Biweekly links for 04/17/2009
- Pooling of Unshared Information in Group Decision Making: Biased Information Sampling During Discussion
- “Decision-making groups can potentially benefit from pooling members’ information, particularly when members individually have partial and biased information but collectively can compose an unbiased characterization of the decision alternatives. The proposed biased sampling model of group discussion, however, suggests that group members often fail to effectively pool their information because discussion tends to be dominated by (a) information that members hold in common before discussion and (b) information that supports members’ existent preferences. In a political caucus simulation, group members individually read candidate descriptions that contained partial information biased against the most favorable candidate and then discussed the candidates as a group. Even though groups could have produced unbiased composites of the candidates through discussion, they decided in favor of the candidate initially preferred by a plurality rather than the most favorable candidate…”
- SciBarCamp Toronto 2
- SciBarCamp Toronto 2 is happening May 8-9, Hart House, Toronto. See the Participant page to register!
- Killer Bean Forever
- Feature-length animated movie animated entirely by one person, Jeff Lew (of the Matrix Reloaded). Will be released on DVD in July (US and Canada).
- arXiview: A New iPhone App for the arXiv
- Browse the preprint arXiv from your iPhone.
- A Comparison of Approaches to Large-Scale Data Analysis
- “There is currently considerable enthusiasm around the MapReduce (MR) paradigm.. Although the basic control flow of this framework has existed in parallel SQL database management systems (DBMS) for over 20 years, some have called MR a dramatically new computing model [8, 17]. In this paper… we evaluate both kinds of systems in terms of performance and development complexity… we define a benchmark consisting of a collection of tasks that we have run on an open source version of MR as well as on two parallel DBMSs. For each task, we measure each system’s performance for various degrees of parallelism on a cluster of 100 nodes… Although the process to load data into and tune the execution of parallel DBMSs took much longer than the MR system, the observed performance of these DBMSs was strikingly better. We speculate about the causes of the dramatic performance difference and consider implementation concepts that future systems should take from both kinds of architectures”
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