Showing papers by "Duncan J. Watts published in 2009"

Financial incentives and the "performance of crowds"

Abstract: The relationship between financial incentives and performance, long of interest to social scientists, has gained new relevance with the advent of web-based "crowd-sourcing" models of production. Here we investigate the effect of compensation on performance in the context of two experiments, conducted on Amazon's Mechanical Turk (AMT). We find that increased financial incentives increase the quantity, but not the quality, of work performed by participants, where the difference appears to be due to an "anchoring" effect: workers who were paid more also perceived the value of their work to be greater, and thus were no more motivated than workers paid less. In contrast with compensation levels, we find the details of the compensation scheme do matter---specifically, a "quota" system results in better work for less pay than an equivalent "piece rate" system. Although counterintuitive, these findings are consistent with previous laboratory studies, and may have real-world analogs as well.

Origins of Homophily in an Evolving Social Network

Abstract: The authors investigate the origins of homophily in a large university community, using network data in which interactions, attributes, and affiliations are all recorded over time. The analysis indicates that highly similar pairs do show greater than average propensity to form new ties; however, it also finds that tie formation is heavily biased by triadic closure and focal closure, which effectively constrain the opportunities among which individuals may select. In the case of triadic closure, moreover, selection to “friend of a friend” status is determined by an analogous combination of individual preference and structural proximity. The authors conclude that the dynamic interplay of choice homophily and induced homophily, compounded over many “generations” of biased selection of similar individuals to structurally proximate positions, can amplify even a modest preference for similar others, via a cumulative advantage–like process, to produce striking patterns of observed homophily.

Web-based experiments for the study of collective social dynamics in cultural markets

Abstract: Social scientists are often interested in understanding how the dynamics of social systems are driven by the behavior of individuals that make up those systems. However, this process is hindered by the difficulty of experimentally studying how individual behavioral tendencies lead to collective social dynamics in large groups of people interacting over time. In this study, we investigate the role of social influence, a process well studied at the individual level, on the puzzling nature of success for cultural products such as books, movies, and music. Using a "multiple-worlds" experimental design, we are able to isolate the causal effect of an individual-level mechanism on collective social outcomes. We employ this design in a Web-based experiment in which 2,930 participants listened to, rated, and downloaded 48 songs by up-and-coming bands. Surprisingly, despite relatively large differences in the demographics, behavior, and preferences of participants, the experimental results at both the individual and collective levels were similar to those found in Salganik, Dodds, and Watts (2006). Further, by comparing results from two distinct pools of participants, we are able to gain new insights into the role of individual behavior on collective outcomes. We conclude with a discussion of the strengths and weaknesses of Web-based experiments to address questions of collective social dynamics.

Characterizing individual communication patterns

Abstract: The increasing availability of electronic communication data, such as that arising from e-mail exchange, presents social and information scientists with new possibilities for characterizing individual behavior and, by extension, identifying latent structure in human populations. Here, we propose a model of individual e-mail communication that is sufficiently rich to capture meaningful variability across individuals, while remaining simple enough to be interpretable. We show that the model, a cascading non-homogeneous Poisson process, can be formulated as a double-chain hidden Markov model, allowing us to use an efficient inference algorithm to estimate the model parameters from observed data. We then apply this model to two e-mail data sets consisting of 404 and 6,164 users, respectively, that were collected from two universities in different countries and years. We find that the resulting best-estimate parameter distributions for both data sets are surprisingly similar, indicating that at least some features of communication dynamics generalize beyond specific contexts. We also find that variability of individual behavior over time is significantly less than variability across the population, suggesting that individuals can be classified into persistent "types". We conclude that communication patterns may prove useful as an additional class of attribute data, complementing demographic and network data, for user classification and outlier detection-a point that we illustrate with an interpretable clustering of users based on their inferred model parameters.

Characterizing Individual Communication Patterns

Abstract: The increasing availability of electronic communication data, such as that arising from e-mail exchange, presents social and information scientists with new possibilities for characterizing individual behavior and, by extension, identifying latent structure in human populations. Here, we propose a model of individual e-mail communication that is sufficiently rich to capture meaningful variability across individuals, while remaining simple enough to be interpretable. We show that the model, a cascading non-homogeneous Poisson process, can be formulated as a double-chain hidden Markov model, allowing us to use an efficient inference algorithm to estimate the model parameters from observed data. We then apply this model to two e-mail data sets consisting of 404 and 6,164 users, respectively, that were collected from two universities in different countries and years. We find that the resulting best-estimate parameter distributions for both data sets are surprisingly similar, indicating that at least some features of communication dynamics generalize beyond specific contexts. We also find that variability of individual behavior over time is significantly less than variability across the population, suggesting that individuals can be classified into persistent "types". We conclude that communication patterns may prove useful as an additional class of attribute data, complementing demographic and network data, for user classification and outlier detection--a point that we illustrate with an interpretable clustering of users based on their inferred model parameters.

Social search in "Small-World" experiments

Abstract: The "algorithmic small-world hypothesis" states that not only are pairs of individuals in a large social network connected by short paths, but that ordinary individuals can find these paths. Although theoretically plausible, empirical evidence for the hypothesis is limited, as most chains in "small-world" experiments fail to complete, thereby biasing estimates of "true" chain lengths. Using data from two recent small-world experiments, comprising a total of 162,328 message chains, and directed at one of 30 "targets" spread across 19 countries, we model heterogeneity in chain attrition rates as a function of individual attributes. We then introduce a rigorous way of estimating true chain lengths that is provably unbiased, and can account for empirically-observed variation in attrition rates. Our findings provide mixed support for the algorithmic hypothesis. On the one hand, it appears that roughly half of all chains can be completed in 6-7 steps--thus supporting the "six degrees of separation" assertion--but on the other hand, estimates of the mean are much longer, suggesting that for at least some of the population, the world is not "small" in the algorithmic sense. We conclude that search distances in social networks are fundamentally different from topological distances, for which the mean and median of the shortest path lengths between nodes tend to be similar.