Room Assignment A Market Approach Hassaan Khan and Sam Yellen Dec. 4, 2007.

Slides:



Advertisements
Similar presentations
The Basics of Game Theory
Advertisements

An Efficient Dynamic Auction for Heterogeneous Commodities (Lawrence M.Ausubel - september 2000) Authors: Oren Rigbi Damian Goren.
Testing Linear Pricing Algorithms for use in Ascending Combinatorial Auctions (A5) Giro Cavallo David Johnson Emrah Kostem.
Chapter 17: Making Complex Decisions April 1, 2004.
Local Residential Sorting and Public Goods Provision: A Classroom Demonstration Local Residential Sorting and Public Goods Provision: A Classroom Demonstration.
By Michael W. Zhang. Race to the Bottom In government regulation, a race to the bottom is a theoretical phenomenon which occurs when competition between.
3.1 Fair Division: To divide S into shares (one for each player) in such a way that each player gets a fair share. Fair Division: To divide S into shares.
The Voting Problem: A Lesson in Multiagent System Based on Jose Vidal’s book Fundamentals of Multiagent Systems Henry Hexmoor SIUC.
1 Auctions, I V.S. Subrahmanian. Fall 2002, © V.S. Subrahmanian 2 Auction Types Ascending auctions (English) Descending auctions (Dutch) Vickrey Auctions.
Prompt Mechanisms for Online Auctions Speaker: Shahar Dobzinski Joint work with Richard Cole and Lisa Fleischer.
An Approximate Truthful Mechanism for Combinatorial Auctions An Internet Mathematics paper by Aaron Archer, Christos Papadimitriou, Kunal Talwar and Éva.
Multi-item auctions with identical items limited supply: M items (M smaller than number of bidders, n). Three possible bidder types: –Unit-demand bidders.
Federal Communications Commission NSMA Spectrum Management Conference May 20, 2008 Market Based Forces and the Radio Spectrum By Mark Bykowsky, Kenneth.
Multiagent Coordination Using a Distributed Combinatorial Auction Jose M. Vidal University of South Carolina AAAI Workshop on Auction Mechanisms for Robot.
Auction Theory Class 3 – optimal auctions 1. Optimal auctions Usually the term optimal auctions stands for revenue maximization. What is maximal revenue?
By: Bloomfield and Luft Presenter: Sara Aliabadi October,9,
A Prior-Free Revenue Maximizing Auction for Secondary Spectrum Access Ajay Gopinathan and Zongpeng Li IEEE INFOCOM 2011, Shanghai, China.
Liz DiMascio Paige Warren- Shriner Mitch Justus DUTCH AND ENGLISH AUCTIONS IN RELATION TO THE TULIP MARKET.
Preference Elicitation Partial-revelation VCG mechanism for Combinatorial Auctions and Eliciting Non-price Preferences in Combinatorial Auctions.
Biased Price Signals and Reaction of Bidders in Vickrey Auction IPOs Aytekin Ertan December 2009.
P.V. VISWANATH FOR A FIRST COURSE IN INVESTMENTS.
Mechanism Design and the VCG mechanism The concept of a “mechanism”. A general (abstract) solution for welfare maximization: the VCG mechanism. –This is.
Endogenous Coalition Formation in Contests Santiago Sánchez-Pagés Review of Economic Design 2007.
Reducing Costly Information Acquisition in Auctions Kate Larson, University of Waterloo Presented by David Thompson, University of British Columbia July.
AP Statistics Chapter 5 Notes.
Why are auction based IPOs underpriced? Panos Patatoukas.
Stat 512 – Lecture 14 Analysis of Variance (Ch. 12)
Developing Principles in Bargaining. Motivation Consider a purely distributive bargaining situation where impasse is costly to both sides How should we.
The Practice of Statistics
Collusion and the use of false names Vincent Conitzer
A Principled Study of Design Tradeoffs for Autonomous Trading Agents Ioannis A. Vetsikas Bart Selman Cornell University.
Overview Aggregating preferences The Social Welfare function The Pareto Criterion The Compensation Principle.
AP Bell Ringer Sit in your regular number seat On as Sheet of Paper Define: Control Group Treatment Group Variable Independent Variable Dependent Variable.
Experiments and Observational Studies. Observational Studies In an observational study, researchers don’t assign choices; they simply observe them. look.
Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Chapter 13 Experiments and Observational Studies.
CPS 173 Mechanism design Vincent Conitzer
Auction Seminar Optimal Mechanism Presentation by: Alon Resler Supervised by: Amos Fiat.
Slide 13-1 Copyright © 2004 Pearson Education, Inc.
Brian Kelly '06 Chapter 13: Experiments. Observational Study n Observational Study: A type of study in which individuals are observed or certain outcomes.
Session 8 University of Southern California ISE544 June 18, 2009 Geza P. Bottlik Page 1 Outline Two party distributive negotiations (Win/Lose) –Case history.
Part III Gathering Data.
MAP: Multi-Auctioneer Progressive Auction in Dynamic Spectrum Access Lin Gao, Youyun Xu, Xinbing Wang Shanghai Jiaotong University.
Strategyproof Auctions For Balancing Social Welfare and Fairness in Secondary Spectrum Markets Ajay Gopinathan, Zongpeng Li University of Calgary Chuan.
Auctions Shyam Sunder, Yale University Kozminski Academy Warsaw, June 22, 2013.
Experiments on Risk Taking and Evaluation Periods Misread as Evidence of Myopic Loss Aversion Ganna Pogrebna June 30, 2007 Experiments on Risk Taking and.
Introduction to Survey Research. Survey Research is About Asking Questions About…  Behaviors  Opinions/Attitudes  Facts  Beliefs  There are lots.
Chapter 3.1.  Observational Study: involves passive data collection (observe, record or measure but don’t interfere)  Experiment: ~Involves active data.
Chapter 8 : Estimation.
Check whether these things are on your desk. If not, please raise your hand. –Pen –Receipt –“Summary of the experiment” Fill in the receipt following the.
Part III – Gathering Data
Auctions serve the dual purpose of eliciting preferences and allocating resources between competing uses. A less fundamental but more practical reason.
Experimental Design Econ 176, Fall Some Terminology Session: A single meeting at which observations are made on a group of subjects. Experiment:
Principles of exp. design Control for effects of lurking variables Randomization to keep personal biases or other preferences out of the study Replication.
Dynamic Programming.  Decomposes a problem into a series of sub- problems  Builds up correct solutions to larger and larger sub- problems  Examples.
Instructor: Shengyu Zhang 1. Resource allocation General goals:  Maximize social welfare.  Fairness.  Stability. 2.
Multi-Agents System CMSC 691B Gunjan Kalra Peter DSouza.
False-name Bids “The effect of false-name bids in combinatorial
Generalized Agent-mediated procurement auctions
Shyam Sunder, Yale University Kozminski Academy Warsaw, June 23, 2012
Auctions and Competitive Bidding
Grading on a curve, and other effect of group size on all-pay auctions
EXPERIMENT DESIGN.
Failures of the VCG Mechanism in Combinatorial Auctions and Exchanges
Part III – Gathering Data
Internet Economics כלכלת האינטרנט
Authors: Oren Rigbi Damian Goren
Learning under different market protocols (full vs
CHAPTER 10 Comparing Two Populations or Groups
Behavioral Finance Economics 437.
Chapter 34 Welfare Key Concept: Arrow’s impossibility theorem, social welfare functions Limited support of how market preserves fairness.
Presentation transcript:

Room Assignment A Market Approach Hassaan Khan and Sam Yellen Dec. 4, 2007

Room Assignment - The Status Quo Housing is a very stressful experience for undergraduates Residential Colleges divided into Suites which are comprised of rooms

Reasons for Stress Scarcity of Desirable suites Individual Preferences – Partners – Rooms Heterogeneous distribution Stickiness

Existing System 1.Form teams to enter “room draw” 2.Teams advised by rooming committee about availability 3.Each team draws a number 4.Teams choose suites, largest to smallest according to lottery number 5.Unsuccessful team broken up and redraw for a smaller room.

A Market Solution?

Why a Market Solution? Information asymmetry – Among groups – Within groups – Regulatory – (between housing committee and student body) Temporal Rigidity – If groups are awarded in a set order, certain behaviors are incentivized.

Markets Aggregate Preferences Information asymmetry – Price can signal preference For a individual choosing a group For groups choosing rooms Temporal Rigidity – Bids can be entered and modified simultaneously – Students can immediately bid on the rooms they find most desirable.

Market Design Each Student assigned 100 points. Scarcity of points forces bids to reflect preferences Two ways to bid – On individual room in a suite – Join a team and bid for a suite collectively Bidders can diversify the spending of their points, so that if they do not win a certain room they still have a chance to win a different room. Soft Ending – Auction closes a set amount of time after last bid.

Experimental Design 2 runs, 2 treatments each Each player assigned affinities for other players Status Quo vs. Market-Based Participants asked to team up with other players to maximize utility, the sum of affinities

Rooming in Residential Colleges There is enough housing for every student Rooms are grouped into suites

New Residential College President Levin announces the creation of a new residential college, Sunder College. Named after a professor who gave a very large donation. This college has 5 total suites, all rooms are singles – 1 quintet (r = 5) – 2 triples (r = 3) – 2 doubles (r = 2)

Lottery Treatment 1.Utilities Assigned 1.Partner Utilities 2.Form teams to enter “room draw” 3.Each team draws a number 4.Teams broken up and redraw for a smaller room.

Two Approaches Lottery Auction

Utility Maximization Total Utility = Who Who – Utilities Assigned to student ID’s

An Example 1.Two groups (G1 and G2) attempt to get a suite for 9 people. There is only one 9 person suite. 2.G1 draws a 5 and G2 draws a 10 3.G2 wins, G1 must break up and attempt to get a smaller room. 4.Process repeated for suites of size (9, 8, 7, 6, 5, 4, 3, 2).

Bidding System 1.Each Student assigned 100 points. 2.Two ways to bid 1.On individual room in a suite 2.Join a team and bid for a suite collectively 3.An example will be provided later 3.Bidders can diversify the spending of their points, so that if they do not win a certain room they still have a chance to win a different room. 4.Ending - last 30 seconds, bidders will be only able to change one bid.

Individual vs. Team bidding An example based on a double Z, with rooms Z1 and Z2 Bidder1 bids 20 on Z1 Bidder2 bids 30 on Z2 A team comprised of Bidder3 (bids 15) and Bidder4 (bids 40) bid a sum of 55 The team wins the suite

Individual vs. Team bidding cont. An example based on a double Z, with rooms Z1 and Z2 Bidder1 bids 20 on Z1 Bidder2 bids 30 on Z2 A team comprised of Bidder3 (bids 15) and Bidder4 (bids 30) bid a sum of 45 Bidder1 and Bidder2 win the rooms

Additional Instructions Don’t bid on 2 rooms in the same suite Restrain bidding activity in the final ending period. Partner Utilities – On a scale from -1 to 2 -1 : dislike 0: indifferent 1 : mild preference 2 : strong preference non mentioned utilities are assumed to be 0

Results Market and Status Quo outcomes Identical

Conclusions – Market Based Approach at least as efficient as status quo in allocating rooms. Other Observation: – Increased Utility of Larger rooms Larger groups can have more friends No additional preference for singles or smaller rooms

Problems Biased Group – groups sought to replicate success in previous round Order of treatments not reversed Suites were homogenous other than size Group too small Stickiness proportional to the number of possible groups In class, approximately 15 students taken 3 at a time = 455 possible combinations 200 students in groups of 5 – possible groups 7 orders of magnitude difference!

What do to next Redo Experiment – Same set of utilities but assigned to different students in each treatment – Increase Complexity Use a larger group of students Increase heterogeneity of rooms using floor maps Use homegrown affinities by letting groups of friends test the system. – Utility will be measured by using a questionnaire – Convince Pierson College to Accept Our System