16 resultados para Information Behavior

em QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast


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The problem of topology control is to assign per-node transmission power such that the resulting topology is energy efficient and satisfies certain global properties such as connectivity. The conventional approach to achieve these objectives is based on the fundamental assumption that nodes are socially responsible. We examine the following question: if nodes behave in a selfish manner, how does it impact the overall connectivity and energy consumption in the resulting topologies? We pose the above problem as a noncooperative game and use game-theoretic analysis to address it. We study Nash equilibrium properties of the topology control game and evaluate the efficiency of the induced topology when nodes employ a greedy best response algorithm. We show that even when the nodes have complete information about the network, the steady-state topologies are suboptimal. We propose a modified algorithm based on a better response dynamic and show that this algorithm is guaranteed to converge to energy-efficient and connected topologies. Moreover, the node transmit power levels are more evenly distributed, and the network performance is comparable to that obtained from centralized algorithms.

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We propose a strategy for perfect state transfer in spin chains based on the use of an unmodulated coupling Hamiltonian whose coefficients are explicitly time dependent. We show that, if specific and nondemanding conditions are satisfied by the temporal behavior of the coupling strengths, our model allows perfect state transfer. The paradigm put forward by our proposal holds the promises to set an alternative standard to the use of clever encoding and coupling-strength engineering for perfect state transfer.

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Contestants are predicted to adjust the cost of a fight in line with the perceived value of the resource and this provides a way of determining whether the resource has been assessed. An assessment of resource value is predicted to alter an animal's motivational state and we note different methods of measuring that state. We provide a categorical framework in which the degree of resource assessment may be evaluated and also note limitations of various approaches. We place studies in six categories: (1) cases of no assessment, (2) cases of internal state such as hunger influencing apparent value, (3) cases of the contestants differing in assessment ability, (4) cases of mutual and equal assessment of value, (5) cases where opponents differ in resource value and (6) cases of particularly complex assessment abilities that involve a comparison of the value of two resources. We examine the extent to which these studies support game theory predictions and suggest future areas of research. (C) 2008 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.

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Decision-making requires the perception of relevant information variables that emerge from the player–environment interaction. The purpose of the present article is to empirically assess whether players’ decisional behavior about which type of pass to make is influenced by the spatio-temporal variable tau. Time series positional data of rugby players were analyzed from video footage taken in real match scenarios. The tau of the distance motion gap between attacker and defender was calculated, along with the duration of the next pass. Results revealed that the initial tau value predicted 64% of the variance found in pass duration. A qualitative distinction of tau dynamics between two periods of the approach between the attacker and the defender was also observed. We argue that the time-to-contact between the attacker and the defender may yield information about future pass possibilities. Additionally, the informational fields constraining attacker–defender interaction may be viewed as a convergent channeling of possibilities towards a single pass solution.

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Migrating bats are among the most poorly understood of migratory taxa, with relatively little information available on their behavior and ecology during migration compared to other taxa. This arises because of the

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Conditional branches frequently exhibit similar behavior (bias, time-varying behavior,...), a property that can be used to improve branch prediction accuracy. Branch clustering constructs groups or clusters of branches with similar behavior and applies different branch prediction techniques to each branch cluster. We revisit the topic of branch clustering with the aim of generalizing branch clustering. We investigate several methods to measure cluster information, with the most effective the storage of information in the branch target buffer. Also, we investigate alternative methods of using the branch cluster identification in the branch predictor. By these improvements we arrive at a branch clustering technique that obtains higher accuracy than previous approaches presented in the literature for the gshare predictor. Furthermore, we evaluate our branch clustering technique in a wide range of predictors to show the general applicability of the method. Branch clustering improves the accuracy of the local history (PAg) predictor, the path-based perceptron and the PPM-like predictor, one of the 2004 CBP finalists.

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A general approach to information correction and fusion for belief functions is proposed, where not only may the information items be irrelevant, but sources may lie as well. We introduce a new correction scheme, which takes into account uncertain metaknowledge on the source’s relevance and truthfulness and that generalizes Shafer’s discounting operation. We then show how to reinterpret all connectives of Boolean logic in terms of source behavior assumptions with respect to relevance and truthfulness. We are led to generalize the unnormalized Dempster’s rule to all Boolean connectives, while taking into account the uncertainties pertaining to assumptions concerning the behavior of sources. Eventually, we further extend this approach to an even more general setting, where source behavior assumptions do not have to be restricted to relevance and truthfulness.We also establish the commutativity property between correction and fusion processes, when the behaviors of the sources are independent.

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This paper studies the impact of belief elicitation on informational efficiency and individual behavior in experimental parimutuel betting markets. In one treatment, groups of eight participants, who possess a private signal about the eventual outcome, play a sequential betting game. The second treatment is identical, except that bettors are observed by eight other participants who submit incentivized beliefs about the winning probabilities of each outcome. In the third treatment, the same individuals make bets and assess the winning probabilities of the outcomes. Market probabilities more accurately reflect objective probabilities in the third than in the other two treatments. Submitting beliefs reduces the favorite-longshot bias and making bets improves the accuracy of elicited beliefs. A level-k framework provides some insights about why belief elicitation improves the capacity of betting markets to aggregate information. (C) 2012 Elsevier B.V. All rights reserved.

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This paper examines the occurrence and fragility of information cascades in two laboratory experiments. One group of low informed participants sequentially guess which of two states has been randomly chosen. In a matched pairs design, another group of high informed participants make similar guesses after having observed the guesses of the low informed participants. In the second experiment, participants' beliefs about the chosen state are elicited. In equilibrium, low informed players who observe an established pattern of identical guesses herd without regard to their private information whereas high informed players always guess according to their private information. Equilibrium behavior implies that information cascades emerge in the group of low informed participants, the belief based solely on cascade guesses is stationary, and information cascades are systematically broken by high informed participants endowed with private information contradicting the cascade guesses. Experimental results show that the behavior of low informed participants is qualitatively in line with the equilibrium prediction. Information cascades often emerge in our experiments. The tendency of low informed participants to engage in cascade behavior increases with the number of identical guesses. Our main finding is that information cascades are not fragile. The behavior of high informed participants differs markedly from the equilibrium prediction. Only one-third of laboratory cascades are broken by high informed participants endowed with private information contradicting the cascade guesses. The relative frequency of cascade breaks is 15% for the situations where five or more identical guesses are observed. Participants' elicited beliefs are strongly consistent with their own behavior and show that, unlike in equilibrium, the more cascade guesses participants observe the more they believe in the state favored by those guesses.

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This article explores the literature concerning responses to pain of both premature and term-born newborn infants, the evidence for short-term and long-term effects of pain, and behavioral sequelae in individuals who have experienced repeated early pain in neonatal life as they mature. There is no doubt that pain causes stress in babies and this in turn may adversely affect long-term neurodevelopmental outcome. Although there are methods for assessing dimensions of acute reactivity to pain in an experimental setting, there are no very good measures available at the present time that can be used clinically. In the clinical setting repeated or chronic pain is more likely the norm rather than infrequent discrete noxious stimuli of the sort that can be readily studied. The wind-up phenomenon suggests that, exposed to a cascade of procedures as happens with clustering of care in the clinical setting in an attempt to provide periods of rest for stressed babies, an infant may in fact perceive procedures that are not normally viewed as noxious, as pain. Pain exposure during lifesaving intensive medical care of ELBW neonates may also affect subsequent reactivity to pain in the neonatal period, but behavioral differences are probably not likely to be clinically significant in the long term. Prolonged and repeated untreated pain in the newborn period, however, may produce a relatively permanent shift in basal autonomic arousal related to prior NICU pain experience, which may have long-term sequelae. In the long run, the most significant clinical effects of early pain exposure may be on neurodevelopment, contributing to later attention, learning, and behavior problems in these vulnerable children. Although there is considerable evidence to support a variety of adverse effects of early pain, there is less information about the long-term effects of opiates and benzodiazepines on the developing central nervous system. Current evidence reviewed suggests that judicious use of morphine for adjustment to mechanical ventilation may ameliorate the altered autonomic response. It may be very important, however, to distinguish stress from pain. Animal evidence suggests that the neonatal brain is affected differently when exposed to morphine administered in the absence of pain than in the presence of pain. Pain control may be important for many reasons but overuse of morphine or benzodiazepines may have undesirable long-term effects. This is a rapidly evolving area of knowledge of clear relevance to clinical management likely to affect long-term outcomes of high-risk children.

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Product Line software Engineering depends on capturing the commonality and variability within a family of products, typically using feature modeling, and using this information to evolve a generic reference architecture for the family. For embedded systems, possible variability in hardware and operating system platforms is an added complication. The design process can be facilitated by first exploring the behavior associated with features. In this paper we outline a bidirectional feature modeling scheme that supports the capture of commonality and variability in the platform environment as well as within the required software. Additionally, 'behavior' associated with features can be included in the overall model. This is achieved by integrating the UCM path notation in a way that exploits UCM's static and dynamic stubs to capture behavioral variability and link it to the feature model structure. The resulting model is a richer source of information to support the architecture development process.