Abstract. We introduce the concept of a trust network—a decentralized payment infrastructure in which payments are routed as IOUs between trusted entities. The trust network has directed links between pairs of agents, with capacities that are related to the credit an agent is willing to extend another; payments may be routed between any two agents that are connected by a path in the network. The network structure introduces group budget constraints on the payments from a subset of agents to another on the trust network: this generalizes the notion of individually budget constrained bidders. We consider a multi-unit auction of identical items among bidders with unit demand, when the auctioneer and bidders are all nodes on a trust network. We define a generalized notion of social welfare for such budgetconstrained bidders, and show that the winner determination problem under this notion of social welfare is NP-hard; however the flow structure in a trust network can be exploited to approximate the solution with a factor of 1 − 1/e. We then present a pricing scheme that leads to an incentive compatible, individually rational mechanism with feasible payments that respect the trust network’s payment constraints and that maximizes the modified social welfare to within a factor 1 − 1/e. 1

One of the key requirement for Grid infrastructures is the ability to share resources with nontrivial qualities of service. However, resource management in a decentralized infrastructure is a complex task as it has to cope with different policies and objectives of the different resource providers and the resource users. Recent research indicate that agreement-based resource management will solve many of these problems as it supports the reliable interaction of different providers and users. Here, negotiation is needed to create such bi-lateral agreements between Grid parties. Such a negotiation process should be automated with no or minimal human interaction, considering the potential scale of Grid systems and the amount of necessary transactions. Therefore, strategic negotiation models play an important role. In this paper, a negotiation model and a learning-based negotiation strategy are proposed and examined. Simulations have been conducted to evaluate the presented system. The results demonstrate that the proposed negotiation model and the learning based negotiation strategies are suitable and effective for Grid environments. 1

Abstract — This paper reports on the design and comparison of two economically-inspired mechanisms for task allocation in environments where sellers have finite production capacities and a cost structure composed of a fixed overhead cost and a constant marginal cost. Such mechanisms are required when a system consists of multiple self-interested stakeholders that each possess private information that is relevant to solving a system-wide problem. Against this background, we first develop a computationally tractable centralised mechanism that finds the set of producers that have the lowest total cost in providing a certain demand (i.e. it is efficient). We achieve this by extending the standard Vickrey-Clarke-Groves mechanism to allow for multi-attribute bids and by introducing a novel penalty scheme such that producers are incentivised to truthfully report their capacities and their costs. Furthermore our extended mechanism is able to handle sellers ’ uncertainty about their production capacity and ensures that individual agents find it profitable to participate in the mechanism. However, since this first mechanism is centralised, we also develop a complementary decentralised mechanism based around the continuous double auction. Again because of the characteristics of our domain, we need to extend the standard form of this protocol by introducing a novel clearing rule based around an order book. With this modified protocol, we empirically demonstrate (with simple trading strategies) that the mechanism achieves high efficiency. In particular, despite this simplicity, the traders can still derive a profit from the market which makes our mechanism attractive since these results are a likely lower bound on their expected returns. Index Terms — distributed decision making, decision theory, multi-agent systems, market-based control. I.

Abstract: The paper presents the generic conceptual framework for modeling rational negotiation processes. The central piece of the presented work is the upper-level negotiation ontology. This ontology is designed to be capable to model the arbitrary types of negotiation dialogues by providing the upper-level namespace for that. The models of the specific negotiation types refine this namespace by providing the instances of its concepts, applying additional restrictions on the properties, possibly adding new subclasses and properties. The approach to model negotiations with the Generic Negotiation Ontology (GNO) is initially evaluated by applying to contracting negotiation type. The choice of the negotiation type for the evaluation is suggested by the fact that contracting negotiations are the typical encounters among the agents taking part in the simulation of a Dynamic Engineering Design Process (DEDP). This modeling activity is performed in the frame of our PSI project 1.

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Abstract. This paper attends to the problem of a mechanism designer seeking to influence the outcome of a strategic game based on her creditability. The mechanism designer offers additional payments to the players depending on their mutual choice of strategies in order to steer them to certain decisions. Of course, the mechanism designer aims at spending as little as possible and yet implementing her desired outcome. We present several algorithms for this optimization problem both for singleton target strategy profiles and target strategy profile regions. Furthermore, the paper shows how a bankrupt mechanism designer can decide efficiently whether strategy profiles can be implemented at no cost at all. Finally, risk-averse players and dynamic games are examined. 1

Abstract. In this paper, we present a novel multi-layered framework for designing strategies for trading agents. The objective of this work is to provide a framework that will assist strategy designers with the different aspects involved in designing a strategy. At present, such strategies are typically designed in an ad-hoc and intuitive manner with little regard for discerning best practice or attaining reusability in the design process. Given this, our aim is to put such developments on a more systematic engineering footing. After we describe our framework, we then go on to illustrate how it can be used to design strategies for a particular type of market mechanism (namely the Continuous Double Auction), and how it was used to design a novel strategy for the Travel Game of the International Trading Agent Competition. 1

Providing efficient mechanism for allocation and management of resources is essential for realizing a computational grid. Since resource providers and consumers may be independent bodies in a computing grid platform, negotiation among these participants is necessary. This position paper discusses (i) the design considerations of e-negotiation agents for grid commerce, and (ii) the possible application of Market-driven agents (MDAs) as negotiation mechanisms for managing resources in a computational grid. MDAs are negotiation agents designed with the flexibility of (i) making adjustable amounts of concession taking into account factors such as market rivalry, time preferences, and outside options, and (ii) relaxing trade expectation in the face of intense pressure. In addition to having stable strategies, making prudent compromises and optimizing utility, additional desirable properties of MDAs that are suitable for a computational grid include: adaptivity to changes in market conditions, and flexibility of reaching faster consensus.

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. 1

Vickrey-Clarke-Groves (VCG) mechanisms are often used to allocate tasks to selfish and rational agents. VCG mechanisms are incentive compatible, direct mechanisms that are efficient (i.e., maximise social utility) and individually rational (i.e., agents prefer to join rather than opt out). However, an important assumption of these mechanisms is that the agents will always successfully complete their allocated tasks. Clearly, this assumption is unrealistic in many real-world applications, where agents can, and often do, fail in their endeavours. Moreover, whether an agent is deemed to have failed may be perceived differently by different agents. Such subjective perceptions about an agent’s probability of succeeding at a given task are often captured and reasoned about using the notion of trust. Given this background, in this paper we investigate the design of novel mechanisms that take into account the trust between agents when allocating tasks. Specifically, we develop a new class of mechanisms, called trust-based mechanisms, that can take into account multiple subjective measures of the probability of an agent succeeding at a given task and produce allocations that maximise social utility, whilst ensuring that no agent obtains a negative utility. We then show that such mechanisms pose a challenging new combinatorial