16,174 research outputs found
Learning and Strongly Truthful Multi-Task Peer Prediction: A Variational Approach
Peer prediction mechanisms incentivize agents to truthfully report their
signals even in the absence of verification by comparing agents' reports with
those of their peers. In the detail-free multi-task setting, agents respond to
multiple independent and identically distributed tasks, and the mechanism does
not know the prior distribution of agents' signals. The goal is to provide an
-strongly truthful mechanism where truth-telling rewards agents
"strictly" more than any other strategy profile (with additive
error), and to do so while requiring as few tasks as possible. We design a
family of mechanisms with a scoring function that maps a pair of reports to a
score. The mechanism is strongly truthful if the scoring function is "prior
ideal," and -strongly truthful as long as the scoring function is
sufficiently close to the ideal one. This reduces the above mechanism design
problem to a learning problem -- specifically learning an ideal scoring
function. We leverage this reduction to obtain the following three results. 1)
We show how to derive good bounds on the number of tasks required for different
types of priors. Our reduction applies to myriad continuous signal space
settings. This is the first peer-prediction mechanism on continuous signals
designed for the multi-task setting. 2) We show how to turn a soft-predictor of
an agent's signals (given the other agents' signals) into a mechanism. This
allows the practical use of machine learning algorithms that give good results
even when many agents provide noisy information. 3) For finite signal spaces,
we obtain -strongly truthful mechanisms on any stochastically
relevant prior, which is the maximal possible prior. In contrast, prior work
only achieves a weaker notion of truthfulness (informed truthfulness) or
requires stronger assumptions on the prior.Comment: 39 pages, 1 figur
Fairness Incentives for Myopic Agents
We consider settings in which we wish to incentivize myopic agents (such as
Airbnb landlords, who may emphasize short-term profits and property safety) to
treat arriving clients fairly, in order to prevent overall discrimination
against individuals or groups. We model such settings in both classical and
contextual bandit models in which the myopic agents maximize rewards according
to current empirical averages, but are also amenable to exogenous payments that
may cause them to alter their choices. Our notion of fairness asks that more
qualified individuals are never (probabilistically) preferred over less
qualified ones [Joseph et al].
We investigate whether it is possible to design inexpensive {subsidy} or
payment schemes for a principal to motivate myopic agents to play fairly in all
or almost all rounds. When the principal has full information about the state
of the myopic agents, we show it is possible to induce fair play on every round
with a subsidy scheme of total cost (for the classic setting with
arms, , and for the -dimensional linear contextual
setting ). If the principal has much more limited
information (as might often be the case for an external regulator or watchdog),
and only observes the number of rounds in which members from each of the
groups were selected, but not the empirical estimates maintained by the myopic
agent, the design of such a scheme becomes more complex. We show both positive
and negative results in the classic and linear bandit settings by upper and
lower bounding the cost of fair subsidy schemes
Optimum Statistical Estimation with Strategic Data Sources
We propose an optimum mechanism for providing monetary incentives to the data
sources of a statistical estimator such as linear regression, so that high
quality data is provided at low cost, in the sense that the sum of payments and
estimation error is minimized. The mechanism applies to a broad range of
estimators, including linear and polynomial regression, kernel regression, and,
under some additional assumptions, ridge regression. It also generalizes to
several objectives, including minimizing estimation error subject to budget
constraints. Besides our concrete results for regression problems, we
contribute a mechanism design framework through which to design and analyze
statistical estimators whose examples are supplied by workers with cost for
labeling said examples
Incentivizing Exploration with Heterogeneous Value of Money
Recently, Frazier et al. proposed a natural model for crowdsourced
exploration of different a priori unknown options: a principal is interested in
the long-term welfare of a population of agents who arrive one by one in a
multi-armed bandit setting. However, each agent is myopic, so in order to
incentivize him to explore options with better long-term prospects, the
principal must offer the agent money. Frazier et al. showed that a simple class
of policies called time-expanded are optimal in the worst case, and
characterized their budget-reward tradeoff.
The previous work assumed that all agents are equally and uniformly
susceptible to financial incentives. In reality, agents may have different
utility for money. We therefore extend the model of Frazier et al. to allow
agents that have heterogeneous and non-linear utilities for money. The
principal is informed of the agent's tradeoff via a signal that could be more
or less informative.
Our main result is to show that a convex program can be used to derive a
signal-dependent time-expanded policy which achieves the best possible
Lagrangian reward in the worst case. The worst-case guarantee is matched by
so-called "Diamonds in the Rough" instances; the proof that the guarantees
match is based on showing that two different convex programs have the same
optimal solution for these specific instances. These results also extend to the
budgeted case as in Frazier et al. We also show that the optimal policy is
monotone with respect to information, i.e., the approximation ratio of the
optimal policy improves as the signals become more informative.Comment: WINE 201
A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems
Bike sharing provides an environment-friendly way for traveling and is
booming all over the world. Yet, due to the high similarity of user travel
patterns, the bike imbalance problem constantly occurs, especially for dockless
bike sharing systems, causing significant impact on service quality and company
revenue. Thus, it has become a critical task for bike sharing systems to
resolve such imbalance efficiently. In this paper, we propose a novel deep
reinforcement learning framework for incentivizing users to rebalance such
systems. We model the problem as a Markov decision process and take both
spatial and temporal features into consideration. We develop a novel deep
reinforcement learning algorithm called Hierarchical Reinforcement Pricing
(HRP), which builds upon the Deep Deterministic Policy Gradient algorithm.
Different from existing methods that often ignore spatial information and rely
heavily on accurate prediction, HRP captures both spatial and temporal
dependencies using a divide-and-conquer structure with an embedded localized
module. We conduct extensive experiments to evaluate HRP, based on a dataset
from Mobike, a major Chinese dockless bike sharing company. Results show that
HRP performs close to the 24-timeslot look-ahead optimization, and outperforms
state-of-the-art methods in both service level and bike distribution. It also
transfers well when applied to unseen areas
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