98 research outputs found
Integrating experts’ weights generated dynamically into the consensus reaching process and its applications in managing non-cooperative behaviors
This work was supported in part by the NSF of China under grants 71171160 and 71571124, in part by the SSEM Key Research Center at Sichuan Province under grant xq15b01, in part by the FEDER funds under grant TIN2013-40658-P, and in part by Andalusian Excellence Project under grant TIC-5991.The consensus reaching process (CRP) is a dynamic and iterative process for improving the consensus level among experts in group decision making. A large number of non-cooperative behaviors exist in the CRP. For example, some experts will express their opinions dishonestly or refuse to change their opinions to further their own interests. In this study, we propose a novel consensus framework for managing non-cooperative behaviors. In the proposed framework, a self-management mechanism to generate experts' weights dynamically is presented and then integrated into the CRP. This self-management mechanism is based on multi-attribute mutual evaluation matrices (MMEMs). During the CRP, the experts can provide and update their MMEMs regarding the experts' performances (e.g., professional skill, cooperation, and fairness), and the experts' weights are dynamically derived from the MMEMs. Detailed simulation experiments and comparison analysis are presented to justify the validity of the proposed consensus framework in managing the non-cooperative behaviors.National Natural Science Foundation of China
71171160
71571124SSEM Key Research Center at Sichuan Province
xq15b01European Union (EU)
TIN2013-40658-PAndalusian Excellence Project
TIC-599
Breaking the -Pass Barrier: A Streaming Algorithm for Maximum Weight Bipartite Matching
Given a weighted bipartite graph with vertices and edges, the
\emph{maximum weight bipartite matching} problem is to find a set of
vertex-disjoint edges with the maximum weight. This classic problem has been
extensively studied for over a century.
In this paper, we present a new streaming algorithm for the maximum weight
bipartite matching problem that uses space and
passes, which breaks the -pass barrier. All the
previous streaming algorithms either require passes or only
find an approximate solution. Our streaming algorithm constructs a subgraph
with edges of the input graph in passes, such
that the subgraph admits the optimal matching with good probability.
Our method combines various ideas from different fields, most notably the
construction of \emph{space-efficient} interior point method (IPM), SDD system
solvers, the isolation lemma, and LP duality. To the best of our knowledge,
this is the first work that implements the SDD solvers and IPMs in the
streaming model in spaces for graph matrices; previous IPM
algorithms only focus on optimizing the running time, regardless of the space
usage
Tight Revenue Gaps among Multi-Unit Mechanisms
This paper considers Bayesian revenue maximization in the -unit setting,
where a monopolist seller has copies of an indivisible item and faces
unit-demand buyers (whose value distributions can be non-identical). Four basic
mechanisms among others have been widely employed in practice and widely
studied in the literature: {\sf Myerson Auction}, {\sf Sequential
Posted-Pricing}, {\sf -th Price Auction with Anonymous Reserve}, and
{\sf Anonymous Pricing}. Regarding a pair of mechanisms, we investigate the
largest possible ratio between the two revenues (a.k.a.\ the revenue gap), over
all possible value distributions of the buyers.
Divide these four mechanisms into two groups: (i)~the discriminating
mechanism group, {\sf Myerson Auction} and {\sf Sequential Posted-Pricing}, and
(ii)~the anonymous mechanism group, {\sf Anonymous Reserve} and {\sf Anonymous
Pricing}. Within one group, the involved two mechanisms have an asymptotically
tight revenue gap of . In contrast, any two
mechanisms from the different groups have an asymptotically tight revenue gap
of
Velocity acoustic oscillations on Cosmic Dawn 21 cm power spectrum as a probe of small-scale density fluctuations
We investigate the feasibility of using the velocity acoustic oscillations
(VAO) features on the Cosmic Dawn 21 cm power spectrum to probe small-scale
density fluctuations. In the standard cold dark matter (CDM) model, Pop III
stars form in minihalos and affect the 21 cm signal through Ly and
X-ray radiation. Such a process is modulated by the relative motion between
dark matter and baryons, generating the VAO wiggles on the 21 cm power
spectrum. In the fuzzy or warm dark matter models for which the number of
minihalos is reduced, the VAO wiggles are weaker or even fully invisible. We
investigate the wiggle features in the CDM with different astrophysical models
and in different dark matter models. We find: 1) In the CDM model the relative
streaming velocities can generate the VAO wiggles for broad ranges of
parameters , and , though for
different parameters the wiggles would appear at different redshifts and have
different amplitudes. 2) For the axion model with
eV, the VAO wiggles are negligible. In the mixed model, the VAO signal is
sensitive to the axion fraction. For example, the wiggles almost disappear when
for eV. Therefore, the VAO signal
can be an effective indicator for small-scale density fluctuations and a useful
probe of the nature of dark matter. The SKA-low with 2000 hour
observation time has the ability to detect the VAO signal and constraint dark
matter models.Comment: 22 pages, 21 figures, accepted for publication by Ap
A graph model with minimum cost to support conflict resolution and mediation in technology transfer of new product co-development.
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Successful new product development advocate for collaboration among different institutions in which technology transfer dispute widely exists. Although several studies have discussed conflict modelling and resolution in technology transfer dispute, scant research attempted to model third-party (or mediator) mediation, let alone develop effective approaches to minimize cost in the conflict resolution process. This study uses a graph model and minimum cost to investigate the conflict resolution and mediation in technology transfer dispute of new product collaborative development. On the one hand, the conflict in technology transfer of new product collaborative development is modelled using the graph model theory, in which the stakeholders (or decision-makers), their options, the feasible states, and the preferences of decision-makers are analyzed. On the other hand, an inverse graph model with minimum cost is designed to tackle the problem of specifying which decision-makers’ preferences lead to a desired solution, thereby making it easier for a mediator or other third party to influence the course of the conflict. In the inverse graph model with minimum cost, two 0-1 mixed linear approaches are constructed to judge the Nash and General Merataionality stabilities within the graph model, and several optimization-based models that minimize mediation cost are designed for the mediator to guide the technology transfer conflict resolution process to achieve the desired solution. Finally, the proposed methodology is applied to a technology transfer dispute case study
Ranking range based approach to MADM under incomplete context and its application in venture investment evaluation
In real-world Multiple Attribute Decision Making (MADM) problem, the attribute weights information may be unknown or partially known. Several approaches have been suggested to address this kind of incomplete MADM problem. However, these approaches depend on the determination of attribute weights, and setting different attribute weight vectors may result in different ranking positions of alternatives. To deal with this issue, this paper develops a novel MADM approach: the ranking range based MADM approach. In the novel MADM approach, the minimum and maximum ranking positions of every alternative are generated using several optimization models, and the average ranking position of every alternative is produced applying the Monte Carlo simulation method. Then, the minimum, maximum and average ranking positions of the alternative are integrated into a new ranking position of the alternative. This novel approach is capable of dealing with venture investment evaluation problems. However, in the venture investment evaluation process, decision makers will present different risk attitudes. To deal with this issue, two ranking range based MADM approaches with risk attitudes are further designed. A case study and a simulation experiment are presented to show the validity of the proposal
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