How to Do Things With Words

An Introduction to MultiAgent Systems.

Nos últimos trinta trinta anos, houve profundas mudanças na forma como compreendemos o crime e a justiça criminal. O crime tornou-se um evento simbólico, um verdadeiro teste para a ordem social e para as políticas governamentais, um desafio para a sociedade civil, para a democracia e para os direitos humanos. Segundo David Garland, professor da Faculdade de Direito da New York University, um dos principais autores no campo da Sociologia da Punição e com artigo publicado na Revista de Sociologia e Política , número 13, na modernidade tardia houve uma verdadeira obsessão securitária, direcionando as políticas criminais para um maior rigor em relação às penas e maior intolerância com o criminoso. Há trinta anos, nos EUA e na Inglaterra essa tendência era insuspeita. O livro mostra que os dois países compartilham intrigantes similaridades em suas práticas criminais, a despeito da divisão racial, das desigualdades econômicas e da letalidade violenta que marcam fortemente o cenário americano. Segundo David Garland, encontram-se nos dois países os “mesmos tipos de riscos e inseguranças, a mesma percepção a respeito dos problemas de um controle social não-efetivo, as mesmas críticas da justiça criminal tradicional, e as mesmas ansiedades recorrentes sobre mudança e ordem sociais”1 (GARLAND, 2001, p. 2). O argumento principal da obra é o seguinte: a modernidade tardia, esse distintivo padrão de relações sociais, econômicas e culturais, trouxe consigo um conjunto de riscos, inseguranças e problemas de controle social que deram uma configuração específica às nossas respostas ao crime, ao garantir os altos custos das políticas criminais, o grau máximo de duração das penas e a excessivas taxas de encarceramento.

http://www.scielo.br/pdf/rsocp/n20/n20a15.pdf

The culture of control: crime and social order in contemporary, society

人教版高中英语新课程教材中,语言运用（Using Language）是每个单元必不可少的部分,提供了围绕单元中心话题的听、说、读、写的综合性练习,是单元中心话题的延续和升华.如何设计Using Language部分的教学,使自己的教学模式既不落俗套,又能真正体现新课程标准所倡导的教学理念,正是广大一线英语教师一直努力探索的问题.

打磨Using Language,倡导新理念

We explore deep reinforcement learning methods for multi-agent domains. We begin by analyzing the difficulty of traditional algorithms in the multi-agent case: Q-learning is challenged by an inherent non-stationarity of the environment, while policy gradient suffers from a variance that increases as the number of agents grows. We then present an adaptation of actor-critic methods that considers action policies of other agents and is able to successfully learn policies that require complex multi-agent coordination. Additionally, we introduce a training regimen utilizing an ensemble of policies for each agent that leads to more robust multi-agent policies. We show the strength of our approach compared to existing methods in cooperative as well as competitive scenarios, where agent populations are able to discover various physical and informational coordination strategies.

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

Consequentialist Conditional Cooperation in Social Dilemmas with Imperfect Information (Short Workshop Version).

Systems, methods, and non-transitory computer-readable media can acquire a set of individual time series associated with a set of users. Each of the individual time series can be associated with a respective user out of the set of the users. A plurality of variables represented via the set of individual time series can be selected. The plurality of variables can include at least a first variable and a second variable. One or more regression techniques can be applied to at least the first variable and the second variable. A set of sensitivity metrics for the set of users can be determined based on the one or more regression techniques. A respective sensitivity metric out of the set of sensitivity metrics can be determined for each of the users.

Systems and methods for providing data analysis based on applying regression

Dot product latent space embedding is a common form of representation learning in undirected graphs (e.g. social networks, co-occurrence networks). We show that such models have problems dealing with 'intransitive' situations where A is linked to B, B is linked to C but A is not linked to C. Such situations occur in social networks when opposites attract (heterophily) and in co-occurrence networks when there are substitute nodes (e.g. the presence of Pepsi or Coke, but rarely both, in otherwise similar purchase baskets). We present a simple expansion which we call the attract-repel (AR) decomposition: a set of latent attributes on which similar nodes attract and another set of latent attributes on which similar nodes repel. We demonstrate the AR decomposition in real social networks and show that it can be used to measure the amount of latent homophily and heterophily. In addition, it can be applied to co-occurrence networks to discover roles in teams and find substitutable ingredients in recipes.

An Attract-Repel Decomposition of Undirected Networks.

A social networking system builds a quality controlled and desired population-representative pool of human raters to provide ratings on content items to improve a feed ranking model used for providing its users with more relevant content. The system identifies a pool of candidate human raters for providing ratings on a feed of content items. For each candidate human rater of the pool of candidate human raters, the system presents a feed of content items based on a feed ranking model, obtains ratings on the feed of content items, and determines a score representing the consistency of the obtained ratings, the representativeness of the pool of human raters, or the relevance of the content provided by the ranking model. The system uses the computed scores to modify the ranking model used to present content to its users for improving the relevance of the presented content.

Content relevance in a social networking system by simulating feed ranking models

Systems, methods, and non-transitory computer-readable media train a machine learning model to forecast growth of a content item, the growth being measured based at least in part on a count of user interactions with the content item, wherein the model is trained to adjust growth forecasts for the content item in response to one or more users interacting with the content item. A first growth forecast for the content item can be determined for a unit of time using the machine learning model. A determination is made that a first user has interacted with the content item. A second growth forecast for the content item can be determined for the unit of time using the machine learning model and based at least in part on the first user interacting with the content item.

Alexander Peysakhovich

Papers

Consequentialist Conditional Cooperation in Social Dilemmas with Imperfect Information (Short Workshop Version).

Systems and methods for providing data analysis based on applying regression

An Attract-Repel Decomposition of Undirected Networks.

Content relevance in a social networking system by simulating feed ranking models

Systems and methods for forecasting trends