Collaborative Solving in a Human Computing Game Using a Market, Skills and Challenges

Crowdsourcing with human-computing games is now a well-established approach to help solving difficult computational problems (e.g. Foldit, Phylo). The current strategies used to distribute problems among participants are currently limited to (i) delivering the full problem to every single user and ask them to explore the complete search space (e.g. Foldit), or (ii) decomposing the initial problem into smaller sub-problems and aggregate the solutions returned by gamers (e.g. Phylo). The second approach could be used to explore larger search spaces while harnessing collective intelligence, but popular crowdsourcing systems making use of the Amazon Mechanical Turk deliberately forbid communication between participants to avoid group-think phenomena. In this paper, we design a novel multi-player game-with-a-purpose, and analyze the impact of multiple game mechanisms on the performance of the system. We present a highly collaborative human-computing game that uses a market, skills and a challenge system to help the players collectively solve a graph problem. The results obtained during 12 game sessions of 10 players show that the market helps players to build larger solutions. We also show that a skill system and, to a lesser extent, a challenge system can be used to influence and guide the players towards producing better solutions. Our collaborative game-with-a-purpose is open-source, and aims to serve as a universal platform for further independent studies.