529 research outputs found
Gradient Descent Only Converges to Minimizers: Non-Isolated Critical Points and Invariant Regions
Given a non-convex twice differentiable cost function f, we prove that the
set of initial conditions so that gradient descent converges to saddle points
where \nabla^2 f has at least one strictly negative eigenvalue has (Lebesgue)
measure zero, even for cost functions f with non-isolated critical points,
answering an open question in [Lee, Simchowitz, Jordan, Recht, COLT2016].
Moreover, this result extends to forward-invariant convex subspaces, allowing
for weak (non-globally Lipschitz) smoothness assumptions. Finally, we produce
an upper bound on the allowable step-size.Comment: 2 figure
Informational Substitutes
We propose definitions of substitutes and complements for pieces of
information ("signals") in the context of a decision or optimization problem,
with game-theoretic and algorithmic applications. In a game-theoretic context,
substitutes capture diminishing marginal value of information to a rational
decision maker. We use the definitions to address the question of how and when
information is aggregated in prediction markets. Substitutes characterize
"best-possible" equilibria with immediate information aggregation, while
complements characterize "worst-possible", delayed aggregation. Game-theoretic
applications also include settings such as crowdsourcing contests and Q\&A
forums. In an algorithmic context, where substitutes capture diminishing
marginal improvement of information to an optimization problem, substitutes
imply efficient approximation algorithms for a very general class of (adaptive)
information acquisition problems.
In tandem with these broad applications, we examine the structure and design
of informational substitutes and complements. They have equivalent, intuitive
definitions from disparate perspectives: submodularity, geometry, and
information theory. We also consider the design of scoring rules or
optimization problems so as to encourage substitutability or complementarity,
with positive and negative results. Taken as a whole, the results give some
evidence that, in parallel with substitutable items, informational substitutes
play a natural conceptual and formal role in game theory and algorithms.Comment: Full version of FOCS 2016 paper. Single-column, 61 pages (48 main
text, 13 references and appendix
Behavioral Mechanism Design: Optimal Contests for Simple Agents
Incentives are more likely to elicit desired outcomes when they are designed
based on accurate models of agents' strategic behavior. A growing literature,
however, suggests that people do not quite behave like standard economic agents
in a variety of environments, both online and offline. What consequences might
such differences have for the optimal design of mechanisms in these
environments? In this paper, we explore this question in the context of optimal
contest design for simple agents---agents who strategically reason about
whether or not to participate in a system, but not about the input they provide
to it. Specifically, consider a contest where potential contestants with
types each choose between participating and producing a submission
of quality at cost , versus not participating at all, to maximize
their utilities. How should a principal distribute a total prize amongst
the ranks to maximize some increasing function of the qualities of elicited
submissions in a contest with such simple agents?
We first solve the optimal contest design problem for settings with
homogenous participation costs . Here, the optimal contest is always a
simple contest, awarding equal prizes to the top contestants for a
suitable choice of . (In comparable models with strategic effort choices,
the optimal contest is either a winner-take-all contest or awards possibly
unequal prizes, depending on the curvature of agents' effort cost functions.)
We next address the general case with heterogeneous costs where agents' types
are inherently two-dimensional, significantly complicating equilibrium
analysis. Our main result here is that the winner-take-all contest is a
3-approximation of the optimal contest when the principal's objective is to
maximize the quality of the best elicited contribution.Comment: This is the full version of a paper in the ACM Conference on
Economics and Computation (ACM-EC), 201
Crowdsourcing atop blockchains
Traditional crowdsourcing systems, such as Amazon\u27s Mechanical Turk (MTurk), though once acquiring great economic successes, have to fully rely on third-party platforms to serve between the requesters and the workers for basic utilities. These third-parties have to be fully trusted to assist payments, resolve disputes, protect data privacy, manage user authentications, maintain service online, etc. Nevertheless, tremendous real-world incidents indicate how elusive it is to completely trust these platforms in reality, and the reduction of such over-reliance becomes desirable.
In contrast to the arguably vulnerable centralized approaches, a public blockchain is a distributed and transparent global consensus computer that is highly robust. The blockchain is usually managed and replicated by a large-scale peer-to-peer network collectively, thus being much more robust to be fully trusted for correctness and availability. It, therefore, becomes enticing to build novel crowdsourcing applications atop blockchains to reduce the over-trust on third-party platforms.
However, this new fascinating technology also brings about new challenges, which were never that severe in the conventional centralized setting. The most serious issue is that the blockchain is usually maintained in the public Internet environment with a broader attack surface open to anyone. This not only causes serious privacy and security issues, but also allows the adversaries to exploit the attack surface to hamper more basic utilities. Worse still, most existing blockchains support only light on-chain computations, and the smart contract executed atop the decentralized consensus computer must be simple, which incurs serious feasibility problems. In reality, the privacy/security issue and the feasibility problem even restrain each other and create serious tensions to hinder the broader adoption of blockchain.
The dissertation goes through the non-trivial challenges to realize secure yet still practical decentralization (for urgent crowdsourcing use-cases), and lay down the foundation for this line of research. In sum, it makes the next major contributions.
First, it identifies the needed security requirements in decentralized knowledge crowdsourcing (e.g., data privacy), and initiates the research of private decentralized crowdsourcing. In particular, the confidentiality of solicited data is indispensable to prevent free-riders from pirating the others\u27 submissions, thus ensuring the quality of solicited knowledge. To this end, a generic private decentralized crowdsourcing framework is dedicatedly designed, analyzed, and implemented.
Furthermore, this dissertation leverages concretely efficient cryptographic design to reduce the cost of the above generic framework. It focuses on decentralizing the special use-case of Amazon MTurk, and conducts multiple specific-purpose optimizations to remove needless generality to squeeze performance. The implementation atop Ethereum demonstrates a handling cost even lower than MTurk.
In addition, it focuses on decentralized crowdsourcing of computing power for specific machine learning tasks. It lets a requester place deposits in the blockchain to recruit some workers for a designated (randomized) programs. If and only if these workers contribute their resources to compute correctly, they would earn well-deserved payments. For these goals, a simple yet still useful incentive mechanism is developed atop the blockchain to deter rational workers from cheating.
Finally, the research initiates the first systematic study on crowdsourcing blockchains\u27 full nodes to assist superlight clients (e.g., mobile phones and IoT devices) to read the blockchain\u27s records. This dissertation presents a novel generic solution through the powerful lens of game-theoretic treatments, which solves the long-standing open problem of designing generic superlight clients for all blockchains
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
Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges
Participatory sensing is a powerful paradigm which takes advantage of
smartphones to collect and analyze data beyond the scale of what was previously
possible. Given that participatory sensing systems rely completely on the
users' willingness to submit up-to-date and accurate information, it is
paramount to effectively incentivize users' active and reliable participation.
In this paper, we survey existing literature on incentive mechanisms for
participatory sensing systems. In particular, we present a taxonomy of existing
incentive mechanisms for participatory sensing systems, which are subsequently
discussed in depth by comparing and contrasting different approaches. Finally,
we discuss an agenda of open research challenges in incentivizing users in
participatory sensing.Comment: Updated version, 4/25/201
Coverage, Matching, and Beyond: New Results on Budgeted Mechanism Design
We study a type of reverse (procurement) auction problems in the presence of
budget constraints. The general algorithmic problem is to purchase a set of
resources, which come at a cost, so as not to exceed a given budget and at the
same time maximize a given valuation function. This framework captures the
budgeted version of several well known optimization problems, and when the
resources are owned by strategic agents the goal is to design truthful and
budget feasible mechanisms, i.e. elicit the true cost of the resources and
ensure the payments of the mechanism do not exceed the budget. Budget
feasibility introduces more challenges in mechanism design, and we study
instantiations of this problem for certain classes of submodular and XOS
valuation functions. We first obtain mechanisms with an improved approximation
ratio for weighted coverage valuations, a special class of submodular functions
that has already attracted attention in previous works. We then provide a
general scheme for designing randomized and deterministic polynomial time
mechanisms for a class of XOS problems. This class contains problems whose
feasible set forms an independence system (a more general structure than
matroids), and some representative problems include, among others, finding
maximum weighted matchings, maximum weighted matroid members, and maximum
weighted 3D-matchings. For most of these problems, only randomized mechanisms
with very high approximation ratios were known prior to our results
Truthful Multi-unit Procurements with Budgets
We study procurement games where each seller supplies multiple units of his
item, with a cost per unit known only to him. The buyer can purchase any number
of units from each seller, values different combinations of the items
differently, and has a budget for his total payment.
For a special class of procurement games, the {\em bounded knapsack} problem,
we show that no universally truthful budget-feasible mechanism can approximate
the optimal value of the buyer within , where is the total number of
units of all items available. We then construct a polynomial-time mechanism
that gives a -approximation for procurement games with {\em concave
additive valuations}, which include bounded knapsack as a special case. Our
mechanism is thus optimal up to a constant factor. Moreover, for the bounded
knapsack problem, given the well-known FPTAS, our results imply there is a
provable gap between the optimization domain and the mechanism design domain.
Finally, for procurement games with {\em sub-additive valuations}, we
construct a universally truthful budget-feasible mechanism that gives an
-approximation in polynomial time with a
demand oracle.Comment: To appear at WINE 201
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