The Web contains thousands of white papers and blog postings related to specific reputation issues, such as ratings bias and abusing karma. The list here is a representative sample. We maintain an updated, comprehensive list on their Delicious bookmarks: http://delicious.com/frandallfarmer/reputation and http://delicious.com/soldierant/reputation.

A Framework for Building Reputation Systems, by Phillip J. Windley, Ph.D., Kevin Tew, Devlin Daley, dept. of computer science Brigham Young University. One of the few papers that proposes a platform approach to reputation systems.

Designing Social Interfaces, by Christian Crumlish and Erin Malone from O’Reilly and Yahoo! Press. It covers not only the reputation patterns, but social patterns of all types—a definite companion for our book.

“Designing Your Reputation System,” a slideshow presentation by Bryce Glass, initially presented before we started on this: book.

“Reputation As Property in Virtual Economies,” by Joseph Blocher, discusses the idea that online reputation may become real-world property.

The Reputation Pattern Library at the Yahoo! Developer Network, where some of our thoughts were first refined into clear patterns.

The Reputation Research Network, a clearinghouse for some older reputation systems research papers.

“Who Is Grady Harp? Amazon’s Top Reviewers and the fate of the literary amateur,” by Garth Risk Hallberg. One of many articles talking about the side effects of having karma associated with commercial gain. See our Delicious bookmarks for similar articles about YouTube, Yelp, SlashDot, and more.

Though only briefly mentioned in this book, recommender systems are an important form of web reputations, especially for entities. There are extensive libraries of research papers available on the Web. In particular, you should check out the following resources:

Visit http://presnick.people.si.umich.edu/. The site is maintained by Paul Resnick, professor at the University of Michigan School of Information. He is one of the lead researchers in reputation and recommender systems and is a prolific author of relevant works.

GroupLens is a research lab at the University of Minnesota with a focus in recommender systems.

Robert E. Kraut is another important researcher who focuses on recommender and collaboration systems. Visit his site at http://www.cs.cmu.edu/~kraut/RKraut.site.files/research/research.html.

The ACM Recommender Systems conference site contains some great links to support materials, including slide decks.

The “Broken Windows” effect is cited in this book in several chapters. There is some popular debate about its effect on human behavior, highlighted in two popular books:

Gladwell, Malcolm. The Tipping Point: How Little Things Can Make a Big Difference. MA: Back Bay Books, 2002.

Levitt, Steven D., and Stephen J. Dubner. Freakonomics: A Rogue Economist Explores the Explores the Hidden Side of Everything. NY: Harper Perennial, 2009.

They focus on the question of the effects (or lack thereof) on crime based on the New York Police Department’s strict enforcement. Though we don’t take a position on that specific example, we want to point out a few additional references that support the broken windows effect in other contexts:

Johnson, Carolyn Y. “Breakthrough on Broken Windows.” The Boston Globe, February 8, 2009.

“The Broken Windows Theory of Crime is Correct.” The Economist, November 20, 2008.

The emerging field of behavioral economics is deeply relevant to using reputation as user incentive. Papers and books are starting to emerge, but we recommend this primer for all readers:

Ariely, Dan. Predictably Irrational. NY: Harper Perennial, 2010.

Howe, Jeff. Crowdsourcing: Why the Power of the Crowd Is Driving the Future of Business. NY: Three Rivers Press, 2009. This book provides some useful insight into group motivation.

Several patent applications were cited in this book, and we’ve gathered their references here for convenience. Contributors to this section are encouraged to include other relevant intellectual property for consideration by their peers.

U.S. Patent Application 11/774,460:Detecting Spam Messages Using Rapid Sender Reputation Feedback Analysis, Miles Libbey, F. Randall Farmer, Mohammad Mohsenzadeh, Chip Morningstar, Neal Sample

U.S. Patent Application 11/945,911:Real-Time Asynchronous Event Aggregation Systems, F. Randall Farmer, Mohammad Mohsenzadeh, Chip Morningstar, Neal J. Sample

U.S. Patent Application 11/350,981:Interestingness ranking of media objects, Daniel S. Butterfield, Caterina Fake, Callum James Henderson-Begg, Serguei Mourachov

U.S. Patent Application 11/941,009:Trust Based Moderation, Ori Zaltzman and Quy Dinh Le