marketplaces in two different cases: where competing marketplaces can only charge the same type of fees and where competing marketplaces can charge different types of fees. This analysis provides insights which can be used to guide the charging behaviour of competing marketplaces. We also analyse whether two marketplaces can co-exist in equilibrium. We find that, when both marketplaces are limited to charging the same type of fees, traders will eventually converge to one marketplace. However, when different types of fees are allowed, traders may converge to different marketplaces (i.e. multiple marketplaces can co-exist)

Automated Auction Mechanism Design with Competing Markets

Resource allocation is a major issue in multiple areas of computer science. Despite the wide range of resource types across these areas, for example real commodities in e-commerce and computing resources in distributed computing, auctions are commonly used in solving the optimization problems involved in these areas, since well designed auctions achieve desirable economic outcomes. Auctions are markets with strict regulations governing the information available to traders in the market and the possible actions they can take. Auction mechanism design aims to manipulate the rules of an auction in order to achieve specific goals. Economists traditionally use mathematical methods, mainly game theory, to analyze auctions and design new auction forms. However, due to the high complexity of auctions, the mathematical models are typically simplified to obtain results, and this makes it difficult to apply results derived from such models to market environments in the real world. As a result, researchers are turning to empirical approaches. Following this line of work, we present what we call a grey-box approach to automated auction mechanism design using reinforcement learning and evolutionary computation methods. We first describe a new strategic game, called \cat, which were designed to run multiple markets that compete to attract traders and make profit. The CAT game enables us to address the imbalance between prior work in this field that studied auctions in an isolated environment and the actual competitive situation that markets face. We then define a novel, parameterized framework for auction mechanisms, and present a classification of auction rules with each as a building block fitting into the framework. Finally we evaluate the viability of building blocks, and acquire auction mechanisms by combining viable blocks through iterations of CAT games. We carried out experiments to examine the effectiveness of the grey-box approach. The best mechanisms we learnt were able to outperform the standard mechanisms against which learning took place and carefully hand-coded mechanisms which won tournaments based on the CAT game. These best mechanisms were also able to outperform mechanisms from the literature even when the evaluation did not take place in the context of CAT games. These results suggest that the grey-box approach can generate robust double auction mechanisms and, as a consequence, is an effective approach to automated mechanism design. The contributions of this work are two-fold. First, the grey-box approach helps to design better auction mechanisms which can play a central role in solutions to resource allocation problems in various application domains of computer science. Second, the parameterized view and the reinforcement learning-based search method can be used in other strategic, competitive situations where decision making processes are complex and difficult to design and evaluate manually

A Grey-Box Approach to Automated Mechanism Design

Auctions play an important role in electronic commerce, and have been used to solve problems in distributed computing. Automated approaches to designing effective auction mechanisms are helpful in reducing the burden of traditional game theoretic, analytic approaches and in searching through the large space of possible auction mechanisms. This paper presents an approach to automated mechanism design (AMD) in the domain of double auctions. We describe a novel parametrized space of double auctions, and then introduce an evolutionary search method that searches this space of parameters. The approach evaluates auction mechanisms using the framework of the TAC Market Design Game and relates the performance of the markets in that game to their constituent parts using reinforcement learning. Experiments show that the strongest mechanisms we found using this approach not only win the Market Design Game against known, strong opponents, but also exhibit desirable economic properties when they run in isolation.Comment: 18 pages, 2 figures, 2 tables, and 1 algorithm. Extended abstract to appear in the proceedings of AAMAS'201

Setting Fees in Competing Double Auction Marketplaces: An Equilibrium Analysis

In this paper, we analyse competing double auction marketplaces that vie for traders and need to set appropriate fees to make a profit. Specifically, we show how competing marketplaces should set their fees by analysing the equilibrium behaviour of two competing marketplaces. In doing so, we focus on two different types of market fees: registration fees charged to traders when they enter the marketplace, and profit fees charged to traders when they make transactions. In more detail, given the market fees, we first derive equations to calculate the marketplaces expected profits. Then we analyse the equilibrium charging behaviour of marketplaces in two different cases: where competing marketplaces can only charge the same type of fees and where competing marketplaces can charge different types of fees. This analysis provides insights which can be used to guide the charging behaviour of competing marketplaces. We also analyse whether two marketplaces can co-exist in equilibrium. We find that, when both marketplaces are limited to charging the same type of fees, traders will eventually converge to one marketplace. However, when different types of fees are allowed, traders may converge to different marketplaces (i.e. multiple marketplaces can co-exist). © 2012 Springer-Verlag

An Investigation Report on Auction Mechanism Design

Auctions are markets with strict regulations governing the information available to traders in the market and the possible actions they can take. Since well designed auctions achieve desirable economic outcomes, they have been widely used in solving real-world optimization problems, and in structuring stock or futures exchanges. Auctions also provide a very valuable testing-ground for economic theory, and they play an important role in computer-based control systems. Auction mechanism design aims to manipulate the rules of an auction in order to achieve specific goals. Economists traditionally use mathematical methods, mainly game theory, to analyze auctions and design new auction forms. However, due to the high complexity of auctions, the mathematical models are typically simplified to obtain results, and this makes it difficult to apply results derived from such models to market environments in the real world. As a result, researchers are turning to empirical approaches. This report aims to survey the theoretical and empirical approaches to designing auction mechanisms and trading strategies with more weights on empirical ones, and build the foundation for further research in the field