On calibrated weights in stratified sampling

In this paper, we propose a calibration estimator of population mean in stratified sampling using the known mean and variance information from multi-auxiliary variables. The problem of determining the optimum calibrated weights is formulated as a Nonlinear Programming Problem (NLPP) that is solved using the Lagrange multiplier technique. Numerical example with real data is presented to illustrate the computational details of the proposed estimator. A comparison study is also carried out using real and simulated data to evaluate the performance and the usefulness of the proposed estimator. The study reveals that the proposed estimator with multi-auxiliary information is more efficient estimator of the population mean as it provides least estimated variance and highest gain in relative efficiency (RE). References Jean Claude Deville and Carl Erik Sarndal. Calibration estimators in survey sampling. Journal of the American statistical Association, 87(418):376–382, 1992. doi:10.1080/01621459.1992.10475217. Victor M Estevao and Carl Erik Sarndal. Survey estimates by calibration on complex auxiliary information. International Statistical Review, 74(2):127–147, 2006. doi:110.1111/j.1751-5823.2006.tb00165.x Patrick J Farrell and Sarjinder Singh. Model-assisted higher-order calibration of estimators of variance. Australian and New Zealand Journal of Statistics, 47(3):375–383, 2005. doi:10.1111/j.1467-842X.2005.00402.x Wolfram Research, Inc. Mathematica, Version 11.3. Champaign, IL, 2018. Jong Min Kim, Engin A Sungur, and Tae Young Heo. Calibration approach estimators in stratified sampling. Statistics and probability letters, 77(1):99–103, 2007. doi:10.1016/j.spl.2006.05.015 Phillip S Kott. Using calibration weighting to adjust for nonresponse and coverage errors. Survey Methodology, 32(2):133, 2006. Dinesh K Rao. Mathematical programing in stratified random sampling. PhD thesis, School of Computing, Information and Mathematical Sciences, The University of the South Pacific, Fiji, February 2017. Dinesh K. Rao, Tokaua. Tekabu, and Mohammad G M Khan. New calibration estimators in stratified sampling. In Proceedings of Asia-Pacific World Congress on Computer Science and Engineering, pages 66–70. IEEE, 2016. Gurmindar K Singh, Dinesh K Rao, and Mohammed GM Khan. Calibration estimator of population mean in stratified random sampling. In Proceedings of Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE), pages 1–5. IEEE, 2014. Sarjindar Singh, Stephen Horn, and Frank Yu. Estimation of variance of general regression estimator: Higher level calibration approach. Survey Methodology, 24:41–50, 1998. Sarjinder Singh. Advanced Sampling Theory With Applications: How Michael "Selected" Amy, volume I and II. Kluwer Academic Publishers, Netherlands, 2003. Sarjinder Singh. On the calibration of design weights using a displacement function. Metrika, 75(1):85–107, 2012. doi:10.1007/s00184-010-0316-6 Sarjinder Singh, Stephen Horn, Sadeq Chowdhury, and Frank Yu. Theory and methods: Calibration of the estimators of variance. Australian and New Zealand Journal of Statistics, 41(2):199–212, 1999. doi:10.1111/1467-842X.00074 D S Tracy, S Singh, and R Arnab. Note on calibration in stratified and double sampling. Survey Methodology, 29(1):99–104, 2003. Changbao Wu and Randy R Sitter. A model-calibration approach to using complete auxiliary information from survey data. Journal of the American Statistical Association, 96(453):185–193, 2001. doi:10.1198/01621450175033305

On calibrated weights in stratified sampling

In this paper, we propose a calibration estimator of population mean in stratified sampling using the known mean and variance information from multi-auxiliary variables. The problem of determining the optimum calibrated weights is formulated as a Nonlinear Programming Problem (NLPP) that is solved using the Lagrange multiplier technique. Numerical example with real data is presented to illustrate the computational details of the proposed estimator. A comparison study is also carried out using real and simulated data to evaluate the performance and the usefulness of the proposed estimator. The study reveals that the proposed estimator with multi-auxiliary information is more efficient estimator of the population mean as it provides least estimated variance and highest gain in relative efficiency (RE). References Jean Claude Deville and Carl Erik Sarndal. Calibration estimators in survey sampling. Journal of the American statistical Association, 87(418):376–382, 1992. doi:10.1080/01621459.1992.10475217. Victor M Estevao and Carl Erik Sarndal. Survey estimates by calibration on complex auxiliary information. International Statistical Review, 74(2):127–147, 2006. doi:110.1111/j.1751-5823.2006.tb00165.x Patrick J Farrell and Sarjinder Singh. Model-assisted higher-order calibration of estimators of variance. Australian and New Zealand Journal of Statistics, 47(3):375–383, 2005. doi:10.1111/j.1467-842X.2005.00402.x Wolfram Research, Inc. Mathematica, Version 11.3. Champaign, IL, 2018. Jong Min Kim, Engin A Sungur, and Tae Young Heo. Calibration approach estimators in stratified sampling. Statistics and probability letters, 77(1):99–103, 2007. doi:10.1016/j.spl.2006.05.015 Phillip S Kott. Using calibration weighting to adjust for nonresponse and coverage errors. Survey Methodology, 32(2):133, 2006. Dinesh K Rao. Mathematical programing in stratified random sampling. PhD thesis, School of Computing, Information and Mathematical Sciences, The University of the South Pacific, Fiji, February 2017. Dinesh K. Rao, Tokaua. Tekabu, and Mohammad G M Khan. New calibration estimators in stratified sampling. In Proceedings of Asia-Pacific World Congress on Computer Science and Engineering, pages 66–70. IEEE, 2016. Gurmindar K Singh, Dinesh K Rao, and Mohammed GM Khan. Calibration estimator of population mean in stratified random sampling. In Proceedings of Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE), pages 1–5. IEEE, 2014. Sarjindar Singh, Stephen Horn, and Frank Yu. Estimation of variance of general regression estimator: Higher level calibration approach. Survey Methodology, 24:41–50, 1998. Sarjinder Singh. Advanced Sampling Theory With Applications: How Michael "Selected" Amy, volume I and II. Kluwer Academic Publishers, Netherlands, 2003. Sarjinder Singh. On the calibration of design weights using a displacement function. Metrika, 75(1):85–107, 2012. doi:10.1007/s00184-010-0316-6 Sarjinder Singh, Stephen Horn, Sadeq Chowdhury, and Frank Yu. Theory and methods: Calibration of the estimators of variance. Australian and New Zealand Journal of Statistics, 41(2):199–212, 1999. doi:10.1111/1467-842X.00074 D S Tracy, S Singh, and R Arnab. Note on calibration in stratified and double sampling. Survey Methodology, 29(1):99–104, 2003. Changbao Wu and Randy R Sitter. A model-calibration approach to using complete auxiliary information from survey data. Journal of the American Statistical Association, 96(453):185–193, 2001. doi:10.1198/01621450175033305

On potential cognitive abilities in the machine kingdom

The final publication is available at Springer via http://dx.doi.org/10.1007/s11023-012-9299-6Animals, including humans, are usually judged on what they could become, rather than what they are. Many physical and cognitive abilities in the ‘animal kingdom’ are only acquired (to a given degree) when the subject reaches a certain stage of development, which can be accelerated or spoilt depending on how the environment, training or education is. The term ‘potential ability’ usually refers to how quick and likely the process of attaining the ability is. In principle, things should not be different for the ‘machine kingdom’. While machines can be characterised by a set of cognitive abilities, and measuring them is already a big challenge, known as ‘universal psychometrics’, a more informative, and yet more challenging, goal would be to also determine the potential cognitive abilities of a machine. In this paper we investigate the notion of potential cognitive ability for machines, focussing especially on universality and intelligence. We consider several machine characterisations (non-interactive and interactive) and give definitions for each case, considering permanent and temporal potentials. From these definitions, we analyse the relation between some potential abilities, we bring out the dependency on the environment distribution and we suggest some ideas about how potential abilities can be measured. Finally, we also analyse the potential of environments at different levels and briefly discuss whether machines should be designed to be intelligent or potentially intelligent.We thank the anonymous reviewers for their comments, which have helped to significantly improve this paper. 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Evaluating how agent methodologies support the specification of the normative environment through the development process

[EN] Due to the increase in collaborative work and the decentralization of processes in many domains, there is an expanding demand for large-scale, flexible and adaptive software systems to support the interactions of people and institutions distributed in heterogeneous environments. Commonly, these software applications should follow specific regulations meaning the actors using them are bound by rights, duties and restrictions. Since this normative environment determines the final design of the software system, it should be considered as an important issue during the design of the system. Some agent-oriented software engineering methodologies deal with the development of normative systems (systems that have a normative environment) by integrating the analysis of the normative environment of a system in the development process. This paper analyses to what extent these methodologies support the analysis and formalisation of the normative environment and highlights some open issues of the topic.This work is partially supported by the PROMETEOII/2013/019, TIN2012-36586-C03-01, FP7-29493, TIN2011-27652-C03-00, CSD2007-00022 projects, and the CASES project within the 7th European Community Framework Program under the grant agreement No 294931.Garcia Marques, ME.; Miles, S.; Luck, M.; Giret Boggino, AS. (2014). Evaluating how agent methodologies support the specification of the normative environment through the development process. Autonomous Agents and Multi-Agent Systems. 1-20. https://doi.org/10.1007/s10458-014-9275-zS120Cossentino, M., Hilaire, V., Molesini, A., & Seidita, V. (Eds.). (2014). Handbook on agent-oriented design processes (Vol. VIII, 569 p. 508 illus.). Berlin: Springer.Akbari, O. (2010). A survey of agent-oriented software engineering paradigm: Towards its industrial acceptance. 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Verhagen (Eds.), Normative multi-agent systems, number 09121 in Dagstuhl seminar proceedings.Boella, G., Torre, L., & Verhagen, H. (2006). Introduction to normative multiagent systems. Computational and Mathematical Organization Theory, 12(2–3), 71–79.Bogdanovych, A., Esteva, M., Simoff, S., Sierra, C., & Berger, H. (2008). A methodology for developing multiagent systems as 3d electronic institutions. In M. Luck & L. Padgham (Eds.), Agent-Oriented Software Engineering VIII (Vol. 4951, pp. 103–117). Lecture Notes in Computer Science. Berlin: Springer.Boissier, O., Padget, J., Dignum, V., Lindemann, G., Matson, E., Ossowski, S., Sichman, J., & Vazquez-Salceda, J. (2006). Coordination, organizations, institutions and norms in multi-agent systems. LNCS (LNAI) (Vol. 3913).Bordini, R. H., Fisher, M., Visser, W., & Wooldridge, M. (2006). Verifying multi-agent programs by model checking. In Autonomous agents and multi-agent systems (Vol. 12, pp. 239–256). 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Regulated open multi-agent systems based on contracts. In Information Systems Development (pp. 243–255).Garcia, E., Tyson, G., Miles, S., Luck, M., Taweel, A., Staa, T. V., & Delaney, B. (2012). An analysis of agent-oriented engineering of e-health systems. In 13th international eorkshop on sgent-oriented software engineering (AOSE-AAMAS).Garcia, E., Tyson, G., Miles, S., Luck, M., Taweel, A., Staa, T. V., and Delaney, B. (2013). Analysing the Suitability of Multiagent Methodologies for e-Health Systems. In Agent-Oriented Software Engineering XIII, volume 7852, pages 134–150. Springer-Verlag.Garrido, A., Giret, A., Botti, V., & Noriega, P. (2013). mWater, a case study for modeling virtual markets. In New perspectives on agreement technologies (Vol. Law, Gover, pp. 563–579). Springer.Gteau, B., Boissier, O., & Khadraoui, D. (2006). Multi-agent-based support for electronic contracting in virtual enterprises. 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Real-time agreement and fulfilment of SLAs in Cloud Computing environments

A Cloud Computing system must readjust its resources by taking into account the demand for its services. This raises the need for designing protocols that provide the individual components of the Cloud architecture with the ability to self-adapt and to reach agreements in order to deal with changes in the services demand. Furthermore, if the Cloud provider has signed a Service Level Agreement (SLA) with the clients of the services that it offers, the appropriate agreement mechanism has to ensure the provision of the service contracted within a specified time. This paper introduces real-time mechanisms for the agreement and fulfilment of SLAs in Cloud Computing environments. On the one hand, it presents a negotiation protocol inspired by the standard WSAgreement used in web services to manage the interactions between the client and the Cloud provider to agree the terms of the SLA of a service. On the other hand, it proposes the application of a real-time argumentation framework for redistributing resources and ensuring the fulfilment of these SLAs during peaks in the service demand.This work is supported by the Spanish government Grants CONSOLIDER-INGENIO 2010 CSD2007-00022, TIN2011-27652-C03-01, TIN2012-36586-C03-01 and TIN2012-36586-C03-03.De La Prieta, F.; Heras Barberá, SM.; Palanca Cámara, J.; Rodríguez, S.; Bajo, J.; Julian Inglada, VJ. (2014). Real-time agreement and fulfilment of SLAs in Cloud Computing environments. 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Agent Recommendation for Agent-Based Urban-Transportation Systems. IEEE Intelligent Systems, 26(6), 77-81. doi:10.1109/mis.2011.94[15]Y.Y. Cheng, M. Low, S. Zhou, W. Cai and C.S. Choo, Evolving agent-based simulations in the clouds, in: 3rd International Workshop on Advanced Computational Intelligence (IWACI), 2010, pp. 244–249.[16]F. Dignum and H. Weigand, Communication and Deontic Logic, in: Information Systems – Correctness and Reusability. Selected Papers from the IS-CORE Workshop, R. Wieringa and R. Feenstra, eds, World Scientific Publishing Co., 1995, pp. 242–260.Erdogmus, H. (2009). Cloud Computing: Does Nirvana Hide behind the Nebula? IEEE Software, 26(2), 4-6. doi:10.1109/ms.2009.31[19]J.O. Fitó, I. Goiri and J. Guitart, SLA-driven elastic cloud hosting provider, in: 18th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), IEEE Computer Society, 2010, pp. 111–118.Fuentes-Fernández, R., Hassan, S., Pavón, J., Galán, J. M., & López-Paredes, A. (2012). Metamodels for role-driven agent-based modelling. Computational and Mathematical Organization Theory, 18(1), 91-112. doi:10.1007/s10588-012-9110-5Heras, S., Botti, V., & Julián, V. (2009). Challenges for a CBR framework for argumentation in open MAS. The Knowledge Engineering Review, 24(4), 327-352. doi:10.1017/s0269888909990178Heras, S., Jordán, J., Botti, V., & Julián, V. (2013). Argue to agree: A case-based argumentation approach. International Journal of Approximate Reasoning, 54(1), 82-108. doi:10.1016/j.ijar.2012.06.005[24]M. Jensen, J. Schwenk, N. Gruschka and L. Iacono, On technical security issues in cloud computing, in: IEEE International Conference on Cloud Computing, IEEE Press, 2009, pp. 109–116.Kakas, A., Maudet, N., & Moraitis, P. (2005). Modular Representation of Agent Interaction Rules through Argumentation. 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Route Planning in Transportation Networks

We survey recent advances in algorithms for route planning in transportation networks. For road networks, we show that one can compute driving directions in milliseconds or less even at continental scale. A variety of techniques provide different trade-offs between preprocessing effort, space requirements, and query time. Some algorithms can answer queries in a fraction of a microsecond, while others can deal efficiently with real-time traffic. Journey planning on public transportation systems, although conceptually similar, is a significantly harder problem due to its inherent time-dependent and multicriteria nature. Although exact algorithms are fast enough for interactive queries on metropolitan transit systems, dealing with continent-sized instances requires simplifications or heavy preprocessing. The multimodal route planning problem, which seeks journeys combining schedule-based transportation (buses, trains) with unrestricted modes (walking, driving), is even harder, relying on approximate solutions even for metropolitan inputs.Comment: This is an updated version of the technical report MSR-TR-2014-4, previously published by Microsoft Research. This work was mostly done while the authors Daniel Delling, Andrew Goldberg, and Renato F. Werneck were at Microsoft Research Silicon Valle