18,586 research outputs found
Estimating Dynamic Traffic Matrices by using Viable Routing Changes
Abstract: In this paper we propose a new approach for dealing with the ill-posed nature of traffic matrix estimation. We present three solution enhancers: an algorithm for deliberately changing link weights to obtain additional information that can make the underlying linear system full rank; a cyclo-stationary model to capture both long-term and short-term traffic variability, and a method for estimating the variance of origin-destination (OD) flows. We show how these three elements can be combined into a comprehensive traffic matrix estimation procedure that dramatically reduces the errors compared to existing methods. We demonstrate that our variance estimates can be used to identify the elephant OD flows, and we thus propose a variant of our algorithm that addresses the problem of estimating only the heavy flows in a traffic matrix. One of our key findings is that by focusing only on heavy flows, we can simplify the measurement and estimation procedure so as to render it more practical. Although there is a tradeoff between practicality and accuracy, we find that increasing the rank is so helpful that we can nevertheless keep the average errors consistently below the 10% carrier target error rate. We validate the effectiveness of our methodology and the intuition behind it using commercial traffic matrix data from Sprint's Tier-1 backbon
Advances in ranking and selection: variance estimation and constraints
In this thesis, we first show that the performance of ranking and selection (R&S) procedures in steady-state simulations depends highly on the quality of the variance estimates that are used. We study the performance of R&S procedures using three variance estimators --- overlapping area, overlapping Cramer--von Mises, and overlapping modified jackknifed Durbin--Watson estimators --- that show better long-run performance than other estimators previously used in conjunction with R&S procedures for steady-state simulations. We devote additional study to the development of the new overlapping modified jackknifed Durbin--Watson estimator and demonstrate some of its useful properties.
Next, we consider the problem of finding the best simulated system under a primary performance measure, while also satisfying stochastic constraints on secondary performance measures, known as constrained ranking and selection. We first present a new framework that allows certain systems to become dormant, halting sampling for those systems as the procedure continues. We also develop general procedures for constrained R&S that guarantee a nominal probability of correct selection, under any number of constraints and correlation across systems. In addition, we address new topics critical to efficiency of the these procedures, namely the allocation of error between feasibility check and selection, the use of common random numbers, and the cost of switching between simulated
systems.Ph.D.Committee Co-chairs: Sigrun Andradottir, Dave Goldsman and Seong-Hee Kim; Committee Members:Shabbir Ahmed and Brani Vidakovi
Significance Relations for the Benchmarking of Meta-Heuristic Algorithms
The experimental analysis of meta-heuristic algorithm performance is usually
based on comparing average performance metric values over a set of algorithm
instances. When algorithms getting tight in performance gains, the additional
consideration of significance of a metric improvement comes into play. However,
from this moment the comparison changes from an absolute to a relative mode.
Here the implications of this paradigm shift are investigated. Significance
relations are formally established. Based on this, a trade-off between
increasing cycle-freeness of the relation and small maximum sets can be
identified, allowing for the selection of a proper significance level and
resulting ranking of a set of algorithms. The procedure is exemplified on the
CEC'05 benchmark of real parameter single objective optimization problems. The
significance relation here is based on awarding ranking points for relative
performance gains, similar to the Borda count voting method or the Wilcoxon
signed rank test. In the particular CEC'05 case, five ranks for algorithm
performance can be clearly identified.Comment: 6 pages, 2 figures, 1 tabl
Sequential Design for Ranking Response Surfaces
We propose and analyze sequential design methods for the problem of ranking
several response surfaces. Namely, given response surfaces over a
continuous input space , the aim is to efficiently find the index of
the minimal response across the entire . The response surfaces are not
known and have to be noisily sampled one-at-a-time. This setting is motivated
by stochastic control applications and requires joint experimental design both
in space and response-index dimensions. To generate sequential design
heuristics we investigate stepwise uncertainty reduction approaches, as well as
sampling based on posterior classification complexity. We also make connections
between our continuous-input formulation and the discrete framework of pure
regret in multi-armed bandits. To model the response surfaces we utilize
kriging surrogates. Several numerical examples using both synthetic data and an
epidemics control problem are provided to illustrate our approach and the
efficacy of respective adaptive designs.Comment: 26 pages, 7 figures (updated several sections and figures
Clustered marginalization of minorities during social transitions induced by co-evolution of behaviour and network structure
Large-scale transitions in societies are associated with both individual
behavioural change and restructuring of the social network. These two factors
have often been considered independently, yet recent advances in social network
research challenge this view. Here we show that common features of societal
marginalization and clustering emerge naturally during transitions in a
co-evolutionary adaptive network model. This is achieved by explicitly
considering the interplay between individual interaction and a dynamic network
structure in behavioural selection. We exemplify this mechanism by simulating
how smoking behaviour and the network structure get reconfigured by changing
social norms. Our results are consistent with empirical findings: The
prevalence of smoking was reduced, remaining smokers were preferentially
connected among each other and formed increasingly marginalised clusters. We
propose that self-amplifying feedbacks between individual behaviour and dynamic
restructuring of the network are main drivers of the transition. This
generative mechanism for co-evolution of individual behaviour and social
network structure may apply to a wide range of examples beyond smoking.Comment: 16 pages, 5 figure
How does Clubs' Organizational Design Affect Competition Among Clubs?
We analyze competition among clubs in which the status of club members is the crucial added value accruing to fellow club members through social interaction within the club (e.g. in country clubs, academic faculties, or internet communities). In the course of competition for new members, clubs trade off the effect of entry on average status of the club and candidates’ monetary payment via an entrance fee. We show that the best candidates join the best clubs but they pay higher entrance fees than some lowerranking candidates. We distinguish among various decision rules and organizational set-ups, including majority voting, unanimity, and meritocracy. We find that, from a second-best welfare perspective, the unanimity rule leads to inefficient exclusion of some candidates, while meritocracy leads to inefficient inclusion. Our main policy implication is that consensus-based clubs, such as many academic faculties in Europe, could improve the well-being of their members if they liberalized their internal decision making processes.club theory;status organizations;design of decision making;collective action
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