New classes of Lorenz curves by maximizing Tsallis entropy under mean and Gini equality and inequality constraints

Preda, Vasile; Dedu, Silvia; Gheorghe, Carmen

doi:10.1016/j.physa.2015.05.092

Cited by 26 publications

(11 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, the suitability to implement the MaxEnt principle into different inventory systems (e.g., multi-echelon supply chains) may be investigated. In addition, the entropy maximization principle may be applied to generalized entropies, e.g., to Tsallis entropy [48]. Finally, a more general formulation of the expected shortage may also be considered.…”

Section: Conclusion and Further Remarksmentioning

confidence: 99%

Stochastic Reorder Point-Lot Size (r,Q) Inventory Model under Maximum Entropy Principle

Castellano

2015

Entropy

View full text Add to dashboard Cite

This paper takes into account the continuous-review reorder point-lot size (r,Q) inventory model under stochastic demand, with the backorders-lost sales mixture. Moreover, to reflect the practical circumstance in which full information about the demand distribution lacks, we assume that only an estimate of the mean and of the variance is available. Contrarily to the typical approach in which the lead-time demand is supposed Gaussian or is obtained according to the so-called minimax procedure, we take a different perspective. That is, we adopt the maximum entropy principle to model the lead-time demand distribution. In particular, we consider the density that maximizes the entropy over all distributions with given mean and variance. With the aim of minimizing the expected total cost per time unit, we then propose an exact algorithm and a heuristic procedure. The heuristic method exploits an approximated expression of the total cost function achieved by means of an ad hoc first-order Taylor polynomial. We finally carry out numerical experiments with a twofold objective. On the one hand we examine the efficiency of the approximated solution procedure. On the other hand we investigate the performance of the maximum entropy principle in approximating the true lead-time demand distribution.

show abstract

Section: Conclusion and Further Remarksmentioning

confidence: 99%

Stochastic Reorder Point-Lot Size (r,Q) Inventory Model under Maximum Entropy Principle

Castellano

2015

Entropy

View full text Add to dashboard Cite

show abstract

“…However, it has been applied also to study the economical characteristics of certain classes of Italian cities in [ 41 ], recently, to improve the performance of clustering algorithms [ 42 ] or for measuring inequality in water usage [ 43 ]. On the relationship between Gini and b-ary entropy, it is worth to mention [ 44 , 45 ] where the Gini and Tsallis’ entropy are used to model income inequalities or, as in [ 46 ], to improve the performance of decision trees classifiers in the field of machine learning.…”

Section: Introductionmentioning

confidence: 99%

Entropic Analysis of Votes Expressed in Italian Elections between 1948 and 2018

Marmani

Ficcadenti

Kaur

et al. 2020

Entropy

View full text Add to dashboard Cite

In Italy, the elections occur often, indeed almost every year the citizens are involved in a democratic choice for deciding leaders of different administrative entities. Sometimes the citizens are called to vote for filling more than one office in more than one administrative body. This phenomenon has occurred 35 times after 1948; it creates the peculiar condition of having the same sample of people expressing decisions on political bases at the same time. Therefore, the Italian contemporaneous ballots constitute the occasion to measure coherence and chaos in the way of expressing political opinion. In this paper, we address all the Italian elections that occurred between 1948 and 2018. We collect the number of votes per party at each administrative level and we treat each election as a manifestation of a complex system. Then, we use the Shannon entropy and the Gini Index to study the degree of disorder manifested during different types of elections at the municipality level. A particular focus is devoted to the contemporaneous elections. Such cases implicate different disorder dynamics in the contemporaneous ballots, when different administrative level are involved. Furthermore, some features that characterize different entropic regimes have emerged.

show abstract

“…Applications of M E can be found in various fields of computer science, in statistical learning, especially natural language processing [13,17]. The M E method was applied to obtain new classes of Lorenz curves by maximizing Tsallis entropy under mean and Gini equality and inequality constraints [28]. For more details on entropy optimization we sugest the reader to see [29].…”

Section: Introductionmentioning

confidence: 99%