Dueling algorithms

Social Computing Models
Moshe Tennenholtz
Search
Search
• Say we know probability
distribution over pages
that the user desires.
movie reviews
prob. website
1.
18%
2.
16%
3.
15%
4.
13%
5.
12%
6.
9%
7.
8%
8.
8%
9.
7%
10.
6%
http://rottentomatoes.com
http://movies.yahoo.com
http://www.metacritic.com/movies
http://rogerebert.suntimes.com
http://www.movies.com/movie-reviews
http://www.moviefone.com/reviews
http://moviereviews.com
http://www.imdb.com
http://netflix.com/reviews
http://mrqe.com
http://movies.msn.com
• Trivial greedy algorithm
is optimal.
Which search engine
is better for me??
I’ll see which one ranks
http://movies.yahoo.com
(my favorite) higher?
(my favorie movie
From now on,
I’ll use Google.
movie reviews
http://rottentomatoes.com
http://movies.yahoo.com
http://www.metacritic.com/movies
http://rogerebert.suntimes.com
http://www.movies.com/movie-reviews
http://www.moviefone.com/reviews
http://moviereviews.com
http://www.kids-in-my-mind.com
http://pluggedin.com/movies.aspx
movie reviews
http://rottentomatoes.com
http://rogerebert.suntimes.com
http://moviereviews.com
http://pluggedin.com/movies.aspx
http://www.kids-in-my-mind.com
http://movies.yahoo.com
http://www.metacritic.com/movies
http://www.movies.com/movie-reviews
http://www.moviefone.com/reviews
Perils of greed
Search
movie reviews
movie reviews
prob. website
18%
16%
15%
13%
12%
9%
8%
8%
7%
6%
http://rottentomatoes.com
http://movies.yahoo.com
http://www.metacritic.com/movies
http://rogerebert.suntimes.com
http://www.movies.com/movie-reviews
http://www.moviefone.com/reviews
http://moviereviews.com
http://www.imdb.com
http://netflix.com/reviews
http://mrqe.com
http://movies.msn.com
http://movies.yahoo.com
http://www.metacritic.com/movies
http://rogerebert.suntimes.com
http://www.movies.com/movie-reviews
http://www.moviefone.com/reviews
http://moviereviews.com
http://www.imdb.com
http://netflix.com/reviews
http://mrqe.com
http://movies.msn.com
http://rottentomatoes.com
Perils of predictability
Any deterministic algorithm can be badly beaten.
Search
movie reviews
movie reviews
prob. website
9%
18%
13%
15%
8%
9%
6%
8%
7%
12%
http://moviereviews.com
http://rottentomatoes.com
http://www.metacritic.com/movies
http://movies.yahoo.com
http://rogerebert.suntimes.com
http://www.movies.com/movie-reviews
http://www.moviefone.com/reviews
http://www.imdb.com
http://netflix.com/reviews
http://mrqe.com
http://movies.msn.com
http://rottentomatoes.com
http://www.metacritic.com/movies
http://movies.yahoo.com
http://rogerebert.suntimes.com
http://www.movies.com/movie-reviews
http://www.moviefone.com/reviews
http://www.imdb.com
http://netflix.com/reviews
http://mrqe.com
http://movies.msn.com
http://moviereviews.com
Optimization → Games
Ranking → Ranking duel
movie reviews
http://rottentomatoes.com
http://movies.yahoo.com
http://www.metacritic.com/movies
http://rogerebert.suntimes.com
http://www.movies.com/moviereviews
http://www.moviefone.com/reviews
http://moviereviews.com
http://www.kids-in-my-mind.com
http://pluggedin.com/movies.aspx
1
𝑛
-beatable
Hiring problem duel
“Secretary alg.” is beatable
Routing
?
movie reviews
http://rottentomatoes.com
http://rogerebert.suntimes.com
http://moviereviews.com
http://pluggedin.com/movies.aspx
http://www.kids-in-my-mind.com
http://movies.yahoo.com
http://www.metacritic.com/movies
http://www.movies.com/moviereviews
http://www.moviefone.com/revie
ws
Greedy is
1−
Binary search duel
Solving duel is
→ Racecomputationally
harder
?
Binary search is
62.5%-beatable
Compression duel
Huffman coding is
66-75%-beatable
?
?
?
?
?
?
Shortest (expected) path is
1 − 𝜖 -beatable
TSP → Parking duel
TSP is
1 − 𝜖 -beatable
Routing
Which route should an agent take?
Taking the route that goes through s is α slower than taking
the route the goes through f, but service is splitted when
shared among agents.
Agent 1
Agent 2
f
s
Target
Pricing
Store
based
pricing
Pricing
Location based
differential
pricing
Display Advertising
Sponsored Search
Ranking Systems
On-line voting
• Voting (as preference aggregation) is popular in social networks
– For example, “likes” and “dislikes” in Facebook
– Usually, sequential voting: vote count shown as voting unfolds
– Other examples: voting in committees, US presidential primaries
On-line voting
• Voting (as preference aggregation) is popular in social networks
• Since vote is public, probably dislike voting against actual winner
– Facebook: disutility from disagreeing with most of my friends
– Committees: candidate may end up as a new faculty member
– Primaries: candidate may end up in a position of power
(Social) Trust Systems
Predicting and learning social structure
– How are friendships structured?
Zachary’s Karate Club – Social Network
from: “An Information Flow Model for Conflict and Fission in Small
Groups” W. W. Zachary, J. of Anthropological Research 33:4, 1977
• Conflict arose over price of lessons
• Eventually the club split to two clubs
• Structure -> Dynamics
• How do things spread in society?
– Epidemics, Information
“A History of Influenza”, C.W. Potter, J. of applied microbiology, 2001, 91,572—579.
Parts of the course will include material from the
more advanced material in the book:
“Networks, Crowds, and Markets:
Reasoning About a Highly Connected
World” by David Easley and Jon Kleinberg.
– A preprint version is available online at:
http://www.cs.cornell.edu/home/kleinber/networks-book/
‫מחקר בנושא‬
‫"בינה מלאכותית ותורת המשחקים"‬
‫• רקע נדרש‪ :‬מיועד לסטודנטים מצטיינים במתמטיקה‪,‬‬
‫מדעי המחשב‪ ,‬הנדסה (עם רקע מתמטי חזק)‪ ,‬או‬
‫כלכלה (עם רקע מתמטי חזק ודגש על תיאוריה‬
‫כלכלית)‪ .‬הרקע האופטימלי הנדרש לשם השתלבות‬
‫במחקר בתחום מתקדם זה מורכב מידע נרחב‬
‫במתמטיקה‪ ,‬תורת המשחקים‪ ,‬מדעי המחשב‬
‫ומערכות מידע‪ ,‬ותיאוריה כלכלית‪ .‬בשל העובדה‬
‫שלמרבית הסטודנטים אין רקע מספק בכל הנושאים‬
‫ישקלו רק סטודנטים מצטיינים המסוגלים להשלים‬
‫רקע רלוונטי באופן יעיל ובעלי פוטנציאל בולט‬
‫לעבודה מקורית ועצמאית‪ .‬לפרטים נוספים ניתן‬
‫לפנות לפרופ' משה טננהולץ‪ ,‬בדואר אלקטרוני‬
‫‪[email protected]‬‬