31,412 research outputs found

    A coalition formation mechanism based on inter-agent trust relationships

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    Knowing who likes who: The early developmental basis of coalition understanding

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    Group biases based on broad category membership appear early in human development. However, like many other primates humans inhabit social worlds also characterised by small groups of social coalitions which are not demarcated by visible signs or social markers. A critical cognitive challenge for a young child is thus how to extract information concerning coalition structure when coalitions are dynamic and may lack stable and outwardly visible cues to membership. Therefore, the ability to decode behavioural cues of affiliations present in everyday social interactions between individuals would have conferred powerful selective advantages during our evolution. This would suggest that such an ability may emerge early in life, however, little research has investigated the developmental origins of such processing. The present paper will review recent empirical research which indicates that in the first 2 years of life infants achieve a host of social-cognitive abilities that make them well adapted to processing coalition-affiliations of others. We suggest that such an approach can be applied to better understand the origins of intergroup attitudes and biases. Copyright © 2010 John Wiley & Sons, Ltd

    Human-agent collectives

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    We live in a world where a host of computer systems, distributed throughout our physical and information environments, are increasingly implicated in our everyday actions. Computer technologies impact all aspects of our lives and our relationship with the digital has fundamentally altered as computers have moved out of the workplace and away from the desktop. Networked computers, tablets, phones and personal devices are now commonplace, as are an increasingly diverse set of digital devices built into the world around us. Data and information is generated at unprecedented speeds and volumes from an increasingly diverse range of sources. It is then combined in unforeseen ways, limited only by human imagination. People’s activities and collaborations are becoming ever more dependent upon and intertwined with this ubiquitous information substrate. As these trends continue apace, it is becoming apparent that many endeavours involve the symbiotic interleaving of humans and computers. Moreover, the emergence of these close-knit partnerships is inducing profound change. Rather than issuing instructions to passive machines that wait until they are asked before doing anything, we will work in tandem with highly inter-connected computational components that act autonomously and intelligently (aka agents). As a consequence, greater attention needs to be given to the balance of control between people and machines. In many situations, humans will be in charge and agents will predominantly act in a supporting role. In other cases, however, the agents will be in control and humans will play the supporting role. We term this emerging class of systems human-agent collectives (HACs) to reflect the close partnership and the flexible social interactions between the humans and the computers. As well as exhibiting increased autonomy, such systems will be inherently open and social. This means the participants will need to continually and flexibly establish and manage a range of social relationships. Thus, depending on the task at hand, different constellations of people, resources, and information will need to come together, operate in a coordinated fashion, and then disband. The openness and presence of many distinct stakeholders means participation will be motivated by a broad range of incentives rather than diktat. This article outlines the key research challenges involved in developing a comprehensive understanding of HACs. To illuminate this agenda, a nascent application in the domain of disaster response is presented

    Integrative Use of Information Extraction, Semantic Matchmaking and Adaptive Coupling Techniques in Support of Distributed Information Processing and Decision-Making

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    In order to press maximal cognitive benefit from their social, technological and informational environments, military coalitions need to understand how best to exploit available information assets as well as how best to organize their socially-distributed information processing activities. The International Technology Alliance (ITA) program is beginning to address the challenges associated with enhanced cognition in military coalition environments by integrating a variety of research and development efforts. In particular, research in one component of the ITA ('Project 4: Shared Understanding and Information Exploitation') is seeking to develop capabilities that enable military coalitions to better exploit and distribute networked information assets in the service of collective cognitive outcomes (e.g. improved decision-making). In this paper, we provide an overview of the various research activities in Project 4. We also show how these research activities complement one another in terms of supporting coalition-based collective cognition

    The Emergence of Institutions

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    This paper analyzes how institutions aimed at coordinating economic interactions may appear. We build a model in which agents play a prisoners’ dilemma game in a hypothetical state of nature. Agents can delegate the task of enforcing cooperation in interactions to one of them in exchange for a proper compensation. Two basic commitment problems stand in the way of institution formation. The first one is the individual commitment problem that arises because an agent chosen to run the institution may prefer to renege ex post. The second one is a “collective commitment” problem linked to the lack of binding agreements on the fee that will be charged by the centre once it is designated. This implies first that a potentially socially efficient institution may fail to arise because of the lack of individual incentives, and second that even if it arises, excessive rent extraction by the institution may imply a sub-optimal efficiency level, explaining the heterogeneity of observed institutional arrangements. An institution is less likely to arise in small groups with limited endowments, but also when the underlying commitment problem is not too severe. Finally, we show that the threat of secession by a subset of agents may endogenously solve part of the second commitment problem.Institution, Coordination, State of nature, Secession.

    Federated AI for building AI Solutions across Multiple Agencies

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    The different sets of regulations existing for differ-ent agencies within the government make the task of creating AI enabled solutions in government dif-ficult. Regulatory restrictions inhibit sharing of da-ta across different agencies, which could be a significant impediment to training AI models. We discuss the challenges that exist in environments where data cannot be freely shared and assess tech-nologies which can be used to work around these challenges. We present results on building AI models using the concept of federated AI, which al-lows creation of models without moving the training data around.Comment: Presented at AAAI FSS-18: Artificial Intelligence in Government and Public Sector, Arlington, Virginia, US

    Network models of innovation and knowledge diffusion

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    Much of modern micro-economics is built from the starting point of the perfectly competitive market. In this model there are an infinite number of agents — buyers and sellers, none of whom has the power to influence the price by his actions. The good is well-defined, indeed it is perfectly standardized. And any interactions agents have is mediated by the market. That is, all transactions are anonymous, in the sense that the identities of buyer and seller are unimportant. Effectively, the seller sells “to the market” and the buyer buys “from the market”. This follows from the standardization of the good, and the fact that the market imposes a very strong discipline on prices. Implicit here is one (or both) of two assumptions. Either all agents are identical in every relevant respect, apart, possibly, from the prices they ask or offer; or every agent knows every relevant detail about every other agent. If the former, then obviously my only concern as a buyer is the prices asked by the population of sellers since in every other way they are identical. If the latter, then each seller has a unique good, and again what I am concerned with is the price of it. In either case, we see that prices capture all relevant information and are enough for every agent to make all the decisions he needs to make....economics of technology ;
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