4,814 research outputs found
Aggregating Dependency Graphs into Voting Agendas in Multi-Issue Elections
Many collective decision making problems have a
combinatorial structure: the agents involved must
decide on multiple issues and their preferences over
one issue may depend on the choices adopted for
some of the others. Voting is an attractive method
for making collective decisions, but conducting a
multi-issue election is challenging. On the one hand,
requiring agents to vote by expressing their preferences
over all combinations of issues is computationally
infeasible; on the other, decomposing the
problem into several elections on smaller sets of
issues can lead to paradoxical outcomes. Any pragmatic
method for running a multi-issue election will
have to balance these two concerns. We identify
and analyse the problem of generating an agenda
for a given election, specifying which issues to vote
on together in local elections and in which order to
schedule those local elections
Bribeproof mechanisms for two-values domains
Schummer (Journal of Economic Theory 2000) introduced the concept of
bribeproof mechanism which, in a context where monetary transfer between agents
is possible, requires that manipulations through bribes are ruled out.
Unfortunately, in many domains, the only bribeproof mechanisms are the trivial
ones which return a fixed outcome.
This work presents one of the few constructions of non-trivial bribeproof
mechanisms for these quasi-linear environments. Though the suggested
construction applies to rather restricted domains, the results obtained are
tight: For several natural problems, the method yields the only possible
bribeproof mechanism and no such mechanism is possible on more general domains.Comment: Extended abstract accepted to SAGT 2016. This ArXiv version corrects
typos in the proofs of Theorem 7 and Claims 28-29 of prior ArXiv versio
MODELING, LEARNING AND REASONING ABOUT PREFERENCE TREES OVER COMBINATORIAL DOMAINS
In my Ph.D. dissertation, I have studied problems arising in various aspects of preferences: preference modeling, preference learning, and preference reasoning, when preferences concern outcomes ranging over combinatorial domains. Preferences is a major research component in artificial intelligence (AI) and decision theory, and is closely related to the social choice theory considered by economists and political scientists. In my dissertation, I have exploited emerging connections between preferences in AI and social choice theory. Most of my research is on qualitative preference representations that extend and combine existing formalisms such as conditional preference nets, lexicographic preference trees, answer-set optimization programs, possibilistic logic, and conditional preference networks; on learning problems that aim at discovering qualitative preference models and predictive preference information from practical data; and on preference reasoning problems centered around qualitative preference optimization and aggregation methods. Applications of my research include recommender systems, decision support tools, multi-agent systems, and Internet trading and marketing platforms
On the Hardness of Bribery Variants in Voting with CP-Nets
We continue previous work by Mattei et al. (Mattei, N., Pini, M., Rossi, F.,
Venable, K.: Bribery in voting with CP-nets. Ann. of Math. and Artif. Intell.
pp. 1--26 (2013)) in which they study the computational complexity of bribery
schemes when voters have conditional preferences that are modeled by CP-nets.
For most of the cases they considered, they could show that the bribery problem
is solvable in polynomial time. Some cases remained open---we solve two of them
and extend the previous results to the case that voters are weighted. Moreover,
we consider negative (weighted) bribery in CP-nets, when the briber is not
allowed to pay voters to vote for his preferred candidate.Comment: improved readability; identified Cheapest Subsets to be the
enumeration variant of K.th Largest Subset, so we renamed it to K-Smallest
Subsets and point to the literatur; some more typos fixe
Statistical-mechanical lattice models for protein-DNA binding in chromatin
Statistical-mechanical lattice models for protein-DNA binding are well
established as a method to describe complex ligand binding equilibriums
measured in vitro with purified DNA and protein components. Recently, a new
field of applications has opened up for this approach since it has become
possible to experimentally quantify genome-wide protein occupancies in relation
to the DNA sequence. In particular, the organization of the eukaryotic genome
by histone proteins into a nucleoprotein complex termed chromatin has been
recognized as a key parameter that controls the access of transcription factors
to the DNA sequence. New approaches have to be developed to derive statistical
mechanical lattice descriptions of chromatin-associated protein-DNA
interactions. Here, we present the theoretical framework for lattice models of
histone-DNA interactions in chromatin and investigate the (competitive) DNA
binding of other chromosomal proteins and transcription factors. The results
have a number of applications for quantitative models for the regulation of
gene expression.Comment: 19 pages, 7 figures, accepted author manuscript, to appear in J.
Phys.: Cond. Mat
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