NDLI: Matching while Learning

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Fair Algorithms for Machine Learning

Dominant-Strategy versus Bayesian Multi-item Auctions: Maximum Revenue Determination and Comparison

The Double Clinching Auction for Wagering

The Stochastic Matching Problem: Beating Half with a Non-Adaptive Algorithm

A "Quantal Regret" Method for Structural Econometrics in Repeated Games

The Scope of Sequential Screening with Ex Post Participation Constraints

Accounting for Strategic Response in an Agent-Based Model of Financial Regulation

Stable Secretaries

Making Right Decisions Based on Wrong Opinions

A Simple and Approximately Optimal Mechanism for a Buyer with Complements: Abstract

Optimal Signaling Mechanisms in Unobservable Queues with Strategic Customers

Combinatorial Cost Sharing

Makespan Minimization via Posted Prices

Convex Program Duality, Fisher Markets, and Nash Social Welfare

Online Auctions and Multi-scale Online Learning

The Tragedy of your Upstairs Neighbors: Is the Negative Externality of Airbnb Internalized?

Truthful Allocation Mechanisms Without Payments: Characterization and Implications on Fairness

Planning with Multiple Biases

Approximating Gains from Trade in Two-sided Markets via Simple Mechanisms

Nash Social Welfare Approximation for Strategic Agents

Graphons: A Nonparametric Method to Model, Estimate, and Design Algorithms for Massive Networks

Stability, Strategy-Proofness, and Cumulative Offer Mechanisms

Simple Approximate Equilibria in Games with Many Players

Deferred-Acceptance Auctions for Multiple Levels of Service

Forecast Aggregation

Facilitating the Search for Partners on Matching Platforms: Restricting Agent Actions

The Theory is Predictive, but is it Complete?: An Application to Human Perception of Randomness

Dynamic Mechanisms with Martingale Utilities

Empirical Mechanism Design for Optimizing Clearing Interval in Frequent Call Markets

Computing Equilibrium in Matching Markets

Voting in the Limelight

Price Doubling and Item Halving: Robust Revenue Guarantees for Item Pricing

Information Sharing and Privacy in Networks

Truth and Regret in Online Scheduling

Controlled Dynamic Fair Division

Joint Pricing and Inventory Management with Strategic Customers

Interacting User Generated Content Technologies: How Q&As Affect Ratings & Reviews

From Monetary to Non-Monetary Mechanism Design via Artificial Currencies

Multidimensional Binary Search for Contextual Decision-Making

Approximately Efficient Two-Sided Combinatorial Auctions

Fair Public Decision Making

Stable Matching with Proportionality Constraints

Theoretical and Practical Advances on Smoothing for Extensive-Form Games

The Optimal Mechanism for Selling to a Budget Constrained Buyer: The General Case

Machine-Learning Aided Peer Prediction

Matching while Learning

Comparison-based Choices

Repeated Sales with Multiple Strategic Buyers

Potential Function Minimizers of Combinatorial Congestion Games: Efficiency and Computation

Communication Requirements and Informative Signaling in Matching Markets

Metric Distortion of Social Choice Rules: Lower Bounds and Fairness Properties

The Competition Complexity of Auctions: A Bulow-Klemperer Result for Multi-Dimensional Bidders

Algorithmic Persuasion with No Externalities

Cost-Sharing Methods for Scheduling Games under Uncertainty

A Lower Bound for Equitable Cake Cutting

Pricing and Optimization in Shared Vehicle Systems: An Approximation Framework

Learning in the Repeated Secretary Problem

Gibbard-Satterthwaite Success Stories and Obvious Strategyproofness

Bifurcation Mechanism Design - from Optimal Flat Taxes to Improved Cancer Treatments

Learning in Repeated Auctions with Budgets: Regret Minimization and Equilibrium

Approximation Algorithms for Maximin Fair Division

Making it Safe to Use Centralized Markets: Epsilon - Dominant Individual Rationality and Applications to Market Design

A Network Game of Dynamic Traffic

Optimal Multi-Unit Mechanisms with Private Demands

Peer Prediction with Heterogeneous Users

Redesigning the Israeli Psychology Master's Match

Combinatorial Auctions Do Need Modest Interaction

Posted Price Mechanisms for a Random Stream of Customers

Surge Pricing Solves the Wild Goose Chase

Complementary Inputs and the Existence of Stable Outcomes in Large Trading Networks

Of the People: Voting Is More Effective with Representative Candidates

Assortment Optimisation under a General Discrete Choice Model: A Tight Analysis of Revenue-Ordered Assortments

Fairness Incentives for Myopic Agents

Multidimensional Dynamic Pricing for Welfare Maximization

Diffusion in Networks and the Unexpected Virtue of Burstiness

How (Not) to Allocate Affordable Housing

A Polynomial Time Algorithm for Spatio-Temporal Security Games

Matching while Learning

Content Provider	ACM Digital Library
Author	Kanoria, Yash Johari, Ramesh Kamble, Vijay
Abstract	We consider the problem faced by a service platform that needs to match supply with demand but also to learn attributes of new arrivals in order to match them better in the future. We introduce a benchmark model with heterogeneous workers and jobs that arrive over time. Job types are known to the platform, but worker types are unknown and must be learned by observing match outcomes. Workers depart after performing a certain number of jobs. The payoff from a match depends on the pair of types and the goal is to maximize the steady-state rate of accumulation of payoff. Our main contribution is a complete characterization of the structure of the optimal policy in the limit that each worker performs many jobs. The platform faces a trade-off for each worker between myopically maximizing payoffs (exploitation) and learning the type of the worker (exploration). This creates a multitude of multi-armed bandit problems, one for each worker, coupled together by the constraint on the availability of jobs of different types (capacity constraints). We find that the platform should estimate a shadow price for each job type, and use the payoffs adjusted by these prices, first, to determine its learning goals and then, for each worker, (i) to balance learning with payoffs during the exploration phase, and (ii) to myopically match after it has achieved its learning goals during the exploitation phase.
Starting Page	119
Ending Page	119
Page Count	1
File Format	PDF
ISBN	9781450345279
DOI	10.1145/3033274.3084095
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-06-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Learning Matching Multi-armed bandits Two-sided platforms Capacity constraints
Content Type	Text
Resource Type	Article

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