Modeling Collaboration in Academia:

A Game Theoretic Approach

Graham Cormode, Qiang Ma,

S. Muthukrishnan, and Brian Thompson

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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 dataThe Academic Collaboration game, where researchers

collaborate to maximize their academic successAnalysis of collaboration strategies and game equilibria

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Modeling Collaboration in Academia: A Game Theoretic Approach

Model one researcher’s papers and citations over time [Hirsch’ 05]

Related Work

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+3 +3 +3 +3 +3 +3+6 +6+6 +6+9 +9

Analyze the coauthorship graph [AAH’ 10, KPMVD’ 10]

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

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Model Design and Validation

Hypothesis: is correlated with the academic success of its authors up to that point and the amount of effort they put into the paper

Dataset: DBLP + Google Scholar, 1M researchers, 2M publications

Experimental set-up: We consider three scenarios:1. single-author paper, his/her only paper that year

2. two-author paper, their only paper that year

3. multiple papers by an author in the same year

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Model Design and Validation

1. Single-author, no other publications that year

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Observation: # of citations grows linearly with h-index

Model Design and Validation

2. Two-author, no other publications that year

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Observation: # of citations received by a paper is additive over the h-indices of the co-authors

Model Design and Validation

3. Multiple publications in the same year

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Observation: # of citations received by an author is additive over multiple publications

The Academic Collaboration Game

Players: A set of researchers

Utility: Each researcher wants to maximize his/her academic success as

Actions: In year , each researcher can distribute units of “research potential” between individual and collaborative projects

Outcome: Each project produces a paper that will receive citations equal to the total research potential invested by the authors

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Main Results

A researcher’s h-index grows asymptotically faster when collaborating than when working independently – versus

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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

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

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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

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