The Continuous Double Auction (CDA) is the dominant market institution for real-world trading of equities, commodities, derivatives, etc. We describe a series of laboratory experiments that, for the first time, allow human subjects to interact with software bidding agents in a CDA. Our bidding agents use strategies based on extensions of the Gjerstad-Dickhaut and Zero-Intelligence-Plus algorithms. We find that agents consistently obtain significantly larger gains from trade than their human counterparts. This was unexpected because both humans and agents have approached theoretically perfect efficiency in prior all-human or allagent CDA experiments. Another unexpected finding is persistent far-from-equilibrium trading, in sharp contrast to the robust convergence observed in previous all-human or all-agent experiments. We consider possible explanations for our empirical findings, and speculate on the implications for future agent-human interactions in electronic markets.

Abstract not found

Auctions can be thought of as resource allocation mechanisms.

We develop a general framework in which real-time Dynamic Programming (DP) can be used to formulate agent bidding strategies in a broad class of auctions characterized by sequential bidding and continuous clearing. In this framework, states are represented primarily by an agent’s holdings, and transition probabilities are estimated from the market event history, along the lines of the “belief function ” approach of Gjerstad and Dickhaut [7]. We use the belief function, combined with a forecast of how it changes over time, as an approximate state-transition model in the DP formulation. The DP is then solved from scratch each time the agent has an opportunity to bid. The resulting algorithm optimizes cumulative long-term discounted profitability, whereas most previous strategies such as Gjerstad-Dickhaut (GD) merely optimize immediate profits. We test our algorithm in a simplified model of a Continuous Double Auction (CDA). Our results show that the DP-based approach reproduces the behavior of GD for small discount parameter γ, and is clearly superior for large values of γ close to 1. We suggest that this algorithm may offer the best performance of any published CDA bidding strategy. The framework our algorithm provides is extensible and can accommodate many market and research aspects.

Gode and Sunder's (1993) results from using "zero-intelligence" (zi) traders, that act randomly within a structured market, appear to imply that convergence to the theoretical equilibrium price in continuous double-auction markets is determined more by market structure than by the intelligence of the traders in that market. However, it is demonstrated here that the average transaction prices of zi traders can vary significantly from the theoretical equilibrium value when the market supply and demand are asymmetric, and that the degree of difference from equilibrium is predictable from a priori probabilistic analysis. In this sense, it is shown here that Gode and Sunder's results are artefacts of their experimental regime. Following this, `zero-intelligence-plus' (zip) traders are introduced: like zi traders, these simple agents make stochastic bids. Unlike zi traders, they employ an elementary form of machine learning. Groups of zip traders interacting in experimental markets...

online auction marketplaces, e-marketplaces, automated market mechanism design, trader-agents, ZIP traders, genetic algorithms This paper describes the use of a genetic algorithm (GA) to find optimal parameter-values for trading agents that operate in virtual online auction “e-marketplaces”, where the rules of those marketplaces are also under simultaneous control of the GA. The aim is to use the GA to automatically design new mechanisms for agent-based e-marketplaces that are more efficient than online markets designed by (or populated by) humans. The space of possible auction-types explored by the GA includes the Continuous Double Auction (CDA) mechanism (as used in most of the world’s financial exchanges), and also two purely one-sided mechanisms. Surprisingly, the GA did not always settle on the CDA as an optimum. Instead, novel hybrid auction mechanisms were evolved, which are unlike any existing market mechanisms. In this paper we show that, when the market supply and demand schedules undergo sudden “shock ” changes partway through the evaluation process, two-sided hybrid market mechanisms can evolve which may be unlike any human-designed auction and yet may also be significantly more efficient than any human-designed market mechanism.

This paper analyzes the entrants to the 2007 TAC Market Design competition. It presents a classification of the entries to the competition, and uses this classification to compare these entries. The paper also attempts to relate market dynamics to the auction rules adopted by these entries and their adaptive strategies via a set of post-tournament experiments. Based on this analysis, the paper speculates about the design of effective auction mechanisms, both in the setting of this competition and in the more general case. Categories and Subject Descriptors

Abstract. In this paper we describe an analysis of two double auction markets— the clearing house auction and the continuous double auction. The complexity of these institutions is such that they defy analysis using traditional game-theoretic techniques, and so we use heuristic-strategy approximation to provide an approximated game-theoretic analysis. As well as finding heuristic-strategy equilibria for these mechanisms, we subject them to an evolutionary game-theoretic analysis which allows us to quantify which equilibria are more likely to occur. We then weight the design objectives for each mechanism according to the probability distribution over equilibria, which allows us to provide more realistic estimates for the efficiency of each mechanism. 1

Auction mechanism design is a subfield of game theory dedicated to manipulating the rules of an auction so as to achieve specific goals. The complexity of the dynamics of auctions, especially continuous double auctions, makes it difficult to apply the usual analytic game-theoretic methods to do this. This paper takes an experimental approach, searching a parameterized space of possible auction types, and presents a new pricing policy for continuous double auctions. The paper further demonstrates how this policy, together with a shout improvement rule, helps to reduce the fluctuation of transaction prices in auctions involving agents with minimum intelligence while keeping the overall efficiency high.

We develop a novel bidding strategy that software agents can use to buy and sell goods in Continuous Double Auctions (CDAs). Our strategy involves the agent forming a bid or ask by assessing the degree of risk involved and making a prediction about the competitive equilibrium that is likely to be reached in the marketplace. We benchmark our strategy against two of the most common strategies for CDAs, namely the Zero-Intelligence and the ZeroIntelligence Plus strategies, and we show that our agents outperform these benchmarks. Specifically, our agents win in 100% of the simulations against the ZI agents and, on average, 75% of the games against the ZIP agents.