This thesis work is in the area of modeling trust in multi-agent systems, systems of software agents designed to act on behalf of users (buyers and sellers), in applications such as e-commerce. The focus is on developing an approach for buyers to model the trustworthiness of sellers in order to make effective decisions about which sellers to select for business. One challenge is the problem of unfair ratings, which arises when modeling the trust of sellers relies on ratings provided by other buyers (called advisors). Existing approaches for coping with this problem fail in scenarios where the majority of advisors are dishonest, buyers do not have much personal experience with sellers, advisors try to flood the trust modeling system with unfair ratings, and sellers vary their behavior widely. We propose a novel personalized approach for effectively modeling trustworthiness of advisors, allowing a buyer to 1) model the private reputation of an advisor based on their ratings for commonly rated sellers 2) model the public reputation of the advisor based on all ratings for the sellers ever rated by that agent 3) flexibly weight the private and public reputation into one combined measure of the trustworthiness of the advisor. Our approach tracks ratings provided according to their time windows and limits the ratings accepted, in order to cope with advisors flooding the system and to deal with changes in agents' behavior. Experimental evidence demonstrates that our model outperforms other models in detecting dishonest advisors and is able to assist buyers to gain the largest profit when doing business with sellers. Equipped with this richer method for modeling trustworthiness of advisors, we then embed this reasoning into a novel trust-based incentive mechanism to encourage agents to be honest. In this mechanism, buyers select the most trustworthy advisors as their neighbors from which they can ask advice about sellers, forming a social network. In contrast with other researchers, we also have sellers model the reputation of buyers. Sellers will offer better rewards to satisfy buyers that are well respected in the social network, in order to build their own reputation. We provide precise formulae used by sellers when reasoning about immediate and future profit to determine their bidding behavior and the rewards to buyers, and emphasize the importance for buyers to adopt a strategy to limit the number of sellers that are considered for each good to be purchased. We theoretically prove that our mechanism promotes honesty from buyers in reporting seller ratings, and honesty from sellers in delivering products as promised. We also provide a series of experimental results in a simulated dynamic environment where agents may be arriving and departing. This provides a stronger defense of the mechanism as one that is robust to important conditions in the marketplace. Our experiments clearly show the gains in profit enjoyed by both honest sellers and honest buyers when our mechanism is introduced and our proposed strategies are followed. In general, our research will serve to promote honesty amongst buyers and sellers in e-marketplaces. Our particular proposal of allowing sellers to model buyers opens a new direction in trust modeling research. The novel direction of designing an incentive mechanism based on trust modeling and using this mechanism to further help trust modeling by diminishing the problem of unfair ratings will hope to bridge researchers in the areas of trust modeling and mechanism design.

This thesis work is in the area of modeling trust in multi-agent systems, systems of software agents designed to act on behalf of users (buyers and sellers), in applications such as e-commerce. The focus is on developing an approach for buyers to model the trustworthiness of sellers in order to make effective decisions about which sellers to select for business. One challenge is the problem of unfair ratings, which arises when modeling the trust of sellers relies on ratings provided by other buyers (called advisors). Existing approaches for coping with this problem fail in scenarios where the majority of advisors are dishonest, buyers do not have much personal experience with sellers, advisors try to flood the trust modeling system with unfair ratings, and sellers vary their behavior widely. We propose a novel personalized approach for effectively modeling trustworthiness of advisors, allowing a buyer to 1) model the private reputation of an advisor based on their ratings for commonly rated sellers 2) model the public reputation of the advisor based on all ratings for the sellers ever rated by that agent 3) flexibly weight the private and public reputation into one combined measure of the trustworthiness of the advisor. Our approach tracks ratings provided according to their time windows and limits the ratings accepted, in order to cope with advisors flooding the system and to deal with changes in agents' behavior. Experimental evidence demonstrates that our model outperforms other models in detecting dishonest advisors and is able to assist buyers to gain the largest profit when doing business with sellers. Equipped with this richer method for modeling trustworthiness of advisors, we then embed this reasoning into a novel trust-based incentive mechanism to encourage agents to be honest. In this mechanism, buyers select the most trustworthy advisors as their neighbors from which they can ask advice about sellers, forming a social network. In contrast with other researchers, we also have sellers model the reputation of buyers. Sellers will offer better rewards to satisfy buyers that are well respected in the social network, in order to build their own reputation. We provide precise formulae used by sellers when reasoning about immediate and future profit to determine their bidding behavior and the rewards to buyers, and emphasize the importance for buyers to adopt a strategy to limit the number of sellers that are considered for each good to be purchased. We theoretically prove that our mechanism promotes honesty from buyers in reporting seller ratings, and honesty from sellers in delivering products as promised. We also provide a series of experimental results in a simulated dynamic environment where agents may be arriving and departing. This provides a stronger defense of the mechanism as one that is robust to important conditions in the marketplace. Our experiments clearly show the gains in profit enjoyed by both honest sellers and honest buyers when our mechanism is introduced and our proposed strategies are followed. In general, our research will serve to promote honesty amongst buyers and sellers in e-marketplaces. Our particular proposal of allowing sellers to model buyers opens a new direction in trust modeling research. The novel direction of designing an incentive mechanism based on trust modeling and using this mechanism to further help trust modeling by diminishing the problem of unfair ratings will hope to bridge researchers in the areas of trust modeling and mechanism design.

<p>Despite the high expectations that researchers and practitioners had for business-to- business electronic marketplaces (EMs), EMs have not prospered, for reasons that are not well understood. Research to this point on EM adoption is very limited due to their quickly changing nature and the complexity of the issue, which involves multiple economic, political and technical factors at both macro and micro levels.</p> <p>EM adoption and buyer-supplier relationships are related. Buyer-supplier relationships play an important role in firms' EM adoption decisions since businesses are not willing to change their current relationships with trading partners to adopt EMs and their support functionalities, such as auctions, reverse auctions, transaction support, etc. The adoption of EMs also impacts buyer-supplier relationships. A framework is proposed in this thesis to investigate these mentioned issues. A buyer-supplier relationship perspective is adopted to investigate EM adoption, and both the economic and power dimensions of buyer-supplier relationships are examined. It is proposed that power can speed up the adoption of EM functionalities and that the effect of power is moderated by market structure. Some important contingencies are suggested that underlay buyer supplier relationships, such as transaction uncertainty, transaction frequency, transaction specific investment, complexity of product description and non-contractible factors, and it is proposed that they are likely to affect choice of functionality. At the same time, it is proposed that EMs can make short-term relationships efficient and long-term relationships effective. It is also proposed that the use of EMs causes varying degrees of satisfaction of participants with their online trading partners.</p> <p>A case study approach was adopted to examine the framework. A total of five EMs and some of their participating buyers and suppliers were studied to validate the propositions. Some important findings are reported. The first finding was that the classification of EMs should not be a dichotomy, but a continuum. It was also found that complexity of product description could not explain why companies choose to use different functionalities, since simple products tended to be involved in EM trading, in all the functionalities that we studied. It was confirmed that relationship efficiency and effectiveness gains were moderated by the drawbacks of EMs and the lack of participant system and process integration into EM systems. Finally, based on the results of the case studies and the confirmed propositions, a refined framework is presented and described.</p> / Doctor of Philosophy (PhD)

In electronic marketplaces, buying and selling agents may be used to represent buyers and sellers respectively. When these marketplaces are large, repeated transactions between traders may be rare. This makes it difficult for buying agents to judge the reliability of selling agents, discouraging participation in the market. A variety of trust and reputation systems have been proposed to help traders to find trustworthy partners. Unfortunately, as our investigations reveal, there are a number of common vulnerabilities present in such models---security problems that may be exploited by `attackers' to cheat without detection/repercussions. Inspired by these findings, we set out to develop a model of trust with more robust security properties than existing proposals. Our Trunits model represents a fundamental re-conception of the notion of trust. Instead of viewing trust as a measure of predictability, Trunits considers trust to be a quality that one possesses. Trust is represented using abstract trust units, or `trunits', in much the same way that money represents quantities of value. Trunits flow in the course of transactions (again, similar to money); a trader's trunit balance determines if he is trustworthy for a given transaction. Faithful execution of a transaction results in a larger trunit balance, permitting the trader to engage in more transactions in the future---a built-in economic incentive for honesty. We present two mechanisms (sets of rules that govern the operation of the marketplace) based on this model: Basic Trunits, and an extension known as Commodity Trunits, in which trunits may be bought and sold. Seeking to precisely characterize the protection provided to market participants by our models, we develop a framework for security analysis of trust and reputation systems. Inspired by work in cryptography, our framework allows security guarantees to be developed for trust/reputation models--provable claims of the degree of protection provided, and the conditions under which such protection holds. We focus in particular on characterizing buyer security: the properties that must hold for buyers to feel secure from cheating sellers. Beyond developing security guarantees, this framework is an important research tool, helping to highlight limitations and deficiencies in models so that they may be targeted for future investigation. Application of this framework to Basic Trunits and Commodity Trunits reveals that both are able to deliver provable security to buyers.

In electronic marketplaces, buying and selling agents may be used to represent buyers and sellers respectively. When these marketplaces are large, repeated transactions between traders may be rare. This makes it difficult for buying agents to judge the reliability of selling agents, discouraging participation in the market. A variety of trust and reputation systems have been proposed to help traders to find trustworthy partners. Unfortunately, as our investigations reveal, there are a number of common vulnerabilities present in such models---security problems that may be exploited by `attackers' to cheat without detection/repercussions. Inspired by these findings, we set out to develop a model of trust with more robust security properties than existing proposals. Our Trunits model represents a fundamental re-conception of the notion of trust. Instead of viewing trust as a measure of predictability, Trunits considers trust to be a quality that one possesses. Trust is represented using abstract trust units, or `trunits', in much the same way that money represents quantities of value. Trunits flow in the course of transactions (again, similar to money); a trader's trunit balance determines if he is trustworthy for a given transaction. Faithful execution of a transaction results in a larger trunit balance, permitting the trader to engage in more transactions in the future---a built-in economic incentive for honesty. We present two mechanisms (sets of rules that govern the operation of the marketplace) based on this model: Basic Trunits, and an extension known as Commodity Trunits, in which trunits may be bought and sold. Seeking to precisely characterize the protection provided to market participants by our models, we develop a framework for security analysis of trust and reputation systems. Inspired by work in cryptography, our framework allows security guarantees to be developed for trust/reputation models--provable claims of the degree of protection provided, and the conditions under which such protection holds. We focus in particular on characterizing buyer security: the properties that must hold for buyers to feel secure from cheating sellers. Beyond developing security guarantees, this framework is an important research tool, helping to highlight limitations and deficiencies in models so that they may be targeted for future investigation. Application of this framework to Basic Trunits and Commodity Trunits reveals that both are able to deliver provable security to buyers.

The document deals with the topic of electronic procurement with emphasis on process of its electronization using electronic tool Softender. The text is divided in two main parts -- First, theoretical part focuses on Public Produce Act analysis in relation to its forthcoming amendment. The practical part aims at functional description of electronic tool Softender. Inovations of such software solution and usage models are also discussed such as the marketing plan for the tool.