Urban characteristics attributable to density-driven tie formation

Motivated by empirical evidence on the interplay between geography, population density and societal interaction, we propose a generative process for the evolution of social structure in cities. Our analytical and simulation results predict both super-linear scaling of social tie density and information flow as a function of the population. We demonstrate that our model provides a robust and accurate fit for the dependency of city characteristics with city size, ranging from individual-level dyadic interactions (number of acquaintances, volume of communication) to population-level variables (contagious disease rates, patenting activity, economic productivity and crime) without the need to appeal to modularity, specialization, or hierarchy.Comment: Early version of this paper was presented in NetSci 2012 as a contributed talk in June 2012. An improved version of this paper is published in Nature Communications in June 2013. It has 14 pages and 5 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

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

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

Values of unpredictability and inequality from simulated (polya) and original (SDW) markets.

*<p>NS = non-social</p>**<p>averaged from sub-populations</p><p>The inequality (measured by Gini) is a mean of the results of 8 worlds. In the original experiments, the value for non-social unpredictability was determined by taking subpopulation of the independent condition. The simulated value for unpredictability is determined from 8 simulated non-social worlds.</p

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