Game Aspect: An Approach to Separation of Concerns in Crowdsourced Data Management

In data-centric crowdsourcing, it is well known that the incentive structure connected to workers' behavior greatly affects output data. This paper proposes to use a declarative language to deal with both of data computation and the incentive structure explicitly. In the language, computation is modeled as a set of Datalog-like rules, and the incentive structures for the crowd are modeled as games in which the actions taken by players (workers) affect how much payoff they will obtain. The language is unique in that it introduces the game aspect that separates the code for the incentive structure from the other logic encoded in the program. This paper shows that the game aspect not only makes it easier to analyze and maintain the incentive structures, it gives a principled model of the fusion of human and machine computations. In addition, it reports the results of experiments with a real set of data.