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Course: CIS 2, Fall 2008
School: UPenn
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in Experiments Behavioral Network Science 2: Kings and Pawns Networked Life CSE 112 Spring 2007 Michael Kearns & Stephen Judd Crucial Information The experiments take place tomorrow, March 16, beginning at 3 PM sharp. Please arrive a few minutes early. The location is 207 Moore. Find out how to get there well in advance. Go to the bathroom before you arrive. You are responsible for showing up if youre...

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in Experiments Behavioral Network Science 2: Kings and Pawns Networked Life CSE 112 Spring 2007 Michael Kearns & Stephen Judd Crucial Information The experiments take place tomorrow, March 16, beginning at 3 PM sharp. Please arrive a few minutes early. The location is 207 Moore. Find out how to get there well in advance. Go to the bathroom before you arrive. You are responsible for showing up if youre on the list. No excuses. 2 Overview Experiments in collective problem-solving in network settings As with the February 16 Coloring & Consensus experiments: each participant sits at a workstation and interacts with others only through the system each participant controls the behavior of a single vertex in a network of 36 vertices each participant has only a local, first neighborhood view network structure changes from experiment to experiment two different game types (even more closely related than Feb 16) background and information for game players system walk-through compensation details logistics, ground rules, etc. You see your own vertex, your neighbors (black edges), and connections between neigbhors (red edges) each neighbor annotated by their number of non-local, invisible neighbors Our immediate agenda: Please ask questions if you have them! 3 Kings and Pawns, Version 1 Each player controls a vertex Player action is to choose to be either a King or a Pawn Being a King pays more highly than being a Pawn --- unless you have a neighboring King Furthermore, pay is accumulated continuously at a rate that depends on your current choice and those of your neighbors Payment rates: $1 per minute for being a King with no neighboring King (Lone King) $0.50 per minute for being a Pawn $0 per minute for being a King with a neighboring King (Fighting King) Again, rates are expressed per minute, but are accumulated continuously! All games will last exactly 2 minutes --- no early stopping! Unlike Coloring and Consensus, all players receiving max possible payoff no possible 19 experiments total In principle, an individual could make $38 on Version 1 4 5 6 7 Version 2: Kings and Pawns with Tips Same as before, but now Kings can offer tips or side payments to neighboring A pawns Solo King can offer any fraction of its $2/minute to neighboring pawns This fraction will be split equally among all neighboring pawns no differential tips $1/min tips for being a Lone King $0.50/min + tips (summed over all neighboring Lone Kings) for being a Pawn $0/min for being a Fighting King Again, payment accumulated continuously based on current state Payment rates: All games will last exactly 2 minutes; no early stopping 19 experiments total Individuals could again make $38 on Version 2 8 9 10 11 A Related Computational Problem For any network or graph G, an independent set in G is a set S of vertices such that no pair of vertices in S are connected by an edge in G The opposite or dual of a clique Maximum independent set: largest possible in G Computationally difficult to compute a maximum independent even for centralized computation expensive of being a Conflicting King + Pawn profit encourages Kings to be an independent set most profitable to be a Solo King encourages set of Kings to be as large as possible in particular, maximum social welfare achieved by a maximum independent set Relationship to Kings and Pawns: However, play however you like! 12 Important Notes Experiments are tomorrow (Fri March 16) at 3 PM Experiments will take place in the workstation lab of...

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UPenn - CIS - 2
Introduction to Game Theory and NetworksNetworked Life CSE 112 Spring 2007 Prof. Michael KearnsGame Theory A mathematical theory designed to model: how rational individuals should behave when individual outcomes are determined by collective beh
UPenn - CIS - 07
Economic Exchange on NetworksNetworked Life CSE 112 Spring 2007 Prof. Michael KearnsExchange Economies Suppose there are a bunch of different goods orcommodities We may all have different initial amounts or endowments I might have 10 sacks of r
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Internet EconomicsNetworked Life CSE 112 Spring 2007 Prof. Michael KearnsModern Networks are Economic Systems(whether we like it or not) Highly decentralized and diverse Disparate network administrators operate by local incentives Users may su
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News and Notes 3/18 Two readings in game theory assigned Short lecture today due to 10 AM fire drill HW 2 handed back today, midterm handed back Tuesday No MK OHs todayIntroduction to Game TheoryNetworked Life CSE 112 Spring 2004 Prof. Michael
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S T A N F O R DBayesian Estimation for Autonomous Object Manipulation Based on Tactile PerceptionAnna Petrovskaya, Oussama Khatib, Sebastian Thrun, Andrew Y. NgEmail: anya@cs.stanford.edu Website: http:/cs.stanford.edu/~anyaMotivationToday rob
UPenn - WIML - 06
S T A N F O R DBayesian Estimation for Autonomous Object Manipulation Based on Tactile PerceptionAnna Petrovskaya, Oussama Khatib, Sebastian Thrun, Andrew Y. NgEmail: anya@cs.stanford.edu Website: http:/cs.stanford.edu/~anyaMotivationToday rob
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Strategies for improving face recognition from videoDeborah Thomas, Nitesh V. Chawla, Kevin W. Bowyer, and Patrick J. FlynnComputer Vision Research Lab, University of Notre Dame (http:/www.nd.edu/~cvrl)Goals Improve performance of face recogniti
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Strategies for improving face recognition from videoDeborah Thomas, Nitesh V. Chawla, Kevin W. Bowyer, and Patrick J. FlynnComputer Vision Research Lab, University of Notre Dame (http:/www.nd.edu/~cvrl)Goals Improve performance of face recogniti
UPenn - SEAS - 06
DISTRIBUTED DATA MINING ON ASTRONOMY CATALOGSCross Matching : Alignment of Astronomy Catalogs Tuple ID P1 P2 P3 Astronomy Sky Surveys (SDSS , 2MASS) Observes Galaxies, Quasars, Stars Serendipity Objects Raw Data from Telescope is pre-processed H
UPenn - WIML - 06
DISTRIBUTED DATA MINING ON ASTRONOMY CATALOGSCross Matching : Alignment of Astronomy Catalogs Tuple ID P1 P2 P3 Astronomy Sky Surveys (SDSS , 2MASS) Observes Galaxies, Quasars, Stars Serendipity Objects Raw Data from Telescope is pre-processed H
UPenn - CIS - 05
Course Overview and IntroductionNetworked Life CSE 112 Spring 2005 Prof. Michael KearnsWhat do the following questions How does Google find what you want? How do tolerant populations become segregated? How many friends between you and Kevin
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The Networked Nature of SocietyNetworked Life CSE 112 Spring 2005 Prof. Michael KearnsWhat is a Network? A collection of individual or atomic entities Referred to as nodes or vertices Collection of links or edges between vertices Links repr
UPenn - CIS - 05
Agenda: Tuesday, Jan 25Reports from the Field: Updates on course web page TA office hours New readings Friendster, Love and Money : Monday NY Times (Katy Keenan) Thats Soooo High School : Monday MSNBC (Jake Wiseman) BitTorrent: Today
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Agenda: Thursday, Feb 3 Midterm date: Thursday, March 3 New readings in Watts Our navigation experiment: some analysis Brief introduction to graph theoryNews and Notes: Tuesday Feb 8 From the Field: NY Times article 2/8 on hate groups on Or
UPenn - CIS - 05
The Web as NetworkNetworked Life CSE 112 Spring 2005 Prof. Michael KearnsThe Web as Network Consider the web as a network vertices: individual (html) pages edges: hyperlinks between pages will view as both a directed and undirected graph Wha
UPenn - CIS - 2
Introduction to Game TheoryNetworked Life CSE 112 Spring 2005 Prof. Michael KearnsGame Theory A mathematical theory designed to model: how rational individuals should behave when individual outcomes are determined by collective behavior strate
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Interdependent Security Games and NetworksNetworked Life CSE 112 Spring 2005 Prof. Michael KearnsGame Theory: Whirlwind Review Matrix (normal form) games, mixed strategies, Nash equil. Repeated matrix games Correlated equilibria the basic obje
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Exchange Economies and NetworksNetworked Life CSE 112 Spring 2005 Prof. Michael Kearns Suppose there are a bunch of different goods wheat, rice, paper, raccoon pelts, matches, grain alcohol, no differences or distinctions within a good: rice is
UPenn - CIS - 05
Pre-Play Preparation:Network Exchange Experiments: Tuesday, April 12Round 1 Play: 1. Check in with the TAs to discover whether you will be a buyer or a seller today. You will then be issued 2 envelopes. 2. On the outside of each envelope,
UPenn - CIS - 05
Behavioral Game Theory: A Brief IntroductionNetworked Life CSE 112 Spring 2005 Prof. Michael Kearns Supplementary slides courtesy of Colin Camerer, CalTechBehavioral Game Theory and Game Practice Game theory: how rational individuals should behav
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Game Theory and the InternetNetworked Life CSE 112 Spring 2005 Prof. Michael KearnsModern Networks are Economic Systems(whether we like it or not) Highly decentralized and diverse Disparate network administrators operate by local incentives Us
UPenn - CIS - 06
Course Introduction and OverviewNetworked Life CSE 112 Spring 2006 Prof. Michael Kearns A purely technological network? Points are physical machines Links are physical wires Interaction is electronic What more is there to say?Internet, Rout
UPenn - CIS - 06
News and Notes, 1/12 Please give your completed handout from Tue to Jenn now Reminder: Mandatory out-of-class experiments 1/24 and 1/25 likely time: either 5-7PM or 6-8 PM both sessions are required if you are registered and cannot make one or b
UPenn - CIS - 06
Contagion and Tipping in NetworksNetworked Life CSE 112 Spring 2006 Prof. Michael KearnsGladwell, page 7:The Tipping Point is the biography of the ideathat the best way to understand the emergence of fashion trends, the ebb and flow of crime wa
UPenn - CIS - 06
Social Network TheoryNetworked Life CSE 112 Spring 2006 Prof. Michael KearnsNatural Networks and Universality Consider the many kinds of networks we have examined: These networks tend to share certain informal properties: large scale; conti
UPenn - CIS - 06
The Web as NetworkNetworked Life CSE 112 Spring 2006 Prof. Michael KearnsThe Web as Network Consider the web as a network vertices: individual (html) pages edges: hyperlinks between pages will view as both a directed and undirected graph Wha
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Exchange Economies on NetworksNetworked Life CSE 112 Spring 2006 Prof. Michael Kearns Suppose there are a bunch of different goodsExchange Economies wheat, rice, paper, raccoon pelts, matches, grain alcohol, no differences or distinctions wit
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Behavioral Graph ColoringMichael Kearns Computer and Information Science University of Pennsylvania Collaborators: Nick Montfort Siddharth Suri Special Thanks: Colin Camerer, Duncan Watts, Huanlei NiBackground and Motivation Network Structure Inf
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UPenn - SEAS - 06
Some recent advances in near-neighbor learningMaya R. GuptaUniversity of WashingtonEric Garcia Univ. Washington William Mortenson Univ. Washington Andrey Stroilov GoogleMichael Friedlander Univ. British Columbia Richard Olshen Stanford Robert Gr
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Learning User Preferences for Sets of ObjectsMarie desJardinsUniversity of Maryland Baltimore County Workshop on Machine Learning: Theory, Applications, Experience October 4, 2006Joint work with Eric Eaton and Kiri WagstaffThis work was support
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Language and Popular Culture: LING057First things firstFocus of this course:Contrasting A with B:A: What we know about language how it works, how we acquire it, what is its structure, and how it works in society (a.k.a.) Sociolinguistics B: Popu
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What is Quality?Workshop on Quality Assurance and Quality Measurement for Language and Speech ResourcesChristopher Cieri Linguistic Data Consortium {ccieri}@ldc.upenn.edus LREC2006: The 5th Language Resource and Evaluation Conference, Genoa, May
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More Data and Tools for More Languages and Research Areas:A Progress Report on LDC ActivitiesChristopher Cieri, Mark Liberman Linguistic Data Consortium {ccieri|myl}@ldc.upenn.edus LREC2006: The 5th Language Resource and Evaluation Conference, Ge
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UPenn - PAPERS - 2006
Corpora Development and PublicationStephanie Strassel Andrew W. Cole University of Pennsylvania, Linguistic Data Consortium strassel@ldc.upenn.edu andrew.cole@ldc.upenn.edu www.ldc.upenn.eduNOTES 2. Each publication, release, updated version, etc.
UPenn - PAPERS - 2006
Integrated Linguistic Resources for Language Exploitation TechnologiesStephanie Strassel, Christopher Cieri, Andy Cole, Denise DiPersio, Mark Liberman, Xiaoyi Ma, Mohamed Maamouri, Kazuaki Maeda {strassel, ccieri, acole2, dipersio, myl, xma, maamour
UPenn - PAPERS - 2006
Towards an Integrated Understanding of Speaking Rate in ConversationJiahong Yuan, Mark Liberman, Christopher CieriUniversity of Pennsylvania Sept. 18, 2006IntroductionFactors that affect speaking rate2 Demographic factors:slower speaking r
UPenn - PAPERS - 2004
The Mixer Corpus of Multilingual, Multichannel Speaker Recognition DataChristopher Cieri1, Joseph P. Campbell2, Hirotaka Nakasone3, David Miller1, Kevin Walker11University of Pennsylvania, Linguistic Data Consortium, Philadelphia, PA, USA 2 MIT L
UPenn - PAPERS - 2004
recent activities in resource creation and distribution and the development of tools and standards Christopher Cieri, Mark Liberman{ccieri,myl}@ldc.upenn.edu University of Pennsylvania Linguistic Data Consortium and Department of Linguistics 3600 Ma
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Dialectal Arabic Telephone Speech Corpus: Principles, Tool design, and Transcription ConventionsMohamed Maamouri, Tim Buckwalter, Christopher CieriLinguistic Data Consortium University of Pennsylvania maamouri@ldc.upenn.edu, timbuck2@ldc.upenn.edu,
UPenn - PAPERS - 2003
Robust Sociolinguistic Methodology:Tools, Data and Best PracticesChristopher Cieri, Stephanie Strassel {ccieri, strassel}@ldc.upenn.edu University of Pennsylvania Linguistic Data Consortium and Department of Linguistics 3600 Market Street, Philadel
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Resources for Arabic Natural Language ProcessingMohamed Maamouri, Christopher Cieri {maamouri,ccieri}@ldc.upenn.eduUniversity of Pennsylvania Linguistic Data Consortium and Department of Linguistics www.ldc.upenn.edus International Symposium on P
UPenn - PHYS - 1
Lecture 2 IntroRunway Problem, pg. 1Runway Prob., pg. 2Lect. Quest. 1: ramp prob.Lect. Quest. 1: ramp contd.Ramp Prob. Soln., pg. 1Ramp prob. Soln: time c-gLect. Quest. 2: vel. vs. timeLect. Quest. 2: choose graphsLect. Quest. 3: re
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Lecture 3 IntroDefinition of DynamicsNewtons Laws of MotionTypes of ForcesSpring ForcesNormal and friction forcesTensionNormal Forces explainedThe WeightWeight is Normal ForceFind Weight in an ElevatorTension ForcesConnect Que
UPenn - PHYS - 1
Lecture 4 IntroRadioactive DecayRadioactive Decay: statisticalRadioactive Decay: equationRadioactive Decay: exponentialHalf-Life definedNuclear Medicine definedConnect Quest: O15 minimumLect. Quest. 1: max. C leftLect. Quest. 2: 1/1
UPenn - PHYS - 1
Lecture 5 IntroIntro. To Uniform Circ. MotionDefinition of Uniform circ. Mot.Definition of tangential vel.Connect Quest.: Tangential Acc.Definition of RadiansRadians and degreesKinematics of circ. motionDefinition of period and ang. V
UPenn - PHYS - 1
Lecture 6 Intro PageOscillatory Motion - IntroConnection to Circular MotionOsc. And circles - use ScreenshotPhysical picture of diff. Eqs.Robert Hooke and his LawHookes Law and SpringsConnect Question: Eq. For Osc.Ang. Frequency for r
UPenn - PHYS - 1
Lecture 7 - Intro SlideConversations About ConservationDescartes Bold AssertionNewtons Bolder AssertionNewtonian SynthesisWhy A New Second Law?The Incomplete F = maNew Definition of MotionDefining the SystemDefining the System - col
UPenn - PHYS - 1
Lecture 8 IntroMore ConservationThere is More to Conserve!Kinetic Energy and ForcesThe Definition of WorkRefining the Work DefinitionStatement of Work-Energy TheoremExample 1: Work done on boxConnect Question 1: Centripetal Motion and
UPenn - PHYS - 1
Lecture 9 - Rotational MotionThe Universe and RotationRewind of Angular MotionRigid Body Angular MotionRigid Body Angular MotionRigid Body Angular DynamicsRadial Acceleration ReviewedWhy Use Angular Variables?Connect Question: calcula
UPenn - PHYS - 1
Lecture 10 - IntroductionRotations and The Universe continuedTranslation and RotationRotational and Linear Equations for motionMore Rotational and Linear EquationsPure Rolling MotionConnecting Rotation to TranslationWhat Is No Slipping?
UPenn - PHYS - 10
Lecture 10 - IntroductionRotations and The Universe continuedTranslation and RotationRotational and Linear Equations for motionMore Rotational and Linear EquationsPure Rolling MotionConnecting Rotation to TranslationWhat Is No Slipping?
UPenn - PHYS - 1
Lecture 11 - IntroCentral Forces and GravitationOrbital ViewSidereal Not SynodicWhy Arent Orbits Simple?The Law of Universal GravitationWhy an Inverse Square Law?Remarks on the TheoryMore Remarks.And One Last RemarkWhy g is Consta
UPenn - PHYS - 11
Lecture 11 - IntroCentral Forces and GravitationOrbital ViewSidereal Not SynodicWhy Arent Orbits Simple?The Law of Universal GravitationWhy an Inverse Square Law?Remarks on the TheoryMore Remarks.And One Last RemarkWhy g is Consta
UPenn - CML - 3
Neighborhood Information SystemCreated by Cartographic Modeling Lab University of Pennsylvania Funded by William Penn Foundation The Pew Charitable Trusts University of PennsylvaniaCore Concept Work with City agencies to create an integrated parc
UPenn - LDC - 2004
Dialectal Arabic Telephone Speech Corpus: Principles, Tool design, and Transcription ConventionsMohamed Maamouri, Tim Buckwalter, Christopher CieriLinguistic Data Consortium University of Pennsylvania maamouri@ldc.upenn.edu, timbuck2@ldc.upenn.edu,
UPenn - SSC - 2005
CONFIDENTIALEducation for the Examination vs. Education for Holistic Development-The Transformation of Teacher Beliefs and Practices in Rural Northwest ChinaDocument Date Tanja Carmel Sargent University of Pennsylvania, Graduate School of Educat
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DISE: A Programmable Macro Engine for Customizing ApplicationsMarc Corliss, E Lewis, Amir Roth University of PennsylvaniaCorliss, Lewis, + Roth ISCA-30Overview Application customization functions (ACFs) E.g., safety-checking, debugging, decomp
UPenn - CIS - 05
Low Overhead Debugging DISEwithMarc L. Corliss E Christopher Lewis Amir Roth Department of Computer and Information Science University of PennsylvaniaOverview Goal: Low overhead interactive debugging Solution: Implement efficient debugging pr