223 research outputs found
Promoting Honesty in Electronic Marketplaces: Combining Trust Modeling and Incentive 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
Toward Secure Trust and Reputation Systems for Electronic Marketplaces
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
Proceedings of RSEEM 2006 : 13th Research Symposium on Emerging Electronic Markets
Electronic markets have been a prominent topic of research for the past decade. Moreover, we have seen the rise but also the disappearance of many electronic marketplaces in practice. Today, electronic markets are a firm component of inter-organisational exchanges and can be observed in many branches.
The Research Symposium on Emerging Electronic Markets is an annual conference bringing together researchers working on various topics concerning electronic markets in research and practice. The focus theme of the13th Research Symposium on Emerging Electronic Markets (RSEEM 2006) was ?Evolution in Electronic Markets?. Looking back at more than 10 years of research activities in electronic markets, the evolution can be well observed. While electronic commerce activities were based largely on catalogue-based shopping, there are now many examples that go beyond pure catalogues. For example, dynamic and flexible electronic transactions such as electronic negotiations and electronic auctions are enabled. Negotiations and auctions are the basis for inter-organisational trade exchanges about services as well as products. Mass customisation opens up new opportunities for electronic markets. Multichannel electronic commerce represents today?s various requirements posed on information and communication technology as well as on organisational structures. In recent years, service-oriented architectures of electronic markets have enabled ICT infrastructures for supporting flexible e-commerce and e-market solutions.
RSEEM 2006 was held at the University of Hohenheim, Stuttgart, Germany in September 2006. The proceedings show a variety of approaches and include the selected 8 research papers. The contributions cover the focus theme through conceptual models and systems design, application scenarios as well as evaluation research approaches
Trust-based Incentive Mechanisms for Community-based Multiagent Systems
In this thesis we study peer-based communities which are online communities whose services are provided by their participant agents. In order to improve the services an agent enjoys in these communities, we need to improve the services other agents offer. Towards this goal, we propose a novel solution which allows communities to share the experience of their members with other communities. The experience of a community with an agent is captured in the evaluation rating of the agent within the community, which can either represent the trustworthiness or the reputation of the agent. We argue that exchanging this information is the right way to improve the services the agent offers since it: i) exploits the information that each community accumulates to allow other communities to decide whether to accept the agent while it also puts pressure on the agent to behave well, since it is aware that any misbehaviour will be spread to the
communities it might wish to join in the future, ii) can prevent the agent from overstretching itself among many
communities, since this may lead the agent to provide very limited
services to each of these communities due to its limited
resources, and thus its trustworthiness and reputation might be compromised.
We study mechanisms that can be used to facilitate the exchange of trust or reputation information between communities.
We make two key contributions. First, we propose a graph-based model which allows a particular community to determine which other communities to ask information from. We leverage consistency of past information and provide an equilibrium analysis showing that communities are best-off when they truthfully report the requested information, and describe how payments should be made to support the equilibrium. Our second contribution is a promise-based trust model where agents are judged based on the contributions they promise and deliver to the community. We outline a set of desirable properties such a model must exhibit, provide an instantiation, and an empirical evaluation
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A Market-Based Approach to Software Evolution
Software correctness has bedeviled the field of computer science since its inception. Software complexity has increased far more quickly than our ability to control it, reaching sizes that are many orders of magnitude beyond the reach of formal or automated verification techniques.
We propose a new paradigm for evaluating "correctness" based on a rich market ecosystem in which coalitions of users bid for features and fixes. Developers, testers, bug reporters, and analysts share in the rewards for responding to those bids. In fact, we suggest that the entire software development process can be driven by a disintermediated market-based mechanism driven by the desires of users and the capabilities of developers.
The abstract, unspecifiable, and unknowable notion of absolute correctness is then replaced by quantifiable notions of correctness demand (the sum of bids for bugs) and correctness potential (the sum of the available profit for fixing those bugs). We then sketch the components of a market design intended to identify bugs, elicit demand for fixing bugs, and source workers for fixing bugs. The ultimate goal is to achieve a more appropriate notion of correctness, in which market forces drive software towards a correctness equilibrium in which all bugs for which there is enough value, and with low enough cost to fix, are fixed.Engineering and Applied Science
Essays on Market Design
“Market Design […] strives to understand how the design of marketplaces influences the functioning of markets” (Roth 2018, p. 1609). The simple but powerful rationale of market design is to improve markets by actively designing them, guided by economic theory, empirical data, and carefully designed economic experiments. In recent years, economists have been successful in designing a variety of institutions, including spectrum auctions, electricity markets, feedback systems, kidney exchanges, and school choice (Chen et al., 2020). This thesis consists of four chapters, all devoted to different aspects and areas of market design. Another unifying element of this thesis is the methodology. In all chapters, laboratory experiments are conducted, data are analyzed, and the results are linked to real-world applications. Laboratory experimental studies are a particularly useful tool in the context of market design. They are often compared to a wind tunnel, where the performance of existing designs is studied in a simplified environment or even new design ideas are tested in a controlled environment (Chen et al., 2020).
The first chapter looks at auction design. We investigate the puzzle behind the popularity of a non-binding soft reserve price in practice. Here, we use the laboratory as a "wind tunnel" to compare the performance of different existing auction designs in a controlled environment. Chapter two focuses on the design of feedback systems. In this chapter, we propose a small but very effective modification to existing feedback withdrawal mechanisms. Therefore, we use the possibility of laboratory experiments to test a new design idea that has not yet been implemented in practice and for which, of course, no field data are available. The third chapter is concerned with the area of school choice. Here, I investigate the value of fairness to participants in school choice markets, which can guide a market designer in choosing an appropriate algorithm. A laboratory experiment allows for the observation and control of student preferences that are typically unobservable in field data. Finally, chapter four focuses on norm information acquisition. When designing real-world institutions, incentives must be aligned with behavior in terms of underlying goals (Bolton and Ockenfels 2012). Therefore, social norms, which are known to be a powerful force influencing behavior, are of great importance for market design. We study how economic agents choose between different types of norm information in a social choice context with uncertainty
Incorporating behavioral principles in primary data analysis with application to beer demand
Recent advancements in microeconomics have resurrected a need for modern economists to grapple with principles of consumer behavior. This dissertation uses the American beer market as a starting place to present three ways applied researchers can incorporate behavioral principles into economic theory. The first essay uses choice experiments designed to estimate the price sensitivity of alcohol consumption to explore the efficacy of prompts targeted at reducing inattention bias. Upon receiving feedback, inattentive respondents are given the opportunity to re-answer a so-called "trap question" that checks for attentiveness. We find that individuals who miss trap questions and do not correctly revise their responses have significantly different choice patterns as compared to individuals who correctly answer the trap question. The second essay proposes an instrumental variable approach to address the endogeneity issues associated with distinguishing preferences from perceptions. Even after correction, we find beliefs/perceptions substantially affect consumer choices of beer brands, and that perceived taste and brand familiarity are key determinants of choice. In the final essay, we empirically tests the effectiveness of two institutional nudges on the ECE in a field experiment at a bar. Focusing on craft beer sales, we manipulate the number of options on the menu and use institutional nudges (a control menu, a menu with a special prominently displayed, and a menu with Beer Advocate scores). In the field experiment, the ECE was alive and well using the control menu, but the effect reversed itself when the menu included Beer Advocate Scores. Our results suggest the ECE might be turned on and off by manipulating search costs. Taken together, these three essays show that that behavioral principles can enrich understanding of human action as it relates to consumer decision-making
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