Is it a good idea to encourage all employees to trade in these markets? Should insiders and/or highly uninformed people be allowed to trade? Do they help or hurt the market?

Today, organizations are exploring and taking advantage of different social networks such as blogs, wikis, collaborative filtering, and now, predictions markets. Prediction market is another technology that could be helpful to an organization in many ways. It can help generate predictions that efficiently aggregate many employees’ information and augment existing forecasting methods and provide insight into how organizations process information and how the same one flows within the organization

For predictions markets to work, even spread of information across the organization is recommended.
In markets where highly informed employees or insiders are allowed to trade some problems might occur:
• Employees from areas not related to the subject on the trading market will lose on average, and their incentives and motivation to trade will be reduced. Thus, the number of trades in the market will be hurt.
• Insiders or highly informed traders may also reveal highly protected information to the markets. They can also attempt to mislead the market about the outcomes in order to gain personal profit.

To avoid this problem and make the market work better, the organizations using prediction markets can try to evenly spread information across the organization, reducing the insider’s advantage. The organization could also do what happens in the stock market and prohibit insiders or group of employees highly informed about the subject in the market to trade in that specific market.

On the other hand, highly uniformed employees or employees who do not take the time to research and make and informed trade, sooner or later, will stop trading in the markets because they will have high probabilities to loose all what they have, and this will be an strong incentive for them to stop participating.

In what other industries or areas would Threadless’ community-driven product development model work well? And not so well?

Online social networks have become ubiquitous in the past few years. What forms of value do users get from these services and who is most likely to sign up on LinkedIn versus other sites?
Today, social networking has become deeply embedded in the lifestyles of all kinds of people. Through social networking, people can fulfill their desire to communicate better with the people they already know. They can also meet their need to become more popular and connect with a lot of people from different places, and social levels. People can also feel they are empowered to express their opinions, views, and knowledge about their topics of interest.

Some of the most common social network activities are the following:
• Posting messages
• Downloading music and videos
• Uploading music
• Updating personal Web sites or online profiles
• Posting photos
• Blogging
• Creating and sharing virtual objects
• Creating new characters
• Participating in collaborative projects
• Sending suggestions or ideas to Web sites
• Submitting articles to Web sites, and so on

In the last few years, there has been an explosive growth in social networking activities and sites that are competing for attention against television. Social Networking sites are positioned differently and try to target different segments of the market, which have different interests, needs, and lifestyles. For instance, sites such a LinkedIn are most likely to target the professionals and students segment of the market that are looking for to create their business networks while other sites such as Facebook and MySpace are likely to target the segment of the market that is looking for to satisfy their social needs by sharing their interests, connecting with old friends, and so on.

How do Wikipedia’s processes for creating and modifying articles ever lead to high-quality results?

Wikipedia is the online encyclopedia created by volunteers. Many people have asked themselves how the Wikipedia model has motivated individuals to give away their time and effort to write articles. According to some experts, the incentives for contributors to write to the open content encyclopedia are similar to those of the scientific community. Like scientists, people seek to collaboratively identify and publish true facts about the world. However, the increased size of collaboration can lead to ambiguous results in quality. In general, the quality of Wikipedia articles vary depending on the relevance of the same one, and the overall quality of Wikipedia is very difficult to measure, but its content is undoubtedly very useful to the community.

A study conducted by Dennis Wilkinson and Bernardo Huberman from Hewlett-Packard Labs(October 2007) found that high-quality articles in Wikipedia are distinguished from the rest by a larger number of edits and distinct editors. These findings are in contrast to observations of cooperative efforts in other domains where result quality does not necessarily increase with the number of collaborators.

Another important point leading to high quality results is that while it is difficult to eliminate vandalism, jokes, or entry of false information, the wiki technology makes easier to undo damage than to do damage to the encyclopedia. Due to the track of versions features it just takes few seconds to undo damage that might have taken hours or many minutes to do.