Evolution of Cooperation when Feedback to Reputation Scores is Voluntary

Reputation systems are used to facilitate interaction between strangers in one-shot social dilemmas, like transactions in e-commerce. The functioning of various reputation systems depend on voluntary feedback derived from the participants in those social dilemmas. In this paper a model is presented under which frequencies of providing feedback to positive and negative experiences in reputation systems explain observed levels of cooperation. The results from simulations show that it is not likely that reputation scores alone will lead to high levels of cooperation.Trust, Reputation, One-Shot Prisoner Dilemma, Voluntary Feedback, Symbols

Fairness Emergence in Reputation Systems

Reputation systems have been used to support users in making decisions under uncertainty or risk that is due to the autonomous behavior of others. Research results support the conclusion that reputation systems can protect against exploitation by unfair users, and that they have an impact on the prices and income of users. This observation leads to another question: can reputation systems be used to assure or increase the fairness of resource distribution? This question has a high relevance in social situations where, due to the absence of established authorities or institutions, agents need to rely on mutual trust relations in order to increase fairness of distribution. This question can be formulated as a hypothesis: in reputation (or trust management) systems, fairness should be an emergent property. The notion of fairness can be precisely defined and investigated based on the theory of equity. In this paper, we investigate the Fairness Emergence hypothesis in reputation systems and prove that , under certain conditions, the hypothesis is valid for open and closed systems, even in unstable system states and in the presence of adversaries. Moreover, we investigate the sensitivity of Fairness Emergence and show that an improvement of the reputation system strengthens the emergence of fairness. Our results are confirmed using a trace-driven simulation from a large Internet auction site.Trust, Simulation, Fairness, Equity, Emergence, Reputation System

Trust-Based Mechanisms for Robust and Efficient Task Allocation in the Presence of Execution Uncertainty

Vickrey-Clarke-Groves (VCG) mechanisms are often used to allocate tasks to selfish and rational agents. VCG mechanisms are incentive-compatible, direct mechanisms that are efficient (i.e. maximise social utility) and individually rational (i.e. agents prefer to join rather than opt out). However, an important assumption of these mechanisms is that the agents will always successfully complete their allocated tasks. Clearly, this assumption is unrealistic in many real-world applications where agents can, and often do, fail in their endeavours. Moreover, whether an agent is deemed to have failed may be perceived differently by different agents. Such subjective perceptions about an agent’s probability of succeeding at a given task are often captured and reasoned about using the notion of trust. Given this background, in this paper, we investigate the design of novel mechanisms that take into account the trust between agents when allocating tasks. Specifically, we develop a new class of mechanisms, called trust-based mechanisms, that can take into account multiple subjective measures of the probability of an agent succeeding at a given task and produce allocations that maximise social utility, whilst ensuring that no agent obtains a negative utility. We then show that such mechanisms pose a challenging new combinatorial optimisation problem (that is NP-complete), devise a novel representation for solving the problem, and develop an effective integer programming solution (that can solve instances with about 2×105 possible allocations in 40 seconds).

Choosing between Auctions and Negotiations in Online B2B Markets for IT Services: The Effect of Prior Relationships and Performance

The choice of contract allocation mechanism in procurement affects such aspects of transactions as information exchange between buyer and supplier, supplier competition, pricing and, eventually, performance. In this study we investigate the buyer’s choice between reverse auctions and bilateral negotiations as an allocation mechanism for IT services contracts. Prior studies into allocation mechanism choice focused on factors pertaining to discrete exchange situation, such as con-tract complexity or availability of suppliers. We broaden the research by focusing on buyers’ past exchange relationships with vendors. Based on the literature on the economics of contracting and agency theory, we hypothesize that prior re-peat interaction with vendors favors the use of negotiations over auctions in the next transaction, while the need to explore the marketplace due to buyer’s inexperience or dissatisfaction with vendor’s performance in the most recent project leads to the use of auctions instead of negotiations. We find support for these hypotheses in a longitudinal dataset of 2,081 IT projects realized by 91 repeat buyers at a leading online services marketplace over a period of eight years. Taken together, the results show that analyzing B2B auctions and negotiations should move beyond analyzing discrete instances and instead analyze them in the context of the individual firm’s history and supplier strategy.outsourcing;IT services;online marketplace;reverse auctions

Reputation systems and recruitment in online labor markets: insights from an agent-based model

Online labor markets—freelance marketplaces, where digital labor is distributed via a web-based platform—commonly use reputation systems to overcome uncertainties in the hiring process, that can arise from a lack of objective information about employees’ abilities. Research shows, however, that reputation systems tend to create winner-takes-all dynamics, in which differences in candidates’ reputations become disconnected from differences in their objective abilities. In this paper, we use an empirically validated agent-based computational model to investigate the extent to which reputation systems can create segmented hiring patterns that are biased toward freelancers with good reputation. We explore how jobs and earnings become distributed on a stylized platform, under different contextual conditions of information asymmetry. Our results suggest that information asymmetry influences the extent to which reputation systems may lead to inequality between freelancers, but contrary to our expectations, lower levels of information asymmetry can facilitate higher inequality in outcomes

The Value of Third-Party Certification of Preconditioning Claims at Iowa Feeder Cattle Auctions

After controlling a variety of feeder cattle characteristics and market and sale conditions, we estimate the price premiums for preconditioning (vaccinations and minimum 30 days weaning) claims with and without third-party certification (TPC) as $6.15/cwt and$3.40/cwt, respectively, in Iowa feeder cattle auctions. These premiums differ statistically (p-value less than 0.0001) and their difference exceeds the additional participation cost of TPC ($1/cwt) on average. This indicates that the third party certification is valued in the market to credibly signal preconditioning investment under asymmetric information.

On the Simulation of Global Reputation Systems

Reputation systems evolve as a mechanism to build trust in virtual communities. In this paper we evaluate different metrics for computing reputation in multi-agent systems. We present a formal model for describing metrics in reputation systems and show how different well-known global reputation metrics are expressed by it. Based on the model a generic simulation framework for reputation metrics was implemented. We used our simulation framework to compare different global reputation systems to find their strengths and weaknesses. The strength of a metric is measured by its resistance against different threat-models, i.e. different types of hostile agents. Based on our results we propose a new metric for reputation systems.Reputation System, Trust, Formalization, Simulation