Despite a lasting surge in media, business, and academic interest, proven mechanisms via which to harness the wisdom of crowds remain in short supply. Idea markets have existed for many years, as have the “opinion aggregation” systems in websites (i.e. the user-generated product rankings found in Amazon.com). The chief obstacle is and always has been: how to properly incentivize the participants in a system, such that they generate meaningful, unbiased input. There is, however, one well-known mechanism that does an amazing job of incentivizing people to think seriously and passionately about a given set of problems. A mechanism that compels people to meaningfully compete, against other people or against themselves, for no monetary benefit whatsoever. That’s right — video games.
For many years now, developers have been creating games that revolve around real-world problems such as resource development, political maneuvering, etc. One of the most famous of these is called SimCity; in it, players are taught to grapple with zoning issues, tax rates, etc. What if games that encouraged people to solve real-world problems (as a means of accomplishing larger objectives) were developed in tandem with corporate or government sponsors? Not “business games”, but commercially-viable, entertaining games that consumers might not even recognize as out of the ordinary?
Imagine a SimCity-esq game in which the player is given the financial reins to a region. The game could be set in a real location (i.e. California), incorporate real world constraints (i.e you can’t indulge in deficit spending forever), and could dynamically import the latest available real-world regional data via the Internet (i.e. demographic figures, current spending levels, etc). That way, when players begin a new game, they are immersed in a situation that closely resembles whatever situation California’s politicians are currently grappling with. But here’s the catch: once players get out of the tutorial phase, the game can begin recording their decisions and transmitting them to a central database, where they are aggregated into a form of “collective vote” on what actions to take (i.e. raise the sales tax or lower the sales tax). If the Wisdom of Crowds is correct, the collective choices of 100,000 game players in California (which would include knowledgeable people as well as many less-knowledgeable people) may very well be better than the choices of 1,000 Californian policy experts.
Jenkins goes on to discuss the confusions, and difference between the “wisdom of crowds” (which is applicable towards aggregating dispersed knowledge about quantifiable, objective data) and “collective intelligence” (which is intelligence that derives from collective behavior and stigmergic, and/or consensus decision making).
Jenkins talks about these differences, because they are fundamental in design choices for games that try to harness either the wisdom of crowds, or collective intelligence. He talks about how two different models for creating Serious Games are emerging, based around these fundamental differences.
Jenkins cites Surowiecki’scontexts for how the Wisdom Of Crowds applies:
There are four key qualities that make a crowd smart. It needs to be diverse, so that people are bringing different pieces of information to the table. It needs to be decentralized, so that no one at the top is dictating the crowd’s answer. It needs a way of summarizing people’s opinions into one collective verdict. And the people in the crowd need to be independent, so that they pay attention mostly to their own information, and not worrying about what everyone around them thinks.
The need for independence among “crowd” members contrasts with the requirement for connection and collaboration to see collective intelligence work. This distinction is actually important for all Collective Problem Solving issues. Jenkins writes:
The Wisdom of Crowds model focuses on (aggregating) isolated inputs: the Collective Intelligence model focuses on the process of knowledge production.
The Wisdom of Crowds generally breaks down when information sharing/group think starts to skew and bias people towards errors. Collective Intelligence overcomes this by looking at different ways that groups can systematically enhance and improve collaboration and cooperation.