A pull versus push framework for reputation

Reputation is a powerful driver of human behavior. Reputation systems incentivize 'actors' to take reputation-enhancing actions, and 'evaluators' to reward actors with positive reputations by preferentially cooperating with them. This article proposes a reputation framework that centers the perspective of evaluators by suggesting that reputation systems can create two fundamentally different incentives for evaluators to reward positive reputations. Evaluators may be pulled towards 'good' actors to benefit directly from their reciprocal cooperation, or pushed to cooperate with such actors by normative pressure. I discuss how psychology and behavior might diverge under pull versus push mechanisms, and use this framework to deepen our understanding of the empirical reputation literature and suggest ways that we may better leverage reputation for social good.

Data Trustworthiness Evaluation in Mobile Crowdsensing Systems with Users' Trust Dispositions' Consideration

Mobile crowdsensing is a powerful paradigm that exploits the advanced sensing capabilities and ubiquity of smartphones in order to collect and analyze data on a scale that is impossible with fixed sensor networks. Mobile crowdsensing systems incorporate people and rely on their participation and willingness to contribute up-to-date and accurate information, meaning that such systems are prone to malicious and erroneous data. Therefore, trust and reputation are key factors that need to be addressed in order to ensure sustainability of mobile crowdsensing systems. The objective of this work is to define the conceptual trust framework that considers human involvement in mobile crowdsensing systems and takes into account that users contribute their opinions and other subjective data besides the raw sensing data generated by their smart devices. We propose a novel method to evaluate the trustworthiness of data contributed by users that also considers the subjectivity in the contributed data. The method is based on a comparison of users' trust attitudes and applies nonparametric statistic methods. We have evaluated the performance of our method with extensive simulations and compared it to the method proposed by Huang that adopts Gompertz function for rating the contributions. The simulation results showed that our method outperforms Huang's method by 28.6% on average and the method without data trustworthiness calculation by 33.6% on average in different simulation settings.

Reputation offsets trust judgments based on social biases among Airbnb users

To provide social exchange on a global level, sharing-economy companies leverage interpersonal trust between their members on a scale unimaginable even a few years ago. A challenge to this mission is the presence of social biases among a large heterogeneous and independent population of users, a factor that hinders the growth of these services. We investigate whether and to what extent a sharing-economy platform can design artificially engineered features, such as reputation systems, to override people's natural tendency to base judgments of trustworthiness on social biases. We focus on the common tendency to trust others who are similar (i.e., homophily) as a source of bias. We test this argument through an online experiment with 8,906 users of Airbnb, a leading hospitality company in the sharing economy. The experiment is based on an interpersonal investment game, in which we vary the characteristics of recipients to study trust through the interplay between homophily and reputation. Our findings show that reputation systems can significantly increase the trust between dissimilar users and that risk aversion has an inverse relationship with trust given high reputation. We also present evidence that our experimental findings are confirmed by analyses of 1 million actual hospitality interactions among users of Airbnb.

Reputation and Reward: Two Sides of the Same Bitcoin

In Mobile Crowd Sensing (MCS), the power of the crowd, jointly with the sensing capabilities of the smartphones they wear, provides a new paradigm for data sensing. Scenarios involving user behavior or those that rely on user mobility are examples where standard sensor networks may not be suitable, and MCS provides an interesting solution. However, including human participation in sensing tasks presents numerous and unique research challenges. In this paper, we analyze three of the most important: user participation, data sensing quality and user anonymity. We tackle the three as a whole, since all of them are strongly correlated. As a result, we present PaySense, a general framework that incentivizes user participation and provides a mechanism to validate the quality of collected data based on the users’ reputation. All such features are performed in a privacy-preserving way by using the Bitcoin cryptocurrency. Rather than a theoretical one, our framework has been implemented, and it is ready to be deployed and complement any existing MCS system.

How feedback biases give ineffective medical treatments a good reputation

BACKGROUND

Medical treatments with no direct effect (like homeopathy) or that cause harm (like bloodletting) are common across cultures and throughout history. How do such treatments spread and persist? Most medical treatments result in a range of outcomes: some people improve while others deteriorate. If the people who improve are more inclined to tell others about their experiences than the people who deteriorate, ineffective or even harmful treatments can maintain a good reputation.

OBJECTIVE

The intent of this study was to test the hypothesis that positive outcomes are overrepresented in online medical product reviews, to examine if this reputational distortion is large enough to bias people's decisions, and to explore the implications of this bias for the cultural evolution of medical treatments.

METHODS

We compared outcomes of weight loss treatments and fertility treatments in clinical trials to outcomes reported in 1901 reviews on Amazon. Then, in a series of experiments, we evaluated people's choice of weight loss diet after reading different reviews. Finally, a mathematical model was used to examine if this bias could result in less effective treatments having a better reputation than more effective treatments.

RESULTS

Data are consistent with the hypothesis that people with better outcomes are more inclined to write reviews. After 6 months on the diet, 93% (64/69) of online reviewers reported a weight loss of 10 kg or more while just 27% (19/71) of clinical trial participants experienced this level of weight change. A similar positive distortion was found in fertility treatment reviews. In a series of experiments, we show that people are more inclined to begin a diet with many positive reviews, than a diet with reviews that are representative of the diet's true effect. A mathematical model of medical cultural evolution shows that the size of the positive distortion critically depends on the shape of the outcome distribution.

CONCLUSIONS

Online reviews overestimate the benefits of medical treatments, probably because people with negative outcomes are less inclined to tell others about their experiences. This bias can enable ineffective medical treatments to maintain a good reputation.

Detecting unknown attacks in wireless sensor networks that contain mobile nodes

As wireless sensor networks are usually deployed in unattended areas, security policies cannot be updated in a timely fashion upon identification of new attacks. This gives enough time for attackers to cause significant damage. Thus, it is of great importance to provide protection from unknown attacks. However, existing solutions are mostly concentrated on known attacks. On the other hand, mobility can make the sensor network more resilient to failures, reactive to events, and able to support disparate missions with a common set of sensors, yet the problem of security becomes more complicated. In order to address the issue of security in networks with mobile nodes, we propose a machine learning solution for anomaly detection along with the feature extraction process that tries to detect temporal and spatial inconsistencies in the sequences of sensed values and the routing paths used to forward these values to the base station. We also propose a special way to treat mobile nodes, which is the main novelty of this work. The data produced in the presence of an attacker are treated as outliers, and detected using clustering techniques. These techniques are further coupled with a reputation system, in this way isolating compromised nodes in timely fashion. The proposal exhibits good performances at detecting and confining previously unseen attacks, including the cases when mobile nodes are compromised.

Improving social odometry robot networks with distributed reputation systems for collaborative purposes

The improvement of odometry systems in collaborative robotics remains an important challenge for several applications. Social odometry is a social technique which confers the robots the possibility to learn from the others. This paper analyzes social odometry and proposes and follows a methodology to improve its behavior based on cooperative reputation systems. We also provide a reference implementation that allows us to compare the performance of the proposed solution in highly dynamic environments with the performance of standard social odometry techniques. Simulation results quantitatively show the benefits of this collaborative approach that allows us to achieve better performances than social odometry.