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Sharing Mart: An Experimental Platform for Socio-Technological Networks Research

Sharing Mart: An Experimental Platform for Socio-Technological Networks Research

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Sharing Mart: An Experimental Platform for Socio-Technological Networks Research

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  1. Sharing Mart: An Experimental Platform for Socio-Technological Networks Research Dr. Hazer Inaltekin Department of Electrical Engineering Princeton University Princeton University

  2. Agenda Today • Introduction and Motivation • Quick Introduction to the Sharing Mart System • Multi-unit Sharing Mart Auctions • Sharing Mart Experiments

  3. Introduction and Motivation Time 1950 1998 2006 Present Math Math Math Electrical Engineers Computer Science Computer Science Sociology Computer Science Electrical Engineers Sociology Sociology Electrical Engineers Physics Physics Physics Lots of funding (NSF, DARPA, ARMY) on human-network interactions and joint socio-technological network characterization !!! Milgram Granovetter Watts Barabasi Kleinberg

  4. Introduction and Motivation Social Overlay - Communication Underlay Networks • Two Modes of Connections: • Physical connections via communication networks, • virtual connections via social networks. • Human Factor in Play: • Needs of people, • virtual ties among them, • how they use communication nodes. • P2P + Social Networks:More than 60% of total data traffic in Internet. • Facebook: A Web Based Social Network. • More than 150 million active users, • has an average 250K registrants per day since January 2007, • has an average weekly growth rate around 3%.

  5. Millions Years Introduction and Motivation Social Overlay - Communication Underlay Networks • An exponential increase in the number of social networking sites. • An exponential increase in the number of active members. • Bottom line: Understand human behavior and social ties among people better design next generation communication networks.

  6. Introduction and Motivation Social Overlay - Communication Underlay Networks

  7. Vision and Motivation Social Overlay - Communication Underlay Networks • Examples: • Distributed decision making, • trust formation, • traffic engineering, • network capacity and network connectivity calculations, • network resource allocation, • wireless social networking. • Collaborators: • Prof. Matthew Salganik from Sociology Department at Princeton University. • Prof. Jacob Shapiro from Political Science Department at Princeton University/ • Prof. Junshan Zhang from Electrical Engineering Department at Arizona State University.

  8. Introduction and Motivation Time/Resources v.s. Research Task Trade-off 1-) Sharing Mart Project 1-) Delay Simulations 2-) Yahoo Communication Network Data. (500 Gbyte) Time and Resources Required 1-) Topology of Socio-technological Networks and Delay Characterization Testbed Design and Empirical Data Collection / Analysis Mathematical Modeling / Analysis Agent Based Simulation and Empirical Data Analysis

  9. Agenda Today • Introduction and Motivation • Quick Introduction to the Sharing Mart System • Multi-unit Sharing Mart Auctions • Sharing Mart Experiments

  10. File Transactions Fixed Price or S-mart Auction User Generated Advertisements Group Formation User Behavior Characterization Content and User Rating Content Request Digital Rights Management Quick Introduction to Sharing Mart • Sharing Mart, S-Mart, is a virtual money (token) based social file sharing platform for Web users to come together and exchange their files. • Innovative research agenda with fun functional modules. • Current Status: • Active with 250 students in SEAS.

  11. Quick Introduction to Sharing Mart Sharing Mart System http://sharingmart.princeton.edu S-Mart Main Web Page Student Interests at SEAS

  12. Quick Introduction to Sharing Mart Personalized Home Pages

  13. Quick Introduction to Sharing Mart Personalized Web Stores

  14. Quick Introduction to Sharing Mart Content Upload

  15. Quick Introduction to Sharing Mart Ad Upload

  16. Quick Introduction to Sharing Mart Content Request

  17. Quick Introduction to Sharing Mart Group Formation

  18. Agenda Today • Introduction and Motivation • Quick Overview of the Sharing Mart System • Multi-unit Sharing Mart Auctions • Sharing Mart Experiments

  19. Multi-unit Sharing Mart Auctions Auctions • Auctions: A simple solution used since 500 B.C. to discover market value of goods. Single Unit Multi-unit First Price Second Price Discriminatory Uniform Vickery Ascending Price English Auctions Vickery Auctions Descending Price Dutch Auctions Multi-unit Descending Price Dutch Auction Sharing Mart Auctions Ausubel Auctions Ebay Auctions

  20. Multi-unit Sharing Mart Auctions Some Properties • Sharing Mart Auction: is a uniform price, unit demand and multiple winner file auction. • Parameters: Auction duration, minimum price, number of copies to be sold K. • Can be specific to groups. • Market Clearing Price in Sharing Mart Auctions: (K+1)st highest bid. • Definition: An auction is efficient if it allocates the goods to highest bidders. • Definition: An auction is incentive compatible if it induces a bidder to submit a bid that sincerely reflects her value for the item. • Revenue Equivalence: A Sharing Mart auction is revenue equivalent to other two auction types (discriminatory and Vickery multi-unit auctions).

  21. Multi-unit Sharing Mart Auctions Auction Interface

  22. Agenda Today • Introduction and Motivation • Quick Overview of the Sharing Mart System • Multi-unit Sharing Mart Auctions • Sharing Mart Experiments

  23. Sharing Mart Experiments Experiment 1 - General Set-up • Number of Participants: 19 undergraduate students. • Number of Auctions: 4 • Bidding Behavior: • Manual bidding • Bidding strategy can change over time • No restriction • Open Economy: • Initial budget: 500 [Tokens] • Can change over time by selling files and inviting friends in SEAS to buy their files • Close to collecting plain data in eBay

  24. Sharing Mart Experiments Experiment 1 - Auction 1 Set-up • Auction Parameters: • Number of students = 19 • Minimum price = 20 [Tokens] • Number of copies = 2 • Start Date: 11/1/2008 11:30:00 AM • End Date: 11/4/2008 11:59:00 PM • Duration: 304,140 [Seconds] • Auction Results: • Final Price: 612 [Tokens] • Number of unique bidders: 10 • Total Income: 1224 [Tokens]

  25. Sharing Mart Experiments Experiment 1 - Auction 1 Results 65 62, bids 612. Token balance 612 65 69 59 69 62

  26. Sharing Mart Experiments Experiment 1 - Auction 1 Results

  27. Sharing Mart Experiments Experiment 1 - Auction 1 Budget Distribution 62 - Token balance 612 Winners 55 65 69

  28. Sharing Mart Experiments Experiment 1 - Auction 2 Set-up • Auction Parameters: • Number of students = 19 • Minimum price = 20 [Tokens] • Number of copies = 5 • Start Date: 11/5/2008 2:00:00 AM • End Date: 11/8/2008 11:59:00 PM • Duration: 338,340 [Seconds] • Auction Results: • Final Price: 551 [Tokens] • Number of unique bidders: 14 • Total Income: 2755 [Tokens]

  29. Sharing Mart Experiments Experiment 1 - Auction 2 Results 62, bids 616. Token balance 616 60 bid amount = 710 60 bid amount = 707 54 37 55 59 57

  30. Sharing Mart Experiments Experiment 1 - Auction 2 Results

  31. Sharing Mart Experiments Experiment 1 - Auction 2 Budget Distribution They are among winners 37, 62, 54 But they are not winners They do not bid They are among winners 57, 59 55 65 69 60, token balance = 710

  32. Sharing Mart Experiments Experiment 1 - Auction 3 and 4 • Auction 3: • Number of copies = 7 • Total Income: 3500 [Tokens] • Auction 4: • Number of copies = 9 • Total Income: 180 [Tokens]

  33. Sharing Mart Experiments Seller Revenue Optimization

  34. Sharing Mart Experiments Community Structure SEAS Intra-community Links ELE 382 Inter-community Links

  35. Sharing Mart Experiments Experiment 2 - General Set-up • Number of Participants: 7 graduate students. • Bidding Behavior: • Automated bidding. • Bidding strategy does not change over time. • Closed Economy: • Initial budget: 1800 [Tokens]. • Can change over time by selling/buying files from other 6 graduate students. • 96-hours Auction Competition: • Each student sets 3 auctions to sell - Seller Strategy: • Free Parameters: Number of items, duration and minimum price. • Each student submits an automated bidding agent to bid 18 auctions - Buyer Strategy. • Total Number of Points Collected: • Number of Auctions Won * 100 + Total Revenue in 3 Auctions. • Experiment 2 collects auction data in a controlled manner.

  36. Sharing Mart Experiments Experiment 2 - General Set-up

  37. Sharing Mart Experiments Experiment 2 - Nash Equilibrium • Points of Student i: • is the number of items sold in auction k from student i. • is the final sale price in auction k from student i. • is equal to 1 if student i wins auction k from student j. • Score Function to Optimize: : Minimum sale price in auction k from student i : Duration of auction k from student i : Bidding strategy of student j

  38. Sharing Mart Experiments Experiment 2 - Nash Equilibrium • Theorem: The above experiment has a symmetric and socially optimal Nash equilibrium at which: • Note: At this equilibrium, all students earn 3600 points, therefore a score of 100.

  39. Sharing Mart Experiments Experiment 2 Results - Revenue v.s. Number of Copies • Average Number of Copies (Averaged over 3 Best Seller Strategies) is 5.33. • Nash equilibrium predicts this number to be 6. Conclusion 1 All students benefit from setting number of copies to 6. Conclusion 2 Average revenue increase is 20%.

  40. Sharing Mart Experiments Experiment 2 Results - Revenue v.s. Number of Copies Minimum Price = 97 Conclusion 3 Empirical revenue versus number of copies curve peaks around 5-6 copies.

  41. Sharing Mart Experiments Experiment 2 Results - Effects of Other Parameters • Minimum Price: • Average minimum price over all auctions is 97 [Tokens]. • Average minimum price over three best sellers is 95 [Tokens]. • High minimum price decreases the percentage of copies sold. • All copies are sold if the minimum price is below 80 but none of the all copies sold if minimum price is above 100. • Conclusion 4: Nash equilibrium strategy minimum price matches with the empirically observed minimum price. • Bidding Strategy: • Three most successful bidders with success rate 97% snipe within the last 60 seconds. • Average number of bids per auction from the three most successful bidders is 1.3. • Continuous bidding with 8.6 bids per auction reduces to the success rate to 62%. • Conclusion 5: Small average number of bids per auction is in accordance with the efficiency and incentive compatibility property of Sharing Mart auctions.

  42. Summary and Conclusions • Boundaries between sociology, physics, computer science and electrical engineering are disappearing. • New Networking Paradigm: Social Overlay - Communication Underlay • Sharing Mart: Collect data in