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Using Uncensored Communication Channels to Divert Spam Traffic

42 Pages Posted: 2 Nov 2006  

Benjamin Chiao

Shanghai University of Finance and Economics

Jeffrey K. MacKie-Mason

UC Berkeley; University of Michigan

Multiple version iconThere are 2 versions of this paper

Date Written: March 2009


We offer a microeconomic model of the two-sided market for the dominant form of spam: bulk, unsolicited, and commercial advertising email. Most most spam is advertising, and thus should be modeled as a problem in the market supply and demand for advertising, rather than the usual approach of modeling spam as pure social cost to be eliminated. We adopt an incentive-centered design approach to develop a simple, feasible improvement to the current email system using an uncensored (open) communication channel. Such a channel could be an email folder or account, to which properly tagged commercial solicitations are routed without any
blocking or filtering along the way. We characterize the circumstances under which spammers would voluntarily move much of their spam into the open channel, leaving the traditional email channel dominated by person-to-person, non-spam mail. We show that under certain conditions all email recipients are better off when an open channel is introduced. Only recipients wanting spam will use the open channel enjoying the less disguised messages and cheaper sale prices, and for all recipients the dissatisfaction associated with both undesirable mail received and desirable
mail filtered out decreases.

Keywords: incentive-centered design, internet services, incentives, spam, email

Suggested Citation

Chiao, Benjamin and MacKie-Mason, Jeffrey K., Using Uncensored Communication Channels to Divert Spam Traffic (March 2009). NET Institute Working Paper No. 06-20 Earlier version presented at the 34th Research Conference on Communication, Information and Internet Policy. Available at SSRN: or

Benjamin Chiao (Contact Author)

Shanghai University of Finance and Economics ( email )

777 Guoding Road
Shanghai, Shanghai 200433

Jeffrey K. MacKie-Mason

UC Berkeley ( email )

102 South Hall
Berkeley, CA 94720-4600
United States


University of Michigan ( email )

Ann Arbor, MI 48109-1092
United States

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