The peer-to-peer (P2P) economy relies on establishing trust in distributed networked systems, where the reliability of a user is assessed through digital peer-review processes that aggregate ratings into reputation scores. Here we present evidence of a network effect which biases digital reputation, revealing that P2P networks display exceedingly high levels of reciprocity. In fact, these are much higher than those compatible with a null assumption that preserves the empirically observed level of agreement between all pairs of nodes, and rather close to the highest levels structurally compatible with the networks' reputation landscape. This indicates that the crowdsourcing process underpinning digital reputation can be significantly distorted by the attempt of users to mutually boost reputation, or to retaliate, through the exchange of ratings. We uncover that the least active users are predominantly responsible for such reciprocity-induced bias, and that this fact can be exploited to obtain more reliable reputation estimates. Our findings are robust across different P2P platforms, including both cases where ratings are used to vote on the content produced by users and to vote on user profiles.
Evaluation relationships are pivotal for maintaining a cooperative society. A formation of the evaluation relationships has been discussed in terms of indirect reciprocity, by modeling dynamics of good or bad reputations among individuals. Recently, a situation that individuals independently evaluate others with errors (i.e., noisy and private reputation) is considered, where the reputation structure (from what proportion of individuals in the population each receives good reputations, defined as goodness here) becomes complex, and thus has been studied mainly with numerical simulations. The p…
Incomplete Reputation Information and Punishment in Indirect Reciprocity
Pukyong National University（釜庆国立大学）；RIKEN Center for Interdisciplinary Theoretical and Mathematical Science (iTHEMS)（理化学研究所交叉理论数学科学中心（iTHEMS））；RIKEN Center for Computational Science（理化学研究所计算科学中心）
We analyze an agent-based model to estimate how the costs and benefits of users in an online social network (OSN) impact the robustness of the OSN. Benefits are measured in terms of relative reputation that users receives from their followers. They can be increased by direct and indirect reciprocity in following each other, which leads to a core-periphery structure of the OSN. Costs relate to the effort to login, to maintain the profile, etc. and are assumed as constant for all users. The robustness of the OSN depends on the entry and exit of users over time. Intuitively, one would expect that…
Reputation is one of key mechanisms to maintain human cooperation, but its analysis gets complicated if we consider the possibility that reputation does not reach consensus because of erroneous assessment. The difficulty is alleviated if we assume that reputation and cooperation do not take binary values but have continuous spectra so that disagreement over reputation can be analysed in a perturbative way. In this work, we carry out the analysis by expanding the dynamics of reputation to the second order of perturbation under the assumption that everyone initially cooperates with good reputati…
University of Pennsylvania（宾夕法尼亚大学）；RIKEN Center for Interdisciplinary Theoretical and Mathematical Science (iTHEMS)（理化学研究所交叉理论与数学科学中心(iTHEMS)）；RIKEN Center for Computational Science（理化学研究所计算科学中心）；Saitama University（埼玉大学）
Individual and collective gains from cooperation and reciprocity in a dynamic-network Prisoner's Dilemma driven by extraversion, openness, and agreeableness
Departamento de Informática e Ingeniería de Sistemas, Universidad de Zaragoza（萨拉戈萨大学计算机与系统工程系）；Instituto de Investigación en Ingeniería de Aragón (I3A), Universidad de Zaragoza（阿拉贡工程研究所 (I3A)，萨拉戈萨大学）
