2026-09-18T21:47:02Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1190852021-07-05T14:03:20Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
César Hidalgo.
Guruprasad, Sanjay
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
2018-11-15T16:35:55Z
2018-11-15T16:35:55Z
2018
2018
http://hdl.handle.net/1721.1/119085
1057897623
Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 26-28).
Many behaviors spread through social contact. However, different behaviors seem to require different degrees of social reinforcement to spread within a network. Some behaviors spread via simple contagion, where a single contact with an "activated node" is sufficient for transmission, while others require complex contagion, with reinforcement from multiple nodes to adopt the behavior. But why do some behaviors require more social reinforcement to spread than others? Here we hypothesize that learning more difficult behaviors requires more social reinforcement. We test this hypothesis by analyzing the programming language adoption of hundreds of thousands of programmers on the social coding platform Github. We show that adopting more difficult programming languages requires more reinforcement from the collaboration network. This research sheds light on the role of collaboration networks in programming language acquisition.
by Sanjay Guruprasad.
S.M.
28 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Program in Media Arts and Sciences ()
The influence of collaboration networks on programming language acquisition
Thesis
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<Abstract>Many behaviors spread through social contact. However, different behaviors seem to require different degrees of social reinforcement to spread within a network. Some behaviors spread via simple contagion, where a single contact with an "activated node" is sufficient for transmission, while others require complex contagion, with reinforcement from multiple nodes to adopt the behavior. But why do some behaviors require more social reinforcement to spread than others? Here we hypothesize that learning more difficult behaviors requires more social reinforcement. We test this hypothesis by analyzing the programming language adoption of hundreds of thousands of programmers on the social coding platform Github. We show that adopting more difficult programming languages requires more reinforcement from the collaboration network. This research sheds light on the role of collaboration networks in programming language acquisition.</Abstract>
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