53,596 research outputs found
Incentive Compatible Active Learning
We consider active learning under incentive compatibility constraints. The
main application of our results is to economic experiments, in which a learner
seeks to infer the parameters of a subject's preferences: for example their
attitudes towards risk, or their beliefs over uncertain events. By cleverly
adapting the experimental design, one can save on the time spent by subjects in
the laboratory, or maximize the information obtained from each subject in a
given laboratory session; but the resulting adaptive design raises
complications due to incentive compatibility. A subject in the lab may answer
questions strategically, and not truthfully, so as to steer subsequent
questions in a profitable direction.
We analyze two standard economic problems: inference of preferences over risk
from multiple price lists, and belief elicitation in experiments on choice over
uncertainty. In the first setting, we tune a simple and fast learning algorithm
to retain certain incentive compatibility properties. In the second setting, we
provide an incentive compatible learning algorithm based on scoring rules with
query complexity that differs from obvious methods of achieving fast learning
rates only by subpolynomial factors. Thus, for these areas of application,
incentive compatibility may be achieved without paying a large sample
complexity price.Comment: 22 page
An Incentive Compatible Multi-Armed-Bandit Crowdsourcing Mechanism with Quality Assurance
Consider a requester who wishes to crowdsource a series of identical binary
labeling tasks to a pool of workers so as to achieve an assured accuracy for
each task, in a cost optimal way. The workers are heterogeneous with unknown
but fixed qualities and their costs are private. The problem is to select for
each task an optimal subset of workers so that the outcome obtained from the
selected workers guarantees a target accuracy level. The problem is a
challenging one even in a non strategic setting since the accuracy of
aggregated label depends on unknown qualities. We develop a novel multi-armed
bandit (MAB) mechanism for solving this problem. First, we propose a framework,
Assured Accuracy Bandit (AAB), which leads to an MAB algorithm, Constrained
Confidence Bound for a Non Strategic setting (CCB-NS). We derive an upper bound
on the number of time steps the algorithm chooses a sub-optimal set that
depends on the target accuracy level and true qualities. A more challenging
situation arises when the requester not only has to learn the qualities of the
workers but also elicit their true costs. We modify the CCB-NS algorithm to
obtain an adaptive exploration separated algorithm which we call { \em
Constrained Confidence Bound for a Strategic setting (CCB-S)}. CCB-S algorithm
produces an ex-post monotone allocation rule and thus can be transformed into
an ex-post incentive compatible and ex-post individually rational mechanism
that learns the qualities of the workers and guarantees a given target accuracy
level in a cost optimal way. We provide a lower bound on the number of times
any algorithm should select a sub-optimal set and we see that the lower bound
matches our upper bound upto a constant factor. We provide insights on the
practical implementation of this framework through an illustrative example and
we show the efficacy of our algorithms through simulations
Partial Strategyproofness: Relaxing Strategyproofness for the Random Assignment Problem
We present partial strategyproofness, a new, relaxed notion of
strategyproofness for studying the incentive properties of non-strategyproof
assignment mechanisms. Informally, a mechanism is partially strategyproof if it
makes truthful reporting a dominant strategy for those agents whose preference
intensities differ sufficiently between any two objects. We demonstrate that
partial strategyproofness is axiomatically motivated and yields a parametric
measure for "how strategyproof" an assignment mechanism is. We apply this new
concept to derive novel insights about the incentive properties of the
probabilistic serial mechanism and different variants of the Boston mechanism.Comment: Working Pape
Efficiency Guarantees in Auctions with Budgets
In settings where players have a limited access to liquidity, represented in
the form of budget constraints, efficiency maximization has proven to be a
challenging goal. In particular, the social welfare cannot be approximated by a
better factor then the number of players. Therefore, the literature has mainly
resorted to Pareto-efficiency as a way to achieve efficiency in such settings.
While successful in some important scenarios, in many settings it is known that
either exactly one incentive-compatible auction that always outputs a
Pareto-efficient solution, or that no truthful mechanism can always guarantee a
Pareto-efficient outcome. Traditionally, impossibility results can be avoided
by considering approximations. However, Pareto-efficiency is a binary property
(is either satisfied or not), which does not allow for approximations.
In this paper we propose a new notion of efficiency, called \emph{liquid
welfare}. This is the maximum amount of revenue an omniscient seller would be
able to extract from a certain instance. We explain the intuition behind this
objective function and show that it can be 2-approximated by two different
auctions. Moreover, we show that no truthful algorithm can guarantee an
approximation factor better than 4/3 with respect to the liquid welfare, and
provide a truthful auction that attains this bound in a special case.
Importantly, the liquid welfare benchmark also overcomes impossibilities for
some settings. While it is impossible to design Pareto-efficient auctions for
multi-unit auctions where players have decreasing marginal values, we give a
deterministic -approximation for the liquid welfare in this setting
Auctions with Heterogeneous Items and Budget Limits
We study individual rational, Pareto optimal, and incentive compatible
mechanisms for auctions with heterogeneous items and budget limits. For
multi-dimensional valuations we show that there can be no deterministic
mechanism with these properties for divisible items. We use this to show that
there can also be no randomized mechanism that achieves this for either
divisible or indivisible items. For single-dimensional valuations we show that
there can be no deterministic mechanism with these properties for indivisible
items, but that there is a randomized mechanism that achieves this for either
divisible or indivisible items. The impossibility results hold for public
budgets, while the mechanism allows private budgets, which is in both cases the
harder variant to show. While all positive results are polynomial-time
algorithms, all negative results hold independent of complexity considerations
Computational Mechanism Design: A Call to Arms
Game theory has developed powerful tools for analyzing decision making in systems with multiple autonomous actors. These tools, when tailored to computational settings, provide a foundation for building multiagent software systems. This tailoring gives rise to the field of computational mechanism design, which applies economic principles to computer systems design
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