Eric J. Friedman

Reputation systems

Systems T he Internet offers vast new opportunities to interact with total strangers. These interactions can be fun, informative, even profitable. But they also involve risk. Is the advice of a self-proclaimed expert at expertcentral.com reliable? Will an unknown dotcom site or eBay seller ship items promptly with appropriate packaging? Will the product be the same one described online? Prior to the Internet, such questions were answered, in part, through personal and corporate reputations. Vendors provided references, Better Business Bureaus tallied complaints, and past personal experience and person-to-person gossip told you on whom you could rely and on whom you could not. Participants’ standing in their communities, including their roles in church and civic organizations, served as a valuable hostage. Internet services operate on a vastly larger scale

Dodging the grabbing hand: the determinants of unofficial activity in 69 countries

Across 69 countries, higher tax rates are associated with less unofficial activity as a percent of GDP but corruption is associated with more unofficial activity. Entrepreneurs go underground not to avoid official taxes but to reduce the burden of bureaucracy and corruption. Dodging the Grabbing Hand in this way reduces tax revenues as a percent of both official and total GDP. As a result, corrupt governments become small governments and only relatively uncorrupt governments can sustain high tax rates.

Sybilproof reputation mechanisms

Due to the open, anonymous nature of many P2P networks, new identities - or sybils - may be created cheaply and in large numbers. Given a reputation system, a peer may attempt to falsely raise its reputation by creating fake links between its sybils. Many existing reputation mechanisms are not resistant to these types of strategies.Using a static graph formulation of reputation, we attempt to formalize the notion of sybilproofness. We show that there is no symmetric sybilproof reputation function. For nonsymmetric reputations, following the notion of reputation propagation along paths, we give a general asymmetric reputation function based on flow and give conditions for sybilproofness.

Pricing WiFi at Starbucks: issues in online mechanism design

We consider the problem of designing mechanisms for online problems in which agents arrive over time and truthfully announce their arrival. These problems are becoming extremely common in a wide variety of problems involving wireless networking and webserving. We show how the standard results of mechanism design can be modified to apply to this setting, provide conditions under which efficient and incentive compatible mechanisms exist and analyze several important online models including wireless networks and web serving.

Efficiency and nash equilibria in a scrip system for P2P networks

A model of providing service in a P2P network is analyzed. It is shown that by adding a scrip system, a mechanism that admits a reasonable Nash equilibrium that reduces free riding can be obtained. The effect of varying the total amount of money (scrip) in the system on efficiency (i.e., social welfare) is analyzed, and it is shown that by maintaining the appropriate ratio between the total amount of money and the number of agents, efficiency is maximized. The work has implications for many online systems, not only P2P networks but also a wide variety of online forums for which scrip systems are popular, but formal analyses have been lacking.

Algorithmic Game Theory: Manipulation-Resistant Reputation Systems

This chapter is an overview of the design and analysis of reputation systems for strategic users. We consider three specific strategic threats to reputation systems: the possibility of users with poor reputations starting afresh (whitewashing); lack of effort or honesty in providing feedback; and sybil attacks, in which users create phantom feedback from fake identities to manipulate their own reputation. In each case, we present a simple analytical model that captures the essence of the strategy, and describe approaches to solving the strategic problem in the context of this model. We conclude with a discussion of open questions in this research area. 27.

Hierarchical scheduling for diverse datacenter workloads

There has been a recent industrial effort to develop multi-resource hierarchical schedulers. However, the existing implementations have some shortcomings in that they might leave resources unallocated or starve certain jobs. This is because the multi-resource setting introduces new challenges for hierarchical scheduling policies. We provide an algorithm, which we implement in Hadoop, that generalizes the most commonly used multi-resource scheduler, DRF [1], to support hierarchies. Our evaluation shows that our proposed algorithm, H-DRF, avoids the starvation and resource inefficiencies of the existing open-source schedulers and outperforms slot scheduling.

Fairness and efficiency in web server protocols

We consider the problem of designing a preemptive protocol that is both fair and efficient when one is only concerned with the sojourn time of the job and not intermediate results. Our Fair Sojourn Protocol (FSP) is both efficient, in a strong sense (similar to the shortest remaining processing time protocol: SRPT), and fair, in the sense of guaranteeing that it weakly outperforms processor sharing (PS) for every job on any sample path.Our primary motivation is web serving in which the standard protocol is PS, while recent work proposes using SRPT or variants. Our work suggests both a framework in which to evaluate proposed protocols and an attractive new protocol, FSP.

Test–Retest Reliability of Computational Network Measurements Derived from the Structural Connectome of the Human Brain

Structural magnetic resonance (MR) connectomics holds promise for the diagnosis, outcome prediction, and treatment monitoring of many common neurodevelopmental, psychiatric, and neurodegenerative disorders for which there is currently no clinical utility for MR imaging (MRI). Before computational network metrics from the human connectome can be applied in a clinical setting, their precision and their normative intersubject variation must be understood to guide the study design and the interpretation of longitudinal data. In this work, the reproducibility of commonly used graph theoretic measures is investigated, as applied to the structural connectome of healthy adult volunteers. Two datasets are examined, one consisting of 10 subjects scanned twice at one MRI facility and one consisting of five subjects scanned once each at two different facilities using the same imaging platform. Global graph metrics are calculated for unweighed and weighed connectomes, and two levels of granularity of the connectome are evaluated: one based on the 82-node cortical and subcortical parcellation from FreeSurfer and one based on an atlas-free parcellation of the gray-white matter boundary consisting of 1000 cortical nodes. The consistency of the unweighed and weighed edges and the module assignments are also computed for the 82-node connectomes. Overall, the results demonstrate good-to-excellent test-retest reliability for the entire connectome-processing pipeline, including the graph analytics, in both the intrasite and intersite datasets. These findings indicate that measurements of computational network metrics derived from the structural connectome have sufficient precision to be tested as potential biomarkers for diagnosis, prognosis, and monitoring of interventions in neurological and psychiatric diseases.

Learning in Network Contexts: Experimental Results from Simulations

This paper describes the results of simulation experiments performed on a suite of learning algorithms. We focus on games in {\em network contexts}. These are contexts in which (1) agents have very limited information about the game; users do not know their own (or any other agents) payoff function, they merely observe the outcome of their play. (2) Play can be extremely asynchronous; players update their strategies at very different rates. There are many proposed learning algorithms in the literature. We choose a small sampling of such algorithms and use numerical simulation to explore the nature of asymptotic play. In particular, we explore the extent to which the asymptotic play depends on three factors, namely: limited information, asynchronous play, and the degree of responsiveness of the learning algorithm.