The Evolution of Trust and Reputation: Results from Simulation Experiments

In online interactions in general, but especially in interactions between buyers and sellers on internet-auction platforms, the interacting parties must deal with trust and cooperation problems. Whether a rating system is able to foster trust and cooperation through reputation and without an external enforcer is an open question. We therefore explore through ecological analysis different buyer and seller strategies in terms of their success and their contribution to supporting or impeding trust and cooperation. In our agent-based model, the interaction between a buyer and a seller is defined by a one-shot trust game with a reputation mechanism. In every interaction, a buyer has complete information about a seller's past behavior. We find that cooperation evolves under two conditions even in the absence of an external sanctioning authority. On the one hand, some minimal fraction of buyers must make use of the sellers’ reputation in their buying strategies and, on the other hand, trustworthy sellers must be given opportunities to gain a good reputation through their cooperative behavior. Despite the apparent usefulness of the reputation mechanism, a small number of deceitful sellers are able to hold their ground.trust game, reputation, agent-based simulation

Trust among Strangers

The trust building process is basic to social science. We investigate it in a laboratory setting using a novel multi-stage trust game where social gains are achieved if players trust each other in each stage. And in each stage, players have an opportunity to appropriate these gains or be trustworthy by sharing them. Players are strangers because they do not know the identity of others and they will not play them again in the future. Thus there is no prospect of future interaction to induce trusting behavior. So, we study the trust building process where there is little scope for social relations and networks. Standard game theory, which assumes all players are opportunistic, untrustworthy, and should have zero trust for others is used to construct a null hypothesis. We test whether people are trusting or trustworthy and examine how inferring the intentions of those who trust affects trustworthiness. We also investigate the effect of stake on trust, and study the evolution of trust. Results show subjects exhibit some degree of trusting behavior though a majority of them are not trustworthy and claim the entire social gain. Players are more reluctant to trust in later stages than in earlier ones and are more trustworthy if they are certain of the trustee’s intention. Surprisingly, subjects are more trusting and trustworthy when the stake size increases. Finally, we find the sub- population who invests in initiating the trust building process modifies its trusting behavior based on the relative fitness of trust.Experimental Economics, Behavioral Economics

How Effective are Electronic Reputation Mechanisms? An Experimental Investigation

Electronic reputation or "feedback" mechanisms aim to mitigate the moral hazard problems associated with exchange among strangers by providing the type of information available in more traditional close-knit groups, where members are frequently involved in one another's dealings. In this paper, we compare trading in a market with online feedback (as implemented by many Internet markets) to a market without feedback, as well as to a market in which the same people interact with one another repeatedly (partners market). We find that, while the feedback mechanism induces quite a substantial improvement in transaction efficiency, it also exhibits a kind of public goods problem in that, unlike in the partners market, the benefits of trust and trustworthy behavior go to the whole community and are not completely internalized. We discuss the implications of this perspective for improving feedback systems.

Trust and Experience in Online Auctions

This paper aims to shed light on the complexities and difficulties in predicting the effects of trust and the experience of online auction participants on bid levels in online auctions. To provide some insights into learning by bidders, a field study was conducted first to examine auction and bidder characteristics from eBay auctions of rare coins. We proposed that such learning is partly because of institutional-based trust. Data were then gathered from 453 participants in an online experiment and survey, and a structural equation model was used to analyze the results. This paper reveals that experience has a nonmonotonic effect on the levels of online auction bids. Contrary to previous research on traditional auctions, as online auction bidders gain more experience, their level of institutional-based trust increases and leads to higher bid levels. Data also show that both a bidder’s selling and bidding experiences increase bid levels, with the selling experience having a somewhat stronger effect. This paper offers an in-depth study that examines the effects of experience and learning and bid levels in online auctions. We postulate this learning is because of institutional-based trust. Although personal trust in sellers has received a significant amount of research attention, this paper addresses an important gap in the literature by focusing on institutional-based trust

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

Enabling Privacy-preserving Auctions in Big Data

We study how to enable auctions in the big data context to solve many upcoming data-based decision problems in the near future. We consider the characteristics of the big data including, but not limited to, velocity, volume, variety, and veracity, and we believe any auction mechanism design in the future should take the following factors into consideration: 1) generality (variety); 2) efficiency and scalability (velocity and volume); 3) truthfulness and verifiability (veracity). In this paper, we propose a privacy-preserving construction for auction mechanism design in the big data, which prevents adversaries from learning unnecessary information except those implied in the valid output of the auction. More specifically, we considered one of the most general form of the auction (to deal with the variety), and greatly improved the the efficiency and scalability by approximating the NP-hard problems and avoiding the design based on garbled circuits (to deal with velocity and volume), and finally prevented stakeholders from lying to each other for their own benefit (to deal with the veracity). We achieve these by introducing a novel privacy-preserving winner determination algorithm and a novel payment mechanism. Additionally, we further employ a blind signature scheme as a building block to let bidders verify the authenticity of their payment reported by the auctioneer. The comparison with peer work shows that we improve the asymptotic performance of peer works' overhead from the exponential growth to a linear growth and from linear growth to a logarithmic growth, which greatly improves the scalability

The Limits of Trust in Economic Transactions - Investigations of Perfect Reputation Systems

nonetrust, reputation systems, eBay

Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges

Participatory sensing is a powerful paradigm which takes advantage of smartphones to collect and analyze data beyond the scale of what was previously possible. Given that participatory sensing systems rely completely on the users' willingness to submit up-to-date and accurate information, it is paramount to effectively incentivize users' active and reliable participation. In this paper, we survey existing literature on incentive mechanisms for participatory sensing systems. In particular, we present a taxonomy of existing incentive mechanisms for participatory sensing systems, which are subsequently discussed in depth by comparing and contrasting different approaches. Finally, we discuss an agenda of open research challenges in incentivizing users in participatory sensing.Comment: Updated version, 4/25/201