Economics Dictionary of ArgumentsHome
| |||
|
| |||
| Misinformation: Misinformation is false or misleading assertion that is spread, regardless of whether there is intent to deceive. See also Social Media, Internet, Newspapers, Information, Assertions._____________Annotation: The above characterizations of concepts are neither definitions nor exhausting presentations of problems related to them. Instead, they are intended to give a short introduction to the contributions below. – Lexicon of Arguments. | |||
| Author | Concept | Summary/Quotes | Sources |
|---|---|---|---|
|
Economic Theories on Misinformation - Dictionary of Arguments
Kranton I 423 Misinformation/Fake News/Economic Theories: ((s) the term “fake news” is not used by the cited authors). In one set of models, opinions spread like diseases; that is, individuals become infected (adopt an opinion) by contact with another agent with that disease (see, e.g., chapter 7 of Jackson, 2008)(1). Such diffusion processes are also studied in computer science, statistical physics, and sociology. In such models, biased agents are always better off when there are more biased agents (…). In a second set of models, opinion formation in social networks builds on DeGroot (1974)(2). Agents, with possibly different initial priors, repeatedly “exchange” their beliefs with their neighbors and adopt some statistic (the weighted average, say) of their neighbors’ opinions. Such agents fail to take into account the repetition of information that can propagate through a network, leading to a persuasion bias as referred to by DeMarzo et al. (2003)(3). Golub and Jackson (2010)(4) find sufficient network conditions under which such a naive rule leads to convergence to the truth—there can be no prominent groups, for example, that have disproportionate influence. Research on Bayesian learning in networks (e.g., Bala and Goyal, 1998(5); Gale and Kariv, 2003(6); Acemoglu et al., 2011(7)) characterizes convergence or not to common opinions for different network architectures. A new literature studies individuals’ incentives to communicate private information to others. Niehaus (2011)(8) adds a cost to sharing information; an agent will weigh the benefits to her friends and neighbors against the personal cost. Other papers study influence in networks; agents all have private information and have an incentive to share their information because, for example, agents benefit when others’ adopt the same action (Hagenbach and Koessler, 2010(9); Galeotti et al., 2013(10); Calvo´ -Armengol et al., 2015(11)). Chatterjee and Dutta (2016)(12) [are probably the closest to the line of work by Bloch, Demange and Kranton 2018(13)]. [Their paper focuses] on the credibility of messages received by agents in a social network when the message can be false. Kranton I 424 (…) this article features a situation in which information is not widely held, and unbiased agents strategically spread information so that a correct public decision is taken. A large economic literature also studies the transmission and communication of information through the observation of other agents’ actions. Observation helps discern the true state of the world. Knowledge or information costlessly spreads (Banerjee, 1992(14), 1993(15); Bikhchandani et al., 1992(16)), or spills over, to others, as occurs when people observe others’ use of a new technology (e.g., Foster and Rosenzweig, 1995(17); Conley and Udry, 2010(18)). In these models, though individuals influence others through their actions, they derive no benefit in influencing them and, contrary to [the article by Bloch, Demange, Kranton 2018 (13)], any decision to communicate is not strategic. >Network Models/Kranton, >Communication Models/Kranton, >Communication Filters/Kranton, >Misinformation/Kranton. 1. JACKSON, M., Social and Economic Networks (Princeton: Princeton University Press, 2008). 2. DEGROOT,M. H., “Reaching a Consensus,” Journal of the American Statistical Association 69 (345) (1974), 118–21. 3. DEMARZO, P. M.,D.VAYANOS, AND J. ZWEIBEL, “Persuasion Bias, Social Influence, and Uni-Dimensional Opinions,” Quarterly Journal of Economics 113 (3) (2003), 909–68. 4. GOLUB, B., AND M. JACKSON, “Naive Learning in Social Networks and the Wisdom of Crowds,” American Economic Journal: Microeconomics 2 (2010), 112–49. 5. BALA, V., AND S. GOYAL, “Learning from Neighbors,” The Review of Economic Studies 65 (3) (1998), 595–621. 6. GALE, D., AND S. KARIV, “Bayesian Learning in Social Networks,” Games and Economic Behavior 45 (2) (2003), 329–46. 7. ACEMOGLU, D.,M.DAHLEH, I. LOBEL, AND A.OZDAGLAR, “Bayesian Learning in Social Networks,” Review of Economic Studies 78 (2011), 1201–36. 8. NIEHAUS, P., “Filtered Social Learning,” Journal of Political Economy 119 (4) (2011), 686–720. 9. HAGENBACH, J., AND F. KOESSLER, “Strategic Communication in Networks,” Review of Economic Studies 77 (3) (2010), 1072–99. 10. GALEOTTI, A., C.GHIGLINO, AND F. SQUINTANI, “Strategic Information in Networks,” Journal of Economic Theory 148 (5) (2013), 1751–69. 11. CALVO´ -ARMENGOL,A., J. DEMART´I, ANDA. PRAT, “Communication and Influence,” Theoretical Economics 10 (2015), 649–90. 12. CHATTERJEE, K., AND B.DUTTA, “Credibility and Strategic Learning in Networks,” International Economic Review 57 (3) (2016), 759–86. 13. BLOCH, F., G. DEMANGE, AND R. KRANTON, "Rumors And Social Networks," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 59(2)(2018), pages 421-448, May. 14. BANERJEE, A., “A Simple Model of Herd Behavior,” Quarterly Journal of Economics 107 (3) (1992), 797–817. 15. BANERJEE, A., “The Economics of Rumours,” Review of Economic Studies 60 (1993), 309–27. 16. BIKHCHANDANI, S., D. HIRSHLEIFER, AND I. WELCH, “A Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades,” Journal of Political Economy 100 (1992), 992–1026. 17. FOSTER, A., AND M. ROSENZWEIG, “Learning by Doing and Learning from Others: Human Capital and Technical Change in Agriculture,” Journal of Political Economy 103 (1995), 1176–209. 18. CONLEY, T., AND C.UDRY, “Learning about a New Technology: Pineapple in Ghana,” American Economic Review 100 (2010), 35–69. Francis Bloch, Gabrielle Demange & Rachel Kranton, 2018. "Rumors And Social Networks," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 59(2), pages 421-448._____________Explanation of symbols: Roman numerals indicate the source, arabic numerals indicate the page number. The corresponding books are indicated on the right hand side. ((s)…): Comment by the sender of the contribution. Translations: Dictionary of Arguments The note [Concept/Author], [Author1]Vs[Author2] or [Author]Vs[term] resp. "problem:"/"solution:", "old:"/"new:" and "thesis:" is an addition from the Dictionary of Arguments. If a German edition is specified, the page numbers refer to this edition. |
Economic Theories Kranton I Rachel E. Kranton Francis Bloch Gabrielle Demange, Rumors And Social Networks 2018 Kranton II Rachel E. Kranton George A. Akerlof Identity Economics: How Our Identities Shape Our Work, Wages, and Well-Being Princeton 2011 |
||
> Counter arguments against Economic Theories
> Counter arguments in relation to Misinformation
