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UT CS 395T - Lecture notes

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Adaptive Market Design with Linear Charging and Adaptive k-Pricing Policy Jaesuk Ahn and Chris Jones Department of Electrical and Computer Engineering, The University of Texas at Austin {jsahn, coldjones}@lips.utexas.edu Abstract. We describe a possible design strategy for a market specialist in the TAC Market Design Competition. Specifically, we describe both a linear charging policy that steadily increases the fees charged to trading agents interacting with the market, and an adaptive pricing policy that alters the trading price of goods based on the relative numbers of buyers and sellers in the market and the previous history of the market condition. We offer up two different sets of experimental data, the first to illustrate some basic properties about successful markets in the Market Design Competition, and the second to show the relative superiority of our chosen strategies over several possible alternatives. Keywords: Auction Mechanism, Market Design. 1 Introduction The Trading Agent Competition has, until now, primarily focused on developing strategies for agents proactively competing in various simulated economic activities. More particularly, the classic Trading Agent Competition features automated trading agents competing and trading with one another to maximize profit on a limited supply of goods on behalf of simulated clients [1], while the TAC Supply Chain Management competition features agents competing directly with each other for customers and supplier output in a multi-faceted supply chain simulation [2]. Recently, however, a group of researchers spearheaded by Gerding, Jennings, McBurney and Phelps proposed the development of a TAC Market Design Competition. Such a mechanism would allow participants to explore how different market design strategies promote or suppress certain global system properties, as well as which strategies might successfully attract various types of automated agents [3]. The TAC Market Design Competition represents a simulated double auction market, such as a stock or commodities exchange, where dedicated buyer and seller agents seek to find complementary partners to complete trades with. These trading agents are fully automated and are outside of the competition participants’ control, but must trade their goods by participating in markets designed by the participants. More particularly, participants program specialist agents which control the fees a market charges, the pricing policy of the market, which controls the exact price a good will trade at, the clearing policy of a market, which controls when a market will execute trades between traders, and a quote accepting policy, which controls which buy or sell orders a market will accept [3]. Participants compete to create specialists which will extract the highest number of fees from a common pool of traders; however, since the automated traders are rational actors (ideally), markets must perform a careful balancing act between implementing policies which will charge high fees and drive traders towards other, cheaper markets, and policies which will charge modest fees but attract many traders.Before the first official TAC Market Design Competition in Summer of 2007, students of Dr. Peter Stone’s Agent-Based Electronic Commerce class at the University of Texas at Austin were offered the opportunity to work with an early version of the Market Design testbed software by developing their own market specialists and running a competition between themselves. The following sections describe one such strategy in detail. Specifically, Section 2 outlines a set of preliminary experiments which illustrate certain properties of the trading agents within the Market Design testbed. Section 3 describes in detail a strategy derived from these observations, and gives a brief overview of several alternative charging policies. Section 4 describes a set of experiments which compare the presented market specialists to the alternative strategies in various market conditions, while Section 5 provides analysis of the experimental results and presents some concluding remarks. 2 Market Design Competition Observation 2.1 Effect of Market Charging Policy The two primary variables determining the income earned by any given market in the competition are the number of agents in the market, and the level of fees charged to each agent. Because agents have the ability to move between markets on different simulated “days” of the competition, relatively high fees might be expected to drive agents away from a given market, while lower fees might be expected to attract agents to a given market. However, it is reasonable to suspect that fees lowered past a certain level cannot remain competitive, regardless of how many agents those fees attract. For example, suppose only two specialists existed in a competition – one controlling a first market that charged a flat fee of 1 credit per agent, and one controlling a second market that charged a flat fee of 500 per agent. In a trading competition with 100 agents, even a single agent selecting the second market would mean that the first market would lose the competition. Such a scenario could happen for any number of reasons – agents are not always perfectly rational, agents must operate without perfect information about fees, agents may find that a given good or service can only be obtained within a given market. Accordingly, we performed a simple set of experiments with the basic “Fixed Charging Policy” specialists provided as default implementations by the testbed. Specifically, we took seven different specialists and placed them in competition with each other, with one specialist charging the maximum set of fees allowed by the student competition, and the six remaining specialist charging 90%, 75%, 50%, 25%, 10% and 0% of these maximum fees respectively. The experiments featured 100 trading agents evenly divided between both buyers and sellers, and divided between the various trading strategies provided by the testbed trader agent implementation. Figures 1 and 2 show the average results of 5 trials of these experiments. As can be seen in Figure 1, although the 75% specialist charges 25% less in fees than the 100% specialist, it earns approximately as much in total. This is almost certainly due to the fact that the 75% specialist attracts a slightly higher number of traders, as seen in Figure 2. At the same time, however, 75% seems to


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UT CS 395T - Lecture notes

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