Networked Markets Ashish Goel Mukund Sundararajan

Networked Markets Markets that could not have been possible without the Internet “at scale” Will briefly describe the concepts and business ideas underlying these markets Will largely focus on the mathematical and technical aspects that lie at the heart of how these markets use and are used by human beings Can not cover all these technical topics in great detail, but will give you a good understanding of the underlying technical ideas

Examples Business: Groupon Technical ideas: Group Action; Network Formation; Virality

Examples Business: Netflix and Amazon Technical ideas: The long tail of human interests; personalization; reviews and ratings

Examples Business: eBay Technical ideas: The long tail of production; new pricing models; reputation systems

Examples Business: TripAdvisor, Yelp Technical ideas: Reviews and ratings

Examples Business: Mechanical Turk and oDesk Technical ideas: crowdsourcing

Examples Business: Search Technical ideas: Crowd-sourced reputation systems; advertising networks

Examples Business: Social networks, social media Technical ideas: advertising networks; personalization

Examples Business: Online retail and advertising Technical ideas: targeting; personalization; different pricing models for advertisements

Examples Business: Ashley Madison Technical ideas: privacy

Examples Business: Silk Road Technical ideas: virtual currencies; anonymity

Examples Business: Peer-to-Peer Content Sharing Technical ideas: reputation systems; virtual currencies; anonymity

Examples Business: Online education Technical ideas: peer grading (similar to reviews and ratings)

Technical Topics Covered – Groupon: Group action; network formation; virality – Netflix and Amazon: The long tail of human interests; personalization; reviews and ratings – eBay: The long tail of production; new pricing models; reputation systems – TripAdvisor, Yelp: Reviews and ratings – Mechanical Turk and oDesk: distributed work – Search: Crowd-sourced reputation systems; advertising networks – Social networks, social media: advertising networks; personalization – Online retail and advertising: targeting; personalization; different pricing models for advertisements – Ashley Madison: privacy – Silk Road: virtual currencies; anonymity – Peer-to-Peer Content Sharing: reputation systems; virtual currencies; Anonymity – Online education: peer grading (similar to reviews and ratings)

Today: Long Tails Long tails are the signature of human activity. – Christos Papadimitriou Popularized by an article in “Wired” by Chris Anderson – Details in a book called “The Long Tail” by Chris Anderson Another interpretation: Long tails are a balance between human individuality and social behavior Technical details in notes

Implications of Long Tails Challenges for search: search keywords are diverse; can not just process the top few keywords offline; need to assemble search results in real-time! Opportunities for advertising: search keywords express intent; advertisers can use the long tail of search to sell a long tail of products

Implications of Long Tails Example: Blinds.com. Started 1996, and soon grew to be a large business. Blinds are a product in the long tail: they are bought infrequently, but the total demand is still quite large when aggregated over a country (Blinds.com has > 50M in revenues) The Internet allowed them to have a virtual storefront Search advertising allowed them to reach the long tail of customers very efficiently

Implications of Long Tails Example: Netflix’s DVD business DVD licensing fees per rental are negotiated between Netflix and the studios; depend on popularity of the movie If Netflix rents out a less popular DVD, its costs are lower Enter the recommendation system: by offering you personalized recommendations, Netflix makes good use of the long tail in people’s tastes