Crowdsourcing & Open Innovation

The Laboratory of Innovation Science at Harvard is currently working on a number of studies, experiments, and projects that center around Crowdsourcing & Open Innovation. Such initiatives allow for the optimization of research for solutions to complex problems by calling on the crowd, instead of limiting knowledge to what is available within an organization. Listed below are some examples of research which have greatly benefited from utilizing exterior knowledge and not limiting resources to those employed in-house, including a number of crowdsourcing challenges from our partners at NASA, the Broad Institute, and others. Browse LISH’s Crowdsourcing & Open Innovation projects and papers below.


Elizabeth E. Richard, Jeffrey R. Davis, Jin H. Paik, and Karim R. Lakhani. Working Paper. “Establishing a Center of Excellence to Scale and Sustain Open Innovation”.Abstract
Organizations face many issues in scaling and sustaining successful pilot programs in open innovation. This paper describes a set of recommendations to accelerate these practices in order to develop a Center of Excellence (CoE) that can increase adoption. The experience of the Human Health and Performance Directorate (HH&P) at the NASA Johnson Space Center spanned more than seven years from initially learning about open innovation to the successful establishment of a CoE; this paper provides recommendations on how to decrease this timeline to three to four years. Organizations must anticipate success with initial pilot programs and conduct many future activities in parallel to achieve the recommended timeline. Simultaneously, organizations must develop strategies to overcome the internal resistance and cultural barriers to finding novel ideas and solutions to fully realize the potential of open innovation.


Herman B. Leonard, Mitchell B. Weiss, Jin H. Paik, and Kerry Herman. 2018. SOFWERX: Innovation at U.S. Special Operations Command. Harvard Business School Case. Harvard Business School. Publisher's VersionAbstract
James “Hondo” Geurts, the Acquisition Executive for U.S. Special Operations Command was in the middle of his Senate confirmation hearing in 2017 to become Assistant Secretary of the Navy for Research, Development and Acquisition. The questions had a common theme: how would Geurts’s experience running an innovative procurement effort for U.S. Special Forces units enable him to change a much larger—and much more rigid—organization like the U.S. Navy? In one of the most secretive parts of the U.S. military, Geurts founded an open platform called SOFWERX to speed the rate of ideas to Navy SEALs, Army Special Forces, and the like. His team even sourced the idea for a hoverboard from a YouTube video. But how should things like SOFWERX and protypes like the EZ-Fly find a place within the Navy writ large?
Luke Boosey, Philip Brookins, and Dmitry Ryvkin. 2018. “Contests between groups of unknown size.” Games and Economic Behavior. Publisher's VersionAbstract
We study group contests where group sizes are stochastic and unobservable to participants at the time of investment. When the joint distribution of group sizes is symmetric, with expected group size , the symmetric equilibrium aggregate investment is lower than in a symmetric group contest with commonly known fixed group size . A similar result holds for two groups with asymmetric distributions of sizes. For the symmetric case, the reduction in individual and aggregate investment due to group size uncertainty increases with the variance in relative group impacts. When group sizes are independent conditional on a common shock, a stochastic increase in the common shock mitigates the effect of group size uncertainty unless the common and idiosyncratic components of group size are strong complements. Finally, group size uncertainty undermines the robustness of the group size paradox otherwise present in the model.
Philip Brookins, John P. Lightle, and Dmitry Ryvkin. 2018. “Sorting and communication in weak-link group contests.” Journal of Economic Behavior & Organization, 152, Pp. 64-80. Publisher's VersionAbstract
We experimentally study the effects of sorting and communication in contests between groups of heterogeneous players whose within-group efforts are perfect complements. Contrary to the common wisdom that competitive balance bolsters performance in contests, in this setting theory predicts that aggregate output increases in the variation in abilities between groups, i.e., it is maximized by the most unbalanced sorting of players. However, the data does not support this prediction. In the absence of communication, we find no effect of sorting on aggregate output, while in the presence of within-group communication aggregate output is 33% higher under the balanced sorting as compared to the unbalanced sorting. This reversal of the prediction is in line with the competitive balance heuristic. The results have implications for the design of optimal groups in organizations using relative performance pay.
Karim R. Lakhani, Andrew Hill, Po-Ru Loh, Ragu B. Bharadwaj, Pascal Pons, Jingbo Shang, Eva C. Guinan, Iain Kilty, and Scott Jelinsky. 2017. “Stepwise Distributed Open Innovation Contests for Software Development: Acceleration of Genome-Wide Association Analysis.” GigaScience, 6, 5, Pp. 1-10. Publisher's VersionAbstract

BACKGROUND: The association of differing genotypes with disease-related phenotypic traits offers great potential to both help identify new therapeutic targets and support stratification of patients who would gain the greatest benefit from specific drug classes. Development of low-cost genotyping and sequencing has made collecting large-scale genotyping data routine in population and therapeutic intervention studies. In addition, a range of new technologies is being used to capture numerous new and complex phenotypic descriptors. As a result, genotype and phenotype datasets have grown exponentially. Genome-wide association studies associate genotypes and phenotypes using methods such as logistic regression. As existing tools for association analysis limit the efficiency by which value can be extracted from increasing volumes of data, there is a pressing need for new software tools that can accelerate association analyses on large genotype-phenotype datasets.

RESULTS: Using open innovation (OI) and contest-based crowdsourcing, the logistic regression analysis in a leading, community-standard genetics software package (PLINK 1.07) was substantially accelerated. OI allowed us to do this in <6 months by providing rapid access to highly skilled programmers with specialized, difficult-to-find skill sets. Through a crowd-based contest a combination of computational, numeric, and algorithmic approaches was identified that accelerated the logistic regression in PLINK 1.07 by 18- to 45-fold. Combining contest-derived logistic regression code with coarse-grained parallelization, multithreading, and associated changes to data initialization code further developed through distributed innovation, we achieved an end-to-end speedup of 591-fold for a data set size of 6678 subjects by 645 863 variants, compared to PLINK 1.07's logistic regression. This represents a reduction in run time from 4.8 hours to 29 seconds. Accelerated logistic regression code developed in this project has been incorporated into the PLINK2 project.

CONCLUSIONS: Using iterative competition-based OI, we have developed a new, faster implementation of logistic regression for genome-wide association studies analysis. We present lessons learned and recommendations on running a successful OI process for bioinformatics.

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