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Resolving a Replication That Failed: News on the Macy & Sato Model

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Abstract

The paper at hand aimes at identifying the assumptions that lead to the results presented in an article by Michael Macy and Yoshimichi Sato published in PNAS. In answer to a failed replication, the authors provided the source code of their model and here the results of carefully studying that code are presented. The main finding is that the simulation program implements an assumption that is most probably an unwilling, unintended, and unwanted implication of the code. This implied assumption is never mentioned in Macy and Sato's article and if the authors wanted to program what they describe in their article then it is due to a programming error. After introducing the reader to the discussion, data that stem from a new replication based on the assumptions extracted from the source code is compared with the results published in Macy and Sato's original article. The replicated results are sufficiently similar to serve as a strong indicator that this new replication implements the same relevant assumptions as the original model. Afterwards it is shown that a removal of the dubious assumption leads to results that are dramatically different from those published in Macy and Sato's PNAS article.

Suggested Citation

  • Oliver Will, 2009. "Resolving a Replication That Failed: News on the Macy & Sato Model," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(4), pages 1-11.
    • Handle: RePEc:jas:jasssj:2009-78-2
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    1. Michael Macy & Yoshimichi Sato, 2008. "Reply to Will and Hegselmann," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 11(4), pages 1-11.
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    Cited by:

    1. Wolfgang Radax & Bernhard Rengs, 2010. "Prospects and Pitfalls of Statistical Testing: Insights from Replicating the Demographic Prisoner's Dilemma," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 13(4), pages 1-1.
    2. Juliette Rouchier & Emily Tanimura, 2016. "Learning with Communication Barriers Due to Overconfidence. What a "Model-To-Model Analysis" Can Add to the Understanding of a Problem," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 19(2), pages 1-7.
    3. Flaminio Squazzoni, 2010. "The impact of agent-based models in the social sciences after 15 years of incursions," History of Economic Ideas, Fabrizio Serra Editore, Pisa - Roma, vol. 18(2), pages 197-234.
    4. Edmund Chattoe-Brown, 2013. "Why Sociology Should Use Agent Based Modelling," Sociological Research Online, , vol. 18(3), pages 31-41, August.

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