Journal articles: 'Information inequality'

1

Lei, Xiaowen. "Information and Inequality." Journal of Economic Theory 184 (November 2019): 104937. http://dx.doi.org/10.1016/j.jet.2019.08.007.

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Lee, Doo-Young. "Information Inequality, Information Gap, and Library Information Service." Journal of the Korean Society for Library and Information Science 40, no. 4 (December 30, 2006): 13–19. http://dx.doi.org/10.4275/kslis.2006.40.4.013.

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Greenberg, Jason. "The (Internet) Information Inequality Machine?" Academy of Management Proceedings 2019, no. 1 (August 1, 2019): 10933. http://dx.doi.org/10.5465/ambpp.2019.10933abstract.

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4

The Lancet. "The web of information inequality." Lancet 349, no. 9068 (June 1997): 1781. http://dx.doi.org/10.1016/s0140-6736(97)21025-1.

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SAFINEJAD, F., A. SHAFIEE, and R. MALEEH. "BELL'S INEQUALITY AND CONTEXTUAL INFORMATION." International Journal of Quantum Information 04, no. 04 (August 2006): 641–51. http://dx.doi.org/10.1142/s0219749906002110.

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The properties of the Shannon entropy (concavity and strong additivity) are satisfied in both classical and quantum experiments, if the context of experiment is considered appropriately. We show that these properties hold true in a classical model with the feature of being contextual, where a correlation is observed between events. Our results show that the classical example is similar to an entangled singlet state for spin-½ particles. But, contrary to general opinion, [Formula: see text] is not a necessary and sufficient condition for violating Bell's inequality, since it has been obtained on the basis of a common cause pattern. In other words, it is possible to reconstruct the amount of information contained in a quantum entangled state according to the common cause criterion satisfying local realism.

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Matus, F. "Piecewise linear conditional information inequality." IEEE Transactions on Information Theory 52, no. 1 (January 2006): 236–38. http://dx.doi.org/10.1109/tit.2005.860438.

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Iversen, Torben, and David Soskice. "Information, Inequality, and Mass Polarization." Comparative Political Studies 48, no. 13 (July 28, 2015): 1781–813. http://dx.doi.org/10.1177/0010414015592643.

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Popescu, Pantelimon-George, Florin Pop, Alexandru Herişanu, and Nicolae Ţăpuş. "New Inequalities between Information Measures of Network Information Content." Mathematical Problems in Engineering 2013 (2013): 1–3. http://dx.doi.org/10.1155/2013/175769.

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We refine a classical logarithmic inequality using a discrete case of Bernoulli inequality, and then we refine furthermore two information inequalities between information measures for graphs, based on information functionals, presented by Dehmer and Mowshowitz in (2010) as Theorems 4.7 and 4.8. The inequalities refer to entropy-based measures of network information content and have a great impact for information processing in complex networks (a subarea of research in modeling of complex systems).

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9

Kondratenko, Natalia. "The researching of information inequality in the market of information services." Technology audit and production reserves 1, no. 4(57) (January 20, 2021): 45–48. http://dx.doi.org/10.15587/2706-5448.2021.225533.

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The object of research is information inequality. Information inequality is seen as a socio-economic problem that can be solved with the help of confident actions of the state. Data analysis confirmed the problem of the «digital divide» at the global and regional levels. The transformation of the information services market depends on the quality of the Internet. The growing number of Internet users is a global tendency, but at the regional level it is possible to see clear differences, which creates problems for obtaining quality educational, financial and professional services. Both negative and positive consequences of information inequality are considered. Along with the growing importance of modern information technologies and services in society, inequality between certain segments of the population is growing. Some people for various reasons may have restrictions on access to information, knowledge, information services, new digital products and modern technologies, while others may not have similar restrictions on access to them. The study found that the market for information services is specific in terms of protection of intellectual property rights. Aspects that would contribute to strengthening the protection of intellectual property rights to information services and products, information security are provided. Negative transactional externalities occur in the market of information services precisely when there is a decrease in information security due to violation of intellectual property rights by one person in relation to another, causing the last damage. To reduce the burden of transaction costs on market participants in information services, the directions of reducing transaction costs at the national level are substantiated. In all countries of the world, the COVID-19 pandemic has exacerbated the issue of information inequality. The study presents the principles for overcoming digital inequality.

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Mann, A., M. Revzen, and E. Santos. "Entanglement, information theory and Bell's inequality." Physics Letters A 238, no. 2-3 (February 1998): 85–89. http://dx.doi.org/10.1016/s0375-9601(97)00898-0.

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11

Prelov, V. V., and E. C. van der Meulen. "Mutual information, variation, and Fano’s inequality." Problems of Information Transmission 44, no. 3 (September 2008): 185–97. http://dx.doi.org/10.1134/s0032946008030022.

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12

Jewitt, Ian. "An information inequality for agency problems." Economics Letters 29, no. 4 (1989): 295–98. http://dx.doi.org/10.1016/0165-1765(89)90204-8.

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13

Yu, Liangzhi. "The divided views of the information and digital divides: A call for integrative theories of information inequality." Journal of Information Science 37, no. 6 (November 14, 2011): 660–79. http://dx.doi.org/10.1177/0165551511426246.

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This article defines information inequality as multifaceted disparity between individuals, communities or nations in mobilizing society’s information resources for the benefit of their lives and development. It then examines related research from a wide range of disciplines that focuses either on information inequality in general or on its specific forms, e.g. information poverty, information divide, knowledge gap and digital divide. It shows that it is possible to identify a number of clusters of information inequality research according to their theoretical perspectives, and that these perspectives have inherited to a great extent the traditional divisions of social sciences between structure vs agency, society vs individuals and objectivism vs subjectivism. Following earlier calls for greater dialogue between divisions of related research, this article goes further to call for integrative theorizing of information inequality in the way exemplified by Bourdieu’s research on social inequality.

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14

Srivastava, Amit. "Some New Information Inequalities Involving f-Divergences." Cybernetics and Information Technologies 12, no. 2 (June 1, 2012): 3–10. http://dx.doi.org/10.2478/cait-2012-0008.

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Abstract New information inequalities involving f-divergences have been established using the convexity arguments and some well known inequalities such as the Jensen inequality and the Arithmetic-Geometric Mean (AGM) inequality. Some particular cases have also been discussed.

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15

Melin, Harri. "Inequality and Social Mobility in Information Society." Information Technology, Education and Society 10, no. 2 (January 1, 2009): 79–95. http://dx.doi.org/10.7459/ites/10.2.05.

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Singh, R. P., Rajeev Kumar, and R. K. Tuteja. "Application of Holder’s inequality in information theory." Information Sciences 152 (June 2003): 145–54. http://dx.doi.org/10.1016/s0020-0255(02)00300-6.

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17

Lutwak, Erwin, Songjun Lv, Deane Yang, and Gaoyong Zhang. "Extensions of Fisher Information and Stam's Inequality." IEEE Transactions on Information Theory 58, no. 3 (March 2012): 1319–27. http://dx.doi.org/10.1109/tit.2011.2177563.

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18

Akahira, Masafumi, and Michikazu Sato. "An information inequality for the Bayes risk." Annals of Statistics 24, no. 5 (October 1996): 2288–95. http://dx.doi.org/10.1214/aos/1069362323.

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19

Toscani, Giuseppe. "An information-theoretic proof of Nash’s inequality." Rendiconti Lincei - Matematica e Applicazioni 24, no. 1 (2013): 83–93. http://dx.doi.org/10.4171/rlm/645.

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20

Maudlin, Tim. "Bell's Inequality, Information Transmission, and Prism Models." PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1992, no. 1 (January 1992): 404–17. http://dx.doi.org/10.1086/psaprocbienmeetp.1992.1.192771.

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21

Halcli, Abigail, and Frank Webster. "Inequality and Mobilization in The Information Age." European Journal of Social Theory 3, no. 1 (February 2000): 67–81. http://dx.doi.org/10.1177/13684310022224688.

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22

Adair, Stephen. "The Commodification of Information and Social Inequality." Critical Sociology 36, no. 2 (March 2010): 243–63. http://dx.doi.org/10.1177/0896920509357505.

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23

Ioannides, Yannis M., and Linda Datcher Loury. "Job Information Networks, Neighborhood Effects, and Inequality." Journal of Economic Literature 42, no. 4 (November 1, 2004): 1056–93. http://dx.doi.org/10.1257/0022051043004595.

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This paper explores the theoretical and empirical literature to examine the use by different social groups of informal sources of information provided by friends, relatives, and acquaintances during job search and its consequences for the job market. It also addresses the role of network structure and size, the resource endowments of contacts, and nature of the links between contacts to explain differences in the effects of job information networks. In doing so, the paper also turns to the sociology literature on job information networks and provides an economic perspective on such sociological concepts as strong versus weak ties, inbreeding, distance from structural holes, etc. The paper distinguishes between models of exogenous job information networks, that is where individuals obtain job-related information through a given social structure, and endogenous job information networks, which are social networks that result from individuals' uncoordinated actions. The paper pays special attention to such issues as physical and social proximity and sharing of information and discusses them in the context of the recent social interactions and neighborhood effects literature. Finally, the paper outlines a model that integrates job information networks, where interactions occur in business cycle frequencies, with the dynamics of human capital formation, which include the joint effects of parental, community and neighborhood human capital, and are set in life cycle frequencies, for the purpose of organizing suggestions for future research and examining earned income inequality.

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24

Kristal, Tali. "Why Has Computerization Increased Wage Inequality? Information, Occupational Structural Power, and Wage Inequality." Work and Occupations 47, no. 4 (July 16, 2020): 466–503. http://dx.doi.org/10.1177/0730888420941031.

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This article offers a new account of rising inequality by providing a new explanation for the observed correlation between computerization and earnings. The argument is that as computers transformed work into a more knowledge-intensive activity, occupations located at critical junctions of information flow have gained greater structural power, and thereby higher wages. Combining occupational measures for location in the information flow based on the Occupational Information Network with the 1979–2016 Current Population Surveys, the analyses reveal a rising wage premium for occupations with greater access to and control of information, independent of the spectrum of skills related to computerization.

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25

Mardison, Eri. "THE OPPORTUNITY TO ACCESS BETTER INFORMATION TECHNOLOGY." Jurnal Teknologi Informasi dan Pendidikan 14, no. 1 (April 15, 2021): 33–39. http://dx.doi.org/10.24036/tip.v14i1.406.

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Children are born without being able to choose a number of factors that are attached to them. This is in fact unfair to him in supporting his future. HOI exists to calculate a number of factors that cause imbalance and how much inequality should be reallocated. The usage of cellphone has covered 63.86 percent. The inequality that must be reallocated is 8.76 percent. Per capita income and residence are the dominant influencing factors. In the case of cellphone ownership, the coverage was still 33.12 percent, with an inequality reallocation of 17.05 percent. Per capita income and residence are the dominant factors. Access coverage on new computers reached 17.35 percent with reallocation of inequalities reaching 28.87 percent. Per capita income, residence and certificate of household head are the dominant factors. Meanwhile internet access has covered 35.02 percent with inequality reallocation reaching 17.85 percent. Per capita income, residence and certificate of household head are the most dominant things. An understanding of reality is very important in taking online policies against children that happen to be implemented during the Covid-19 period and possibly afterward.

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26

Bachir, Mohammed. "The multidirectional mean value inequalities with second order information." Journal of the Australian Mathematical Society 80, no. 2 (April 2006): 159–72. http://dx.doi.org/10.1017/s1446788700013045.

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AbstractWe give a multidirectional mean value inequality with second order information. This result extends the classical Clarke-Ledyaev's inequality to the second order. As application, we give the uniqueness of viscosity solution of second order Hamilton-Jacobi equations in finite dimensions.

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27

Liu, Haimeng, Chuanglin Fang, and Siao Sun. "Digital inequality in provincial China." Environment and Planning A: Economy and Space 49, no. 10 (May 30, 2017): 2179–82. http://dx.doi.org/10.1177/0308518x17711946.

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The rapid spread of information and communications technologies is accelerating the development of our economy and society. While information and communications technologies have become an important development tool that reshapes many aspects of the world’s activities, digital inequality emerges on a worldwide scale due to difference in access to and use of information and communications technologies. Digital inequality, which is a new phenomenon in the current information era, will widen the gap between developed and developing countries/regions. China has witnessed rapid informationization along with its booming economic growth over the past decade. However, digital inequality, as an important aspect of social inequalities in China, has received only scant attention currently. Based on the technique of cartogram, Figures display provincial information and communications technologies development index (IDI) values in 2010 and 2015 in China, exhibiting high inequality. Regions of the highest information and communications technologies levels are distributed along China's eastern coast. On the contrary, the provinces in the west present very low information and communications technologies levels. The coefficient of variation of IDI values dropped from 0.30 to 0.18, indicating declining digital inequality in China. A causal bidirectional relation probably exists between the economic development and information and communications technologies levels.

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28

Fedotov, A. A., P. Harremoes, and F. Topsoe. "Refinements of Pinsker's inequality." IEEE Transactions on Information Theory 49, no. 6 (June 2003): 1491–98. http://dx.doi.org/10.1109/tit.2003.811927.

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29

Deng, Yongping, Hidayat Ullah, Muhammad Adil Khan, Sajid Iqbal, and Shanhe Wu. "Refinements of Jensen’s Inequality via Majorization Results with Applications in the Information Theory." Journal of Mathematics 2021 (August 18, 2021): 1–12. http://dx.doi.org/10.1155/2021/1951799.

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In this study, we present some new refinements of the Jensen inequality with the help of majorization results. We use the concept of convexity along with the theory of majorization and obtain refinements of the Jensen inequality. Moreover, as consequences of the refined Jensen inequality, we derive some bounds for power means and quasiarithmetic means. Furthermore, as applications of the refined Jensen inequality, we give some bounds for divergences, Shannon entropy, and various distances associated with probability distributions.

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30

YOSHIDA, HIROAKI. "REMARKS ON A SEMICIRCULAR PERTURBATION OF THE FREE FISHER INFORMATION." Infinite Dimensional Analysis, Quantum Probability and Related Topics 11, no. 01 (March 2008): 97–108. http://dx.doi.org/10.1142/s0219025708002999.

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In this paper, we shall give the representation of a semicircular perturbation of the free Fisher information [Formula: see text] by the mean of the conditional variance [Formula: see text], where S is a standard semicircular element freely independent of X, and ε > 0. Using this representation, we will give alternative proofs of the free Fisher information inequality, in which the free analogue of Stam's inequality can be obtained as a special case, and of the free entropy power inequality in an infinitesimal approach to the free entropy.

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31

Eubanks, Virginia. "Popular technology: exploring inequality in the information economy." Science and Public Policy 34, no. 2 (March 1, 2007): 127–38. http://dx.doi.org/10.3152/030234207x193592.

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32

Dragomir, S. S., V. Gluščević, and C. E. M. Pearce. "Csiszár f-divergence, Ostrowski’s inequality and mutual information." Nonlinear Analysis: Theory, Methods & Applications 47, no. 4 (August 2001): 2375–86. http://dx.doi.org/10.1016/s0362-546x(01)00361-3.

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33

Fujii, Jun Ichi. "A trace inequality arising from quantum information theory." Linear Algebra and its Applications 400 (May 2005): 141–46. http://dx.doi.org/10.1016/j.laa.2004.11.009.

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34

Maalej, Maroua, Vitor Paisante, Fernando Magno Quintão Pereira, and Laure Gonnord. "Combining range and inequality information for pointer disambiguation." Science of Computer Programming 152 (January 2018): 161–84. http://dx.doi.org/10.1016/j.scico.2017.10.014.

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35

Kaced, Tarik, Andrei Romashchenko, and Nikolai Vereshchagin. "A Conditional Information Inequality and Its Combinatorial Applications." IEEE Transactions on Information Theory 64, no. 5 (May 2018): 3610–15. http://dx.doi.org/10.1109/tit.2018.2806486.

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36

Zhang, Zhen. "On a new non-Shannon type information inequality." Communications in Information and Systems 3, no. 1 (2003): 47–60. http://dx.doi.org/10.4310/cis.2003.v3.n1.a4.

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37

Miller, Steven J. "When the Cramér-Rao Inequality Provides No Information." Communications in Information and Systems 7, no. 3 (2007): 272. http://dx.doi.org/10.4310/cis.2007.v7.n3.a3.

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38

Englert, Berthold-Georg. "Fringe Visibility and Which-Way Information: An Inequality." Physical Review Letters 77, no. 11 (September 9, 1996): 2154–57. http://dx.doi.org/10.1103/physrevlett.77.2154.

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39

Hashimoto, Shintaro, and Ken-Ichi Koike. "Bhattacharyya-Type Information Inequality for the Bayes Risk." Communications in Statistics - Theory and Methods 44, no. 24 (January 3, 2015): 5213–24. http://dx.doi.org/10.1080/03610926.2013.810265.

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40

Li, Yumeng, and Xinyu Wang. "Transportation Cost-Information Inequality for Stochastic Wave Equation." Acta Applicandae Mathematicae 169, no. 1 (October 14, 2019): 145–55. http://dx.doi.org/10.1007/s10440-019-00292-y.

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41

Eckwert, Bernhard, and Itzhak Zilcha. "The Effect of Better Information on Income Inequality." Economic Theory 32, no. 2 (May 20, 2006): 287–307. http://dx.doi.org/10.1007/s00199-006-0120-8.

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42

Gullett, Matt. "Information Inequality: The Deepening Social Crisis in America." Journal of Government Information 30, no. 1 (January 2004): 120–23. http://dx.doi.org/10.1016/j.jgi.2003.12.011.

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43

Rezaei, Tahereh, Shahabeddin M. Aslmarand, Robert Snyder, Behzad Khajavi, Paul M. Alsing, Michael Fanto, Doyeol Ahn, and Warner A. Miller. "Experimental realization of Schumacher's information geometric Bell inequality." Physics Letters A 405 (July 2021): 127444. http://dx.doi.org/10.1016/j.physleta.2021.127444.

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44

Zhou, Yuhang. "Effectiveness of performance compensation commitment under Information Inequality." E3S Web of Conferences 235 (2021): 03082. http://dx.doi.org/10.1051/e3sconf/202123503082.

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The demand of information society promotes the production of performance compensation commitment, which is used to alleviate the information asymmetry between investors and listed companies. The generation of performance compensation commitments has reduced valuation difficulties and other problems to a certain extent, lowered M&A costs and improved M&A efficiency. However, as the sample size increases, its drawbacks have gradually emerged. Youbo Pharmaceutical has stepped on the line for three consecutive years to achieve accurate performance. After the commitment period expires, the performance has dropped by more than 50%, so the authenticity of its performance has been questioned. Through the analysis of its announcement and various indicators, I find that the performance compensation commitment can not bring good market reaction after the commitment period, nor can it improve the integration effect, and even there are some behaviors such as premium, goodwill impairment risk and earnings management.

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45

Jain, K., and Ram Saraswat. "A New Information Inequality and Its Application in Establishing Relation Among Various f-Divergence Measures." Journal of Applied Mathematics, Statistics and Informatics 8, no. 1 (May 1, 2012): 17–32. http://dx.doi.org/10.2478/v10294-012-0002-6.

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A New Information Inequality and Its Application in Establishing Relation Among Various f-Divergence MeasuresAn Information inequality by using convexity arguments and Jensen inequality is established in terms of Csiszar f-divergence measures. This inequality is applied in comparing particular divergences which play a fundamental role in Information theory, such as Kullback-Leibler distance, Hellinger discrimination, Chi-square distance, J-divergences and others.

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46

Sardiana, Anna. "Banking Information Technology on Economic Inequality: An analysis of Electronic Money." Indonesian Journal of Economics, Social, and Humanities 3, no. 1 (March 5, 2021): 27–34. http://dx.doi.org/10.31258/ijesh.3.1.27-34.

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The purpose of this study is to analyze and determine the role of banking technology, specifically the use of electronic money in reducing economic inequality in Indonesia. Gini coefficient analysis was used as method in data analysis to measure economic inequality. The data used in the study was secondary data sourced from the Central Statistics Agency (BPS) and Bank Indonesia (BI). The comparison is done using the Gini coefficient calculation method. The results of analysis in this study indicate that there is an increase that causes economic inequality in Indonesia regarding the use of server-based electronic money. The findings in this study also indicate that chip-based electronic money which is a product of banking technology can reduce the level of economic inequality in Indonesia.

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47

Goharian, Nazli, and Suzan Verberne. "Addressing gender inequality." ACM SIGIR Forum 53, no. 2 (December 2019): 44. http://dx.doi.org/10.1145/3458553.3458557.

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The SIGIR'19 Women in IR session, co-organized by Nazli Goharian and Suzan Verberne, took place on July 23 and was attended by well over 200 participants. After giving attribute to Laura Dietz, one of the co-founders who had initiated the Women in IR session in 2014 and organized it annually, the session started with a keynote by Mounia Lalmas sharing her experiences on the path she took towards learning, confronting challenges, and professional advancements. Next we had a panel comprised of panelists from both academia and industry discussing gender inequality.

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48

Brooks, Clem, and Elijah Harter. "Redistribution Preferences, Inequality Information, and Partisan Motivated Reasoning in the United States." Societies 11, no. 2 (June 21, 2021): 65. http://dx.doi.org/10.3390/soc11020065.

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In an era of rising inequality, the U.S. public’s relatively modest support for redistributive policies has been a puzzle for scholars. Deepening the paradox is recent evidence that presenting information about inequality increases subjects’ support for redistributive policies by only a small amount. What explains inequality information’s limited effects? We extend partisan motivated reasoning scholarship to investigate whether political party identification confounds individuals’ processing of inequality information. Our study considers a much larger number of redistribution preference measures (12) than past scholarship. We offer a second novelty by bringing the dimension of historical time into hypothesis testing. Analyzing high-quality data from four American National Election Studies surveys, we find new evidence that partisanship confounds the interrelationship of inequality information and redistribution preferences. Further, our analyses find the effects of partisanship on redistribution preferences grew in magnitude from 2004 through 2016. We discuss implications for scholarship on information, motivated reasoning, and attitudes towards redistribution.

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49

Xiang, Y., and S. J. Xiong. "Nonlocal information as condition for violations of Bell inequality and information causality." European Physical Journal D 61, no. 1 (October 1, 2010): 249–52. http://dx.doi.org/10.1140/epjd/e2010-00254-9.

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50

Zhou, Peng, Fengwen Chen, Wei Wang, Peixin Song, and Chenliang Zhu. "Does the Development of Information and Communication Technology and Transportation Infrastructure Affect China’s Educational Inequality?" Sustainability 11, no. 9 (May 1, 2019): 2535. http://dx.doi.org/10.3390/su11092535.

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Educational inequality is an important factor in the development of human capital, and limits the output of regional economic activities. The unequal distribution of educational resources has become a hot topic noticed by the public, and has restricted sustainable economic growth. This paper provides a better understanding of educational inequality, and explores the impacts of information and communication technology (ICT) and transportation infrastructure on the distribution of educational resources. The panel data models are constructed to discuss the relationship among ICT, transportation infrastructure, and educational inequality, using the data of 31 provinces in China from 2006 to 2016. The empirical results show that there is a positive relationship between ICT and educational inequality, while transportation infrastructure can restrain the unequal distribution of educational resources. Moreover, there is a significant inverted U-shaped relationship between transportation infrastructure and educational inequality. Since China’s education reform in 2010, the relationship among ICT, transportation infrastructure, and educational inequality has been significantly changed, as well as the influence mechanism of ICT. In addition, transportation infrastructure in China western regions can effectively alleviate the problem of educational inequality, and its impact will increase with the growth of transportation investments. It is necessary to consider the rational allocation of educational resources, and this is essential to relieve the problem of educational inequality. Therefore, our results demonstrate the key roles of information technology and transportation network in the field of education, and provide some new ideas for the solution of educational inequality.

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Journal articles on the topic 'Information inequality'

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