an underlying game-theoretic or economic model, and all are conducted via monetary incentives at the level of individual subjects. They can thus be viewed as experiments in behavioral economics, 1 but taking place in ( artificial) social networks, an area of growing interest but with little prior experimental literature. In some cases we can make detailed comparisons between behavior and equilibrium predictions, and find systematic (and therefore potentially rectifiable) differences, such as networked instances of phenomena like inequality aversion.

˲ Network science. Network Science is itself an interdisciplinary and emerging area9, 25 that seeks to document “universal” structural properties of social and other large-scale networks, and ask how they might form and influence network formation and dynamics. Our experiments can be viewed as extending this line of questioning into a laboratory setting with human subjects, and examining the ways in which network structure influences human behavior, strategies, and performance.

˲Computational social science. While our experimental designs have often emphasized collective problem solving, it is an inescapable fact that individual human subjects make up the collective, and individual decision-making, strategies, and personalities influence the outcomes. What are these influences, and in what ways do they matter? In many of our experiments there are natural and quantifiable notions of traits like stubbornness, stability, and cooperation whose variation across subjects can be measured and correlated with collective behavior and performance, and in turn used to develop simple computational models of individual behavior for predictive and explanatory purposes.

This article surveys our experiments and results to date, emphasizing overall collective performance, behavioral phenomena arising repeatedly across different tasks, task- and network-spe-cific findings that are particularly striking, and the overall methodology and analyses employed. It is worth noting at the outset that one of the greatest challenges posed by this line of work has been the enormous size of the design space: each experimental session involves the selection of a collective prob-

While our
experimental
designs have
often emphasized
collective problem
solving, it is an
inescapable fact
that individual
human subjects
make up the
collective, and
individual decision-
making, strategies,
and personalities
influence the
outcomes.

lem, a set of network structures, their decomposition into local interactions and subject incentives, and values for many other design variables. Early on we were faced with a choice between breadth and depth—that is, designing experiments to try to populate many points in this space, or picking very specific types of problems and networks, and examining these more deeply over the years. Since the overarching goal of the project has been to explore the broad themes and questions here, and to develop early pieces of a behavioral science of human computation in networked settings, we have opted for breadth, making direct comparisons between some of our experiments difficult. Clearly much more work is needed for a comprehensive picture to emerge.

In the remainder of this article, I describe the methodology of our experiments, including the system and its GUIs, human subject methodology, and session design. I then summarize our experiments to date and remark on findings that are common to all or most of the different tasks and highlight more specific experimental results on a task-by-task basis.

 

Experimental methodology All of the experiments discussed here were held over a roughly six-year period, in a series of approximately two-hour sessions in the same laboratory of workstations at the University of Pennsylvania. The experiments used an extensive software, network and visualization platform we have developed for this line of research, and which has been used by colleagues at other institutions as well. In all experiments the number of simultaneous subjects was approximately 36, and almost all of the subjects were drawn from Penn undergraduates taking a survey course on the science of social networks. 12 Each experimental session was preceded by a training and demonstration period in which the task, financial incentives, and GUI were explained, and a practice game was held. Sessions were closely proctored to make sure subjects were attending to their workstation and understood the rules and GUI; under no circumstances was advice on strategy provided. Physical partitions were erected around workstations to ensure subjects could only see their own GUI.