1. Modeling Collaboration in Academia: A Game Theoretic Approach Graham Cormode, Qiang Ma, S. Muthukrishnan, and Brian Thompson 1

2. Outline Goal: Explore the use of Game Theory as a tool for modeling and understanding the dynamics of collaborative behavior Contributions: A model of academic collaboration supported by real-world publication data The Academic Collaboration game, where researchers collaborate to maximize their academic success Analysis of collaboration strategies and game equilibria Modeling Collaboration in Academia: A Game Theoretic Approach 2

3. Model one researcher’s papers and citations over time [Hirsch’ 05] Related Work 3 +3 +6 +9 Analyze the coauthorship graph [AAH’ 10, KPMVD’ 10]

4. Our Approach We present the first generative model to describe the formation of academic collaborations, the resulting papers, and the citations they receive We model the system as a repeated game, where researchers choose collaborators each year in an attempt to maximize their long-term academic success Modeling Collaboration in Academia: A Game Theoretic Approach 4

5. Model Design and Validation Modeling Collaboration in Academia: A Game Theoretic Approach 5

6. Model Design and Validation 1. Single-author, no other publications that year Modeling Collaboration in Academia: A Game Theoretic Approach 6 Observation: # of citations grows linearly with h-index

7. Model Design and Validation 2. Two-author, no other publications that year Modeling Collaboration in Academia: A Game Theoretic Approach 7 Observation: # of citations received by a paper is additive over the h-indices of the co-authors

8. Model Design and Validation 3. Multiple publications in the same year Modeling Collaboration in Academia: A Game Theoretic Approach 8 Observation: # of citations received by an author is additive over multiple publications

9. The Academic Collaboration Game Modeling Collaboration in Academia: A Game Theoretic Approach 9

10. Main Results Modeling Collaboration in Academia: A Game Theoretic Approach 10 In the static multi-player game, each perfect matching on the researchers is in equilibrium In the dynamic multi-player game, however, the perfect matchings are not in equilibrium

11. Take-away Messages Use of static rather than dynamic collaboration models may yield misleading predictions of people’s behavior in collaborative environments Game Theory is a promising tool for studying the dynamics of collaborative behavior The Academic Collaboration game can help study which metrics of academic success encourage behavior that benefits the academic community Modeling Collaboration in Academia: A Game Theoretic Approach 11

12. Future Directions Open question: Do there exist equilibria in the dynamic game? Extend the model to allow mixed strategies Analyze the game under other metrics of academic success besides the h-index Study the price of anarchy and stability under each of these scenarios Modeling Collaboration in Academia: A Game Theoretic Approach 12