http://www.hpl.hp.com/research/idl/ Recent papers from HP Labs' Information Dynamics Lab http://www.hpl.hp.com/personal/Thomas_Sandholm/sandholm2008a.pdf Tradeoffs between using spot and reservation markets. We propose, implement and evaluate three admission models for computational Grids. The models take the expected demand into account and offer a specific performance guarantee. The main issue addressed is how users and providers should make the tradeoff between a best effort (low guarantee) spot market and an admission controlled (high guarantee) reservation market. Using a realistically modeled high performance computing workload and utility models of user preferences, we run experiments highlighting the conditions under which different markets and admission models are efficient. The experimental results show that providers can make large efficiency gains if the admission model is chosen dynamically based on the current load, likewise we show that users have an opportunity to optimize their job performance by carefully picking the right market based on the state of the system, and the characteristics of the application to be run. Finally, we provide simple functional expressions that can guide both users and providers when making decisions about guarantee levels to request or offer. Thomas Sandholm, Kevin Lai, and Scott Clearwater 2008-06-06 12:00:00 http://www.hpl.hp.com/research/idl/papers/opinion_expression/ How opinions evolve online. We performed a massive study of the dynamics of group delibera- tion among several websites containing millions of opinions on topics ranging from books to media. Contrary to the common phenomenon of group polarization observed offline, we measured a strong tendency towards moderate views in the course of time. This phenomenon possi- bly operates through a self-selection bias whereby previous comments and ratings elicit contrarian views that soften the previous opinions. Fang Wu and Bernardo A. Huberman 2008-05-15 17:00:00 Dennis M. Wilkinson 2008-04-10 00:00:00 Online peer production systems have enabled people to coactively create, share, classify, and rate content on an unprecedented scale. This paper describes strong macroscopic regularities in how people contribute to peer production systems, and shows how these regularities arise from simple dynamical rules. First, it is demonstrated that the probability a person stops contributing varies inversely with the number of contributions he has made. This rule leads to a power law distribution for the number of contributions per person in which a small number of very active users make most of the contributions. The rule also implies that the power law exponent is proportional to the effort required to contribute, as justified by the data. Second, the level of activity per topic is shown to follow a lognormal distribution generated by a stochastic reinforcement mechanism. A small number of very popular topics thus accumulate the vast majority of contributions. These trends are demonstrated to hold across hundreds of millions of contributions to four disparate peer production systems of differing scope, interface style, and purpose.. Simple behavioral rules hold across hundreds of millions of contributions to disparate online peer production efforts. http://www.hpl.hp.com/research/scl/papers/regularities/ Scott A. Golder 2008-04-09 00:00:00 The ease and lack of cost associated with taking digital photographs have allowed people to amass large personal photograph collections. These collections contain valuable information about their owners' social relationships. This paper is a preliminary investigation into how digital photo collections can provide useful data for the study of social networks. Results from an analysis of 23 subjects photo collections demonstrate the feasibility of this approach. The relationship between perceived closeness and network position, as well as future questions, are also discussed. Digital photo archives contain valuable information about individuals' social networks. http://www.hpl.hp.com/research/scl/papers/sna-photos/ Tad Hogg and Gabor Szabo 2008-03-25 0:00:00 Web sites where users create and rate content as well as form networks with other users display long-tailed distributions in many aspects of behavior. Using behavior on one such community site, Essembly, we propose and evaluate plausible mechanisms to explain these behaviors. Unlike purely descriptive models, these mechanisms rely on user behaviors based on information available locally to each user. For Essembly, we find the long-tails arise from large differences among user activity rates and qualities of the rated content, as well as the extensive variability in the time users devote to the site. We show that the models not only explain overall behavior but also allow estimating the quality of content from their early behaviors. http://arxiv.org/abs/0803.3482 Diversity among users and the content they create in the Essembly web site Fang Wu and Bernardo A. Huberman 2008-01-24 0:00:00 We analyze the role that popularity and novelty play in attracting the attention of users to dynamic websites. We do so by determining the performance of three different strategies that can be utilized to maximize attention. The first one prioritizes novelty while the second emphasizes popularity. A third strategy looks myopically into the future and prioritizes stories that are expected to generate the most clicks within the next few minutes. We show that the first two strategies should be selected on the basis of the rate of novelty decay, while the third strategy performs sub-optimally in most cases. We also demonstrate that the relative performance of the first two strategies as a function of the rate of novelty decay changes abruptly around a critical value, resembling a phase transition in the physical world. http://www.hpl.hp.com/research/idl/papers/popularity/popularity.pdf Whether to use popularity or novelty to elicit attention Tad Hogg, Gabor Szabo, Dennis M. Wilkinson, and Michael J. Brzozowski 2008-01-12 0:00:00 Traditionally, online social network sites like Facebook and MySpace allow people to form links to "friends" but do little to qualify the semantic meaning of the friendship. As a result, many users "collect" friends on these sites, conflating "acquaintances" with "friends". Essembly, a "fiercely non-partisan social network", on the other hand, lets its users enrich the meaning of their relations to others by explicitly labeling them "friends", "allies", or "nemeses". Essembly then allows its members to post resolves reflecting controversial opinions on political issues. As a defining activity on the site, members can vote on these resolves on a four-point scale ranging from complete agreement to full opposition. We examined how the uncommon link semantics affects users in casting their votes. In particular, Essembly prominently highlights the activities of users' acquaintances, and the question arises if this makes them more likely to participate, and if so, how this information affects votes. It is widely assumed that social networks enhance, if not drive, the popularity of online services; what does an additional layer of link classification add to them? Papers to appear at CHI 2008 and AAAI Spring Symposium on Social Information Processing 2008. http://www.hpl.hp.com/research/idl/papers/essembly Examines the usefulness of distinguishing between friends and similar/dissimilar users in propagating new content in an online social network, and suggests resulting design implications for social content aggregation services and recommender systems. http://www.hpl.hp.com/personal/Thomas_Sandholm/sandholm2007a.pdf Applying Occam's razor to statistics to enable risk preference multiplexing. We study stochastic models to mitigate the risk of poor Quality-of-Service (QoS) in computational markets. Consumers who purchase services expect both price and performance guarantees. They need to predict future demand to budget for sustained performance despite price fluctuations. Conversely, providers need to estimate demand to price future usage. The skewed and bursty nature of demand in large-scale computer networks challenges the common statistical assumptions of symmetry, independence, and stationarity. This discrepancy leads to underestimation of investment risk. We confirm this non-normal distribution behavior in our study of demand in computational markets. Thomas Sandholm and Kevin Lai 2007-07-12 14:08:00