More from these Authors

• Working Paper | HBS Working Paper Series | 2013

  Hidden Structure: Using Network Methods to Map System Architecture

  Carliss Y. Baldwin, Alan MacCormack and John Rusnak

  In this paper, we describe an operational methodology for characterizing the architecture of technical systems and demonstrate its application to a large sample of software releases. Our methodology is based upon network graphs and allows us to identify and define three fundamental architectural patterns, which we call core-periphery, multi-core, and hierarchical. We apply our methodology to a sample of 1,286 software releases from 17 applications and find that 70% to 80% of these systems possess a core-periphery architecture under our classification scheme. This type of architecture is characterized by having a single dominant cyclic group (the Core) that is large relative to other cyclic groups and above a threshold with respect to system size. We find that the size of the Core varies widely, even for systems that perform the same function. These differences appear to be associated with different models of development—open, distributed organizations tend to develop systems with smaller Cores, while closed collocated organizations tend to develop systems with larger Cores. Our findings represent a first step in establishing some "stylized facts" about the fine-grained structure of large, real-world technical systems.

  Citation:

  Baldwin, Carliss Y., Alan MacCormack, and John Rusnak. "Hidden Structure: Using Network Methods to Map System Architecture." Harvard Business School Working Paper, No. 13–093, May 2013.

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• Supplement | HBS Case Collection | 2013

  The Congressional Oversight Panel's Valuation of the TARP Warrants (A) (CW)

  Carliss Y. Baldwin

  Citation:

  Baldwin, Carliss Y. "The Congressional Oversight Panel's Valuation of the TARP Warrants (A) (CW)." Harvard Business School Spreadsheet Supplement 213-704, April 2013.

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• Article | Nature Biotechnology | February, 2013

  Prize-based Contests Can Provide Solutions to Computational Biology Problems

  Karim R. Lakhani, Kevin Boudreau, Eva C. Guinan, Carliss Y. Baldwin, Alan MacCormack, Eric Lonstein, Mike Lydon and Ramy A Arnaout

  Advances in biotechnology have fueled the generation of unprecedented quantities of data across the life sciences. However, finding analysts who can address such "big data" problems effectively has become a significant research bottleneck. Historically, prize-based contests have had striking success in attracting unconventional individuals who can overcome difficult challenges. To determine whether this approach could solve a real big-data biologic algorithm problem, we used a complex immunogenomics problem as the basis for a two-week online contest broadcast to participants outside academia and biomedical disciplines. Participants in our contest produced over 600 submissions containing 89 novel computational approaches to the problem. Thirty submissions exceeded the benchmark performance of the U.S. National Institutes of Health's MegaBLAST. The best achieved both greater accuracy and speed (1,000 times greater). Here we show the potential of using online prize-based contests to access individuals without domain-specific backgrounds to address big-data challenges in the life sciences.

  Citation:

  Lakhani, Karim R., Kevin Boudreau, Eva C. Guinan, Carliss Y. Baldwin, Alan MacCormack, Eric Lonstein, Mike Lydon, and Ramy A Arnaout. "Prize-based Contests Can Provide Solutions to Computational Biology Problems." Nature Biotechnology 31, no. 2 (February, 2013): 108–111.

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