How to Host a Data Competition: Statistical Advice for Design and Analysis of a Data Competition

Data competitions rely on real-time leaderboards to rank competitor entries and stimulate algorithm improvement. While such competitions have become quite popular and prevalent, particularly in supervised learning formats, their implementations by the host are highly variable. Without careful planning, a supervised learning competition is vulnerable to overfitting, where the winning solutions are so closely tuned to the particular set of provided data that they cannot generalize to the underlying problem of interest to the host. This paper outlines some important considerations for strategically designing relevant and informative data sets to maximize the learning outcome from hosting a competition based on our experience. It also describes a post-competition analysis that enables robust and efficient assessment of the strengths and weaknesses of solutions from different competitors, as well as greater understanding of the regions of the input space that are well-solved. The post-competition analysis, which complements the leaderboard, uses exploratory data analysis and generalized linear models (GLMs). The GLMs not only expand the range of results we can explore, they also provide more detailed analysis of individual sub-questions including similarities and differences between algorithms across different types of scenarios, universally easy or hard regions of the input space, and different learning objectives. When coupled with a strategically planned data generation approach, the methods provide richer and more informative summaries to enhance the interpretation of results beyond just the rankings on the leaderboard. The methods are illustrated with a recently completed competition to evaluate algorithms capable of detecting, identifying, and locating radioactive materials in an urban environment.Comment: 36 page

Perception of competition : A measurement of competition from the perspective of the firm

In this report, we study competition from a cognitive psychology, marketing and strategic management perspective and hope to contribute to the notion of competition and competitive processes. In addition, we propose a new method to measure competition that is based on these more psychological insights.��

Collusion through Joint R&D: An Empirical Assessment

This paper tests whether upstream R&D cooperation leads to downstream collusion. We consider an oligopolistic setting where firms enter in research joint ventures (RJVs) to lower production costs or coordinate on collusion in the product market. We show that a sufficient condition for identifying collusive behavior is a decline in the market share of RJV-participating firms, which is also necessary and sufficient for a decrease in consumer welfare. Using information from the US National Cooperation Research Act, we estimate a market share equation correcting for the endogeneity of RJV participation and R&D expenditures. We find robust evidence that large networks between direct competitors – created through firms being members in several RJVs at the same time – are conducive to collusive outcomes in the product market which reduce consumer welfare. By contrast, RJVs among non-competitors are efficiency enhancing