Building Stable Conventions, by Jonathan Newton (Kyoto University)
Building Stable Conventions, by Jonathan Newton (Kyoto University)
– February 28, 2019
BUILDING STABLE CONVENTIONS
Abstract: Strategies of players in a population are updated according to the choice rules of agents, where each agent is a player or a coalition of players. It is shown that choice rules that satisfy a specific type of asymmetry can be combined in a variety of ways while retaining this asymmetry. It is known that, at a global level, this asymmetry implies stochastic stability of a given homogeneous strategy profile. Taken together, these results enable two approaches, one reductive, the other constructive. Firstly, for models in which every agent follows the same choice rule, stochastic stability can be proven by showing that the asymmetry holds for a representative agent. This allows us to easily recover and extend many results from the literature. Secondly, agents who follow choice rules that satisfy the asymmetry can be combined arbitrarily while the same homogeneous strategy profile remains stochastically stable.
Melissa: Modular External Library for In Situ Sensitivity Analysis by Theophile Terraz (Datamove)
– February 14, 2019
Classical sensitivity analysis consists in running different instances of a numerical simulation with different sets of input parameters, store the results to disk, to later read them back from disk to compute the required statistics. A simulation can be multi-dimensional, multivariate, and multivalued, and a global sensitivity analysis often requires thousands of runs. The amount of storage needed can quickly become overwhelming, with the associated long read time that makes statistic computing time consuming. To avoid this pitfall, scientists usualy reduce their study size by running low resolution simulations or down-sampling output data in space and time.
Melissa bypass this limitation by avoiding intermediate file storage. Melissa processes the data in transit, enabling very large scale sensitivity analysis. Melissa is built around two key concepts: iterative statistics algorithms and asynchronous client/server model for data transfer. Simulation outputs are never stored on disc. They are sent by the simulations to a parallel server, which aggregate them to the statistic fields in an iterative fashion, and then throw them away. This allows to compute statistics maps on every mesh element for every timestep on a full scale study (ubiquitous statistics).
Melissa is a file avoiding, adaptive, fault tolerant and elastic framework, enabling very efficient executions on large scale supercomputers.
Melissa comes with iterative algorithms for computing the average, variance and co-variance, skewness, kurtosis, max, min, threshold exceedance, quantiles and Sobol' indices, and can easily be extended with new algorithms.
Building Stable Conventions, by Jonathan Newton (Kyoto University)
– February 28, 2019
BUILDING STABLE CONVENTIONS
Abstract: Strategies of players in a population are updated according to the choice rules of agents, where each agent is a player or a coalition of players. It is shown that choice rules that satisfy a specific type of asymmetry can be combined in a variety of ways while retaining this asymmetry. It is known that, at a global level, this asymmetry implies stochastic stability of a given homogeneous strategy profile. Taken together, these results enable two approaches, one reductive, the other constructive. Firstly, for models in which every agent follows the same choice rule, stochastic stability can be proven by showing that the asymmetry holds for a representative agent. This allows us to easily recover and extend many results from the literature. Secondly, agents who follow choice rules that satisfy the asymmetry can be combined arbitrarily while the same homogeneous strategy profile remains stochastically stable.
A journey to causal advertising: interventions, Datasets & Models by Eustache Diemert (Criteo Grenoble)
– March 7, 2019
Title: A journey to causal advertising: interventions, Datasets & Models
Abstract: In a culture where claims are backed with data, digital advertising shall demonstrate and optimize its causal effect. We will present two use cases from this industry leading to interesting problems. Then we'll describe how to generate datasets to allow for proper counterfactual learning, along with practical optimization tricks and experimental results. Presentation will include material from work published at NeurIPS Causal Learning 2018 and open datasets from Criteo.
Tree Search and the EURO/ROADEF 2018 Challenge (Gescop event)
– March 21, 2019
Tree Search and the EURO/ROADEF 2018 Challenge
We present a simple and competitive heuristic Branch and Bound. It performs
heuristic cuts and pseudo-dominances to solve the EURO/ROADEF cutting glass
problem proposed by Saint-Gobain. It imports ideas from the AI Planning
community that are competitive with classical meta-heuristics. The resulting
program was ranked first over 20 qualified submissions in the final phase of
the challenge.
In this talk, we will describe the cutting glass problem, then introduce the
ideas from AI planning that can be relevant in Operations Research starting
from the basics and arrive to the state of the art. Finally, we will see how
to apply those ideas to the challenge problem.
Link to the challenge web page:
http://www.roadef.org/challenge/2018/en/finalResults.php
Eigenvalues and Graphs by Fanny Dufossé (Datamove)
– March 28, 2019
The first result obtained for eigenvalues applied to graph problems is a bound on the chromatic number of a graph in 1973. Since then, many important applications of eigenvalues were discovered. This presentation will summarize some of these results, including properties of eigenvalues and application to combinatorial optimization problems.
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