Agent-orientated auction mechanism and strategy design

Agent-based technology is playing an increasingly important role in today’s economy. Usually a multi-agent system is needed to model an economic system such as a market system, in which heterogeneous trading agents interact with each other autonomously. Two questions often need to be answered regarding such systems: 1) How to design an interacting mechanism that facilitates efficient resource allocation among usually self-interested trading agents? 2) How to design an effective strategy in some specific market mechanisms for an agent to maximise its economic returns? For automated market systems, auction is the most popular mechanism to solve resource allocation problems among their participants. However, auction comes in hundreds of different formats, in which some are better than others in terms of not only the allocative efficiency but also other properties e.g., whether it generates high revenue for the auctioneer, whether it induces stable behaviour of the bidders. In addition, different strategies result in very different performance under the same auction rules. With this background, we are inevitably intrigued to investigate auction mechanism and strategy designs for agent-based economics. The international Trading Agent Competition (TAC) Ad Auction (AA) competition provides a very useful platform to develop and test agent strategies in Generalised Second Price auction (GSP). AstonTAC, the runner-up of TAC AA 2009, is a successful advertiser agent designed for GSP-based keyword auction. In particular, AstonTAC generates adaptive bid prices according to the Market-based Value Per Click and selects a set of keyword queries with highest expected profit to bid on to maximise its expected profit under the limit of conversion capacity. Through evaluation experiments, we show that AstonTAC performs well and stably not only in the competition but also across a broad range of environments. The TAC CAT tournament provides an environment for investigating the optimal design of mechanisms for double auction markets. AstonCAT-Plus is the post-tournament version of the specialist developed for CAT 2010. In our experiments, AstonCAT-Plus not only outperforms most specialist agents designed by other institutions but also achieves high allocative efficiencies, transaction success rates and average trader profits. Moreover, we reveal some insights of the CAT: 1) successful markets should maintain a stable and high market share of intra-marginal traders; 2) a specialist’s performance is dependent on the distribution of trading strategies. However, typical double auction models assume trading agents have a fixed trading direction of either buy or sell. With this limitation they cannot directly reflect the fact that traders in financial markets (the most popular application of double auction) decide their trading directions dynamically. To address this issue, we introduce the Bi-directional Double Auction (BDA) market which is populated by two-way traders. Experiments are conducted under both dynamic and static settings of the continuous BDA market. We find that the allocative efficiency of a continuous BDA market mainly comes from rational selection of trading directions. Furthermore, we introduce a high-performance Kernel trading strategy in the BDA market which uses kernel probability density estimator built on historical transaction data to decide optimal order prices. Kernel trading strategy outperforms some popular intelligent double auction trading strategies including ZIP, GD and RE in the continuous BDA market by making the highest profit in static games and obtaining the best wealth in dynamic games

Electronic business and electronic commerce (supporting lecture notes for students of dirеction "Management" of all forms of education)

E-Business systems naturally have greater security risks than traditional business systems, therefore it is important for e-business systems to be fully protected against these risks. Customers, suppliers, employees, and numerous other people use any particular e-business system daily and expect their confidential information to stay secure. Hackers are one of the great threats to the security of e-businesses. Some common security concerns for e-Businesses include keeping business and customer information private and confidential, authenticity of data, and data integrity. Some of the methods of protecting e-business security and keeping information secure include physical security measures as well as data storage, data transmission, anti-virus software, firewalls, and encryption to list a few.Розглянуто та рекомендовано до друку на засіданні кафедри інноваційного менеджменту та підприємництва, протокол No1 від 27 серпня 2015 року. Схвалено та рекомендовано до друку на засіданні методичної комісії факультету управління та бізнесу у виробництві Тернопільського національного технічного університету імені Івана Пулюя, протокол No6 від 26 лютого 2016 року.The purpose of thе document is to present the different underlying "technologies" (in reality, organizational modes based on information and communication technologies) and their associated acronyms. The term "e-Business" therefore refers to the integration, within the company, of tools based on information and communication technologies (generally referred to as business software) to improve their functioning in order to create value for the enterprise, its clients, and its partners.Topic 1. Basic concepts of electronic business and electronic commerce 1.1. Basic concepts and principles of e-business. 1.2. Origins and growth of e-commerce. Topic 2. Ecommerce as a part of electronic business 2.1. E-business infrastructure, e-environment and e-business strategy 2.2. Ways of e-business conducting. Online trading. Topic 3. Basis of global computer network internet functioning. 3.1. Basic principles of internet. 3.2. The most common services of Іnternet. 3.3. The concept and structure of Internet marketing. Topic 4. E-commerce systems in corporate sector 4.1. The basic processes of implementation of electronic commerce in the B2B sector. Virtual enterprise, internet incubator, mobile commerce. 4.2. The role of supply-chain management (SCM) and customer relationship management (CRM) in e-commerce. Topic 5. Information management for effective e-commerce building through intranet and extranet 5.1. Basic principles of Intranet functioning. 5.2. Extranet and its security issues. Topic 6. Electronic payment systems 6.1. Electronic payment systems. 6.2. Primary classification of payment systems

Incentive Mechanisms in Peer-to-Peer Networks — A Systematic Literature Review

Centralized networks inevitably exhibit single points of failure that malicious actors regularly target. Decentralized networks are more resilient if numerous participants contribute to the network’s functionality. Most decentralized networks employ incentive mechanisms to coordinate the participation and cooperation of peers and thereby ensure the functionality and security of the network. This article systematically reviews incentive mechanisms for decentralized networks and networked systems by covering 165 prior literature reviews and 178 primary research papers published between 1993 and October 2022. Of the considered sources, we analyze 11 literature reviews and 105 primary research papers in detail by categorizing and comparing the distinctive properties of the presented incentive mechanisms. The reviewed incentive mechanisms establish fairness and reward participation and cooperative behavior. We review work that substitutes central authority through independent and subjective mechanisms run in isolation at each participating peer and work that applies multiparty computation. We use monetary, reputation, and service rewards as categories to differentiate the implementations and evaluate each incentive mechanism’s data management, attack resistance, and contribution model. Further, we highlight research gaps and deficiencies in reproducibility and comparability. Finally, we summarize our assessments and provide recommendations to apply incentive mechanisms to decentralized networks that share computational resources

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Agent-orientated auction mechanism and strategy design

Agent-based technology is playing an increasingly important role in today’s economy. Usually a multi-agent system is needed to model an economic system such as a market system, in which heterogeneous trading agents interact with each other autonomously. Two questions often need to be answered regarding such systems: 1) How to design an interacting mechanism that facilitates efficient resource allocation among usually self-interested trading agents? 2) How to design an effective strategy in some specific market mechanisms for an agent to maximise its economic returns? For automated market systems, auction is the most popular mechanism to solve resource allocation problems among their participants. However, auction comes in hundreds of different formats, in which some are better than others in terms of not only the allocative efficiency but also other properties e.g., whether it generates high revenue for the auctioneer, whether it induces stable behaviour of the bidders. In addition, different strategies result in very different performance under the same auction rules. With this background, we are inevitably intrigued to investigate auction mechanism and strategy designs for agent-based economics. The international Trading Agent Competition (TAC) Ad Auction (AA) competition provides a very useful platform to develop and test agent strategies in Generalised Second Price auction (GSP). AstonTAC, the runner-up of TAC AA 2009, is a successful advertiser agent designed for GSP-based keyword auction. In particular, AstonTAC generates adaptive bid prices according to the Market-based Value Per Click and selects a set of keyword queries with highest expected profit to bid on to maximise its expected profit under the limit of conversion capacity. Through evaluation experiments, we show that AstonTAC performs well and stably not only in the competition but also across a broad range of environments. The TAC CAT tournament provides an environment for investigating the optimal design of mechanisms for double auction markets. AstonCAT-Plus is the post-tournament version of the specialist developed for CAT 2010. In our experiments, AstonCAT-Plus not only outperforms most specialist agents designed by other institutions but also achieves high allocative efficiencies, transaction success rates and average trader profits. Moreover, we reveal some insights of the CAT: 1) successful markets should maintain a stable and high market share of intra-marginal traders; 2) a specialist’s performance is dependent on the distribution of trading strategies. However, typical double auction models assume trading agents have a fixed trading direction of either buy or sell. With this limitation they cannot directly reflect the fact that traders in financial markets (the most popular application of double auction) decide their trading directions dynamically. To address this issue, we introduce the Bi-directional Double Auction (BDA) market which is populated by two-way traders. Experiments are conducted under both dynamic and static settings of the continuous BDA market. We find that the allocative efficiency of a continuous BDA market mainly comes from rational selection of trading directions. Furthermore, we introduce a high-performance Kernel trading strategy in the BDA market which uses kernel probability density estimator built on historical transaction data to decide optimal order prices. Kernel trading strategy outperforms some popular intelligent double auction trading strategies including ZIP, GD and RE in the continuous BDA market by making the highest profit in static games and obtaining the best wealth in dynamic games.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

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

Assessing the relative performance of online advertising media

Online marketing campaigns are the big trend in today’s advertising world. The obvious advantages of online ad media – low costs, multiple tracking options and better segmentation -, are forcing marketing managers to re-allocate big portions of their advertisement budgets from traditional offline marketing media to online ad campaigns. Several digital communication channels are suitable for online advertising, each serving different communication goals - awareness, engagement, traffic generation, lead generation and sales conversion – according to the overall brand strategy. Yet, which channel or mix of channels should advertisers or marketing agencies employ in order to best achieve its communication goals is something that still remains highly unclear. This is particularly true for advertising markets outside the USA. The overall aim of this dissertation was hence to compare the performance of different advertising media channels – E-mail marketing, Social Networked Media and Paid Search -, with performance being assessed from the viewpoint of a digital marketing agency, both in terms of campaign effectiveness (traffic and lead generation) and campaign profitability (ROI). To this end, secondary panel data regarding the features and performance of six online ad campaigns (five of which were conducted in more than one channel), namely click-through rates (CTR; traffic generation), lead conversion rates (LCR; submission of a registration form) and Return-on-Investment (ROI), conducted over the course of the first trimester of 2014 were compiled. OLS multivariate linear regression analysis was then performed to understand which channel and campaign features significantly affected campaign effectiveness and profitability. Moreover, primary data were also collected by selecting a poorly performing campaign, optimizing it based on the results of the secondary data analysis, re-launching it and re-analyzing its performance results. Results show that e-mail marketing campaigns seem to be the most effective and the most profitable ones for digital marketing agencies operating under performance (lead conversion)-based pricing models. Social Networked media such as Facebook, however, seem to perform better when the campaign goal is drive traffic to advertisers’ websites, as CTR rates for this channel tend to higher on average. Regarding the quasi-experiment done, E-mail and Google had similar performance in terms of target effectiveness. The number of conversions was also similar, However, Google had substantial higher costs, due to the number of impressions. After dropping that channel, campaign’s results have improved. Nevertheless, the increase in the number of leads may have been due to E-mail design rather than the channel choice.Atualmente, as campanhas de publicidade on-line são uma grande tendência entre as empresas. As principais vantagens dos canais on-line – como por exemplo os baixos custos associados, as opções de tracking e de segmentação, motivam os Marketers a re-alocar uma parcela maior do orçamento total de Publicidade do tradicional para campanhas online. Existem diversos media on-line que podem ser usados, assim como objetivos diferentes - branding, aumento das vendas ou captação de bases de dados. No entanto, ainda não é claro o que o canal on-line mais apropriado para uma empresa, consoante o objetivo traçado. Ainda, a maioria dos resultados só dizem respeito ao mercado dos EUA. Esta tese visa compreender o desempenho dos canais E-mail Marketing, anúncios do Facebook e Google Ad Words, tendo em conta três objetivos diferentes: geração de tráfego para o site do cliente (número de cliques) , o preenchimento de um registo (número de conversões) e o ROI médio por canal . Devido à parceria com Revshare , uma agência digital Português, todos os resultados são baseados sobre a perspectiva da agência. Foram analisados dados secundários de 6 campanhas, lançadas no primeiro trimestre do presente ano, no mercado Português - 5 das quais são campanhas multi-canal. Foi realizado uma análise descritiva dos dados obtidos. Também foram recolhidos dados primários, com o objetivo de avaliar o impacto da escolha dos canais no desempenho dos mesmo. Foi realizada uma regressão linear OLS com o intuito de entender quais as variáveis que têm impacto no número de cliques, conversões e ROI. Concluindo, o E-mail aparenta ser o canal mais adequado quando o objetivo é a conversão. Além disso, é o único com um ROI positivo. Os anúncios do Facebook são uma boa opção em direcionar o tráfego para o site do cliente. Relativamente à expriência realizada, tanto o canal E-mail como o Google tiveram uma performance semelhante em termos de leads e segmentação. Contudo, devido ao maior número de impressões gerados pelo Google, os custos deste canal foram substancialmente mais elevados. Assim, e depois de ter sido retirado este canal, os resultados da campanha melhoraram. No entanto, esta melhoria poderá ter sido originada pela alteração do design do E-mail e não pela combinação de canais

Agent Based Matchmaking and Clustering: A Decentralized Approach to Search

Brazier, F.M.T. [Promotor]Steen, M.R. van [Promotor