Price Prediction in a Trading Agent Competition

The 2002 Trading Agent Competition (TAC) presented a challenging market game in the domain of travel shopping. One of the pivotal issues in this domain is uncertainty about hotel prices, which have a significant influence on the relative cost of alternative trip schedules. Thus, virtually all participants employ some method for predicting hotel prices. We survey approaches employed in the tournament, finding that agents apply an interesting diversity of techniques, taking into account differing sources of evidence bearing on prices. Based on data provided by entrants on their agents' actual predictions in the TAC-02 finals and semifinals, we analyze the relative efficacy of these approaches. The results show that taking into account game-specific information about flight prices is a major distinguishing factor. Machine learning methods effectively induce the relationship between flight and hotel prices from game data, and a purely analytical approach based on competitive equilibrium analysis achieves equal accuracy with no historical data. Employing a new measure of prediction quality, we relate absolute accuracy to bottom-line performance in the game

An exploration of concepts of community through a case study of UK university web production

The paper explores the inter-relation and differences between the concepts of occupational community, community of practice, online community and social network. It uses as a case study illustration the domain of UK university web site production and specifically a listserv for those involved in it. Different latent occupational communities are explored, and the potential for the listserv to help realize these as an active sense of community is considered. The listserv is not (for most participants) a tight knit community of practice, indeed it fails many criteria for an online community. It is perhaps best conceived as a loose knit network of practice, valued for information, implicit support and for the maintenance of weak ties. Through the analysis the case for using strict definitions of the theoretical concepts is made

Does \u2018bigger\u2019mean \u2018better\u2019? Pitfalls and shortcuts associated with big data for social research

\u2018Big data is here to stay.\u2019 This key statement has a double value: is an assumption as well as the reason why a theoretical reflection is needed. Furthermore, Big data is something that is gaining visibility and success in social sciences even, overcoming the division between humanities and computer sciences. In this contribution some considerations on the presence and the certain persistence of Big data as a socio-technical assemblage will be outlined. Therefore, the intriguing opportunities for social research linked to such interaction between practices and technological development will be developed. However, despite a promissory rhetoric, fostered by several scholars since the birth of Big data as a labelled concept, some risks are just around the corner. The claims for the methodological power of bigger and bigger datasets, as well as increasing speed in analysis and data collection, are creating a real hype in social research. Peculiar attention is needed in order to avoid some pitfalls. These risks will be analysed for what concerns the validity of the research results \u2018obtained through Big data. After a pars distruens, this contribution will conclude with a pars construens; assuming the previous critiques, a mixed methods research design approach will be described as a general proposal with the objective of stimulating a debate on the integration of Big data in complex research projecting

Aggression, Sibling Antagonism, and Theory of Mind During the First Year of Siblinghood: A Developmental Cascade Model

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/133634/1/cdev12530_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/133634/2/cdev12530.pd

Rational bidding using reinforcement learning: an application in automated resource allocation

The application of autonomous agents by the provisioning and usage of computational resources is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic resource provisioning and usage of computational resources, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems. The contributions of the paper are threefold. First, we present a framework for supporting consumers and providers in technical and economic preference elicitation and the generation of bids. Secondly, we introduce a consumer-side reinforcement learning bidding strategy which enables rational behavior by the generation and selection of bids. Thirdly, we evaluate and compare this bidding strategy against a truth-telling bidding strategy for two kinds of market mechanisms – one centralized and one decentralized

An open trial in the NHS of Blues Begone®: A new home based computerised CBT program

Background: Computer based treatment for depression and anxiety has been available for several years and has demonstrated useful clinical effects. Most existing computerized CBT products in the UK that are designed to treat depression and co-morbid anxiety require patients to visit a clinic and require staff input to manage the process. Such intervention adds to the costs and bottlenecks in delivering a clinically effective treatment with mass availability. Internet treatment options are becoming more readily available, although data to support use are not yet strong, and most still require human assessment and telephone support. Blues Begone (R) is a new computerized CBT program that has been designed to be used at home with minimal human support. Method: This pilot project provides data from an open trial of Blues Begone (R) with both primary and secondary care patients. Results: One hundred patients started Blues Begone (R), 58 completed the program, 72% (n = 42) of completers achieved reliable change and (n = 36) 62% achieved both reliable and clinically significant change, and may be considered to have recovered by the end of the program. Conclusion: These data provide the first demonstration of the potential viability of Blues Begone (R) as a home based computerized treatment for depression and anxiety

From simple desires to ordinary beliefs: The early development of everyday psychology

We provide evidence for the claim that before young children construe human action in terms of beliefs and desires they understand action only in terms of simple desires. This type of naive psychology--a simple desire psychology-- constitutes a coherent understanding of human action, but it differs from the belief-desire psychology of slightly older children and adults. In this paper we characterize what we mean by a simple desire psychology and report two experiments. In Experiment 1 we demonstrate that 2-year-olds can predict actions and reactions related to simple desires. In Experiment 2 we demonstrate that many 2-year-olds pass desire reasoning tasks while at the same time failing belief reasoning tasks that are passed by slightly older children, and that are as comparable as possible to the desire tasks they pass with ease.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/28537/1/0000335.pd

Children's conceptions of dreams

Children's conceptions of dreams are an important component of their developing understanding of the mind. Although there is much that even adults do not understand about the nature of dreams, most adults in Western society believe that: Dream entities are not real in the sense that they are nonphysical; they are private in the sense that they are not available to public perception, and are not directly shared with other dreamers; and, dreams are typically fictional in content. Thus, children in our society must confront several dualisms with respect to dreams, such as their physical versus nonphysical, perceptually-public versus perceptually-private, and shared versus individuated nature. Thirty-two children, aged 3- and 4-years-old, were told stories about children who were dreaming about an object, playing with an object, or looking at a photograph of an object, and then were asked questions about the status of these entities with regard to these three dualisms. All children judged dream entities, photographs, and physical objects to be appropriately different in terms of physical versus nonphysical properties and in terms of perceptually-public versus private status. They also understood the fictional nature of dreams. However, whereas most 4-year-olds understood that dreams are individuated, many 3-year-olds believed that dreams are directly shared by more than one person. These findings contrast with earlier research characterizing children's understanding of dreams as realistic. We reconcile these contrasting findings by discussing methodological differences, and we situate our findings regarding children's understanding of dreams within the context of contemporary research on children's theory of mind.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/29966/1/0000328.pd

Theory of mind and emotion understanding predict moral development in early childhood

The current study utilized longitudinal data to investigate how theory of mind (ToM) and emotion understanding (EU) concurrently and prospectively predicted young children's moral reasoning and decision making. One hundred twenty-eight children were assessed on measures of ToM and EU at 3.5 and 5.5 years of age. At 5.5 years, children were also assessed on the quality of moral reasoning and decision making they used to negotiate prosocial moral dilemmas, in which the needs of a story protagonist conflict with the needs of another story character. More sophisticated EU predicted greater use of physical- and material-needs reasoning, and a more advanced ToM predicted greater use of psychological-needs reasoning. Most intriguing, ToM and EU jointly predicted greater use of higher-level acceptance-authority reasoning, which is likely a product of children's increasing appreciation for the knowledge held by trusted adults and children's desire to behave in accordance with social expectations.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/79217/1/026151009X483056.pd

Q-Strategy: A Bidding Strategy for Market-Based Allocation of Grid Services

The application of autonomous agents by the provisioning and usage of computational services is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic service provisioning and usage of Grid services, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems. The contributions of the paper are threefold. First, we present a bidding agent framework for implementing artificial bidding agents, supporting consumers and providers in technical and economic preference elicitation as well as automated bid generation by the requesting and provisioning of Grid services. Secondly, we introduce a novel consumer-side bidding strategy, which enables a goal-oriented and strategic behavior by the generation and submission of consumer service requests and selection of provider offers. Thirdly, we evaluate and compare the Q-strategy, implemented within the presented framework, against the Truth-Telling bidding strategy in three mechanisms – a centralized CDA, a decentralized on-line machine scheduling and a FIFO-scheduling mechanisms