Human Matching Behavior in Social Networks: An Algorithmic Perspective

• Published in: PloS one Vol. 7; no. 8; p. e41900
• Main Authors: Coviello, Lorenzo, Franceschetti, Massimo, McCubbins, Mathew D., Paturi, Ramamohan, Vattani, Andrea
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 22.08.2012; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Behavior; Biology; Computer engineering; Computer Science; Computer simulation; Cooperation; Decision making; Electronic commerce; Engineering; Experiments; Graduate students; Human behavior; Human subjects; Humans; Information processing; Knowledge management; Matching; Mathematical models; Mathematics; Medicine; Models, Theoretical; Network topologies; Networks; Phase transitions; Probability; Social and Behavioral Sciences; Social behavior; Social Networking; Social networks; Social organization; Topology; United States; La Jolla California; United States > US; California
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0041900

We argue that algorithmic modeling is a powerful approach to understanding the collective dynamics of human behavior. We consider the task of pairing up individuals connected over a network, according to the following model: each individual is able to propose to match with and accept a proposal from a neighbor in the network; if a matched individual proposes to another neighbor or accepts another proposal, the current match will be broken; individuals can only observe whether their neighbors are currently matched but have no knowledge of the network topology or the status of other individuals; and all individuals have the common goal of maximizing the total number of matches. By examining the experimental data, we identify a behavioral principle called prudence, develop an algorithmic model, analyze its properties mathematically and by simulations, and validate the model with human subject experiments for various network sizes and topologies. Our results include i) a 1/2-approximate maximum matching is obtained in logarithmic time in the network size for bounded degree networks; ii) for any constant ε > 0, a (1 - ε)-approximate maximum matching is obtained in polynomial time, while obtaining a maximum matching can require an exponential time; and iii) convergence to a maximum matching is slower on preferential attachment networks than on small-world networks. These results allow us to predict that while humans can find a "good quality" matching quickly, they may be unable to find a maximum matching in feasible time. We show that the human subjects largely abide by prudence, and their collective behavior is closely tracked by the above predictions.