Motivated by the difficulty of effecting fundamental change in the architecture of the Internet, in this paper, we study from a theoretical perspective the question of how individuals can join forces toward collective ventures. To that end, we draw on an elementary concept in Internet systems engineering, namely, that of incremental deployability, which we study mathematically and computationally. For example, we show that incremental deployability is at least as general a concept as the Nash equilibrium (in that the latter can be derived from the former). We then draw on this foundation to design and analyze institutional mechanisms that are not only promising to bootstrap emerging Internet architectures but they also have broader applications in social organization beyond its predominant market (and finance)-based character.
Related to the problem of diffusing innovation is that of inciting collective action (in the sense that the decision to act in a particular fashion diffuses in a social system) a problem whose study was initiated in the seminal work of @cite_14 . The study of collective action was later taken up by several authors such as, for example, in the form of critical mass theories (bearing relevance to nuclear fission explosions in physics) . @cite_35 motivates the study of collective action in settings bearing a political nature related to citizen oppression in rogue states, however, models of collective action range from crowdsourcing in the Internet to enabling countermeasures against climate change (such as controlling carbon emissions). Although various authors discuss the problem of inciting collective action through incentives mechanisms, the idea of creating institutions to that effect (as we do in this paper) and proving their efficacy has not, to the extent of our knowledge, been considered before by these earlier works. (An exception is where the analysis is at an elementary stage.)
@cite_35 @cite_14
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Collective awareness about climate change is an ongoing problem because there is such a wealth of information available, which can be confusing, contradictory and difficult to interpret. In order to help citizens understand environmental concerns, and to help organisations better inform and target interested people with campaigns, we have developed an open source toolkit to analyse social media data on the topic of climate change. The toolkit comprises methods for extracting, aggregating, and visualising actionable knowledge, based on automatic analysis of large volumes of text. The key terms, topics and sentiments expressed in online discussions are extracted, along with key indicators of climate change, and are stored in a semantic search tool, which enables complex searches over the huge volumes of data. We describe a scenario using the toolkit to gain insights from a large collection of political tweets, showing how we can analyse this dataset for understanding engagement of the public with respect to the topic of climate change. By lowering the costs of communication, the web promises to enable distributed collectives to act around shared issues. However, many collective action efforts never succeed: while the web's affordances make it easy to gather, these same decentralizing characteristics impede any focus towards action. In this paper, we study challenges to collective action efforts through the lens of online labor by engaging with Amazon Mechanical Turk workers. Through a year of ethnographic fieldwork, we sought to understand online workers' unique barriers to collective action. We then created Dynamo, a platform to support the Mechanical Turk community in forming publics around issues and then mobilizing. We found that collective action publics tread a precariously narrow path between the twin perils of stalling and friction, balancing with each step between losing momentum and flaring into acrimony. However, specially structured labor to maintain efforts' forward motion can help such publics take action. Robots require a broad range of interaction skills in order to work effectively alongside humans. They must have the ability to detect and recognize the actions and intentions of a person, produce functionally valid and situationally appropriate actions, and engage in social interactions through physical cues and dialog. However, social interactions with one of today’s robots will quickly become one-sided and repetitive, even after just a few minutes due to its shallow depth of knowledge and experience. This problem exposes weaknesses in the underlying traditional approaches that aim to pre-code responses to a limited number of inputs. We propose the use of crowdsourcing as a tool for the development of social robots that allow for rich, diverse and natural human-robot interaction. To enable crowdsourcing at a massive scale, we describe a newly implemented system that uses online virtual agents to collect data and then leverages the resulting corpus to train our robot behavior system for use on a real world task. We address the problem of affordance reasoning in diverse scenes that appear in the real world. Affordances relate the agent's actions to their effects when taken on the surrounding objects. In our work, we take the egocentric view of the scene, and aim to reason about action-object affordances that respect both the physical world as well as the social norms imposed by the society. We also aim to teach artificial agents why some actions should not be taken in certain situations, and what would likely happen if these actions would be taken. We collect a new dataset that builds upon ADE20k, referred to as ADE-Affordance, which contains annotations enabling such rich visual reasoning. We propose a model that exploits Graph Neural Networks to propagate contextual information from the scene in order to perform detailed affordance reasoning about each object. Our model is showcased through various ablation studies, pointing to successes and challenges in this complex task. The Twitter Revolutions of 2009 reinvigorated the question of whether new social media have any real effect on contentious politics. In this article, the authors argue that evaluating the relation between transforming communication technologies and collective action demands recognizing how such technologies infuse specific protest ecologies. This includes looking beyond informational functions to the role of social media as organizing mechanisms and recognizing that traces of these media may reflect larger organizational schemes. Three points become salient in the case of Twitter against this background: (a) Twitter streams represent crosscutting networking mechanisms in a protest ecology, (b) they embed and are embedded in various kinds of gatekeeping processes, and (c) they reflect changing dynamics in the ecology over time. The authors illustrate their argument with reference to two hashtags used in the protests around the 2009 United Nations Climate Summit in Copenhagen. Interaction cost is an important but poorly understood factor in visualization design. We propose a framework of interaction costs inspired by Normanpsilas Seven Stages of Action to facilitate study. From 484 papers, we collected 61 interaction-related usability problems reported in 32 user studies and placed them into our framework of seven costs: (1) Decision costs to form goals; (2) system-power costs to form system operations; (3) Multiple input mode costs to form physical sequences; (4) Physical-motion costs to execute sequences; (5) Visual-cluttering costs to perceive state; (6) View-change costs to interpret perception; (7) State-change costs to evaluate interpretation. We also suggested ways to narrow the gulfs of execution (2-4) and evaluation (5-7) based on collected reports. Our framework suggests a need to consider decision costs (1) as the gulf of goal formation. The recent wave of mobilizations in the Arab world and across Western countries has generated much discussion on how digital media is connected to the diffusion of protests. We examine that connection using data from the surge of mobilizations that took place in Spain in May 2011. We study recruitment patterns in the Twitter network and find evidence of social influence and complex contagion. We identify the network position of early participants (i.e. the leaders of the recruitment process) and of the users who acted as seeds of message cascades (i.e. the spreaders of information). We find that early participants cannot be characterized by a typical topological position but spreaders tend to be more central in the network. These findings shed light on the connection between online networks, social contagion, and collective dynamics, and offer an empirical test to the recruitment mechanisms theorized in formal models of collective action. Collective action usually depends on a "critical mass" that behaves differently from typical group members. Sometimes the critical mass provides some level of the good for others who do nothing, while at other times the critical mass pays the start-up costs and induces widespread collective action. Formal analysis supplemented by simulations shows that the first scenario is most likely when the production function relating inputs of resource contributions to outputs of a collective good is decelerating (characterized by diminishing marginal returns), whereas the second scenario is most likely when the production function is accelerating (characterized by increasing marginal returns). Decelerating production functions yield either surpluses of contributors or order effects in which contributions are maximized if the least interested contribute first, thus generating strategic gaming and competition among potential contributors. The start-up costs in accelerating production functions create severe feasibili... This paper proposes a generic method for action recognition in uncontrolled videos. The idea is to use images collected from the Web to learn representations of actions and use this knowledge to automatically annotate actions in videos. Our approach is unsupervised in the sense that it requires no human intervention other than the text querying. Its benefits are two-fold: 1) we can improve retrieval of action images, and 2) we can collect a large generic database of action poses, which can then be used in tagging videos. We present experimental evidence that using action images collected from the Web, annotating actions is possible. This paper proposes a method for handling the frame problem for knowledge-producing actions. An example of a knowledge-producing action is a sensing operation performed by a robot to determine whether or not there is an object of a particular shape within its grasp. The work is an extension of Reiter's approach to the frame problem for ordinary actions and Moore's work on knowledge and action. The properties of our specification are that knowledge-producing actions do not affect fluents other than the knowledge fluent, and actions that are not knowledge-producing only affect the knowledge fluent as appropriate. In addition, memory emerges as a side-effect: if something is known in a certain situation, it remains known at successor situations, unless something relevant has changed. Also, it will be shown that a form of regression examined by Reiter for reducing reasoning about future situations to reasoning about the initial situation now also applies to knowledge-producing actions. Social norms play a pivotal role in sustaining social order by regulating individual behaviors in a society. In normative multiagent systems, social norms have been used as an efficient mechanism to govern virtual agent societies towards cooperation and coordination. In this paper, we study the emergence of social norms via learning from repeated local interactions in networked agent societies. We propose a collective learning framework, which imitates the opinion aggregation process in human decision making, to study the impact of agent local collective behaviors on norm emergence in different situations. In the framework, each agent interacts repeatedly with all of its neighbors. At each step, an agent first takes a best-response action towards each of its neighbors and then combines all of these actions into a final action using ensemble learning methods. We conduct extensive experiments to evaluate the framework with respect to different network topologies, learning strategies, numbers of actions, and so on. Experimental results reveal some significant insights into norm emergence in networked agent societies achieved through local collective behaviors. Sociologists have erred in locating social problems in objective conditions. Instead, social problems have their being in a process of collective definition. This process determines whether social problems will arise, whether they become legitimated, how they are shaped in discussion, how they come to be addressed in official policy, and how they, are reconstituted in putting planned action into effect. Sociological theory and study must respect this process. Quantitative models have proved valuable in predicting consumer behavior in the offline world. These same techniques can be adapted to predict online actions. The use of diffusion models provides a firm foundation to implement and forecast viral marketing strategies. Choice models can predict purchases at online stores and shopbots. Hierarchical Bayesian models provide a framework for implementing versioning and price-segmentation strategies. Bayesian updating is a natural tool for profiling users with clickstream data. A key challenge for practitioners of Internet marketing is to extract value from the huge volume of data that can be collected. These techniques illustrate how this information can be leveraged to create better decisions. In this paper we present a new approach to the problem of planning with incomplete information and sensing. Our approach is based on a higher level, "knowledge-based", representation of the planner's knowledge and of the domain actions. In particular, in our approach we use a set of formulas from a first-order modal logic of knowledge to represent the planner's incomplete knowledge state. Actions are then represented as updates to this collection of formulas. Hence, actions are being modelled in terms of how they modify the knowledge state of the planner rather than in terms of how they modify the physical world. We have constructed a planner to utilize this representation and we use it to show that on many common problems this more abstract representation is perfectly adequate for solving the planning problem, and that in fact it scales better and supports features that make it applicable to much richer domains and problems. Action rules provide hints to a business user what actions (i.e. changes within some values of flexible attributes) should be taken to improve the profitability of customers. That is, taking some actions to re-classify some customers from less desired decision class to the more desired one. However, in previous work, each action rule was constructed from two rules, extracted earlier, defining different profitability classes. In this paper, we make a first step towards formally introducing the problem of mining action rules from scratch and present formal definitions. In contrast to previous work, our formulation provides guarantee on verifying completeness and correctness of discovered action rules. In addition to formulating the problem from an inductive learning viewpoint, we provide theoretical analysis on the complexities of the problem and its variations. Furthermore, we present efficient algorithms for mining action rules from scratch. In an experimental study we demonstrate the usefulness of our techniques. Electronic networks of practice are computer-mediated discussion forums focused on problems of practice that enable individuals to exchange advice and ideas with others based on common interests. However, why individuals help strangers in these electronic networks is not well understood: there is no immediate benefit to the contributor, and free-riders are able to acquire the same knowledge as everyone else. To understand this paradox, we apply theories of collective action to examine how individual motivations and social capital influence knowledge contribution in electronic networks. This study reports on the activities of one electronic network supporting a professional legal association. Using archival, network, survey, and content analysis data, we empirically test a model of knowledge contribution. We find that people contribute their knowledge when they perceive that it enhances their professional reputations, when they have the experience to share, and when they are structurally embedded in the network. Surprisingly, contributions occur without regard to expectations of reciprocity from others or high levels of commitment to the network. Dishonest behaviors in on-line networks include the problems caused by those actions performed by certain elements in a network in order to obtain some kind of benefits from the system. The analysis of this phenomenon concerns the WWW from two points of view: the Web as a collection of interrelated documents, and the social networks. In this work we study the web spam detection and the computation of trust and reputation in on-line social networks. We propose two graph-based ranking algorithms, based on different propagation models that spread the information from a set of elements in the network to compute the global relevance of all the nodes in the system. Creating a good schedule for a large conference such as CHI requires taking into account the preferences and constraints of organizers, authors, and attendees. Traditionally, the onus of planning is placed entirely on the organizers and involves only a few individuals. Cobi presents an alternative approach to conference scheduling that engages the entire community to take active roles in the planning process. The Cobi system consists of a collection of crowdsourcing applications that elicit preferences and constraints from the community, and software that enable organizers and other community members to take informed actions toward improving the schedule based on collected information. We are currently piloting Cobi as part of the CHI 2013 planning process. Deep learning for human action recognition in videos is making significant progress, but is slowed down by its dependency on expensive manual labeling of large video collections. In this work, we investigate the generation of synthetic training data for action recognition, as it has recently shown promising results for a variety of other computer vision tasks. We propose an interpretable parametric generative model of human action videos that relies on procedural generation and other computer graphics techniques of modern game engines. We generate a diverse, realistic, and physically plausible dataset of human action videos, called PHAV for Procedural Human Action Videos. It contains a total of 39,982 videos, with more than 1,000 examples for each action of 35 categories. Our approach is not limited to existing motion capture sequences, and we procedurally define 14 synthetic actions. We introduce a deep multi-task representation learning architecture to mix synthetic and real videos, even if the action categories differ. Our experiments on the UCF101 and HMDB51 benchmarks suggest that combining our large set of synthetic videos with small real-world datasets can boost recognition performance, significantly outperforming fine-tuning state-of-the-art unsupervised generative models of videos. Abstract We consider stochastic dynamic decision problems where at each step two consecutive decisions must be taken, one being what information bearing signal(s) to transmit and the other what control action(s) to exert. For finite-horizon problems involving first-order ARMA models with Gaussian statistics and a quadratic cost criterion, we show that the optimal measurement strategy consists of transmitting the innovation linearly at each stage, which in turn leads to optimality of a linear control law. We then extend this result to infinite-horizon models with discounted costs, showing optimality of linear designs. Subsequently, we show that these appealing results do not necessarily carry over to higher order ARMA models, for which we first characterize the best designs within the affine class, and then derive instances of the problem for which there exist non-linear designs that outperform the optimal designs within the affine class. The paper also includes some illustrative numerical examples on the different classes of problems considered.