« Plus un pays est égalitaire et prospère, moins on trouve de femmes en sciences »

Je suis tombé sur cette phrase au détour d’un article cet après midi, dans La Presse,

« Plus un pays est égalitaire et prospère, moins on trouve de femmes en sciences. C’est difficile à comprendre »

et j’avoue que je suis surpris. Surpris que cette relation soit forte, et significative, au point de l’énoncer comme une loi. Je ne sais pas comment se mesurent l’égalité et la prospérité, mais j’ai tenté l’espérance de vie à la naissance, sur http://data.uis.unesco.org/. Et “researchers FTE” (nombre de chercheurs en équivalents temps plein), femme divisé par le total, pour avoir un ratio de femmes en “Science, technology and innovation“. J’ai du mal à observer la relation décroissante…

A moins d’enlever une dizaines de pays à faible espérance de vie à la naissance (dont le Burundi, l’Éthiopie, la Gambie, l’Inde, Madagascar, le Pakistan, le Togo).

Si quelqu’un sait comment voire cette étonnante relation décroissante, je suis preneur !

Reinforcement Learning in Economics and Finance, a state-of-the-art

Our joint paper, with Romuald Elie and Carl Remlinger entitled Reinforcement Learning in Economics and Finance just appeared in Computational Economics,

Reinforcement learning algorithms describe how an agent can learn an optimal action policy in a sequential decision process, through repeated experience. In a given environment, the agent policy provides him some running and terminal rewards. As in online learning, the agent learns sequentially. As in multi-armed bandit problems, when an agent picks an action, he can not infer ex-post the rewards induced by other action choices. In reinforcement learning, his actions have consequences: they influence not only rewards, but also future states of the world. The goal of reinforcement learning is to find an optimal policy — a mapping from the states of the world to the set of actions, in order to maximize cumulative reward, which is a long term strategy. Exploring might be sub-optimal on a short-term horizon but could lead to optimal long-term ones. Many problems of optimal control, popular in economics for more than forty years, can be expressed in the reinforcement learning framework, and recent advances in computational science, provided in particular by deep learning algorithms, can be used by economists in order to solve complex behavioral problems. In this article, we propose a state-of-the-art of reinforcement learning techniques, and present applications in economics, game theory, operation research and finance.

Big data, the tech giants, and insurance

A few months ago, I published a short article, Big data, the tech giants, and insurance, in the Annales de Mines. The original article was in French, but the Editors shared an English version,

Technology and insurance companies seem like polar opposites in every possible way. The tech giants, agile and fast-acting, are obsessed with the future, whereas insurers, conservative and reflexive, are fascinated with the data that the tech giants collect. However these two sectors are now eyeing each other and have started forming partnerships as they come to understand that, in both cases, their core business is data.

to be continued…

Dynamic Programming in Distributional Reinforcement Learning

Last summer, I supervised a summer intern, Cédric Odin, student at Ecole Normale in Ker Lann, France, on Dynamic Programming in Distributional Reinforcement Learning. A state-of-the-art is now available online https://hal.archives-ouvertes.fr/hal-03168889

The classic approach to reinforcement learning is limited in that it only predicts the expected return. The specialized literature has long tried to remedy this problem by studying risk-sensitive models, but the distributional approach will not emerge until 2017. Since the seminal article Bellemare, Dabney, and Munos 2017 and the state-of-the-art performance of the C51 algorithm in the ATARI 2600 suite of benchmark tasks (Bellemare, Naddaf, et al. 2013), research has focused on understanding the behaviour of distributional algorithms. In this paper we place Bellemare’s original results in distributional dynamic programming in parallel with the classic results.