Taste dynamics, opinion dynamics, sincerely-held-belief dynamics etc

Placeholder for the complex case of diffusion of innovation where the innovations in question are beliefs. Which beliefs prosper and which fail? How much does social dynamics determine how much a belief prospers? How much does factuality? How much earthy folk wisdom?

Innovation diffusion theory

Models such as the Bass diffusion model impose an epidemiological structure on the contagion of products, with a survival analysis flavour. See innovation diffusion.

Invasive arguments

In ecology we have invasive species. In rhetoric we have invasive arguments. I am collecting a library of these.


Is belief a thing related to true states of the world, or it is a pure signifier of group membership, or something else again? Surely belief is a complicated phenomenon, but working out what aggregate of causes underlies any given belief is difficult for us for any given instance. The experience of having a belief is the same, as seen from inside our skulls regardless of what causes it.

The function of belief in individuals

David Banks’s diatribe depicts a particular kind of strategic belief:

“[Radiolab recasts] the political as endlessly unresolved scientific controversies, and act as science concern trolls,” he claims. These “explainerist” nuggets of satisfying factiness - why are they popular? One answer might be that they are a good marker of membership in a tribe that likes a certain kind of cocktail conversation.

What kind of beliefs prosper in society? What is the function of our truth claims? When should you believe “true” things, and what are true things anyway? Are true things about the objects of science the same as true things about society?

Goal: find a way of navigating the pragmatic functions of belief that sidestep the divisions in this anecdote:

I know this sounds like a story from some bad conservative novel, but it is not unheard of for rooms full of PhDs to applaud when someone says that, for example, witchcraft is just another way of knowledge and that disputing factual claims to its power is cultural hegemony.

To my ears it’s the emphases that make this sound uncomfortable, rather than the broad-stroke outline. On one hand I think that empirical fact is special in having a reality independent of human existence. On the other hand, I don’t suppose any of our epistemological methods give us perfect access to the reality I posit. Having claimed my beliefs are not, with 100% certainty, raw and unmediated rays of truth, I have opened the door to negotiating how certain my beliefs are, and admitting that other perspectives might have a point that I cannot dismiss a priori. I am all for admitting that our beliefs are uncertain and our categories subject to revision, otherwise why would I bother with statistics, which is my day job? Any specific claim generated by witchcraft is going to achieve a pretty low prior weighting in my decisions, however.

Also, how about beliefs that are not about facts as such? Does human knowledge transmission at large deal mostly in transmission of precise factual claims about reproducible experiments, or is there a whole bunch of other stuff going on with an indirect relationship to facts about gross physical reality, and some kind of active role in creating whatever passes for facts in the negotiated social reality?

Option B. We need the tools unpack the other propensities in the uses of the language around belief, and disentangle what is going with cheap talk and signalling. We do deploy belief in a variety of ways, often emotional, often figurative. 1

How good are we at forming good facty beliefs? Scott Alexander found the irritating case study of bodybuilders suggests…. tl;dr: We are not very good at facty beliefs?

Antonio García Martínez, in The Holy Church of Christ Without Christ, belabours the point that faith-based engagement is predominantly how we engage with the world. Or, as Herbert Simon and Eliezer Yudkowsky could have co-authored, belief is how a heuristic feels from the inside.

Belief and group identity

See Tribal rituals and beliefs.

Elite capture

See collective action.

Girardian mimetic violence

That’s mimetic not memetic, although there are points of contact. Girard apparently wrote about our desires being often about being something rather than having something. Alex Danco summarizes a few choice morsels:

at a deep neurological level, when we watch other people and pattern our desires off theirs, we are not so much acquiring a desire for that object so much as learning to mimic somebody, and striving to become them or become like them. Girard calls this phenomenon mimetic desire. We don’t want; we want to be.

I do not know what neurological level he is attempting to evoke. Perhaps some of that mirror neuron business. In any case, needs work, citation required.

Modern status forums like Instagram are designed explicitly to bring out this dual admiration/resentment emotion within us. Instagram’s real product isn’t photos; it’s likes. The photos and the events they depict are just the transient objects that bubble up to the surface; what really matters is the relationship between the people. But the fact that Instagram’s product is built around the objects and not the models isn’t an accident: it’s sneaky. It creates way more space and oxygen for resentment and desperation to grow beneath the surface. It’s not about the photo or what it depicts; it’s always about the other person.

Or see Byrne Hobart again:

We’re used to thinking of desire as something that emerges organically: you want something, and you try to get it. Sometimes, it’s easy; sometimes, there’s competition.

To Girard, that’s all wrong: you want something because of competition. Success is just a story you tell yourself about your desire for your rivals to fail.

Stand alone complex

Stand Alone Complex is a handy word in this domain.

A ‘Stand Alone Complex' can be compared to the copycat behavior that often occurs after incidents such as serial murders or terrorist attacks. An incident catches the public’s attention and certain types of people “get on the bandwagon”[…] It is particularly apparent when the incident appears to be the result of well-known political or religious beliefs, but it can also occur in response to intense media attention. For example, a mere fire, no matter the number of deaths, is just a garden variety tragedy. However, if the right kind of people begin to believe it was arson, caused by deliberate action, the threat increases drastically that more arsons will be committed.

What separates the ‘Stand Alone Complex’ from normal copycat behavior is that the originator of the copied action is not even a real person, but merely a rumored figure that commits said action. Even without instruction or leadership a certain type of person will spring into action to imitate the rumored action and move toward the same goal even if only subconsciously.>The result is an epidemic of copied behavior—with no originator. One could say that the Stand Alone Complex is mass hysteria-with purpose.

Directed use of this I have seen referred to as stochastic terrorism, as covered in economics of insurgence.

Hybrid social-pathogenic contagion

TBD. I think Peter Watts wrote a science fiction story about this. More contemporary, the question of what the effect of socially-transmitted beliefs about disease does to socially-transmitted disease. If the anti-vaxxers tend to know each other and meet up, what does this do for broader social immunity?

Hyperselection of transmissible beliefs

Yudkowsky’s memetic collapse post:

I’ve had the sense before that the Internet is turning our society stupider and meaner. My primary hypothesis is “The Internet is selecting harder on a larger population of ideas, and sanity falls off the selective frontier once you select hard enough”.

To review, there’s a general idea that strong (social) selection on a characteristic imperfectly correlated with some other metric of goodness can be bad for that metric, where weak (social) selection on that characteristic was good. If you press scientists a little for publishable work, they might do science that’s of greater interest to others. If you select very harshly on publication records, the academics spend all their time worrying about publishing and real science falls by the wayside. On my feed yesterday was an essay complaining about how the intense competition to get into Harvard is producing a monoculture of students who’ve lined up every single standard accomplishment and how these students don’t know anything else they want to do with their lives. Gentle, soft competition on a few accomplishments might select genuinely stronger students; hypercompetition for the appearance of strength produces weakness, or just emptiness.

A hypothesis I find plausible is that the Internet, and maybe television before it, selected much more harshly from a much wider field of memes; and also allowed tailoring content more narrowly to narrower audiences. The Internet is making it possible for ideas that are optimized to appeal hedonically-virally within a filter bubble to outcompete ideas that have been even slightly optimized for anything else. We’re looking at a collapse of reference to expertise because deferring to expertise costs a couple of hedons compared to being told that all your intuitions are perfectly right, and at the harsh selective frontier there’s no room for that. We’re looking at a collapse of interaction between bubbles because there used to be just a few newspapers serving all the bubbles; and now that the bubbles have separated there’s little incentive to show people how to be fair in their judgment of ideas for other bubbles, it’s not the most appealing Tumblr content. Print magazines in the 1950s were hardly perfect, but they could get away with sometimes presenting complicated issues as complicated, because there weren’t a hundred blogs saying otherwise and stealing their clicks. Or at least, that’s the hypothesis.

This kind of trap sounds like Moloch.

Pluralistic ignorance

A classic stylized phenomenon. See pluralistic ignorance


Acemoglu, Daron, and Asuman Ozdaglar. 2011. Opinion Dynamics and Learning in Social Networks.” Dynamic Games and Applications 1 (1): 3–49.
Ajduković, Dea. 2007. Attitude change and need for cognition in debaters and non-debaters.” Diploma Thesis, Filozofski fakultet Sveučilišta u Zagrebu.
Aral, Sinan. 2012. Social Science: Poked to Vote.” Nature 489 (7415): 212–14.
Aral, Sinan, Lev Muchnik, and Arun Sundararajan. 2009. Distinguishing Influence-Based Contagion from Homophily-Driven Diffusion in Dynamic Networks.” Proceedings of the National Academy of Sciences 106 (51): 21544–49.
Aral, Sinan, and Christos Nicolaides. 2017. Exercise Contagion in a Global Social Network.” Nature Communications 8 (1): 14753.
Aral, Sinan, and Dylan Walker. 2011. Creating Social Contagion Through Viral Product Design: A Randomized Trial of Peer Influence in Networks.” Management Science 57 (9): 1623–39.
———. 2014. Tie Strength, Embeddedness, and Social Influence: A Large-Scale Networked Experiment.” Management Science 60 (6): 1352–70.
Bakshy, Eytan, Dean Eckles, Rong Yan, and Itamar Rosenn. 2012. Social Influence in Social Advertising: Evidence from Field Experiments.” In Proceedings of the 13th ACM Conference on Electronic Commerce, 146–61. EC ’12. New York, NY, USA: Association for Computing Machinery.
Bakshy, Eytan, Itamar Rosenn, Cameron Marlow, and Lada Adamic. 2012. The Role of Social Networks in Information Diffusion.” In Proceedings of the 21st International Conference on World Wide Web, 519–28. WWW ’12. New York, NY, USA: ACM.
Banerjee, Abhijit, Arun G Chandrasekhar, Esther Duflo, and Matthew O Jackson. 2019. Using Gossips to Spread Information: Theory and Evidence from Two Randomized Controlled Trials.” The Review of Economic Studies 86 (6): 2453–90.
Bass, Frank M. 1969. A New Product Growth for Model Consumer Durables.” Management Science 15 (5): 215–27.
———. 2004. Comments on ‘A New Product Growth for Model Consumer Durables The Bass Model’.” Management Science 50 (12_supplement): 1833–40.
Bechtel, Gordon. 2013. Public Opinion about Income Inequality.” Electronic Journal of Applied Statistical Analysis 5 (1).
Bentley, R Alexander, Paul Ormerod, and Michael Batty. 2011. Evolving Social Influence in Large Populations.” Behavioral Ecology and Sociobiology 65 (3): 537–46.
Bentley, R Alexander, Paul Ormerod, and Stephen Shennan. 2011. Population-Level Neutral Model Already Explains Linguistic Patterns.” Proceedings of the Royal Society B: Biological Sciences 278 (1713): 1770–72.
Bessi, Alessandro. 2016. On the Statistical Properties of Viral Misinformation in Online Social Media.” arXiv:1609.09435 [Physics, Stat], September.
Bianchi, Federico, and Flaminio Squazzoni. 2015. Agent-Based Models in Sociology.” WIREs Computational Statistics 7 (4): 284–306.
Bond, Robert M., Christopher J. Fariss, Jason J. Jones, Adam D. I. Kramer, Cameron Marlow, Jaime E. Settle, and James H. Fowler. 2012. A 61-Million-Person Experiment in Social Influence and Political Mobilization.” Nature 489 (7415): 295–98.
Bor, Alexander, and Michael Bang Petersen. 2019. The Psychology of Online Political Hostility: A Comprehensive, Cross-National Test of the Mismatch Hypothesis.” American Political Science Review, December, 1–18.
Bradshaw, S., and P. Howard. 2017. Troops, Trolls and Troublemakers: A Global Inventory of Organized Social Media Manipulation 2017.12.
Carson, Andrea, Aaron J. Martin, and Shaun Ratcliff. 2019. Negative Campaigning, Issue Salience and Vote Choice: Assessing the Effects of the Australian Labor Party’s 2016 ‘Mediscare’ Campaign.” Journal of Elections, Public Opinion and Parties 0 (0): 1–22.
Castellano, Claudio, Santo Fortunato, and Vittorio Loreto. 2009. Statistical Physics of Social Dynamics.” Reviews of Modern Physics 81 (2): 591–646.
Cattani, Gino, and Simone Ferriani. 2008. A Core/Periphery Perspective on Individual Creative Performance: Social Networks and Cinematic Achievements in the Hollywood Film Industry.” Organization Science 19 (6): 824–44.
Centola, Damon. 2018. How Behavior Spreads: The Science of Complex Contagions. 1st edition. Princeton University Press.
Centola, D, and Michael W Macy. 2007. “Complex Contagions and the Weakness of Long Ties.” American Journal of Sociology 113 (3): 702.
Christakis, Nicholas A., and James H. Fowler. 2013. Social Contagion Theory: Examining Dynamic Social Networks and Human Behavior.” Statistics in Medicine 32 (4): 556–77.
Coscia, Michele. 2017. Popularity Spikes Hurt Future Chances for Viral Propagation of Protomemes.” Communications of the ACM 61 (1): 70–77.
DellaPosta, Daniel. 2020. Pluralistic Collapse: The ‘Oil Spill’ Model of Mass Opinion Polarization.” American Sociological Review 85 (3): 507–36.
DellaPosta, Daniel, Yongren Shi, and Michael Macy. 2015. Why Do Liberals Drink Lattes? American Journal of Sociology 120 (5): 1473–1511.
Deschâtres, Fabrice, and Didier Sornette. 2005. Dynamics of Book Sales: Endogenous Versus Exogenous Shocks in Complex Networks.” Physical Review E 72 (1): 016112.
Dodds, Peter Sheridan, and Duncan J. Watts. 2005. A Generalized Model of Social and Biological Contagion.” Journal of Theoretical Biology 232 (4): 587–604.
Elwert, Felix, and Christopher Winship. 2014. Endogenous Selection Bias: The Problem of Conditioning on a Collider Variable.” Annual Review of Sociology 40 (1): 31–53.
Facciani, Matthew, and Matthew E. Brashears. 2019. Sacred Alters: The Effects of Ego Network Structure on Religious and Political Beliefs: Socius, September.
Farrell, Henry. 2012. The Consequences of the Internet for Politics.” Annual Review of Political Science 15 (1): 35–52.
Fernández, Miriam, Alejandro Bellogín, and Iván Cantador. 2021. Analysing the Effect of Recommendation Algorithms on the Amplification of Misinformation.” arXiv:2103.14748 [Cs], March.
Flache, Andreas, Michael Mäs, Thomas Feliciani, Edmund Chattoe-Brown, Guillaume Deffuant, Sylvie Huet, and Jan Lorenz. 2017. “Models of Social Influence: Towards the Next Frontiers.” Journal of Artificial Societies and Social Simulation 20 (4): 2.
Gilens, Martin. 2005. Inequality and Democratic Responsiveness.” Public Opinion Quarterly 69 (5): 778–96.
Goel, Sharad, Ashton Anderson, Jake Hofman, and Duncan J. Watts. 2015. The Structural Virality of Online Diffusion.” Management Science, July, 150722112809007.
Goel, Sharad, Jake M. Hofman, Sébastien Lahaie, David M. Pennock, and Duncan J. Watts. 2010. Predicting Consumer Behavior with Web Search.” Proceedings of the National Academy of Sciences 107 (41): 17486–90.
Goel, Sharad, Winter Mason, and Duncan J. Watts. 2010. Real and Perceived Attitude Agreement in Social Networks.” Journal of Personality and Social Psychology 99 (4): 611–21.
Goel, Sharad, Duncan J. Watts, and Daniel G. Goldstein. 2012. The Structure of Online Diffusion Networks.” In Proceedings of the 13th ACM Conference on Electronic Commerce - EC ’12, 623. Valencia, Spain: ACM Press.
Golub, Benjamin, and Matthew O. Jackson. 2010. Naïve Learning in Social Networks and the Wisdom of Crowds.” American Economic Journal: Microeconomics 2 (1): 112–49.
———. 2011. Network Structure and the Speed of Learning: Measuring Homophily Based on Its Consequences.” SSRN Scholarly Paper ID 1784542. Rochester, NY: Social Science Research Network.
———. 2012. How Homophily Affects the Speed of Learning and Best-Response Dynamics.” The Quarterly Journal of Economics 127 (3): 1287–1338.
Gould, Eric D., and Esteban F. Klor. 2019. Party Hacks and True Believers: The Effect of Party Affiliation on Political Preferences.” Journal of Comparative Economics 47 (3): 504–24.
Greenberg, Steven A. 2009. How Citation Distortions Create Unfounded Authority: Analysis of a Citation Network.” BMJ 339 (July): b2680.
Haghtalab, Nika, Matthew O. Jackson, and Ariel Procaccia. 2020. Belief Polarization in a Complex World: A Learning Theory Perspective.” SSRN Scholarly Paper ID 3606003. Rochester, NY: Social Science Research Network.
Halvorsen, G. S., B. N. Pedersen, and K. Sneppen. 2021. Social Contagion in a World with Asymmetric Influence.” Physical Review E 103 (2): 022303.
Hobart, Byrne, and Tobias Huber. 2019. Manias and Mimesis: Applying René Girard’s Mimetic Theory to Financial Bubbles.” SSRN Scholarly Paper ID 3469465. Rochester, NY: Social Science Research Network.
Houghton, James P. 2021. Interdependent Diffusion: The Social Contagion of Interacting Beliefs.” arXiv:2010.02188 [Physics], January.
Huang, Linan, and Quanyan Zhu. 2021. Combating Informational Denial-of-Service (IDoS) Attacks: Modeling and Mitigation of Attentional Human Vulnerability.” arXiv:2108.08255 [Cs] 13061: 314–33.
Hurd, T. R., and James P. Gleeson. 2012. On Watts’ Cascade Model with Random Link Weights.” arXiv:1211.5708 [Cond-Mat, Physics:physics], November.
Information, Get-Out-the-Vote Messages, and Peer Influence: Causal Effects on Political Behavior in Mozambique.” 2021. Journal of Development Economics 151 (June): 102665.
Iribarren, Jose Luis, and Esteban Moro. 2009. Impact of Human Activity Patterns on the Dynamics of Information Diffusion.” Physical Review Letters 103 (3): 038702.
Jackson, Matthew O., Suraj Malladi, and David McAdams. 2019. Learning Through the Grapevine: The Impact of Noise and the Breadth and Depth of Social Networks.” SSRN Scholarly Paper ID 3269543. Rochester, NY: Social Science Research Network.
Kiley, Kevin, and Stephen Vaisey. 2020. Measuring Stability and Change in Personal Culture Using Panel Data.” American Sociological Review 85 (3): 477–506.
Kim, Yonghwan. 2015. Does Disagreement Mitigate Polarization? How Selective Exposure and Disagreement Affect Political Polarization.” Journalism & Mass Communication Quarterly 92 (4): 915–37.
Lau, Richard R, and David P Redlawsk. 2006. How Voters Decide: Information Processing in Election Campaigns (Cambridge Studies in Public Opinion and Political Psychology). Cambridge University Press.
Lewis, Kevin, Marco Gonzalez, and Jason Kaufman. 2012. Social Selection and Peer Influence in an Online Social Network.” Proceedings of the National Academy of Sciences 109 (1): 68–72.
Lux, Thomas. 1995. Herd Behaviour, Bubbles and Crashes.” The Economic Journal 105.
Lux, Thomas, and Didier Sornette. 2002. On Rational Bubbles and Fat Tails.” Journal of Money, Credit and Banking 34: 589–610.
McElreath, Richard, and Robert Boyd. 2007. Mathematical Models of Social Evolution: A Guide for the Perplexed. University Of Chicago Press.
McElreath, Richard, and Paul E. Smaldino. 2015. Replication, Communication, and the Population Dynamics of Scientific Discovery.” arXiv:1503.02780 [Stat], March.
Meade, N., and Towhidul Islam. 2006. “Modeling and Forecasting the Diffusion of Innovation - A 25 Year Review.” International Journal of Forecasting 22 (January): 529–45.
Mercier, Hugo. 2020. Not Born Yesterday: The Science of Who We Trust and What We Believe. Illustrated edition. Princeton University Press.
Moussaïd, Mehdi, Juliane E. Kämmer, Pantelis P. Analytis, and Hansjörg Neth. 2013. Social Influence and the Collective Dynamics of Opinion Formation.” PLoS ONE 8 (11): e78433.
Müller-Vahl, Kirsten R, Anna Pisarenko, Ewgeni Jakubovski, and Carolin Fremer. 2021. Stop That! It’s Not Tourette’s but a New Type of Mass Sociogenic Illness.” Brain, no. awab316 (August).
Newell, Barry, and Robert Wasson. 2002. “Social System Vs Solar System: Why Policy Makers Need History.” In. Grenoble.
Noelle-Neumann, E. 1991. The Theory of Public Opinion: The Concept of the Spiral of Silence.” Annals of the International Communication Association 14 (1): 256–87.
Noelle-Neumann, Elisabeth. 1974. The Spiral of Silence A Theory of Public Opinion.” Journal of Communication 24 (2): 43–51.
Nyhan, Brendan. 2021. Why the Backfire Effect Does Not Explain the Durability of Political Misperceptions.” Proceedings of the National Academy of Sciences 118 (15).
O’connor, Cailin, and James Owen Weatherall. 2019. The Misinformation Age: How False Beliefs Spread. 1 edition. New Haven: Yale University Press.
Odlyzko, Andrew, and Benjamin Tilly. 2005. “A Refutation of Metcalfe’s Law and a Better Estimate for the Value of Networks and Network Interconnections.”
Oliver, J. Eric, and Thomas J. Wood. 2014. Conspiracy Theories and the Paranoid Style(s) of Mass Opinion.” American Journal of Political Science 58 (4): 952–66.
Ormerod, Paul, and R Alexander Bentley. 2010. “Modelling Creative Innovation.” Cultural Science 3 (1).
Ormerod, Paul, and Greg Wiltshire. 2009. ‘Binge’ Drinking in the UK: A Social Network Phenomenon.” Mind & Society 8 (2): 135–52.
Palen, Leysia, and Kenneth M. Anderson. 2016. Crisis Informatics—New Data for Extraordinary Times.” Science 353 (6296): 224–25.
Pastor-Satorras, Romualdo, Claudio Castellano, Piet Van Mieghem, and Alessandro Vespignani. 2015. Epidemic Processes in Complex Networks.” Reviews of Modern Physics 87 (3): 925–79.
Pavlogiannis, Andreas, Josef Tkadlec, Krishnendu Chatterjee, and Martin A. Nowak. 2018. Construction of Arbitrarily Strong Amplifiers of Natural Selection Using Evolutionary Graph Theory.” Communications Biology 1 (1): 1–8.
Pinheiro, Flávio L., Marta D. Santos, Francisco C. Santos, and Jorge M. Pacheco. 2014. Origin of Peer Influence in Social Networks.” Physical Review Letters 112 (9): 098702.
Powell, Derek, and Kara Weisman. 2018. Articulating Lay Theories Through Graphical Models: A Study of Beliefs Surrounding Vaccination Decisions,” February.
Rawlings, Craig M. 2020. Cognitive Authority and the Constraint of Attitude Change in Groups.” American Sociological Review, November, 0003122420967305.
Rehkopf, David H., M. Maria Glymour, and Theresa L. Osypuk. 2016. The Consistency Assumption for Causal Inference in Social Epidemiology: When a Rose Is Not a Rose.” Current Epidemiology Reports 3 (1): 63–71.
Ribeiro, Manoel Horta, Raphael Ottoni, Robert West, Virgílio A. F. Almeida, and Wagner Meira. 2019. Auditing Radicalization Pathways on YouTube.” arXiv:1908.08313 [Cs], August.
Robison, Joshua, Thomas J. Leeper, and James N. Druckman. 2018. Do Disagreeable Political Discussion Networks Undermine Attitude Strength? Political Psychology 39 (2): 479–94.
Romero, Daniel M., Brendan Meeder, and Jon Kleinberg. 2011. Differences in the Mechanics of Information Diffusion Across Topics: Idioms, Political Hashtags, and Complex Contagion on Twitter.” In Proceedings of the 20th International Conference on World Wide Web, 695–704. WWW ’11. New York, NY, USA: Association for Computing Machinery.
Rossman, Gabriel. 2012. Climbing the Charts: What Radio Airplay Tells Us about the Diffusion of Innovation. Princeton: Princeton University Press.
Rossman, Gabriel, and Jacob C. Fisher. 2021. Network Hubs Cease to Be Influential in the Presence of Low Levels of Advertising.” Proceedings of the National Academy of Sciences 118 (7).
Röttger, Paul, and Balazs Vedres. 2020. “The Information Environment and Its Effects on Individuals and Groups,” 60.
Schich, Maximilian. 2019. Cultural Analysis Situs.”
Serrà, Joan, Álvaro Corral, Marián Boguñá, Martín Haro, and Josep Ll Arcos. 2012. Measuring the Evolution of Contemporary Western Popular Music.” Scientific Reports 2 (July).
Sharma, Amit, Jake M. Hofman, and Duncan J. Watts. 2015. Estimating the Causal Impact of Recommendation Systems from Observational Data.” Proceedings of the Sixteenth ACM Conference on Economics and Computation - EC ’15, 453–70.
Sinha, Rajiv K., and Murali Chandrashekaran. 1992. A Split Hazard Model for Analyzing the Diffusion of Innovations.” Journal of Marketing Research 29 (1): 116–27.
Squazzoni, Flaminio, Wander Jager, and Bruce Edmonds. 2014. Social Simulation in the Social Sciences: A Brief Overview.” Social Science Computer Review 32 (3): 279–94.
Sunstein, Cass R., and Reid Hastie. 2014. Wiser: Getting Beyond Groupthink to Make Groups Smarter. Boston, Massachusetts: Harvard Business Review Press.
Tan, Chenhao, Vlad Niculae, Cristian Danescu-Niculescu-Mizil, and Lillian Lee. 2016. Winning Arguments: Interaction Dynamics and Persuasion Strategies in Good-Faith Online Discussions.” In Proceedings of the 25th International Conference on World Wide Web, 613–24. WWW ’16. Republic and Canton of Geneva, Switzerland: International World Wide Web Conferences Steering Committee.
Thomas, A. C. 2013. The Social Contagion Hypothesis: Comment on ‘Social Contagion Theory: Examining Dynamic Social Networks and Human Behavior’.” Statistics in Medicine 32 (4): 581–90.
Tokita, Christopher K., Andrew M. Guess, and Corina E. Tarnita. 2021. Polarized Information Ecosystems Can Reorganize Social Networks via Information Cascades.” Proceedings of the National Academy of Sciences 118 (50).
Törnberg, Petter. 2018. Echo Chambers and Viral Misinformation: Modeling Fake News as Complex Contagion.” PLOS ONE 13 (9): e0203958.
Tufekci, Zeynep. 2014. Engineering the Public: Big Data, Surveillance and Computational Politics.” First Monday, July.
Ugander, Johan, Lars Backstrom, Cameron Marlow, and Jon Kleinberg. 2012. Structural Diversity in Social Contagion.” Proceedings of the National Academy of Sciences 109 (16): 5962–66.
Ureña, Raquel, Gang Kou, Yucheng Dong, Francisco Chiclana, and Enrique Herrera-Viedma. 2019. A Review on Trust Propagation and Opinion Dynamics in Social Networks and Group Decision Making Frameworks.” Information Sciences 478 (April): 461–75.
Wang, Wei, David Rothschild, Sharad Goel, and Andrew Gelman. 2015. Forecasting Elections with Non-Representative Polls.” International Journal of Forecasting 31 (3): 980–91.
Watson, Richard A, C L Buckley, and Rob Mills. 2011. Optimization in ‘Self-Modeling’ Complex Adaptive Systems.” Complexity 16 (5): 17–26.
Watts, D. J. 2007. Is Justin Timberlake a Product of Cumulative Advantage?\(}\).” New York Times, April 15, 2007.
Watts, Duncan J. 2011. Everything Is Obvious: Once You Know the Answer. 1st ed. New York: Crown Business.
———. 2014. Common Sense and Sociological Explanations.” American Journal of Sociology 120 (2): 313–51.
———. 2018. Small Worlds: The Dynamics of Networks between Order and Randomness. Princeton University Press.
Watts, Duncan J., and Peter Sheridan Dodds. 2007. Influentials, Networks, and Public Opinion Formation.” Journal of Consumer Research 34 (4): 441–58.
Watts, Duncan J., David M. Rothschild, and Markus Mobius. 2021. Measuring the News and Its Impact on Democracy.” Proceedings of the National Academy of Sciences 118 (15).
Watts, Duncan J, and Steven H Strogatz. 1998. Collective Dynamics of ‘Small-World’ Networks.” Nature 393 (6684): 440–42.
Weng, Lilian, Filippo Menczer, and Yong-Yeol Ahn. 2013. Virality Prediction and Community Structure in Social Networks.” Scientific Reports 3 (1): 2522.
Whittaker, Joe, Seán Looney, Alastair Reed, and Fabio Votta. 2021. Recommender Systems and the Amplification of Extremist Content.” Internet Policy Review 10 (2).
Wojcik, Stefan. 2018. Do Birds of a Feather Vote Together, or Is It Peer Influence? Political Research Quarterly 71 (1): 75–87.
Yarkoni, Tal. 2019. The Generalizability Crisis.” Preprint. PsyArXiv.
Zeng, Xiaohua, and Liyuan Wei. 2012. Social Ties and User Content Generation: Evidence from Flickr.” Information Systems Research 24 (1): 71–87.
Zhao, Zhe, Lichan Hong, Li Wei, Jilin Chen, Aniruddh Nath, Shawn Andrews, Aditee Kumthekar, Maheswaran Sathiamoorthy, Xinyang Yi, and Ed Chi. 2019. Recommending What Video to Watch Next: A Multitask Ranking System.” In Proceedings of the 13th ACM Conference on Recommender Systems, 43–51. RecSys ’19. New York, NY, USA: Association for Computing Machinery.
Zhuravskaya, Ekaterina, Maria Petrova, and Ruben Enikolopov. 2020. Political Effects of the Internet and Social Media.” Annual Review of Economics 12 (1): 415–38.

  1. And in any case, scientists at their most precise and factual still uses emotion and metaphor to do communicative work. That is, I suspect, practically unavoidable, or worse, avoiding it would be inefficient.↩︎

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