Gaming With Efficient Networks

            In the last few days of the holiday season, we are still in game playing mode. Our social science friends like to test collaborative aspects of solving complex problems. Often computers are involved, rather than people, as they do what their told. That may not always be a good thing. Real people sometimes do the unexpected.

            The latest game to be reported on is Wildcat Wells, which Mason and Watts used to look at how networks worked in exploring a virtual desert for hidden oilfields (1). The aim was to find as much oil as possible in the game period. Players could drill close to neighbors or move away and explore new regions. So getting the most oil out of the desert meant minimizing dead wells and maximizing long term output – not too many wells in one field.

The participants were recruited via a web crowdsourcing program (Amazon’s Mechanical Turk) and were assembled into networks of one of eight topologies which varied the contact efficiency, that is some networks were more efficient that others. 232 games were played and most players only played a few games.

Efficient networks were found to give the best results. If a field was found, it could be quickly exploited. An efficient network meant less copying so giving a better chance of bringing in “the big one”.

 Real players were more adventurous than computers (or their programmers) in exploring further afield to find “the big one” compared to simulations run by previous investigators. Search strategies were also variable with real players, while simulations had formal rules.

It seems that good information flow and encouraging human ingenuity is still king in problem solving. Yeeha! we're not mushrooms.

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