Why training matters

There has been much chatter in the last couple of years about crowdsourcing: the idea that the insights generated by large numbers of ordinary individuals can rival or exceed the work of a small number of experts working on a problem. The development of the Linux operating system, or Wikipedia, are often cited as examples of the power of the many.

In the field of games and education, this principle is often invoked (implicitly, if not explicitly) to explain how the community of players is able to provide mentoring that is as good or better than would be available from “trained” expert mentors.

But as a recent article, The Crowd Is Wise (When It’s Focused), suggests, that argument may be too simplistic:

The overarching notion is that the Internet opens the door to a new world of democratic idea generation and collaborative production…. In the new model, innovation is often portrayed as a numbers game. The more heads, the better — all weighing in, commenting, offering ideas. Collective knowledge prevails, as if a force of egalitarian inevitability.

But it turns out that in successful examples of crowdsourcing, the process is not just left to chance: there are formal structures of evaluation in place–from outside the “crowd”–that focus the results of the crowd’s input in producive directions:

Open-innovation models are adopted to overcome the constraints of corporate hierarchies. But successful projects are typically hybrids of ideas flowing from a decentralized crowd and a hierarchy winnowing and making decisions.

So the lesson for games–and particularly for mentoring in games–is not that many untrained mentors will be as good or better than a few trained ones. Rather, oversight (and in the case of mentoring, that surely includes training) may be an important part of any crowdsourced solution to a problem.