1,721,078 research outputs found
Designing Risk-Averse Bidding Strategies in Sequential Auctions for Transportation Orders
Full text linkDesigning efficient bidding strategies for agents participating in multiple, sequential auctions remains an important challenge for researchers in agent-mediated electronic markets. The problem is particularly hard if the bidding agents have complementary (i.e. super-additive) utilities for the items being auctioned, such as is often the case in distributed transportation logistics. This paper studies the effect that a bidding agent’s attitude towards taking risks plays in her optimal, decision-theoretic bidding strategy. We model the sequential bidding decision process as an MDP and we analyze, for a category of expectations of future price distributions, the effect that a bidder’s risk aversion profile has on her decision-theoretic optimal bidding policy. Next, we simulate the above strategies, and we study the effect that an agent’s risk aversion has on the chances of winning the desired items, as well as on the market efficiency and expected seller revenue. The paper extends the results presented in our previous work (reported in [1]), not only by providing additional details regarding the analytical part, but also by considering a more complex and realistic market setting for the simulations. This simulation setting is based on a real transportation logistics scenario [2]), in which bidders have to choose between several combinations (bundles) of orders that can be contracted for transportation
Designing bidding strategies in sequential auctions for risk averse agents
Full text linkDesigning 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
Learning the Structure of Utility Graphs Used in Multi-issue Negotiation through Collaborative Filtering
No full textGraphical utility models represent powerful formalisms for modeling complex agent decisions involving multiple issues [2]. In the context of negotiation, it has been shown [10] that using utility graphs enables reaching Pareto-efficient agreements with a limited number of negotiation steps, even for high-dimensional negotiations involving complex complementarity/ substitutability dependencies between multiple issues. This paper considerably extends the results of [10], by proposing a method for constructing the utility graphs of buyers automatically, based on previous negotiation data. Our method is based on techniques inspired from item-based collaborative filtering, used in online recommendation algorithms. Experimental results show that our approach is able to retrieve the structure of utility graphs online, with a high degree of accuracy, even for highly non-linear settings and even if a relatively small amount of data about concluded negotiations is available
A multi-agent platform for auction-based allocation of loads in transportation logistics
Full text linkThis 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
Emergence of consensus and shared vocabularies in collaborative tagging systems
No full textThis article uses data from the social bookmarking site del.icio.us to empirically examine the dynamics of collaborative tagging systems and to study how coherent categorization schemes emerge from unsupervised tagging by individual users. First, we study the formation of stable distributions in tagging systems, seen as an implicit form of “consensus” reached by the users of the system around the tags that best describe a resource. We show that final tag frequencies for most resources converge to power law distributions and we propose an empirical method to examine the dynamics of the convergence process, based on the Kullback-Leibler divergence measure. The convergence analysis is performed for both the most utilized tags at the top of tag distributions and the so-called long tail. Second, we study the information structures that emerge from collaborative tagging, namely tag correlation (or folksonomy) graphs. We show how community-based network techniques can be used to extract simple tag vocabularies from the tag correlation graphs by partitioning them into subsets of related tags. Furthermore, we also show, for a specialized domain, that shared vocabularies produced by collaborative tagging are richer than the vocabularies which can be extracted from large-scale query logs provided by a major search engine. Although the empirical analysis presented in this article is based on a set of tagging data obtained from del.icio.us, the methods developed are general, and the conclusions should be applicable across other websites that employ tagging
Flexibly Priced Options: A New Mechanism for Sequential Auctions with Complementary Goods
No full textIn this paper we present a novel option pricing mechanism for reducing the exposure problem encountered by bidders with complementary valuations when participating in sequential, second-price auction markets. Existing option pricing models have two main drawbacks: they either apply a fixed exercise price, which may deter bidders with low valuations, thereby decreasing allocative efficiency, or options are offered for free, in which case bidders are less likely to exercise them, thereby reducing seller revenues. Our novel mechanism with flexibly priced options addresses these problems by calculating the exercise price as well as the option price based on the bids in an auction. For this novel setting we derive the optimal strategies for a bidding agent with complementary preferences. Furthermore, to compare our approach to existing ones, we derive, for the first time, the bidding strategies for a fixed price mechanism, in which exercise prices for options are fixed by the seller. Finally, we use these strategies to empirically evaluate the proposed option mechanism and compare it to existing ones, both in terms of the seller revenue and the social welfare. We show that our new mechanism achieves higher market efficiency, while still ensuring higher revenues for the seller than direct sale auctions (without options)
A model-based online mechanism with pre-commitment and its application to electric vehicle charging
No full textWe introduce a novel online mechanism that schedules the allocation of an expiring and continuously-produced resource to self-interested agents with private preferences. A key application of our mechanism is the charging of pure electric vehicles, where owners arrive dynamically over time, and each owner requires a minimum amount of charge by its departure to complete its next trip. To truthfully elicit the agents' preferences in this setting, we introduce the new concept of pre-commitment: Whenever an agent is selected, our mechanism pre-commits to charging the vehicle by its reported departure time, but maintains flexibility about when the charging takes place and at what rate. Furthermore, to make effective allocation decisions we use a model-based approach by modifying Consensus, a well-known online optimisation algorithm. We show that our pre-commitment mechanism with modified Consensus incentivises truthful reporting. Furthermore, through simulations based on real-world data, we show empirically that the average utility achieved by our mechanism is 93% or more of the offline optimal
Prediction-of-use games: a cooperative game theory approach to sustainable energy tariffs
Full text linkEfficient buyer groups for prediction-of-use electricity tariffs
Full text linkCopyright © 2014, Association for the Advancement of Artificial Intelligence.Current electricity tariffs do not reflect the real cost that customers incur to suppliers, as units are charged at the same rate, regardless of how predictable each customers consumption is. A recent proposal to address this problem are prediction-of-use tariffs. In such tariffs, a customer is asked in advance to predict her future consumption, and is charged based both on her actual consumption and the deviation from her prediction. Prior work (Vinyals et al. 2014) studied the cost game induced by a single such tariff, and showed customers would have an incentive to minimize their risk, by joining together when buying electricity as a grand coalition. In this work we study the efficient (i.e. cost-minimizing) structure of buying groups for the more realistic setting when multiple, competing prediction-of-use tariffs are available. We propose a polynomial time algorithm to compute efficient buyer groups, and validate our approach experimentally, using a large-scale data set of domestic electricity consumers in the UK
Intention-Aware Routing to Minimise Delays at Electric Vehicle Charging Stations
No full textEn-route charging stations allow electric vehicles to greatly extend their range. However, as a full charge takes a considerable amount of time, there may be significant waiting times at peak hours. To address this problem, we propose a novel navigation system, which communicates its intentions (i.e., routing policies) to other drivers. Using these intentions, our system accurately predicts congestion at charging stations and suggests the most efficient route to its user. We achieve this by extending existing time-dependent stochastic routing algorithms to include the battery's state of charge and charging stations. Furthermore, we describe a novel technique for combining historical information with agent intentions to predict the queues at charging stations. Through simulations we show that our system leads to a significant increase in utility compared to existing approaches that do not explicitly model waiting times or use intentions, in some cases reducing waiting times by over 80% and achieving near-optimal overall journey times.Software and Computer TechnologyElectrical Engineering, Mathematics and Computer Scienc
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