Academic literature on the topic 'Information aggregation'
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Journal articles on the topic "Information aggregation"
XU, Z. S. "CORRELATED LINGUISTIC INFORMATION AGGREGATION." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 17, no. 05 (October 2009): 633–47. http://dx.doi.org/10.1142/s0218488509006182.
Full textJin, LeSheng, Ronald R. Yager, Jana Špirková, Radko Mesiar, Daniel Paternain, and Humberto Bustince. "OWA aggregation with dual preferences for basic uncertain information." Journal of Intelligent & Fuzzy Systems 40, no. 3 (March 2, 2021): 4535–44. http://dx.doi.org/10.3233/jifs-201374.
Full textMorgan, John, and Phillip C. Stocken. "Information Aggregation in Polls." American Economic Review 98, no. 3 (May 1, 2008): 864–96. http://dx.doi.org/10.1257/aer.98.3.864.
Full textCampbell, Donald E., and Jerry S. Kelly. "Information and preference aggregation." Social Choice and Welfare 17, no. 1 (January 2000): 3–24. http://dx.doi.org/10.1007/pl00007172.
Full textYager, Ronald R. "Aggregation of ordinal information." Fuzzy Optimization and Decision Making 6, no. 3 (September 13, 2007): 199–219. http://dx.doi.org/10.1007/s10700-007-9008-8.
Full textVerma, Rajkumar, and Bhudev Sharma. "Prioritized Information Fusion Method for Triangular Fuzzy Information and Its Application to Multiple Attribute Decision Making." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 24, no. 02 (April 2016): 265–89. http://dx.doi.org/10.1142/s0218488516500136.
Full textKoutlis, Christos, Manos Schinas, Symeon Papadopoulos, and Ioannis Kompatsiaris. "GAP: Geometric Aggregation of Popularity Metrics." Information 11, no. 6 (June 15, 2020): 323. http://dx.doi.org/10.3390/info11060323.
Full textEzhilvendan, M., T. Kavitha, S. Chinnadurai, and P. Kumaran. "SIAVA: Secret Information Aggregation Design for Various Applications in Wireless Sensor Networks." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 12, no. 2 (December 28, 2013): 3249–54. http://dx.doi.org/10.24297/ijct.v12i2.3285.
Full textSiga, Lucas, and Maximilian Mihm. "Information aggregation in competitive markets." Theoretical Economics 16, no. 1 (2021): 161–96. http://dx.doi.org/10.3982/te3559.
Full textMIHAELA, COVRIG, TANASESCU PAUL, and MIRCEA IULIAN. "Fuzzy Information Aggregation in Insurance." ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH 52, no. 4/2018 (December 18, 2018): 35–48. http://dx.doi.org/10.24818/18423264/52.4.18.03.
Full textDissertations / Theses on the topic "Information aggregation"
Schulte, Elisabeth. "Information aggregation in organizations." [S.l. : s.n.], 2006. http://nbn-resolving.de/urn:nbn:de:bsz:180-madoc-13540.
Full textMulanda, Chilongo D. "Social network effects on information aggregation." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/55264.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (p. 57-58).
In this thesis, we investigated how sociometric information can be used to improve different methods of aggregating dispersed information. We specifically compared four different approaches of information aggregation: vanilla opinion poll, opinion polls where sociometric data is inferred from the population's own perception of social connectivity, opinion polls where sociometric data is obtained independent of the populations beliefs and data aggregation using market mechanisms. On comparing the entropy of the error of between the prediction of each of these different methods with the truth, preliminary results suggest that sociometric data does indeed improve the enterprise of information aggregation. The results also raise interesting questions about the relevance and application of different kinds of sociometric data as well as the somewhat surprising efficiency of information market mechanisms.
by Chilongo D. Mulanda.
M.Eng.
Han, Simeng. "Statistical Methods for Aggregation of Indirect Information." Thesis, Harvard University, 2014. http://dissertations.umi.com/gsas.harvard:11348.
Full textStatistics
Lobel, Ilan. "Social networks : rational learning and information aggregation." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/54232.
Full textThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (p. 137-140).
This thesis studies the learning problem of a set of agents connected via a general social network. We address the question of how dispersed information spreads in social networks and whether the information is efficiently aggregated in large societies. The models developed in this thesis allow us to study the learning behavior of rational agents embedded in complex networks. We analyze the perfect Bayesian equilibrium of a dynamic game where each agent sequentially receives a signal about an underlying state of the world, observes the past actions of a stochastically-generated neighborhood of individuals, and chooses one of two possible actions. The stochastic process generating the neighborhoods defines the network topology (social network). We characterize equilibria for arbitrary stochastic and deterministic social networks and characterize the conditions under which there will be asymptotic learning -- that is, the conditions under which, as the social network becomes large, the decisions of the individuals converge (in probability) to the right action. We show that when private beliefs are unbounded (meaning that the implied likelihood ratios are unbounded), there will be asymptotic learning as long as there is some minimal amount of expansion in observations. This result therefore establishes that, with unbounded private beliefs, there will be asymptotic learning in almost all reasonable social networks. Furthermore, we provide bounds on the speed of learning for some common network topologies. We also analyze when learning occurs when the private beliefs are bounded.
(cont.) We show that asymptotic learning does not occur in many classes of network topologies, but, surprisingly, it happens in a family of stochastic networks that has infinitely many agents observing the actions of neighbors that are not sufficiently persuasive. Finally, we characterize equilibria in a generalized environment with heterogeneity of preferences and show that, contrary to a nave intuition, greater diversity (heterogeneity) 3 facilitates asymptotic learning when agents observe the full history of past actions. In contrast, we show that heterogeneity of preferences hinders information aggregation when each agent observes only the action of a single neighbor.
by Ilan Lobel.
Ph.D.
Wang, John (John Michael) 1976. "Information aggregation and dissemination in simulated markets." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/80140.
Full textIncludes bibliographical references (leaf 39).
by John Wang.
S.B.and M.Eng.
Kotronis, Stelios. "Information aggregation in dynamic markets under ambiguity." Thesis, University of Southampton, 2017. https://eprints.soton.ac.uk/411958/.
Full textTam, Wing-yan. "Quality of service routing with path information aggregation." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B36782956.
Full textTam, Wing-yan, and 譚泳茵. "Quality of service routing with path information aggregation." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B36782956.
Full textSuen, Benny (Benny Hung Kit) 1975. "Internet information aggregation using the Context Interchange framework." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/46187.
Full textby Benny Suen.
B.S.
M.Eng.
Ozkes, Ali Ihsan. "Essays on hyper-preferences, polarization and information aggregation." Palaiseau, Ecole polytechnique, 2014. https://tel.archives-ouvertes.fr/pastel-01071827/document.
Full textIn this thesis, some important problems and properties of collective decision-making are studied. In particular, first, a stability property of preference aggregation rules is introduced and some well-known classes of rules are tested in this regard. Second, measuring preferential polarization is studied, both theoretically and empirically. Finally, strategic behavior in information aggregation situations is investigated in light of a sort of bounded rationality model, both theoretically and experimentally. The stability notion studied in the first part of the thesis is imposed particularly on social welfare functions and requires that the outcome of these functions should be robust to reduction in preference submission that are argued to take place when individuals submit a ranking of alternatives when the outcomes are also restricted to be rankings. Given the preference profile of a society, that is a collection of rankings of alternatives, a compatible collection of rankings of rankings are extracted and the outcome of social welfare functions in these two levels are compared. It turns out that no scoring rule gives consistent results, although there might exist Condorcet-type rules. Polarization measures studied in second part are in form of aggregation of pairwise antagonisms in a society. The public opinion polarization in the United States for the last three decades is analyzed in light of this view, by using a well-acclaimed measure of polarization introduced in the literature of income inequality. The conclusion is that no significant trend in public opinion polarization can be claimed to exist over the last several decades. Also, an adaptation of the same measure is shown to satisfy desirable properties in lieu of ordinal preference profiles when three alternatives are considered. Furthermore, a measure that is the aggregation of pairwise differences among individuals' preferences is characterized by a set of axioms. In the final part of the thesis, information aggregation situations described as in Condorcet jury model is studied in light of cognitive hierarchy approach to bounded rationality. Specifically, a laboratory experiment is run to test the theoretical predictions of the symmetric Bayesian Nash equilibrium concept. It is observed that behavior in lab is not correctly captured by this concept that assumes a strong notion of rationality and homogeneity among individuals' behaviors. To better describe the findings in the experiment, a novel model of cognitive hierarchy is developed and shown to perform better than both strong rationality approach and previous cognitive hierarchy models. This endogenous cognitive hierarchy model is compared theoretically to previous models of cognitive hierarchy and shown to improve in certain classes of games
Books on the topic "Information aggregation"
Xu, Zeshui, and Xiaoqiang Cai. Intuitionistic Fuzzy Information Aggregation. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29584-3.
Full textBouchon-Meunier, Bernadette, ed. Aggregation and Fusion of Imperfect Information. Heidelberg: Physica-Verlag HD, 1998. http://dx.doi.org/10.1007/978-3-7908-1889-5.
Full textBouchon-Meunier, Bernadette. Aggregation and Fusion of Imperfect Information. Heidelberg: Physica-Verlag HD, 1998.
Find full textChowdhry, Bhagwan. Information aggregation, security design, and currency swaps. Cambridge, MA: National Bureau of Economic Research, 2002.
Find full textXiaoqiang, Cai, and SpringerLink (Online service), eds. Intuitionistic Fuzzy Information Aggregation: Theory and Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.
Find full textAngeletos, Marios. Crises and prices: Information aggregation, multiplicity and volatility. Cambridge, Mass: Massachusetts Institute of Technology, Dept. of Economics, 2004.
Find full textAngeletos, Marios. Crises and prices: Information aggregation, multiplicity and volatility. Cambridge, Mass: National Bureau of Economic Research, 2004.
Find full textAngeletos, Marios. Crises and prices: Information aggregation, multiplicity and volatility. Cambridge, MA: National Bureau of Economic Research, 2004.
Find full textBrogan, Martha L. A survey of digital library aggregation services. Washington, D.C: Digital Library Federation, Council on Library and Information Resources, 2003.
Find full textHolmström, Bengt. Aggregation and linearity in the provision of intertemporal incentives. Stanford, Calif: Institute for Mathematical Studies in the Social Sciences, Stanford University, 1985.
Find full textBook chapters on the topic "Information aggregation"
Wygralak, Maciej. "Aggregation of Information and Aggregation Operators." In Intelligent Counting Under Information Imprecision, 93–109. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-34685-9_4.
Full textHerrera, F., E. Herrera-Viedma, and L. Martinez. "Representation Models for Aggregating Linguistic Information: Issues and Analysis." In Aggregation Operators, 245–59. Heidelberg: Physica-Verlag HD, 2002. http://dx.doi.org/10.1007/978-3-7908-1787-4_8.
Full textXu, Zeshui, and Xiaoqiang Cai. "Intuitionistic Fuzzy Information Aggregation." In Intuitionistic Fuzzy Information Aggregation, 1–102. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29584-3_1.
Full textJordan, James. "Information Aggregation and Prices." In The New Palgrave Dictionary of Economics, 6484–90. London: Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-349-95189-5_2811.
Full textJordan, James. "Information Aggregation and Prices." In The New Palgrave Dictionary of Economics, 1–7. London: Palgrave Macmillan UK, 2008. http://dx.doi.org/10.1057/978-1-349-95121-5_2811-1.
Full textXu, Zeshui, and Xiaoqiang Cai. "Interval-Valued Intuitionistic Fuzzy Information Aggregation." In Intuitionistic Fuzzy Information Aggregation, 103–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29584-3_2.
Full textXu, Zeshui, and Xiaoqiang Cai. "Correlation, Distance and Similarity Measures of Intuitionistic Fuzzy Sets." In Intuitionistic Fuzzy Information Aggregation, 151–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29584-3_3.
Full textXu, Zeshui, and Xiaoqiang Cai. "Decision Making Models and Approaches Based on Intuitionistic Preference Relations." In Intuitionistic Fuzzy Information Aggregation, 189–248. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29584-3_4.
Full textXu, Zeshui, and Xiaoqiang Cai. "Projection Model-Based Approaches to Intuitionistic Fuzzy Multi-Attribute Decision Making." In Intuitionistic Fuzzy Information Aggregation, 249–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29584-3_5.
Full textXu, Zeshui, and Xiaoqiang Cai. "Dynamic Intuitionistic Fuzzy Multi-Attribute Decision Making." In Intuitionistic Fuzzy Information Aggregation, 259–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29584-3_6.
Full textConference papers on the topic "Information aggregation"
Stucken, Sebastian, Fahrettin Gokgoz, and Hans-Christian Schmitz. "Tactical information aggregation." In 2021 International Conference on Military Communication and Information Systems (ICMCIS). IEEE, 2021. http://dx.doi.org/10.1109/icmcis52405.2021.9486412.
Full textGeiger, Bernhard C., and Christoph Temmel. "Information-preserving Markov aggregation." In 2013 IEEE Information Theory Workshop (ITW 2013). IEEE, 2013. http://dx.doi.org/10.1109/itw.2013.6691265.
Full textWen Jiang and Milos Zefran. "Coverage control with information aggregation." In 2013 IEEE 52nd Annual Conference on Decision and Control (CDC). IEEE, 2013. http://dx.doi.org/10.1109/cdc.2013.6760742.
Full textIyer, Krishnamurthy, Ramesh Johari, and Ciamac C. Moallemi. "Information aggregation in smooth markets." In the 11th ACM conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1807342.1807373.
Full textPanwar, Gaurav, Reza Tourani, Satyajayant Misra, and Abderrahmen Mtibaa. "Request aggregation." In ICN '17: 4th International Conference on Information-Centric Networking. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3125719.3132110.
Full textVivona, Doretta, and Maria Divari. "Aggregation operators of general aimed information." In 7th conference of the European Society for Fuzzy Logic and Technology. Paris, France: Atlantis Press, 2011. http://dx.doi.org/10.2991/eusflat.2011.82.
Full textMiletitch, Roman, Vito Trianni, Alexandre Campo, and Marco Dorigo. "Information Aggregation Mechanisms in Social Odometry." In European Conference on Artificial Life 2013. MIT Press, 2013. http://dx.doi.org/10.7551/978-0-262-31709-2-ch016.
Full textAbernethy, Jacob, Sindhu Kutty, Sébastien Lahaie, and Rahul Sami. "Information aggregation in exponential family markets." In EC '14: ACM Conference on Economics and Computation. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2600057.2602896.
Full textLi, Tongxin, Yue Chen, Bo Sun, Adam Wierman, and Steven Low. "Information Aggregation for Constrained Online Control." In SIGMETRICS '21: ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3410220.3461737.
Full textPedronette, Daniel Carlos Guimaraes, and Ricardo da S. Torres. "Exploiting contextual information for rank aggregation." In 2011 18th IEEE International Conference on Image Processing (ICIP 2011). IEEE, 2011. http://dx.doi.org/10.1109/icip.2011.6116726.
Full textReports on the topic "Information aggregation"
Fair, Ray, and Robert Shiller. Econometric Modeling as Information Aggregation. Cambridge, MA: National Bureau of Economic Research, May 1987. http://dx.doi.org/10.3386/w2233.
Full textAlbagli, Elias, Christian Hellwig, and Aleh Tsyvinski. Information Aggregation, Investment, and Managerial Incentives. Cambridge, MA: National Bureau of Economic Research, August 2011. http://dx.doi.org/10.3386/w17330.
Full textHassan, Tarek, and Thomas Mertens. Information Aggregation in a DSGE Model. Cambridge, MA: National Bureau of Economic Research, June 2014. http://dx.doi.org/10.3386/w20193.
Full textYager, Ronald R. Information Fusion and Aggregation for Cooperative Systems. Fort Belvoir, VA: Defense Technical Information Center, May 2007. http://dx.doi.org/10.21236/ada486686.
Full textChowdhry, Bhagwan, Mark Grinblatt, and David Levine. Information Aggregation, Security Design and Currency Swaps. Cambridge, MA: National Bureau of Economic Research, January 2002. http://dx.doi.org/10.3386/w8746.
Full textKung, Fan-chin, and Ping Wang. Information Aggregation and Transmission in Strategic Networks. Cambridge, MA: National Bureau of Economic Research, October 2022. http://dx.doi.org/10.3386/w30585.
Full textAngeletos, George-Marios, and Ivan Werning. Crises and Prices: Information Aggregation, Multiplicity and Volatility. Cambridge, MA: National Bureau of Economic Research, December 2004. http://dx.doi.org/10.3386/w11015.
Full textWallsten, Thomas S. Workshop on Information Aggregation in Group and Individual Decision Making. Fort Belvoir, VA: Defense Technical Information Center, May 2003. http://dx.doi.org/10.21236/ada423004.
Full textTrammell, B., A. Wagner, and B. Claise. Flow Aggregation for the IP Flow Information Export (IPFIX) Protocol. RFC Editor, September 2013. http://dx.doi.org/10.17487/rfc7015.
Full textBanerjee, Abhijit, and Olivier Compte. Consensus and Disagreement: Information Aggregation under (not so) Naive Learning. Cambridge, MA: National Bureau of Economic Research, April 2022. http://dx.doi.org/10.3386/w29897.
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