A multi-agent platform for auction-based allocation of loads in transportation logistics

This paper describes an agent-based platform for the allocation of loads in distributed transportation logistics, developed as a collaboration between CWI, Dutch National Center for Mathematics and Computer Science, Amsterdam and Vos Logistics Organizing, Nijmegen, The Netherlands. The platform follows a real business scenario proposed by Vos, and it involves a set of agents bidding for transportation loads to be distributed from a central depot in the Netherlands to different locations across Germany. The platform supports both human agents (i.e. transportation planners), who can bid through specialized planning and bidding interfaces, as well as automated, software agents. We exemplify how the proposed platform can be used to test both the bidding behaviour of human logistics planners, as well as the performance of automated auction bidding strategies, developed for such settings. The paper first introduces the business problem setting and then describes the architecture and main characteristics of our auction platform. We conclude with a preliminary discussion of our experience from a human bidding experiment, involving Vos planners competing for orders both against each other and against some (simple) automated strategies

Using Options with Set Exercise Prices to Reduce Bidder Exposure in Sequential Auctions

The exposure problem appears whenever an agent with complementary valuations bids to acquire a bundle of items sold sequentially, in separate auctions. In this talk, we review a possible solution that can help solve this problem, which involves selling options for the items, instead of the items themselves. We provide a brief overview of the state of the art in this field and discuss, based on our recent results, under which conditions using option mechanisms would be desirable for both buyers and sellers, by comparison to direct auctioning of items. We conclude with a brief discussion of further research directions in this field, as well as the relation to other techniques proposed to address the problem, such as leveled commitment mechanisms

Using Priced Options to Solve the Exposure Problem in Sequential Auctions

We propose a priced options model for solving the exposure problem of bidders with valuation synergies participating in a sequence of online auctions. We consider a setting in which complementary-valued items are offered sequentially by different sellers, who have the choice of either selling their item directly or through a priced option. In our model, the seller fixes the exercise price for this option, and then sells it through a first-price auction. We analyze this model from a decision-theoretic perspective and we show, for a setting where the competition is formed by local bidders (which desire a single item), that using options can increase the expected profit for both sides. Furthermore, we derive the equations that provide minimum and maximum bounds between which the bids of the synergy buyer are expected to fall, in order for both sides of the market to have an incentive to use the options mechanism. Next, we perform an experimental analysis of a market in which multiple synergy buyers are active simultaneously. We show that, despite the extra competition, some synergy buyers may benefit, because sellers are forced to set their exercise prices for options at levels which encourage participation of all buyers

Designing bidding strategies in sequential auctions for risk averse agents

Designing efficient bidding strategies for sequential auctions represents an important, open problem area in agent-mediated electronic markets. In existing literature, a variety of bidding strategies have been proposed and have been shown to perform with varying degrees of efficiency. However, most of strategies proposed so far do not explicitly model bidders attitudes towards risk which, in mainstream economic literature, is considered an essential attribute in modeling agent preferences and decision making under uncertainty. This paper studies the effect that risk profiles (modeled through the standard Arrow-Pratt risk aversion measure), have on the bidders strategies in sequential auctions. First, the sequential decision process involved in bidding is modeled as a Markov Decision Process. Then, the effect that a bidders risk aversion has on her decision theoretic optimal bidding policy is analyzed, for a category of expectations of future price distributions. This analysis is performed separately for the case of first price and second-price sequential auctions. Next, the bidding strategies developed above are simulated, in order to study the effect that an agents risk aversion has on the chances of winning a set of complementary-valued items. The paper concludes with an experimental study of how the presence of risk-averse bidders affects both bidder profits and auctioneer revenue, for different market scenarios of increasing complexity

Competitive Market-based Allocation of Consumer Attention

The amount of attention space available for recommending suppliers to consumers on e-commerce sites is typically limited. We present a competitive distributed recommendation mechanism based on adaptive software agents for efficiently allocating the "consumer attention space", or banners. In our approach, each agent bids in an auction for the momentary attention of each consumer. Successive auctions allow agents to rapidly adapt their bidding strategy to focus on consumers interested in their offerings. We demonstrate the feasibility of our system by an evolutionary simulation, and reflect on the advantages of this distributed market-based approach

Can priced options solve the exposure problem in sequential auctions?

The exposure problem appears whenever an agent with complementary valuations bids to acquire a bundle of items sold sequentially, in independent auctions. In this letter, we review a possible solution that can help solve this problem, which involves selling options for the items, instead of the items themselves. We provide a brief overview of the state of the art in this field and discuss, based on recent results presented in [Mous et. al. 2008], under which conditions using option mechanisms would be desirable for both buyers and sellers, by comparison to direct auctioning of the items. The paper concludes with a brief discussion of further resea

Approximation of Staircases By Staircases

The simplest nontrivial monotone functions are "staircases." The problem arises: what is the best approximation of some monotone function f(x) by a staircase with M jumps? In particular: what if f(x) is itself a staircase with N , N ? M , steps? This paper considers algorithms for solving, and theorems relating to, this problem. All of the algorithms we propose are space-optimal up to a constant factor and and also runtime-optimal except for at most a logarithmic factor. One application of our results is to "data compression" of probability distributions. We find yet another remarkable property of Monge's inequality, called the "concave cost as a function of zigzag number theorem." This property leads to new ways to get speedups in certain 1-dimensional dynamic programming problems satisfying this inequality. Keywords --- Histograms, data compression, cumulative distribution functions, approximation, monotone functions, dynamic programming, Monge's quadrangle inequality, concave cost..

Computational Intelligence in Economic Games and Policy Design

Dawid H, La Poutre H, Yao X. Computational Intelligence in Economic Games and Policy Design. IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE. 2008;3(4):22-26