In modern urban transportation networks, multiple self-interested travel providers (public transit, micromobility providers, ride-sharing platforms and toll roads) compete for heterogenous transportation users that wish to balance time and cost. Traditional congestion models assume fixed, exogenous costs, while dynamic-pricing frameworks typically focus on a single operator, overlooking the rich strategic interplay among decentralised transportation providers. This paper introduces the Multi-Market Routing Problem (MMRP), a game-theoretic model in which each provider utilises adaptive pricing to maximise profit and heterogeneous transportation users aim to minimise their travel time and cost. We present the MMRP as an extension of traditional congestion games, and extend it to consider online instances for adaptive pricing under dynamic and stochastic congestion. We demonstrate the computational complexity for game-theoretic and exact solutions to the MMRP, reflecting the computational complexity of coordinating routing in dynamic and uncertain settings. To address this, we propose the use of independent Proximal Policy Optimisation as a decentralised and effective solution to the online MMRP, demonstrating reduced travel times and more equitable and fair outcomes for transportation users, and increased profitability for transportation providers. The MMRP framework and learning algorithms offer a principled foundation for competitive, multimodal routing in modern urban transportation networks.

Flying ad hoc networks (FANETs) are collections of Unmanned Aerial Vehicles (UAVs) or nodes which deliver network services to areas lacking fixed infrastructure. The protocols controlling the flow of data in these ad hoc networks are prone to cyber attacks. In this paper, we consider cyber attacks in the form of probabilistic packet dropping or grey hole attacks which are executed by compromised nodes within the network. The defence protocols used to thwart this attack are usually evaluated in restricted environments with a low range of packet dropping attacks. To remedy this, we propose a new competitive evaluation framework, MANET-Rank, which uses empirical game theoretic analysis and bootstrapping to assess the effectiveness of defence protocols in ad hoc networks. Specifically, game theory is used to strategically assess the most effective protocol whilst bootstrapping generates an effective ranking metric from a small number of simulations. To assess the effectiveness of MANET-Rank, we conduct a comparative analysis of two previously proposed protocols by comparing the results of MANET-Rank and those generated by established evaluation methods. As a result, we demonstrate that MANET-Rank yields superior conclusions.

Autonomous intersection management has the potential to reduce road traffic congestion and energy consumption. To realize this potential, efficient algorithms are needed. However, most existing studies locally optimize one intersection at a time, and this can cause negative externalities on the traffic network as a whole. Here, we focus on coordinating multiple intersections, and formulate the problem as a distributed constraint optimisation problem (DCOP). We consider three utility design approaches that trade off efficiency and fairness. Our polynomial-time algorithm for coordinating multiple intersections reduces the traffic delay by about 41 percentage points compared to independent single intersection management approaches.

Ridesharing is a promising approach for reducing congestion and pollution, and many variants have been studied in the literature over the past decades. In this paper, we consider a novel setting where individuals walk to a common pick-up point and ride together to a single drop-off point from where they walk to their final destination. This setting requires finding the optimal composition of riders and pick-up and drop-off meeting points, as well as an equitable distribution of the costs whereby riders are incentivised to participate. Based on game-theoretic principles, we propose a methodology to determine the optimal pick-up and drop-off points, together with a cost allocation method that is equitable in the sense that it ensures proportionality for sharing the costs, i.e., those who walk more should pay less. We present a formal evaluation of our cost allocation method and empirical evaluation against the Shapley value using real-world and simulated data. Our results show that our approach is computationally more tractable than the Shapley value, as it is linear in time while guaranteeing individual rationality under certain conditions.

Abstract Many service providers require permissions to access privacy-sensitive data that are not necessary for their core functionality. To support users’ privacy management, we propose a novel agent-based negotiation framework to negotiate privacy permissions between users and service providers using a new multi-issue alternating-offer protocol based on exchanges of partial and complete offers. Additionally, we introduce a novel approach to learning users’ preferences in negotiation and present two variants of this approach: one variant personalised to each individual user, and one personalised depending on the user’s privacy type. To evaluate them, we perform a user study with participants, using an experimental tool installed on the participants’ mobile devices. We compare the take-it-or-leave-it approach, in which users are required to accept all permissions requested by a service, to negotiation, which respects their preferences. Our results show that users share personal data 2.5 times more often when they are able to negotiate while maintaining the same level of decision regret. Moreover, negotiation can be less mentally demanding than the take-it-or-leave-it approach and it allows users to align their privacy choices with their preferences. Finally, our findings provide insight into users’ data sharing strategies to guide the future of automated and negotiable privacy management mechanisms.

En-route charging stations are essential to ensure the adoption of electric vehicles. However, careful planning is necessary due to high cost in infrastructure and potentially long waiting queues. Existing literature on the placement of charging stations largely disregards competition, sets prices to cover costs and/or disregards queues. In contrast, this work models competing station investors who aim to maximise expected profit, while electric vehicle drivers aim to minimise expected travel costs including queues. Following a game-theoretic approach, investors strategically decide station capacities, locations and charging unit power outputs as well as fees, taking into consideration building and operational costs. Given the complexity of the problem, the solution involves a combination of theoretical and algorithmic techniques to obtain subgame-perfect equilibria of investor and driver choices. Subgame-perfect equilibria are found to be at least 92.85% efficient, for reasonable fluctuations of problem parameters. Furthermore, it is found that charging prices can be up to approximately 5 times higher than marginal cost due to long charging times, and also that better charging technology may not necessarily benefit drivers in the near future. Finally, subsidies towards the purchase of charging units are shown to be beneficial for both drivers and investors, being able to generate up to 14.3% additional value than the cost of the subsidy. In contrast, subsidies on the energy price for stations are found to have small effect and can be abused by investors.

The Internet of Vehicles aims to fundamentally improve transportation by connecting vehicles, drivers, passengers, and service providers together. Several new services such as parking space identification, platooning and intersection control—to name just a few—are expected to improve traffic congestion, reduce pollution, and improve the efficiency, safety and logistics of transportation. Proposed end-user services, however, make extensive use of private information with little consideration for the impact on users and third parties (those individuals whose information is indirectly involved). This article provides the first comprehensive overview of privacy and trust issues in the Internet of Vehicles at the service level. Various concerns over privacy are formalised into four basic categories: personal information privacy, multi-party privacy, trust, and consent to share information. To help analyse services and to facilitate future research, the main relevant end-user services are taxonomised according to voluntary and involuntary information they require and produce. Finally, this work identifies several open research problems and highlights general approaches to address them. These especially relate to measuring the trade-off between privacy and service functionality, automated consent negotiation, trust towards the IoV and its individual services, and identifying and resolving multi-party privacy conflicts.

Ridesharing has great potential to improve transportation efficiency while reducing congestion and pollution. To realize this potential, mechanisms are needed that allocate vehicles optimally and provide the right incentives to riders. However, many existing approaches consider restricted settings (e.g., only one rider per vehicle or a common origin for all riders). Moreover, naive applications of standard approaches, such as the Vickrey-Clarke-Groves or greedy mechanisms, cannot achieve a polynomial-time, truthful, individually rational and budget balanced mechanism. To address this, we formulate a general ridesharing problem and apply mechanism design to develop a novel mechanism which satisfies all four properties and whose social cost is within 8.6% of the optimal on average.

We consider a scenario where self-interested Electric Vehicle (EV) aggregators compete in the day-ahead electricity market in order to purchase the electricity needed to meet EV requirements. We propose a novel decentralised bidding coordination algorithm based on the Alternating Direction Method of Multipliers (ADMM). Our simulations using real market and driver data from Spain show that the algorithm is able to significantly reduce energy costs for all participants. Furthermore, we postulate that strategic manipulation by deviating agents is possible in decentralised algorithms like ADMM. Hence, we describe and analyse different possible attack vectors and propose a mathematical framework to quantify and detect manipulation. Our simulations show that our ADMM-based algorithm can be effectively disrupted by manipulative attacks achieving convergence to a different non-optimal solution which benefits the attacker. At the same time, our proposed manipulation detection algorithm achieves very high accuracy.

Standard machine learning algorithms typically assume that data is sampled independently from the distribution of interest. In attempts to relax this assumption, fields such as adversarial learning typically assume that data is provided by an adversary, whose sole objective is to fool a learning algorithm. However, in reality, it is often the case that data comes from self-interested agents, with less malicious goals and intentions which lie somewhere between the two settings described above. To tackle this problem, we present a Stackelberg competition model for least squares regression, in which data is provided by agents who wish to achieve specific predictions for their data. Although the resulting optimisation problem is nonconvex, we derive an algorithm which converges globally, outperforming current approaches which only guarantee convergence to local optima. We also provide empirical results on two real-world datasets, the medical personal costs dataset and the red wine dataset, showcasing the performance of our algorithm relative to algorithms which are optimal under adversarial assumptions, outperforming the state of the art.

Given the rapid rise of electric vehicles (EVs) worldwide, and the ambitious targets set for the near future, the management of large EV fleets must be seen as a priority. Specifically, we study a scenario where EV charging is managed through self-interested EV aggregators who compete in the day-ahead market in order to purchase the electricity needed to meet their clients' requirements. With the aim of reducing electricity costs and lowering the impact on electricity markets, a centralised bidding coordination framework has been proposed in the literature employing a coordinator. In order to improve privacy and limit the need for the coordinator, we propose a reformulation of the coordination framework as a decentralised algorithm, employing the Alternating Direction Method of Multipliers (ADMM). However, given the self-interested nature of the aggregators, they can deviate from the algorithm in order to reduce their energy costs. Hence, we study the strategic manipulation of the ADMM algorithm and, in doing so, describe and analyse different possible attack vectors and propose a mathematical framework to quantify and detect manipulation. Importantly, this detection framework is not limited to the considered EV scenario and can be applied to general ADMM algorithms. Finally, we test the proposed decentralised coordination and manipulation detection algorithms in realistic scenarios using real market and driver data from Spain. Our empirical results show that the decentralised algorithm's convergence to the optimal solution can be effectively disrupted by manipulative attacks achieving convergence to a different non-optimal solution which benefits the attacker. With respect to the detection algorithm, results indicate that it achieves very high accuracies and significantly outperforms a naive benchmark.

We consider mechanisms for the online allocation of perishable resources such as energy or computational power. A main application is electric vehicle charging where agents arrive and leave over time. Unlike previous work, we consider mechanisms without money, and a range of objectives including fairness and efficiency. In doing so, we extend the concept of envy-freeness to online settings. Furthermore, we explore the trade-offs between different objectives and analyse their theoretical properties both in online and offline settings. We then introduce novel online scheduling algorithms and compare them in terms of both their theoretical properties and empirical performance.

As a result of the very difficult weather in Saudi Arabia during the summer, there is too high power peak demand in the grid and this is expected to increase in the next decade. To fix this problem, power consumers should participate in the power production. Vehicle-to-grid (V2G), one of the efficient sustainable technologies, can offer this opportunity. It is defined as a concept where electric vehicle (EV) provides electric to the grid when parked. This investigation looks at the feasibility of using V2G to mitigate the problem of highest electricity peak demand in the summer period in one of the warmest countries of the world (Saudi Arabia). We conduct a survey in order to serve this issue and we use information from Saudi Arabia electricity authority. We found that, V2G is a promising solution to the peak demand challenge in the summer in Saudi Arabia since there is about 80% of the sample interested in using V2G technology. Moreover, 90% of the participants used their vehicles less than 4 hours daily. Furthermore, in the summer period, most of the participants park their vehicles for the longest time between 13:00 to 18:00, which is the peak demand period.

We consider settings where electric vehicle drivers participate in a market mechanism to charge their vehicles. Existing work typically assumes that participants are fully rational and can report their charging preferences accurately. However, this may not be reasonable in settings with non-experts. To explore this, we design a novel game called Bid2Charge and compare a fully expressive interface that covers the entire space of preferences to two restricted interfaces that offer fewer possible reports. We show that restricting the users' preferences significantly reduces deliberation times while also leading to an increase in utility by up to 70%.

Computers that negotiate on our behalf hold great promise for the future and will even become indispensable in emerging application domains such as the smart grid and the Internet of Things. Much research has thus been expended to create agents that are able to negotiate in an abundance of circumstances. However, up until now, truly autonomous negotiators have rarely been deployed in real-world applications. This paper sizes up current negotiating agents and explores a number of technological, societal and ethical challenges that autonomous negotiation systems have brought about. The questions we address are: in what sense are these systems autonomous, what has been holding back their further proliferation, and is their spread something we should encourage? We relate the automated negotiation research agenda to dimensions of autonomy and distill three major themes that we believe will propel autonomous negotiation forward: accurate representation, long-term perspective, and user trust. We argue these orthogonal research directions need to be aligned and advanced in unison to sustain tangible progress in the field.

Due to the limited availability of fuel resources, there is an urgent need for converting to use renewable sources efficiently. To achieve this, power consumers should participate actively in power production and consumption. Consumers nowadays can produce power and consume a portion of it locally, and then could offer the rest of the power to the grid. Vehicle-to-grid (V2G) which is one of the most effective sustainable solutions, could provide these opportunities. V2G can be defined as a situation where electric vehicles (EVs) offer electric power to the grid when parked. We developed an agent to trade on behalf of V2G users to maximize their profits in a day-ahead price market. We then ran the proposed model in three different scenarios using an optimal algorithm and compared the results of our solution to a benchmark. We show that our solution outperforms the benchmark strategy in the proposed three scenarios 49%, 51%, and 10% respectively in terms of profit.

We consider settings where owners of electric vehicles (EVs) participate in a market mechanism to charge their vehicles. Existing work on such mechanisms has typically assumed that participants are fully rational and can report their preferences accurately via some interface to the mechanism or to a software agent participating on their behalf. However, this may not be reasonable in settings with non-expert human end-users.Thus, our overarching aim in this paper is to determine experimentally if a fully expressive market interface that enables accurate preference reports is suitable for the EV charging domain, or, alternatively, if a simpler, restricted interface that reduces the space of possible options is preferable. In doing this, we measure the performance of an interface both in terms of how it helps participants maximise their utility and how it affects deliberation time. Our secondary objective is to contrast two different types of restricted interfaces that vary in how they restrict the space of preferences that can be reported. To enable this analysis, we develop a novel game that replicates key features of an abstract EV charging scenario. In two experiments with over 300 users, we show that restricting the users' preferences significantly reduces the time they spend deliberating (by up to half in some cases). An extensive usability survey confirms that this restriction is furthermore associated with a lower perceived cognitive burden on the users. More surprisingly, at the same time, using restricted interfaces leads to an increase in the users' performance compared to the fully expressive interface (by up to 70%). We also show that some restricted interfaces have the desirable effect of reducing the energy consumption of their users by up to 20% while achieving the same utility as other interfaces. Finally, we find that a reinforcement learning agent displays similar performance trends to human users, enabling a novel methodology for evaluating market interfaces.

This paper introduces a novel intention-aware routing system (IARS) for electric vehicles. This system enables vehicles to compute a routing policy that minimizes their expected journey time while considering the policies, or intentions, of other vehicles. Considering such intentions is critical for electric vehicles, which may need to recharge en route and face potentially significant queueing times if other vehicles choose the same charging stations. To address this, the computed routing policy takes into consideration predicted queueing times at the stations, which are derived from the current intentions of other electric vehicles. The efficacy of IARS is demonstrated through simulations using realistic settings based on real data from The Netherlands, including charging station locations, road networks, historical travel times, and journey origin-destination pairs. In these settings, IARS is compared with a number of state-of-the-art benchmark routing algorithms and achieves significantly lower average journey times. In some cases, IARS leads to an over 80% improvement in waiting times at charging stations and a more than 50% reduction in overall journey times.

Mobile geo-location advertising, where mobile ads are targeted based on a user’s location, has been identified as a key growth factor for the mobile market. As with online advertising, a crucial ingredient for their success is the development of effective economic mechanisms. An important difference is that mobile ads are shown sequentially over time and information about the user can be learned based on their movements. Furthermore, ads need to be shown selectively to prevent ad fatigue. To this end, we introduce, for the first time, a user model and suitable economic mechanisms which take these factors into account. Specifically, we design two truthful mechanisms which produce an advertisement plan based on the user’s movements. One mechanism is allocatively efficient, but requires exponential compute time in the worst case. The other requires polynomial time, but is not allocatively efficient. Finally, we experimentally evaluate the trade off between compute time and efficiency of our mechanisms.

For many players in financial markets, the price impact of their trading activity represents a large proportion of their transaction costs. This paper proposes a novel machine learning method for predicting the price impact of order book events. Specifically, we introduce a prediction system based on performance weighted ensembles of random forests. The system's performance is benchmarked using ensembles of other popular regression algorithms including: liner regression, neural networks and support vector regression using depth-of-book data from the BATS Chi-X exchange. The results show that recency-weighted ensembles of random forests produce over 15% greater prediction accuracy on out-of-sample data, for 5 out of 6 timeframes studied, compared with all benchmarks.

In this paper we present a smart portfolio management methodology, which advances existing portfolio management techniques at two distinct levels. First, we develop a set of investment models that target regimes found in the data over different time horizons. We then build a meta-model which uses the Kelly criterion to determine an optimal allocation over these investment strategies, thus simultaneously capturing regimes operating in the data over different time horizons. Finally, in order to detect changes in the relevant data regime, and hence investment allocations, we use a forecasting algorithm which relies on a Kalman filter. We call our combined method, that uses both the Kelly criterion and the Kalman filter, the K2 algorithm. Using a large-scale historical dataset of both stocks and indices, we show that our K2 algorithm gives better risk adjusted returns in terms of the Sharpe ratio, better average gain to average loss ratio and higher probability of success compared to existing benchmarks, when measured in out-of-sample tests.

We develop an online mechanism for the allocation of an expiring resource to a dynamic agent population. Each agent has a non-increasing marginal valuation function for the resource, and an upper limit on the number of units that can be allocated in any period. We propose two versions on a truthful allocation mechanism. Each modifies the decisions of a greedy online assignment algorithm by sometimes cancelling an allocation of resources. One version makes this modification immediately upon an allocation decision while a second waits until the point at which an agent departs the market. Adopting a prior-free framework, we show that the second approach has better worst-case allocative efficiency and is more scalable. On the other hand, the first approach (with immediate cancellation) may be easier in practice because it does not need to reclaim units previously allocated. We consider an application to recharging plug-in hybrid electric vehicles (PHEVs). Using data from a real-world trial of PHEVs in the UK, we demonstrate higher system performance than a fixed price system, performance comparable with a standard, but non-truthful scheduling heuristic, and the ability to support 50% more vehicles at the same fuel cost than a simple randomized policy.

Recently there is an increase in smaller, domain-specific search engines that scour the deep web finding information that general-purpose engines are unable to discover. These search engines play a crucial role in the new generation of search paradigms where federated search engines (FSEs) integrate search results from heterogeneous sources. In this paper we pose, for the first time, the problem to design a revenue mechanism that ensures profits both to individual search engines and FSEs as a mechanism design problem. To this end, we extend the sponsored search auction models and we discuss possibility and impossibility results on the implementation of an incentive compatible mechanism. Specifically, we develop an execution-contingent VCG (where payments depend on the observed click behavior) that satisfies both individual rationality and weak budget balance in expectation.

In multi-issue automated negotiation against unknown opponents, a key part of effective negotiation is the choice of concession strategy. In this paper, we develop a principled concession strategy, based on Gaussian processes predicting the opponent's future behaviour. We then use this to set the agent's concession rate dynamically during a single negotiation session. We analyse the performance of our strategy and show that it outperforms the state-of-the-art negotiating agents from the 2010 Automated Negotiating Agents Competition, in both a tournament setting and in self-play, across a variety of negotiation domains.

In this paper we introduce a new option pricing mechanism for reducing the exposure problem encountered by bidding agents with complementary valuations when participating in sequential, second-price auction markets. Existing option pricing models have two main drawbacks: they either apply fixed exercise prices, 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. The proposed mechanism involving flexibly priced options addresses these problems by calculating the exercise price as well as the option price based on the bids received during an auction. For this new model, which extends and encompasses all the previous models examined, we derive the optimal strategies for a bidding agent with complementary preferences. Finally, we use these strategies to evaluate the proposed option mechanism through Monte-Carlo simutions, and compare it to existing mechanisms, both in terms of the seller revenue and the social welfare. We show that our new mechanism achieves higher market efficiency compared to having no options and free options, while achieving higher revenues for the seller than any existing option mechanism.

In this paper, we analyse how traders select marketplaces and bid in a setting with multiple competing marketplaces. Specifically, we use a fictitious play algorithm to analyse the traders' equilibrium strategies for market selection and bidding when their types are continuous. To achieve this, we first analyse traders' equilibrium bidding strategies in a single marketplace and find that they shade their offers in equilibrium and the degree to which they do this depends on the amount and types of fees that are charged by the marketplace. Building on this, we then analyse equilibrium strategies for traders in competing marketplaces in two particular cases. In the first, we assume that traders can only select one marketplace at a time. For this, we show that, in equilibrium, all traders who choose one of the marketplaces eventually converge to the same one. In the second case, we allow buyers to participate in multiple marketplaces at a time, while sellers can only select one marketplace. For this, we show that sellers eventually distribute in different marketplaces in equilibrium and that buyers shade less and sellers shade more in the equilibrium bidding strategy (since sellers have more market power than buyers).

This paper reports on the design of a novel two-stage mechanism, based on strictly proper scoring rules, that motivates selfish rational agents to make a costly probabilistic estimate or forecast of a specified precision and report it truthfully to a centre. Our mechanism is applied in a setting where the centre is faced with multiple agents, and has no knowledge about their costs. Thus, in the first stage of the mechanism, the centre uses a reverse second price auction to allocate the estimation task to the agent who reveals the lowest cost. While, in the second stage, the centre issues a payment based on a strictly proper scoring rule. When taken together, the two stages motivate agents to reveal their true costs, and then to truthfully reveal their estimate. We prove that this mechanism is incentive compatible and individually rational, and then present empirical results comparing the performance of the well known quadratic, spherical and logarithmic scoring rules. We show that the quadratic and the logarithmic rules result in the centre making the highest and the lowest expected payment to agents respectively. At the same time, however, the payments of the latter rule are unbounded, and thus the spherical rule proves to be the best candidate in this setting.

We derive optimal strategies for a bidding agent that participates in multiple, simultaneous second-price auctions with perfect substitutes. We prove that, if everyone else bids locally in a single auction, the global bidder should always place non-zero bids in all available auctions, provided there are no budget constraints. With a budget, however, the optimal strategy is to bid locally if this budget is equal or less than the valuation. Furthermore, for a wide range of valuation distributions, we prove that the problem of finding the optimal bids reduces to two dimensions if all auctions are identical. Finally, we address markets with both sequential and simultaneous auctions, non-identical auctions, and the allocative efficiency of the market.

In this article, we consider the novel approach of a seller and customer negotiating bilaterally about a two-part tariff, using autonomous software agents. An advantage of this approach is that win–win opportunities can be generated while keeping the problem of preference elicitation as simple as possible. We develop bargaining strategies that software agents can use to conduct the actual bilateral negotiation on behalf of their owners. We present a decomposition of bargaining strategies into concession strategies and Pareto-efficient-search methods: Concession and Pareto-search strategies focus on the conceding and win–win aspect of bargaining, respectively. An important technical contribution of this article lies in the development of two Pareto-search methods. Computer experiments show, for various concession strategies, that the respective use of these two Pareto-search methods by the two negotiators results in very efficient bargaining outcomes while negotiators concede the amount specified by their concession strategy. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 99–119, 2006.

Negotiations have been extensively studied theoretically throughout the years. A well-known bilateral approach is the ultimatum game, where two agents negotiate on how to split a surplus or a "dollar"-the proposer makes an offer and responder can choose to accept or reject. In this paper a natural extension of the ultimatum game is presented, in which both agents can negotiate with other opponents in case of a disagreement. This way the basics of a competitive market are modeled, where, for instance, a buyer can try several sellers before making a purchase decision. The game is investigated using an evolutionary simulation. The outcomes appear to depend largely on the information available to the agents. We find that if the agents' number of remaining bargaining opportunities is commonly known, the proposer has the advantage. If this information is held private, however, the responder can obtain a larger share of the surplus. For the first case we also provide a game-theoretic analysis and compare the outcome with evolutionary results. Furthermore, the effects of search costs, uncertainty about future opportunities, and allowing multiple issues to be negotiated simultaneously are investigated

Negotiations are an important way of reaching agreements between selfish autonomous agents. In this paper we focus on one-to-many bargaining within the context of agent-mediated electronic commerce. We consider an approach where a seller agent negotiates over multiple interdependent attributes with many buyer agents in a bilateral fashion. In this setting, "fairness," which corresponds to the notion of envy-freeness in auctions, may be an important business constraint. For the case of virtually unlimited supply (such as information goods), we present a number of one-to-many bargaining strategies for the seller agent, which take into account the fairness constraint, and consider multiple attributes simultaneously. We compare the performance of the bargaining strategies using an evolutionary simulation, especially for the case of impatient buyers. Several of the developed strategies are able to extract almost all the surplus; they utilize the fact that the setting is one-to-many, even though bargaining is bilateral.

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 the example of an electronic shopping mall, the task is delegated to the individual shops, each of which evaluates the information that is available about the consumer and his or her interests (e.g. keywords, product queries, and available parts of a profile). Shops make a monetary bid in an auction where a limited amount of "consumer attention space" for the arriving consumer is sold. Each shop is represented by a software agent that bids for each consumer. This allows shops to rapidly adapt their bidding strategy to focus on consumers interested in their offerings. For various basic and simple models for on-line consumers, shops, and profiles, we demonstrate the feasibility of our system by evolutionary simulations as in the field of agent-based computational economics (ACE). We also develop adaptive software agents that learn bidding-strategies, based on neural networks and strategy exploration heuristics. Furthermore, we address the commercial and technological advantages of this distributed market-based approach. The mechanism we describe is not limited to the example of the electronic shopping mall, but can easily be extended to other domains.

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.