19,278 research outputs found
An Information-Theoretic Model of Voting Systems
We present an information-theoretic model of a voting system, consisting of (a) definitions of the desirable qualities of integrity, privacy and verifiability, and (b) quantitative measures of how close a system is to being perfect with respect to each of the qualities. We describe the well-known trade-off between integrity and privacy in this model, and defines a concept of weak privacy, which is traded off with system verifiability. This is an extension of a talk from WOTE 2006, and contains some new applications of the model and arguments for the model\u27s applicability
Tuning the Diversity of Open-Ended Responses from the Crowd
Crowdsourcing can solve problems that current fully automated systems cannot.
Its effectiveness depends on the reliability, accuracy, and speed of the crowd
workers that drive it. These objectives are frequently at odds with one
another. For instance, how much time should workers be given to discover and
propose new solutions versus deliberate over those currently proposed? How do
we determine if discovering a new answer is appropriate at all? And how do we
manage workers who lack the expertise or attention needed to provide useful
input to a given task? We present a mechanism that uses distinct payoffs for
three possible worker actions---propose,vote, or abstain---to provide workers
with the necessary incentives to guarantee an effective (or even optimal)
balance between searching for new answers, assessing those currently available,
and, when they have insufficient expertise or insight for the task at hand,
abstaining. We provide a novel game theoretic analysis for this mechanism and
test it experimentally on an image---labeling problem and show that it allows a
system to reliably control the balance betweendiscovering new answers and
converging to existing ones
On Rational Delegations in Liquid Democracy
Liquid democracy is a proxy voting method where proxies are delegable. We
propose and study a game-theoretic model of liquid democracy to address the
following question: when is it rational for a voter to delegate her vote? We
study the existence of pure-strategy Nash equilibria in this model, and how
group accuracy is affected by them. We complement these theoretical results by
means of agent-based simulations to study the effects of delegations on group's
accuracy on variously structured social networks.Comment: 17 pages, 3 figures. This paper (without Appendix) appears in the
proceedings of AAAI'1
Learning the Structure and Parameters of Large-Population Graphical Games from Behavioral Data
We consider learning, from strictly behavioral data, the structure and
parameters of linear influence games (LIGs), a class of parametric graphical
games introduced by Irfan and Ortiz (2014). LIGs facilitate causal strategic
inference (CSI): Making inferences from causal interventions on stable behavior
in strategic settings. Applications include the identification of the most
influential individuals in large (social) networks. Such tasks can also support
policy-making analysis. Motivated by the computational work on LIGs, we cast
the learning problem as maximum-likelihood estimation (MLE) of a generative
model defined by pure-strategy Nash equilibria (PSNE). Our simple formulation
uncovers the fundamental interplay between goodness-of-fit and model
complexity: good models capture equilibrium behavior within the data while
controlling the true number of equilibria, including those unobserved. We
provide a generalization bound establishing the sample complexity for MLE in
our framework. We propose several algorithms including convex loss minimization
(CLM) and sigmoidal approximations. We prove that the number of exact PSNE in
LIGs is small, with high probability; thus, CLM is sound. We illustrate our
approach on synthetic data and real-world U.S. congressional voting records. We
briefly discuss our learning framework's generality and potential applicability
to general graphical games.Comment: Journal of Machine Learning Research. (accepted, pending
publication.) Last conference version: submitted March 30, 2012 to UAI 2012.
First conference version: entitled, Learning Influence Games, initially
submitted on June 1, 2010 to NIPS 201
Haplotype Assembly: An Information Theoretic View
This paper studies the haplotype assembly problem from an information
theoretic perspective. A haplotype is a sequence of nucleotide bases on a
chromosome, often conveniently represented by a binary string, that differ from
the bases in the corresponding positions on the other chromosome in a
homologous pair. Information about the order of bases in a genome is readily
inferred using short reads provided by high-throughput DNA sequencing
technologies. In this paper, the recovery of the target pair of haplotype
sequences using short reads is rephrased as a joint source-channel coding
problem. Two messages, representing haplotypes and chromosome memberships of
reads, are encoded and transmitted over a channel with erasures and errors,
where the channel model reflects salient features of high-throughput
sequencing. The focus of this paper is on the required number of reads for
reliable haplotype reconstruction, and both the necessary and sufficient
conditions are presented with order-wise optimal bounds.Comment: 30 pages, 5 figures, 1 tabel, journa
The New Theory of Strategic Voting
This is an analysis of strategic voting under qualified majority voting. Existing formal analyses of the plurality rule predict complete coordination of strategic voting: a strict interpretation of Duverger's Law. This conclusion is rejected. Unlike previous models, the popular support for each option is not commonly certain. Agents base their vote on both public and private signals of popular support. When private signals are the main source of information, the uniquely stable equilibrium entails only limited strategic voting and hence partial coordination. This is due to the surprising presence of negative feedback --- strategic voting is a self-attenuating phenomenon. The theory leads to the conclusion that multi-candidate support in a plurality electoral system is perfectly consistent with rational voting behaviour.
Models of Consensus for Multiple Agent Systems
Models of consensus are used to manage multiple agent systems in order to
choose between different recommendations provided by the system. It is assumed
that there is a central agent that solicits recommendations or plans from other
agents. That agent the n determines the consensus of the other agents, and
chooses the resultant consensus recommendation or plan. Voting schemes such as
this have been used in a variety of domains, including air traffic control.
This paper uses an analytic model to study the use of consensus in multiple
agent systems. The binomial model is used to study the probability that the
consensus judgment is correct or incorrect. That basic model is extended to
account for both different levels of agent competence and unequal prior odds.
The analysis of that model is critical in the investigation of multiple agent
systems, since the model leads us to conclude that in some cases consensus
judgment is not appropriate. In addition, the results allow us to determine how
many agents should be used to develop consensus decisions, which agents should
be used to develop consensus decisions and under which conditions the consensus
model should be used.Comment: Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994
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