Application of game-theoretic methods and models to the design of large-scale human-machine computing systems

Systems using elements of large-scale human-machine information processing (crowdsourcing, crowd computing) have recently become popular in solving a number of practical problems. One of the main problems associated with the use of human-machine computing is the quality of the results obtained in this way. A promising approach to the design of such systems is the use of game-theoretic modeling of the situation (system participant and method of reward distribution), to ensure that the method of reward distribution contributes to the participant applying maximum efforts and, accordingly, achieving the goals of the system designer. The paper presents the results of a review in the field of application of game-theoretic models for the rational design of systems employing large-scale human-machine computing. In particular, assumptions used in game-theoretic modeling of such systems and the main classes of models are identified. The obtained results are grouped in accordance with the typology of large-scale human-machine computing systems; the paper shows what assumptions, modeling goals, and types of models are typical for each distinguished type of such systems. The review may be useful both to practitioners who are constructing software systems that include elements of large-scale human-machine computing, and to researchers working in this field.