Evolving bidding strategies for multiple auctions (original) (raw)

Developing a bidding agent for multiple heterogeneous auctions

ACM Transactions on Internet …, 2003

Due to the proliferation of online auctions, there is an increasing need to monitor and bid in multiple auctions in order to procure the best deal for the desired good. To this end, this paper reports on the development of a heuristic decision making framework that an autonomous agent can exploit to tackle the problem of bidding across multiple auctions with varying start and end times and with varying protocols (including English, Dutch and Vickrey). The framework is flexible, configurable, and enables the agent to adopt varying tactics and strategies that attempt to ensure that the desired item is delivered in a manner consistent with the user’s preferences. Given this large space of possibilities, we employ a genetic algorithm to search (offline) for effective strategies in common classes of environment. The strategies that emerge from this evolution are then codified into the agent’s reasoning behaviour so that it can select the most appropriate strategy to employ in its prevailing circumstances. The proposed framework has been implemented in a simulated marketplace environment and its effectiveness has been empirically demonstrated.

A heuristic bidding strategy for multiple heterogeneous auctions

… of the 5th international conference on …, 2003

Online auctions are increasingly being used as a medium to procure goods and services. As the number of auction sites increases, however, consumers will inevitably want to track and bid in multiple auctions (with multiple protocols) in order to get the best deal for their desired goods. To this end, this paper reports on the development of a heuristic decision making framework that an autonomous agent can exploit to tackle the problem of bidding across multiple heterogeneous auctions. The framework enables the agent to adopt varying tactics and strategies that attempt to ensure that the user's objectives are satisfied. Through empirical evaluation, the agent's performance is shown to be effective even when there are multiple such agents in the environment at the same time and when the agent cannot accurately determine the type of environment that it is situated in.

Autonomous agents for participating in multiple online auctions

Proc. of the IJCAI Workshop …, 2001

The increasing number of online auctions poses a big challenge to e-consumers, especially to those who are actively looking for good deals. In this paper, we present the deisgn of an autonomous agent that can alleviate some of these problems by participating across multiple online auctions (in particular, English, Dutch, and Vickrey auctions). The agent makes decisions on behalf of the consumer and endeavours to guarantee the delivery of the item according to the user's preferences. Our agent monitors and collects information from the ongoing auctions and determines which auction it wishes to participate in. The decision on how much to bid in the selected auction is made based on a series of tactics and strategies. The proposed bidding algorithm has been implemented in a simulated marketplace environment and its performance has been evaluated empirically.

A heuristic bidding strategy for buying multiple goods in multiple english auctions

ACM Transactions on Internet Technology, 2006

This paper presents the design, implementation, and evaluation of a novel bidding algorithm that a software agent can use to obtain multiple goods from multiple overlapping English auctions. Specifically, an Earliest Closest First heuristic algorithm is proposed that uses neurofuzzy techniques to predict the expected closing prices of the auctions and to adapt the agent's bidding strategy to reflect the type of environment in which it is situated. This algorithm first identifies the set of auctions that are most likely to give the agent the best return and then, according to its attitude to risk, it bids in some other auctions that have approximately similar expected returns, but which finish earlier than those in the best return set. We show through empirical evaluation against a number of methods proposed in the multiple auction literature that our bidding strategy performs effectively and robustly in a wide range of scenarios.

Analysis of Ausubel auctions by means of evolutionary computation

… Computation, 2005. The …, 2005

The increasing use of auctions has led to a growing interest in the subject. A recent method used for carrying out examinations on auctions has been the design of computational simulations. The aim of this paper is to develop a genetic algorithm to find bidders' optimal strategies for a specific dynamic multi-unit auction, The algorithm provides the bidding strategy (defined as the action to be taken under different auction conditions) that maximizes the bidder's payoff. The algorithm is tested under several experimental environments, number of bidders and quantity of lots auctioned. The results suggest that the approach leads to strategies that outperform canonical strategies This article has been financed by the Spanish founded research MCyT project

Auctions, evolution, and multi-agent learning

2008

For a number of years we have been working towards the goal of automatically creating auction mechanisms, using a range of techniques from evolutionary and multi-agent learning. This paper gives an overview of this work. The paper presents results from several experiments that we have carried out, and tries to place these in the context of the overall task that we are engaged in.

Internet auctions with artificial adaptive agents: A study on market design

Journal of Economic Behavior & Organization, 2008

Many internet auction sites implement ascending-bid, second-price auctions. Empirically, lastminute or "late" bidding is frequently observed in "hard-close" but not in "soft-close" versions of these auctions. In this paper, we introduce an independent private-value repeated internet auction model to explain this observed difference in bidding behavior. We use finite automata to model the repeated auction strategies. We report results from simulations involving populations of artificial bidders who update their strategies via a genetic algorithm. We show that our model can deliver late or early bidding behavior, depending on the auction closing rule in accordance with the empirical evidence. Among other findings, we observe that hard-close auctions raise less revenue than softclose auctions. We also investigate interesting properties of the evolving strategies and arrive at some conclusions regarding both auction designs from a market design point of view.

ATTac-2000: An adaptive autonomous bidding agent

2001

Abstract The First Trading Agent Competition (TAC) was held from June 22 to July 8, 2000. TAC was designed to create a benchmark problem in the complex domain of e-marketplaces and to motivate researchers to apply unique approaches to a common task. This paper describes\ attac, the first-place finisher in TAC.\ attac\ uses a principled bidding strategy that includes several elements of {adaptivity\/}.

An adaptive bidding agent for multiple English auctions: a neuro-fuzzy approach

2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542)

This paper presents the design, implementation and evaluation of a novel bidding strategy for obtaining goods in multiple overlapping English auctions. The strategy uses fuzzy sets to express trade-offs between multi-attribute goods and exploits neuro-fuzzy techniques to predict the expected closing prices of the auctions and to adapt the agent's bidding strategy to reflect the type of environment in which it is situated. We show, through empirical evaluation against a number of methods proposed in the multiple auction literature, that our strategy performs effectively and robustly in a wide range of scenarios.

Evolutionary Techniques for Reverse Auctions

Winner determination is one of the main challenges in combinatorial auctions. However, not much work has been done to solve this problem in the case of reverse auctions using evolutionary techniques. This has motivated us to propose an improvement of a genetic algorithm based method, we have previously proposed, to address two important issues in the context of combinatorial reverse auctions: determining the winner(s) in a reasonable processing time, and reducing the procurement cost. In order to evaluate the performance of our proposed method in practice, we conduct several experiments on combinatorial reverse auctions instances. The results we report in this paper clearly demonstrate the efficiency of our new method in terms of processing time and procurement cost.