Time Critical Social Mobilization: The DARPA Network Challenge Winning Strategy

It is now commonplace to see the Web as a platform that can harness the collective abilities of large numbers of people to accomplish tasks with unprecedented speed, accuracy and scale. To push this idea to its limit, DARPA launched its Network Challenge, which aimed to "explore the roles the Internet and social networking play in the timely communication, wide-area team-building, and urgent mobilization required to solve broad-scope, time-critical problems." The challenge required teams to provide coordinates of ten red weather balloons placed at different locations in the continental United States. This large-scale mobilization required the ability to spread information about the tasks widely and quickly, and to incentivize individuals to act. We report on the winning team's strategy, which utilized a novel recursive incentive mechanism to find all balloons in under nine hours. We analyze the theoretical properties of the mechanism, and present data about its performance in the challenge.Comment: 25 pages, 6 figure

Quantifying Social Influence in an Online Cultural Market

We revisit experimental data from an online cultural market in which 14,000 users interact to download songs, and develop a simple model that can explain seemingly complex outcomes. Our results suggest that individual behavior is characterized by a two-step process–the decision to sample and the decision to download a song. Contrary to conventional wisdom, social influence is material to the first step only. The model also identifies the role of placement in mediating social signals, and suggests that in this market with anonymous feedback cues, social influence serves an informational rather than normative role

use of knowledge structures in optimal information aggregation

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2006.Includes bibliographical references (leaf 35).In this thesis, I present some novel results pertaining to the relationship between two popular and interesting information aggregation methods: the Condorcet and Borda tallies. I present numerical results showing how the much simpler Borda tally can be used to approximate the outcome of the Condorcet tally with high probability in certain circumstances, a proof that there exist classes of problems for which the two tallies can never agree, and an extension of these results to small-world graphs, which have been of great interest recently due to their practical applicability to many complex problems.bu Galen Pickard.M.Eng

Ověření tvařitelnosti šroubové oceli 23MNB4T pomocí tahových a pěchovacích zkoušek

Import 20/04/2006Prezenční výpůjčkaVŠB - Technická univerzita Ostrava. Fakulta strojní. Katedra (345) mechanické technologi

Quality and appeal are independent.

<p>Values are shown for quality and appeal corresponding to the 48 songs in Experiment 2, independent condition. <i>R</i>² = 0.012.</p

Availability in the independent world of Experiments 1 (A, top) and 2 (B, bottom), indexed to 1.

<p>The availability of a position n describes the likelihood that a song in that position will be sampled (where n = 1 is the top left corner in Experiment 1, and the topmost position in Experiment 2, and n = 48 is the bottom right corner in Experiment 1 and the bottom of the column in Experiment 2). Availability serves as a multiplier in calculating the total probability of a song being sampled, given its position-independent appeal, and its position at a given time in the market. In Experiment 1 (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033785#pone-0033785-g002" target="_blank">Figure 2a</a>), songs on the left side of the grid are more likely to be sampled, all else equal, than songs on the right. In Experiment 2 (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033785#pone-0033785-g002" target="_blank">Figure 2b</a>), songs at the top of the column, as well as the final song, are more likely to be sampled.</p

Inequality (top) and unpredictability (bottom) over the course of the market, with alpha = 900.

<p>Inequality is shown for Experiment 1, world 3. RMSE of simulated market’s unpredictability is = 0.0017, and average of inequality is = 0.093.</p