We study the online learning problem of a bidder who participates in repeated auctions. With the goal of maximizing his total T-period payoff, the bidder wants to determine the optimal allocation of his fixed budget among his bids for K different goods at each period. As a bidding strategy, we propose a polynomial time algorithm, referred to as dynamic programming on discrete set (DPDS), which ...

Combinatorial auctions, where bidders are allowed to submit bids for bundles of items, are preferred to single-item auctions when bidders have complementarities and substitutabilities among items and therefore achieve better social efficiency. A large unexplored area of research is the design of bidding strategies. In this paper, we propose a new adaptive bidding strategy for combinatorial auct...

Combinatorial auction, where bidders can bid on bundles of items, has been the subject of increasing interest in recent years. Although much research work has been conducted on combinatorial auctions, most has been focusing on the winner determination problem. A largely unexplored area of research in combinatorial auctions is the design of bidding strategies, in particular, those that can be us...

Water emission trading (WET) is promising in sustainable development strategy. However, low participation impedes its development. We develop an evolutionary game model of two enterprise populations’ dynamics and stability in the decision-making behavior process. Due to the different perceived value of certain permits, enterprises choose strategy (bidding for permit) or strategy (not bidding). ...

Shill bidding is the act of using bids in an online auction to drive up the final price for the seller, thereby defrauding legitimate bidders. While ‘shilling’ is recognized as a problem and shill bidding is strictly forbidden in online auctions, presently there is little to no established means of defense against shills. This paper presents a software bidding agent that follows a shill bidding...

Two approaches for bidding in games are presented: knowledge-based approach and simulation-based approach. A general knowledge-based decision model for bidding in games with its strategy encoded in a Bayesian network was designed. A program for playing four-player tarok was implemented incorporating a specialised instance of the decision model and a simulation module for bidding. Both approache...

This article summarizes the bidding algorithms developed for the on-line Trading Agent Competition held in July, 2000 in Boston. At its heart, the article describes 12 of the 22 agent strategies in terms of (i) bidding strategy, (ii) allocation strategy, (iii) special approaches, and (iv) team motivations. The common and distinctive features of these agent strategies are highlighted. In additio...

The application of autonomous agents by the provisioning and usage of computational services is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic service provisioning and usage of Grid services, to invent ii) competitive bidding strategies for widely used market mechanisms ...

Auctions are an area of great academic and commercial interest, from tiny auctions for toys on eBay to multi-billion-dollar auctions held by governments for resources or contracts. Although there has been significant research on auction theory, especially from the perspective of auction mechanisms, studies of autonomous bidding agents and their interactions are relatively few and recent. This p...

In this paper, we develop a new method for Þnding the optimal bidding strategy in sequential auctions, using a dynamic programming technique. The existing method assumes that the utility of a user is represented in an additive form. From this assumption, the remaining endowment of money must be explicitly represented in each state, and the calculation of the optimal bidding strategy becomes tim...