Competitive market-based allocation of consumer attention space

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

Autonomous agents in bargaining games : an evolutionary investigation of fundamentals, strategies, and business applications

Bargaining is becoming increasingly important due to developments within the field of electronic commerce, especially the development of autonomous software agents. Software agents are programs which, given instructions from a user, are capable of autonomously and intelligently realise a given task. By means of such agents, the bargaining process can be automated, allowing products and services together with related conditions, such as warranty and delivery time, to be flexible and tuned to the individual preferences of the people concerned. In this theses we concentrate on both fundamental aspects of bargaining as well as business-related applications of automated bargaining using software agents. The fundamental part investigates bargaining outcomes within a stylised world, and the factors that influence these outcomes. This can provide insights for the production of software agents, strategies, and setting up bargaining rules for practical situations. We study these aspects using computational simulations of bargaining agents. Hereby we consider adaptive systems, i.e., where agents learn to adjust their bargaining strategy given past experience. This learning behaviour is simulated using evolutionary algorithms. These algorithms originate from the field of artificial intelligence, and are inspired by the biological theory of evolution. Originally, evolutionary algorithms were designed for solving optimisation problems, but they are now increasingly being used within economics for modelling human learning behaviour. Besides computational simulations, we also consider mathematical solutions from game theory for relatively simple cases. Game theory is mainly concerned with the “rational man”, that is, with optimal outcomes within an stylised setting (or game) where people act rationally. We use the game-theoretic outcomes to validate the computational experiments. The advantage of computer simulations is that less strict assumptions are necessary, and that more complex interactions that are closer to real-world settings can be investigated. First of all, we study a bargaining setting where two players exchange offers and counter offers, the so-called alternating-offers game. This game is frequently used for modelling bargaining about for instance the price of a product or service. It is also important, however, to allow other product- and service-related aspects to be negotiated, such as quality, delivery time, and warranty. This enables compromises by conceding on less important issues and demanding a higher value for relatively important aspects. This way, bargaining is less competitive and the resulting outcome can be mutually beneficial. Therefore, we investigate using computational simulations an extended version of the alternating-offers game, where multiple aspects are negotiated concurrently. Moreover, we apply game theory to validate the results of the computational experiments. The simulation shows that learning agents are capable of quickly finding optimal compromises, also called Pareto-efficient outcomes. In addition, we study the effects of time pressure that arise if negotiations are broken off with a small probability, for example due to external eventualities. In absence of time pressure and a maximum number of negotiation rounds, outcomes are very unbalanced: the player that has the opportunity to make a final offer proposes a take-it-or-leave-it offer in the last round, which leaves the other player with a deal that is only slightly better than no deal at all. With relatively high time pressure, on the other hand, the first offer is most important and almost all agreements are reached in the first round. Another interesting result is that the simulation outcomes after a long period of learning in general coincide with the results from game theory, in spite of the fact that the learning agents are not “rational”. In reality, not only the final outcome is important, but also other factors play a role, such as the fairness of an offer. Using the simulation we study the influence of such fairness norms on the bargaining outcomes. The fairness norms result in much more balanced outcomes, even with no time pressure, and seem to be closer outcomes in the real world. Negotiations are rarely isolated, but can also be influenced by external factors such as additional bargaining opportunities. We therefore also consider bargaining within a market-like setting, where both buyers and sellers can bargain with several opponents before reaching an agreement. The negotiations are executed consecutively until an agreement is reached or no more opportunities are available. Each bargaining game is reduced to a single round, where player 1 makes an offer and player 2 can only respond by rejecting or accepting this offer. Using an evolutionary simulation we study several properties of this market game. It appears that the outcomes depend on the information that is available to the players. If players are informed about the bargaining opportunities of their opponents, the first player in turn has the advantage and always proposes a take-it-or-leave-it deal that leaves the other player with a relatively poor outcome. This outcome is consistent with a game-theoretic analysis which we also present in this thesis. If this information is not available, a theoretical analysis is very hard. The evolutionary simulation, however, shows that in this case the responder obtains a better deal. This occurs because the first player can no longer anticipate the response of the other player, and therefore bids lower to avoid a disagreement. In this thesis, we additionally consider other factors that influence the outcomes of the market game, such as negotiation over multiple issues simultaneously, search costs, and break off probabilities. Besides fundamental issues, this thesis presents a number of business-related applications of automated bargaining, as well as generic bargaining strategies for agents that can be employed in related areas. As a first application, we introduce a framework where negotiation is used for recommending shops to customers, for example on a web page of an electronic shopping mall. Through a market-driven auction a relevant selection of shops is determined in a distributed fashion. This is achieved by selling a limited number of banner spaces in an electronic auction. For each arriving customer on the web page, shops can automatically place bids for this “customer attention space” through their shop agents. These software agents bid based on a customer profile, containing personal data of the customer, such as age, interests, and/or keywords in a search query. The shop agents are adaptive and learn, given feedback from the customers, which profiles to target and how much to bid in the auction. The highest bidders are then selected and displayed to the customer. The feasibility of this distributed approach for matching shops to customers is demonstrated using an evolutionary simulation. Several customer models and auction mechanisms are studied, and we show that the market-based approach results in a proper selection of shops for the customers. Bargaining can be especially beneficial if not only the price, but other aspects are considered as well. This allows for example to customise products and services to the personal preferences of a user. We developed a system makes use of these properties for selling and personalising so-called information goods, such as news articles, software, and music. Using the alternating-offers protocol, a seller agent negotiates with several buyers simultaneously about a fixed price, a per-item price, and the quality of a bundle of information goods. The system is capable of taking into account important business-related conditions such as the fairness of the negotiation. The agents combine a search strategy and a concession strategy to generate offers in the negotiations. The concession strategy determines the amount the agent will concede each round, whereas the search strategy takes care of the personalisation of the offer. We introduce two search strategies in this thesis, and show through computer experiments that the use of these strategies by a buyer and seller agent, result in personalised outcomes, also when combined with various concession strategies. The search strategies presented here can be easily applied to other domains where personalisation is important. In addition, we also developed concession strategies for the seller agent that can be used in settings where a single seller agent bargains with several buyer agents simultaneously. Even if bargaining itself is bilateral (i.e., between two parties), a seller agent can actually benefit from the fact that several such negotiations occur concurrently. The developed strategies are focussed on domains where supply is flexible and can be adjusted to meet demand, like for information goods. We study fixed strategies, time-dependent strategies and introduce several auction-inspired strategies. Auctions are often used when one party negotiates with several opponents simultaneously. Although the latter strategies benefit from the advantages of auctions, the actual negotiation remains bilateral and consists of exchanging offers and counter offers. We developed an evolutionary simulation environment to evaluate the seller agent’s strategies. We especially consider the case where buyers are time-impatient and under pressure to reach agreements early. The simulations show that the auction-inspired strategies are able to obtain almost maximum profits from the negotiations, given sufficient time pressure of the buyers

A decommitment strategy in a competitive multi-agent transportation setting

Decommitment is the action of foregoing of a contract for another (superior) offer. It has been shown that, using decommitment, agents can reach higher utility levels in case of negotiations with uncertainty about future prospects. In this paper, we study the decommitment concept for the novel setting of a large-scale logistics setting with multiple, competing companies. Orders for transportation of loads are acquired by agents of the (competing) companies by bidding in online auctions. We find significant increases in profit when the agents can decommit and postpone the transportation of a load to a more suitable time. Furthermore, we analyze the circumstances for which decommitment has a positive impact if agents are capable of handling multiple contracts simultaneously

Screen real estate ownership based mechanism for negotiating advertisement display

As popularity of online video grows, a number of models of advertising are emerging. It is typically the brokers – usually the operators of websites – who maintain the balance between content and advertising. Existing approaches focus primarily on personalizing advertisements for viewer segments, with minimal decision-making capacity for individual viewers. We take a resource ownership view on this problem. We view consumers’ attention space, which can be abstracted as a display screen for an engaged viewer, as precious resource owned by the viewer. Viewers pay for the content they wish to view in dollars, as well as in terms of their attention. Specifically, advertisers may make partial payment for a viewer’s content, in return for receiving the viewer’s attention to their advertising. Our approach, named “FlexAdSense”, is based on CyberOrgs model, which encapsulates distributed owned resources for multi-agent computations. We build a market of viewers’ attention space in which advertisers can trade, just as viewers can trade in a market of content. We have developed key mechanisms to give viewers flexible control over the display of advertisements in real time. Specific policies needed for automated negotiations can be plugged-in. This approach relaxes the exclusivity of the relationship between advertisers and brokers, and empowers viewers, enhancing their viewing experience. This thesis presents the rationale, design, implementation, and evaluation of FlexAdSense. Feature comparison with existing advertising mechanisms shows how FlexAdSense enables viewers to control with fine-grained flexibility. Experimental results demonstrate the scalability of the approach, as the number of viewers increases. A preliminary analysis of user overhead illustrates minimal attention overhead for viewers as they customize their policies

Arquitetura de software baseada em agentes para gerenciamento de portfólio de fontes de informação existentes na web

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Produção, Florianópolis, 2013.Observando-se as novas tecnologias em software que surgiram com o advento da plataforma da rede Internet, constata-se que algumas ganharam destaque por possibilitar o armazenamento do conhecimento narrado intacto e catalogado como, por exemplo, os wikis e fóruns da rede Internet. Entretanto, os gestores muitas vezes não conseguem utilizar essas informações dentro do processo de tomada de decisão. Esse fato se deve principalmente à impossibilidade de tratamento dessas informações por meio dos sistemas informatizados tradicionais, devido a particularidades do armazenamento de informações nos ambientes distribuídos. Nesse sentido, o objetivo deste trabalho é desenvolver uma arquitetura de software baseada em agentes para gerenciamento de portfólio de fontes de informação existentes na web, com o objetivo de auxiliar o gestor no processo de tomada de decisão. As metodologias da pesquisa utilizadas são as revisões bibliográfica e sistemática, na conceituação dos conceitos que regem o atual panorama tecnológico e inovador atual, a Inteligência Competitiva e os softwares envolvidos, os sistemas agentes e, estudo de caso, na proposta da ferramenta informatizada e grupo focal em sua validação. Os resultados demonstram que é viável a implementação de estruturas de software a fim de efetuar coleta de dados na Internet, para apoiar o gestor em processos de tomada de decisão Abstract : Observing the new software technology that cames with the Internet platform, appears that some gained prominence by store narrated knowledge intact and cataloged, eg, wikis and forums over the Internet. However, managers has fail to use this information in decision-making processes. This fact occurs towards the impossibility of treating these information through traditional information systems, due details of storage information in distributed environments. The focus of this work is to develop a Competitive Intelligence process model and agent based software modeling, to help managers in decision making processes. The research methodologies used are literature systematically review on the bibliographical concepts, that shows the current technological panorama, Competitive Intelligence and involved software, the agents systems; case study, in the models and focus group on the validation of the applied case study. The results shows that it is possible to develop software structures to perform data collection on the Internet, to support the manager in decision-making processes

Towards an agent-based model for risk-based regulation

Risk-based regulation has grown rapidly as a component of Government decision making, and as such, the need for an established evidence-based framework for decisions about risk has become the new mantra. However, the process of brokering scientific evidence is poorly understood and there is a need to improve the transparency of this brokering process and decisions made. This thesis attempts to achieve this by using agent-based simulation to model the influence that power structures and participating personalities has on the brokering of evidence and thereby the confidence-building exercise that characterises risk-based regulation. As a prerequisite to the adoption of agent-based techniques for simulating decisions under uncertainty, this thesis provides a critical review of the influence power structure and personality have on the brokering of scientific evidence that informs risk decisions. Three case studies, each representing a different perspective on risk-based regulation are presented: nuclear waste disposal, the disposal of avian-influenza infected animal carcases and the reduction of dietary salt intake. Semi-structured interviews were conducted with an expert from each case study, and the logical sequence in which decisions were made was mapped out and used to inform the development of an agent-based simulation model. The developed agent-based model was designed to capture the character of the brokering process by transparently setting out how evidence is transmitted from the provider of evidence to the final decision maker. It comprises of two agents, a recipient and provider of evidence, and draws upon a historic knowledge base to permit the user to vary components of the interacting agents and of the decision-making procedure, demonstrating the influence that power structure and personality has on agent receptivity and the confidence attached to a number of different lines of evidence. This is a novel step forward because it goes beyond the scope of current risk management frameworks, for example, permitting the user to explore the influence that participants have in weighing and strengthening different lines of evidence and the impact this has on the final decision outcome.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Recommender systems and market approaches for industrial data management

Industrial companies are dealing with an increasing data overload problem in all aspects of their business: vast amounts of data are generated in and outside each company. Determining which data is relevant and how to get it to the right users is becoming increasingly difficult. There are a large number of datasets to be considered, and an even higher number of combinations of datasets that each user could be using. Current techniques to address this data overload problem necessitate detailed analysis. These techniques have limited scalability due to their manual effort and their complexity, which makes them unpractical for a large number of datasets. Search, the alternative used by many users, is limited by the user’s knowledge about the available data and does not consider the relevance or costs of providing these datasets. Recommender systems and so-called market approaches have previously been used to solve this type of resource allocation problem, as shown for example in allocation of equipment for production processes in manufacturing or for spare part supplier selection. They can therefore also be seen as a potential application for the problem of data overload. This thesis introduces the so-called RecorDa approach: an architecture using market approaches and recommender systems on their own or by combining them into one system. Its purpose is to identify which data is more relevant for a user’s decision and improve allocation of relevant data to users. Using a combination of case studies and experiments, this thesis develops and tests the approach. It further compares RecorDa to search and other mechanisms. The results indicate that RecorDa can provide significant benefit to users with easier and more flexible access to relevant datasets compared to other techniques, such as search in these databases. It is able to provide a fast increase in precision and recall of relevant datasets while still keeping high novelty and coverage of a large variety of datasets