Selective and “Veiled” Demarketing from the Perspective of Black Female Consumers

This study sheds light on the perspective of Black female consumers in regards to certain effects of marketing initiatives adopting the literature on demarketing as a framework. The context examined is their experience with the market of hair beauty and care. Media actions are analyzed along narrative interviews in order to understand the phenomenon. The findings reveal a structural dominant pattern which perpetuates the marginalized status of Black women’s natural traits. Emerging market initiatives point to movements concerning the visibility of such female consumers, who despite being eager to consume, have their demand discouraged. This suggests that they perceive a selective and veiled demarketing, as one of the results of marketing actions. The analysis invites for theoretical reflections on demarketing and veiled racism in Brazil

Asymptotically optimal amplifiers for the Moran process

We study the Moran process as adapted by Lieberman, Hauert and Nowak. This is a model of an evolving population on a graph or digraph where certain individuals, called “mutants” have fitness r and other individuals, called “non-mutants” have fitness 1. We focus on the situation where the mutation is advantageous, in the sense that r > 1. A family of digraphs is said to be strongly amplifying if the extinction probability tends to 0 when the Moran process is run on digraphs in this family. The most-amplifying known family of digraphs is the family of megastars of Galanis et al. We show that this family is optimal, up to logarithmic factors, since every strongly-connected n-vertex digraph has extinction probability Ω(n −1/2 ). Next, we show that there is an infinite family of undirected graphs, called dense incubators, whose extinction probability is O(n −1/3 ). We show that this is optimal, up to constant factors. Finally, we introduce sparse incubators, for varying edge density, and show that the extinction probability of these graphs is O(n/m), where m is the number of edges. Again, we show that this is optimal, up to constant factors

Asymptotically optimal amplifiers for the Moran process

We study the Moran process as adapted by Lieberman, Hauert and Nowak. This is a model of an evolving population on a graph or digraph where certain individuals, called “mutants” have fitness r and other individuals, called “non-mutants” have fitness 1. We focus on the situation where the mutation is advantageous, in the sense that r > 1. A family of digraphs is said to be strongly amplifying if the extinction probability tends to 0 when the Moran process is run on digraphs in this family. The most-amplifying known family of digraphs is the family of megastars of Galanis et al. We show that this family is optimal, up to logarithmic factors, since every strongly-connected n-vertex digraph has extinction probability Ω(n −1/2 ). Next, we show that there is an infinite family of undirected graphs, called dense incubators, whose extinction probability is O(n −1/3 ). We show that this is optimal, up to constant factors. Finally, we introduce sparse incubators, for varying edge density, and show that the extinction probability of these graphs is O(n/m), where m is the number of edges. Again, we show that this is optimal, up to constant factors

Asymptotically optimal amplifiers for the Moran process

We study the Moran process as adapted by Lieberman, Hauert and Nowak. This is a model of an evolving population on a graph or digraph where certain individuals, called “mutants” have fitness r and other individuals, called “non-mutants” have fitness 1. We focus on the situation where the mutation is advantageous, in the sense that r > 1. A family of digraphs is said to be strongly amplifying if the extinction probability tends to 0 when the Moran process is run on digraphs in this family. The most-amplifying known family of digraphs is the family of megastars of Galanis et al. We show that this family is optimal, up to logarithmic factors, since every strongly-connected n-vertex digraph has extinction probability Ω(n −1/2 ). Next, we show that there is an infinite family of undirected graphs, called dense incubators, whose extinction probability is O(n −1/3 ). We show that this is optimal, up to constant factors. Finally, we introduce sparse incubators, for varying edge density, and show that the extinction probability of these graphs is O(n/m), where m is the number of edges. Again, we show that this is optimal, up to constant factors

Exponential upper bounds for the runtime of randomized search heuristics

International audienceWe argue that proven exponential upper bounds on runtimes, an established area in classic algorithms, are interesting also in heuristic search and we prove several such results. We show that any of the algorithms randomized local search, Metropolis algorithm, simulated annealing, and (1+1) evolutionary algorithm can optimize any pseudo-Boolean weakly monotonic function under a large set of noise assumptions in a runtime that is at most exponential in the problem dimension n. This drastically extends a previous such result, limited to the (1+1) EA, the LeadingOnes function, and one-bit or bit-wise prior noise with noise probability at most 1/2, and at the same time simplifies its proof. With the same general argument, among others, we also derive a sub-exponential upper bound for the runtime of the (1, λ) evolutionary algorithm on the OneMax problem when the offspring population size λ is logarithmic, but below the efficiency threshold. To show that our approach can also deal with non-trivial parent population sizes, we prove an exponential upper bound for the runtime of the mutation-based version of the simple genetic algorithm on the OneMax benchmark, matching a known exponential lower bound