Matrix games like Prisoner's Dilemma have guided research on social dilemmas for decades. However... more Matrix games like Prisoner's Dilemma have guided research on social dilemmas for decades. However, they necessarily treat the choice to cooperate or defect as an atomic action. In real-world social dilemmas these choices are temporally extended. Cooperativeness is a property that applies to policies , not elementary actions. We introduce sequential social dilemmas that share the mixed incentive structure of matrix game social dilemmas but also require agents to learn policies that implement their strategic intentions. We analyze the dynamics of policies learned by multiple self-interested independent learning agents, each using its own deep Q-network, on two Markov games we introduce here: 1. a fruit Gathering game and 2. a Wolfpack hunting game. We characterize how learned behavior in each domain changes as a function of environmental factors including resource abundance. Our experiments show how conflict can emerge from competition over shared resources and shed light on how the sequential nature of real world social dilemmas affects cooperation.
Online interactions are increasingly involving images, especially those containing human faces, w... more Online interactions are increasingly involving images, especially those containing human faces, which are naturally attention grabbing and more effective at conveying feelings than text. To understand this new convention of digital culture, we study the collective behavior of sharing selfies on Instagram and present how people appear in selfies and which patterns emerge from such interactions. Analysis of millions of photos shows that the amount of selfies has increased by 900 times from 2012 to 2014. Selfies are an effective medium to grab attention; they generate on average 1.1–3.2 times more likes and comments than other types of content on Ins-tagram. Compared to other content, interactions involving selfies exhibit variations in homophily scores (in terms of age and gender) that suggest they are becoming more widespread. Their style also varies by cultural boundaries in that the average age and majority gender seen in selfies differ from one country to another. We provide explanations of such country-wise variations based on cultural and socioeconomic contexts.
To increase mobile user engagement, photo sharing sites are trying to identify interesting and me... more To increase mobile user engagement, photo sharing sites are trying to identify interesting and memorable pictures. Past proposals for identifying such pictures have relied on either metadata (e.g., likes) or visual features. In practice, techniques based on those two inputs do not always work: metadata is sparse (only few pictures have considerable number of likes), and extracting visual features is computationally expensive. In mobile solutions, geo-referenced content becomes increasingly important. The premise behind this work is that pictures of a neighborhood is linked to the way the neighborhood is perceived by people: whether it is, for instance, distinctive and beautiful or not. Since 1970s, urban theories proposed by Lynch, Milgram and Peterson aimed at systematically capturing the way people perceive neighborhoods. Here we tested whether those theories could be put to use for automatically identifying appealing city pictures. We did so by gathering geo-referenced Flickr pictures in the city of London; selecting six urban qualities associated with those urban theories; computing proxies for those qualities from online social media data; and ranking Flickr pictures based on those proxies. We find that our proposal enjoys three main desirable properties: it is effective, scalable, and aware of contextual changes such as time of day and weather condition. All this suggests new promising research directions for multi-modal learning approaches that automatically identify appealing city pictures.
Matrix games like Prisoner's Dilemma have guided research on social dilemmas for decades. However... more Matrix games like Prisoner's Dilemma have guided research on social dilemmas for decades. However, they necessarily treat the choice to cooperate or defect as an atomic action. In real-world social dilemmas these choices are temporally extended. Cooperativeness is a property that applies to policies , not elementary actions. We introduce sequential social dilemmas that share the mixed incentive structure of matrix game social dilemmas but also require agents to learn policies that implement their strategic intentions. We analyze the dynamics of policies learned by multiple self-interested independent learning agents, each using its own deep Q-network, on two Markov games we introduce here: 1. a fruit Gathering game and 2. a Wolfpack hunting game. We characterize how learned behavior in each domain changes as a function of environmental factors including resource abundance. Our experiments show how conflict can emerge from competition over shared resources and shed light on how the sequential nature of real world social dilemmas affects cooperation.
Online interactions are increasingly involving images, especially those containing human faces, w... more Online interactions are increasingly involving images, especially those containing human faces, which are naturally attention grabbing and more effective at conveying feelings than text. To understand this new convention of digital culture, we study the collective behavior of sharing selfies on Instagram and present how people appear in selfies and which patterns emerge from such interactions. Analysis of millions of photos shows that the amount of selfies has increased by 900 times from 2012 to 2014. Selfies are an effective medium to grab attention; they generate on average 1.1–3.2 times more likes and comments than other types of content on Ins-tagram. Compared to other content, interactions involving selfies exhibit variations in homophily scores (in terms of age and gender) that suggest they are becoming more widespread. Their style also varies by cultural boundaries in that the average age and majority gender seen in selfies differ from one country to another. We provide explanations of such country-wise variations based on cultural and socioeconomic contexts.
To increase mobile user engagement, photo sharing sites are trying to identify interesting and me... more To increase mobile user engagement, photo sharing sites are trying to identify interesting and memorable pictures. Past proposals for identifying such pictures have relied on either metadata (e.g., likes) or visual features. In practice, techniques based on those two inputs do not always work: metadata is sparse (only few pictures have considerable number of likes), and extracting visual features is computationally expensive. In mobile solutions, geo-referenced content becomes increasingly important. The premise behind this work is that pictures of a neighborhood is linked to the way the neighborhood is perceived by people: whether it is, for instance, distinctive and beautiful or not. Since 1970s, urban theories proposed by Lynch, Milgram and Peterson aimed at systematically capturing the way people perceive neighborhoods. Here we tested whether those theories could be put to use for automatically identifying appealing city pictures. We did so by gathering geo-referenced Flickr pictures in the city of London; selecting six urban qualities associated with those urban theories; computing proxies for those qualities from online social media data; and ranking Flickr pictures based on those proxies. We find that our proposal enjoys three main desirable properties: it is effective, scalable, and aware of contextual changes such as time of day and weather condition. All this suggests new promising research directions for multi-modal learning approaches that automatically identify appealing city pictures.
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