60 research outputs found
Automated Auction Mechanism Design with Competing Markets
Resource allocation is a major issue in multiple areas of computer science. Despite the wide range of resource types across these areas, for example real commodities in e-commerce and computing resources in distributed computing, auctions are commonly used in solving the optimization problems involved in these areas, since well designed auctions achieve desirable economic outcomes.
Auctions are markets with strict regulations governing the information available to traders in the market and the possible actions they can take. Auction mechanism design aims to manipulate the rules of an auction in order to achieve specific goals. Economists traditionally use mathematical methods, mainly game theory, to analyze auctions and design new auction forms. However, due to the high complexity of auctions, the mathematical models are typically simplified to obtain results, and this makes it difficult to apply results derived from such models to market environments in the real world. As a result, researchers are turning to empirical approaches.
Following this line of work, we present what we call a grey-box approach to automated auction mechanism design using reinforcement learning and evolutionary computation methods. We first describe a new strategic game, called \cat, which were designed to run multiple markets that compete to attract traders and make profit. The CAT game enables us to address the imbalance between prior work in this field that studied auctions in an isolated environment and the actual competitive situation that markets face. We then define a novel, parameterized framework for auction mechanisms, and present a classification of auction rules with each as a building block fitting into the framework. Finally we evaluate the viability of building blocks, and acquire auction mechanisms by combining viable blocks through iterations of CAT games.
We carried out experiments to examine the effectiveness of the grey-box approach. The best mechanisms we learnt were able to outperform the standard mechanisms against which learning took place and carefully hand-coded mechanisms which won tournaments based on the CAT game. These best mechanisms were also able to outperform mechanisms from the literature even when the evaluation did not take place in the context of CAT games. These results suggest that the grey-box approach can generate robust double auction mechanisms and, as a consequence, is an effective approach to automated mechanism design.
The contributions of this work are two-fold. First, the grey-box approach helps to design better auction mechanisms which can play a central role in solutions to resource allocation problems in various application domains of computer science. Second, the parameterized view and the reinforcement learning-based search method can be used in other strategic, competitive situations where decision making processes are complex and difficult to design and evaluate manually
Learning Strong Substitutes Demand via Queries
This paper addresses the computational challenges of learning strong
substitutes demand when given access to a demand (or valuation) oracle. Strong
substitutes demand generalises the well-studied gross substitutes demand to a
multi-unit setting. Recent work by Baldwin and Klemperer shows that any such
demand can be expressed in a natural way as a finite list of weighted bid
vectors. A simplified version of this bidding language has been used by the
Bank of England.
Assuming access to a demand oracle, we provide an algorithm that computes the
unique list of weighted bid vectors corresponding to a bidder's demand
preferences. In the special case where their demand can be expressed using
positive bids only, we have an efficient algorithm that learns this list in
linear time. We also show super-polynomial lower bounds on the query complexity
of computing the list of bids in the general case where bids may be positive
and negative. Our algorithms constitute the first systematic approach for
bidders to construct a bid list corresponding to non-trivial demand, allowing
them to participate in `product-mix' auctions
ENHANCING PRIVACY IN MULTI-AGENT SYSTEMS
La pérdida de privacidad se está convirtiendo en uno de los mayores problemas
en el mundo de la informática. De hecho, la mayoría de los usuarios
de Internet (que hoy en día alcanzan la cantidad de 2 billones de usuarios
en todo el mundo) están preocupados por su privacidad. Estas preocupaciones
también se trasladan a las nuevas ramas de la informática que están
emergiendo en los ultimos años. En concreto, en esta tesis nos centramos en
la privacidad en los Sistemas Multiagente. En estos sistemas, varios agentes
(que pueden ser inteligentes y/o autónomos) interactúan para resolver problemas.
Estos agentes suelen encapsular información personal de los usuarios
a los que representan (nombres, preferencias, tarjetas de crédito, roles, etc.).
Además, estos agentes suelen intercambiar dicha información cuando interactúan entre ellos. Todo esto puede resultar en pérdida de privacidad para
los usuarios, y por tanto, provocar que los usuarios se muestren adversos a
utilizar estas tecnologías.
En esta tesis nos centramos en evitar la colección y el procesado de información personal en Sistemas Multiagente. Para evitar la colección de información, proponemos un modelo para que un agente sea capaz de decidir
qué atributos (de la información personal que tiene sobre el usuario al que
representa) revelar a otros agentes. Además, proporcionamos una infraestructura
de agentes segura, para que una vez que un agente decide revelar
un atributo a otro, sólo este último sea capaz de tener acceso a ese atributo,
evitando que terceras partes puedan acceder a dicho atributo. Para evitar el
procesado de información personal proponemos un modelo de gestión de las
identidades de los agentes. Este modelo permite a los agentes la utilización
de diferentes identidades para reducir el riesgo del procesado de información. Además, también describimos en esta tesis la implementación de dicho
modelo en una plataforma de agentes.Such Aparicio, JM. (2011). ENHANCING PRIVACY IN MULTI-AGENT SYSTEMS [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/13023Palanci
Modified bargaining protocols for automated negotiation in open multi-agent systems
Current research in multi-agent systems (MAS) has advanced to the development of open MAS, which are characterized by the heterogeneity of agents, free exit/entry and decentralized control. Conflicts of interest among agents are inevitable, and hence automated negotiation to resolve them is one of the promising solutions. This thesis studies three modifications on alternating-offer bargaining protocols for automated negotiation in open MAS. The long-term goal of this research is to design negotiation protocols which can be easily used by intelligent agents in accommodating their need in resolving their conflicts. In particular, we propose three modifications: allowing non-monotonic offers during the bargaining (non-monotonic-offers bargaining protocol), allowing strategic delay (delay-based bargaining protocol), and allowing strategic ignorance to augment argumentation when the bargaining comprises argumentation (ignorance-based argumentation-based negotiation protocol). Utility theory and decision-theoretic approaches are used in the theoretical analysis part, with an aim to prove the benefit of these three modifications in negotiation among myopic agents under uncertainty. Empirical studies by means of computer simulation are conducted in analyzing the cost and benefit of these modifications. Social agents, who use common human bargaining strategies, are the subjects of the simulation. In general, we assume that agents are bounded rational with various degrees of belief and trust toward their opponents. In particular in the study of the non-monotonic-offers bargaining protocol, we assume that our agents have diminishing surplus. We further assume that our agents have increasing surplus in the study of delay-based bargaining protocol. And in the study of ignorance-based argumentation-based negotiation protocol, we assume that agents may have different knowledge and use different ontologies and reasoning engines. Through theoretical analysis under various settings, we show the benefit of allowing these modifications in terms of agents’ expected surplus. And through simulation, we show the benefit of allowing these modifications in terms of social welfare (total surplus). Several implementation issues are then discussed, and their potential solutions in terms of some additional policies are proposed. Finally, we also suggest some future work which can potentially improve the reliability of these modifications
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