226 research outputs found

    Relative Upper Confidence Bound for the K-Armed Dueling Bandit Problem

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    This paper proposes a new method for the K-armed dueling bandit problem, a variation on the regular K-armed bandit problem that offers only relative feedback about pairs of arms. Our approach extends the Upper Confidence Bound algorithm to the relative setting by using estimates of the pairwise probabilities to select a promising arm and applying Upper Confidence Bound with the winner as a benchmark. We prove a finite-time regret bound of order O(log t). In addition, our empirical results using real data from an information retrieval application show that it greatly outperforms the state of the art.Comment: 13 pages, 6 figure

    Individual Confidence-Weighting and Group Decision-Making

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    Group-living species frequently pool individual information so as to reach consensus decisions such as when and where to move, or whether a predator is present. Such opinion-pooling has been demonstrated empirically, and theoretical models have been proposed to explain why group decisions are more reliable than individual decisions. Behavioural ecology theory frequently assumes that all individuals have equal decision-making abilities, but decision theory relaxes this assumption and has been tested in human groups. We summarise relevant theory and argue for its applicability to collective animal decisions. We consider selective pressure on confidence-weighting in groups of related and unrelated individuals. We also consider which species and behaviours may provide evidence of confidence-weighting, paying particular attention to the sophisticated vocal communication of cooperative breeders

    Democracy and the European Constitution: Majority Voting and Small Member States

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    The purpose of this article is to shed light on the relation between large and small member states with regard to the majority principle. Since Maastricht at the latest the institutional discussion centers around the question of how to devise a decision system which pays equal attention to the interests of small and large states in the European Union. This article challenges several underlying assumptions: that size is an important factor determining the political clout of a member state; the existence of ‘natural’ interest divergences and the competitive nature of the European politiy. Finally, it questions the intrinsic relation between majority voting and democracy.democracy; majority voting; European Convention; legitimacy; political science

    Factored Bandits

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    We introduce the factored bandits model, which is a framework for learning with limited (bandit) feedback, where actions can be decomposed into a Cartesian product of atomic actions. Factored bandits incorporate rank-1 bandits as a special case, but significantly relax the assumptions on the form of the reward function. We provide an anytime algorithm for stochastic factored bandits and up to constants matching upper and lower regret bounds for the problem. Furthermore, we show that with a slight modification the proposed algorithm can be applied to utility based dueling bandits. We obtain an improvement in the additive terms of the regret bound compared to state of the art algorithms (the additive terms are dominating up to time horizons which are exponential in the number of arms)

    Experimental Study and Modeling of Three Classes of Collective Problem-Solving Methods

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    People working together can be very successful problem-solvers. Many real-life examples, from Wikipedia to citizen science projects, show that, under the right conditions, crowds can find remarkable solutions to complex problems. Yet, joining the capabilities of many people can be challenging. What factors make some groups more successful than others? How does the nature of the problem and the structure of the environment influence the group's performance? To answer these questions, I consider problem-solving as a search process -- a situation in which individuals are searching for a good solution. I describe and compare three different methods for structuring groups: (1) non-interacting groups, where individuals search independently without exchanging any information, (2) social groups, where individuals freely exchange information during their search, and (3) solution-influenced groups, where individuals repeatedly contribute to a shared collective solution. First, I introduce the idea of transmission chains - a specific type of solution-influenced group where individuals tackle the problem one after another, each one starting from the solution of its predecessor. I apply this method to binary choice problems and compare it to majority voting rules in non-interacting groups. The results show that transmission chains are superior in environments where individual accuracy is low and confidence is a reliable indicator of performance. This type of environment, however, is rarely observed in two experimental datasets. Then, I evaluate the performance of transmission chains for problems that have a complex structure, such as multidimensional optimization tasks. Again, I use non-interacting groups as a comparison, this time by selecting the best out of multiple independent solutions. Simulations and experimental data show that transmission chains outperform independent groups under two environmental conditions: either when problems are rather easy, or when group members are relatively unskilled. Next, I focus on social groups, where individuals influence each other during the search. To understand the social dynamics that operate in such groups, I conduct two studies: I first examine how people search for a solution independently from others, and then study how this individual process is impacted by social influence. The first study presents experimental data to show that the individual search behavior can be described by a take-the-best heuristic, that is, a simple rule-of-thumb that ignores all but one cue at a time. This heuristic reproduces a variety of behavioral patterns observed in different environments. Then, I extend this heuristic to include social interactions where multiple individuals exchange information during their search. My results show that, in this case, individuals tend to converge towards similar solutions. This induces a collective search dilemma: compared to non-interacting groups, the quality of the average individual's solution is improved at the expense of the best solution of the group. Nevertheless, further analyses show that this dilemma disappears for more difficult problems. Overall, this thesis shows that no collective problem-solving method is superior to the others in all environments and for all problems. Instead, the performance of each method depends on numerous factors, such as the nature of the task, the problem difficulty, the group composition, and the skill levels of the individuals. My work helps understanding the role of these different factors and their influence on collective problem-solving

    Pathways of Understanding: the Interactions of Humanity and Global Environmental Change

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    How humans, interacting within social systems, affect and are affected by global change is explored. Recognizing the impact human activities have on the environment and responding to the need to document the interactions among human activities, the Consortium for International Earth Science Information Network (CIESIN) commissioned a group of 12 scientists to develop a framework illustrating the key human systems that contribute to global change. This framework, called the Social Process Diagram, will help natural and social scientists, educators, resource managers and policy makers envision and analyze how human systems interact among themselves and with the natural system. The Social Process Diagram consists of the following blocks that constitute the Diagram's structural framework: (1) fund of knowledge and experience; (2) preferences and expectations; (3) factors of production and technology; (4) population and social structure; (5) economic systems; (6) political systems and institutions; and (7) global scale environmental processes. To demonstrate potential ways the Diagram can be used, this document includes 3 hypothetical scenarios of global change issues: global warming and sea level rise; the environmental impact of human population migration; and energy and the environment. These scenarios demonstrate the Diagram's usefulness for visualizing specific processes that might be studied to evaluate a particular global change issues. The scenario also shows that interesting and unanticipated questions may emerge as links are explored between categories on the Diagram

    Ensemble deep learning: A review

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    Ensemble learning combines several individual models to obtain better generalization performance. Currently, deep learning models with multilayer processing architecture is showing better performance as compared to the shallow or traditional classification models. Deep ensemble learning models combine the advantages of both the deep learning models as well as the ensemble learning such that the final model has better generalization performance. This paper reviews the state-of-art deep ensemble models and hence serves as an extensive summary for the researchers. The ensemble models are broadly categorised into ensemble models like bagging, boosting and stacking, negative correlation based deep ensemble models, explicit/implicit ensembles, homogeneous /heterogeneous ensemble, decision fusion strategies, unsupervised, semi-supervised, reinforcement learning and online/incremental, multilabel based deep ensemble models. Application of deep ensemble models in different domains is also briefly discussed. Finally, we conclude this paper with some future recommendations and research directions
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