Figure 1

On heuristic mechanism and institutional design for communities of scientific practice for the enrichment of the common property resource that is human knowledge. How do diverse underfunded teams manage to advance truth with their weird prestige economy despite the many pitfalls of publication filters and such? What is effective in designing communities, practice and social norms? Both of scientific insiders and outsiders? How much communication is too much? How much iconoclasm is right to defeat groupthink and foster the spread of good ideas? At an individual level, we might wonder about soft methodology. What is the profit model?

A place to file questions like this, in other words ():

Diversity of practice is widely recognised as crucial to scientific progress. If all scientists perform the same tests in their research, they might miss important insights that other tests would yield. If all scientists adhere to the same theories, they might fail to explore other options which, in turn, might be superior. But the mechanisms that lead to this sort of diversity can also generate epistemic harms when scientific communities fail to reach swift consensus on successful theories. In this paper, we draw on extant literature using network models to investigate diversity in science. We evaluate different mechanisms from the modelling literature that can promote transient diversity of practice, keeping in mind ethical and practical constraints posed by real epistemic communities. We ask: what are the best ways to promote the right amount of diversity of practice in such communities?

Figure 2

1 Contrarianism and diversity

Hanania on Tetlock and the Taliban makes a point about the illusory nature of some expertise.

[Tetlock’s results] show that “expertise” as we understand it is largely fake. Should you listen to epidemiologists or economists when it comes to COVID-19? Conventional wisdom says “trust the experts.” The lesson of Tetlock (and the Afghanistan War), is that while you certainly shouldn’t be getting all your information from your uncle’s Facebook Wall, there is no reason to start with a strong prior that people with medical degrees know more than any intelligent person who honestly looks at the available data.

His examples about the science community are neat, although a little cute since Tetlock’s work is more about the limits of expert prediction, that the limits of expert knowledge, so fake is too strong. Then he draws a longer bow and makes some IMO less considered swipes at a weak-manned diversity argument which somewhat ruins the effect for me. Zeynep Tufekci gets at the problem that I think both people who talk about contrarianism and diversity would like to get at: Do the incentives, and especially the incentives in social structures, actually encourage the researchers towards truths, or towards collective fictions?

Sometimes, going against consensus is conflated with contrarianism. Contrarianism is juvenile, and misleads people. It’s not a good habit.

The opposite of contrarianism isn’t accepting elite consensus or being gullible.

Groupthink, especially when big interests are involved, is common. The job is to resist groupthink with facts, logic, work and a sense of duty to the public. History rewards that, not contrarianism.

To get the right lessons from why we fail—be it masks or airborne transmission or failing to regulate tech when we could or Iraq war—it’s key to study how that groupthink occurred. It’s a sociological process: vested interests arguing themselves into positions that benefit them.

Scott Alexander, Contrarians, Crackpots, and Consensus tries to break this idea open with an ontology.

I think a lot of things are getting obscured by the term “scientific establishment” or “scientific consensus”. Imagine a pyramid with the following levels from top to bottom:

FIRST, specialist researchers in a field…

SECOND, non-specialist researchers in a broader field…

THIRD, the organs and administrators of a field who help set guidelines…

FOURTH, science journalism, meaning everyone from the science reporters at the New York Times to the guys writing books with titles like The Antidepressant Wars to random bloggers…

ALSO FOURTH IN A DIFFERENT COLUMN OF THE PYRAMID BECAUSE THIS IS A HYBRID GREEK PYRAMID THAT HAS COLUMNS, “fieldworkers”, aka the professionals we charge with putting the research into practice. … FIFTH, the general public.

A lot of these issues make a lot more sense in terms of different theories going on at the same time on different levels of the pyramid. I get the impression that in the 1990s, the specialist researchers, the non-specialist researchers, and the organs and administrators were all pretty responsible about saying that the serotonin theory was just a theory and only represented one facet of the multifaceted disease of depression. Science journalists and prescribing psychiatrists were less responsible about this, and so the general public may well have ended up with an inaccurate picture.

Bright ():

Du Bois took quite the opposite route from trying to introduce lotteries, with their embrace of chance randomisation. In fact, to a very considerable degree he centrally planned the sort of research his group would carry out so as to form an interlinking whole. Where the status quo system allows for competition between scientists to give funding out piecemeal to whoever seems best at a given moment, Du Bois’ work embodies the attitude that as far as possible our research activities should be coordinated, and not aimed at rewarding individual greatness but rather producing the best overall project. While ideas along these lines have not been totally without support in the history of philosophy of science (see e.g. Neurath 1946, Bernal 1949, Kummerfeld & Zollman 2015), it is safe to say the epistemic merits of this are relatively under-explored. Our brief examination of Du Bois’ plan will thus hopefully form a spur to generate more consideration of this sort of holistic line of action

Figure 3: Star Scientist Funeral

Question: does science advance one funeral at a time?.

There are a lot of models of what scientific consensus might mean. (Kuhnian paradigms, degenerative research programs or whatever Lakatos called them, etc). I don’t have anything practical to add to that right now

Figure 4

Business models for knowledge generation in science. A related problem to public sphere business models.

2 Grant applications in practice

TBD

3 Funding theory and weird ideas

The Dream Machine—by Sarah Constantin introduces Renaissance Philanthropy:

It’s intuitive—but surprisingly rare in the overall world of philanthropy—for a donor to go “that’s cool, I want that to exist, it’s a shame there’s no funding for it yet, let me help you get it off the ground”.

And fundamentally I don’t think it’s because donors aren’t generous, or aren’t interested in innovation, but because creating new projects via donation involves a lot of work that mostly isn’t being done.

There’s:

  • information work

    • donors don’t necessarily know about cool underfunded projects or talented underfunded people
    • donating to one project you’ve heard of is easy; finding the top 100 projects is a substantial research-and-strategy job.

[many more examples]

That’s what RenPhil is trying to fix. Philanthropy is work; we do the work.

4 Incoming

Adam Mastroianni’s funding application diptych:

Grant funding is broken. Here’s how to fix it.

I’ve won grants, I’ve awarded them, and I’ve advised people applying for them. Everything I’ve seen has convinced me this is a terrible way to give away money.

Some of the problems with this system are well known, but we treat them as unavoidable. Other problems are totally overlooked. The result is that virtually all grant money — whether it’s going to fund a lab, sponsor a fellowship, or support a nonprofit — is spent inefficiently and often goes to the wrong people, all while wasting everyone’s time.

Here are five of the worst problems with the way we do grants now. I list them because I believe there’s a better way, and we can only see it when we look squarely at the current system’s shortcomings.

Against All Applications

We apply for everything: scholarships, internships, jobs, promotions, apartments, loans, grants, clubs, conferences, prizes, and even dating apps. A kid today might apply for every school they ever attend, from preschool to graduate school. Even if they get in, they’ll find they’ve merely entered the lobby, and every room beyond requires another application. Want to join the student radio station, take a seminar, or work in a professor’s lab? You’ll have to apply. In undergrad, being unpaid tour guide required an application — and 50% of people were rejected!

[…] The worst part is the subtle, craven way applications irradiate us, mutating our relationships into connections, our passions into activities, and our lives into our track record. Instagram invites you to stand amidst beloved friends in a beautiful place and ask yourself, if we took a selfie right now would it get lots of likes? Applications do the same for your whole life: would this look good on my resume? If you reward people for lives that look good on paper, you’ll get lots of good-looking paper and lots of miserable lives.

Applications might be all right if we knew that they worked really well. But we don’t know that.

5 References

Agassi. 1974. The Logic of Scientific Inquiry.” Synthese.
Alon. 2009. How to Choose a Good Scientific Problem.” Molecular Cell.
Arbesman, and Christakis. 2011. Eurekometrics: Analyzing the Nature of Discovery.” PLoS Comput Biol.
Arvan, Bright, and Heesen. 2022. Jury Theorems for Peer Review.” The British Journal for the Philosophy of Science.
Azoulay, Fons-Rosen, and Zivin. 2015. Does Science Advance One Funeral at a Time? Working Paper 21788.
Bazzoli. 2022. Open Science and Epistemic Pluralism: A Tale of Many Perils and Some Opportunities.” Industrial and Organizational Psychology.
Bhattacharya, and Packalen. 2020. Stagnation and Scientific Incentives.” Working Paper 26752.
Board, and Meyer-ter-Vehn. 2021. Learning Dynamics in Social Networks.” Econometrica.
Bright. 2023. Du Bois on the Centralised Organisation of Science.” In Pluralising Philosophy’s Past.
Campante, Durante, and Tesei. 2022. Media and Social Capital.” Annual Review of Economics.
Chu, and Evans. 2021. Slowed Canonical Progress in Large Fields of Science.” Proceedings of the National Academy of Sciences.
Dang, and Bright. 2021. Scientific Conclusions Need Not Be Accurate, Justified, or Believed by Their Authors.” Synthese.
Devezer, Nardin, Baumgaertner, et al. 2019. Scientific Discovery in a Model-Centric Framework: Reproducibility, Innovation, and Epistemic Diversity.” PLOS ONE.
Dubova, Moskvichev, and Zollman. 2022. Against Theory-Motivated Experimentation in Science.”
Farrow, and Moe. 2019. Rethinking the Role of the Academy: Cognitive Authority in the Age of Post-Truth.” Teaching in Higher Education.
Galesic, Barkoczi, Berdahl, et al. 2022. Beyond Collective Intelligence: Collective Adaptation.”
Gasparyan, Gerasimov, Voronov, et al. 2015. Rewarding Peer Reviewers: Maintaining the Integrity of Science Communication.” Journal of Korean Medical Science.
Greenberg. 2009. How Citation Distortions Create Unfounded Authority: Analysis of a Citation Network.” BMJ.
Healy. 2015. The Performativity of Networks.” European Journal of Sociology.
Heesen, and Bright. 2021. Is Peer Review a Good Idea? The British Journal for the Philosophy of Science.
Hertz, Romand-Monnier, Kyriakopoulou, et al. 2016. Social influence protects collective decision making from equality bias.” Journal of Experimental Psychology. Human Perception and Performance.
Heyard, and Hottenrott. 2021. The Value of Research Funding for Knowledge Creation and Dissemination: A Study of SNSF Research Grants.” Humanities and Social Sciences Communications.
Hirsch. 2013. Social Limits to Growth.” In Social Limits to Growth.
Hoelzemann, and Klein. 2021. Bandits in the Lab.” Quantitative Economics.
Ioannidis, John P. 2005. Why Most Published Research Findings Are False. PLoS Medicine.
Ioannidis, John P. A. 2011. Fund People Not Projects.” Nature.
Jan. 2018. Recognition and Reward System for Peer-Reviewers.” In CEUR Workshop Proceedings.
Kearns, and Gardiner. 2011. The Care and Maintenance of Your Adviser.” Nature.
Lakatos. 1980. The Methodology of Scientific Research Programmes: Volume 1 : Philosophical Papers.
Mark, Marion, and Hoffman. 2010. Natural Selection and Veridical Perceptions.” Journal of Theoretical Biology.
McElreath, and Boyd. 2007. Mathematical Models of Social Evolution: A Guide for the Perplexed.
McElreath, and Smaldino. 2015. Replication, Communication, and the Population Dynamics of Scientific Discovery.” arXiv:1503.02780 [Stat].
Merrifield, and Saari. 2009. Telescope Time Without Tears: A Distributed Approach to Peer Review.” Astronomy & Geophysics.
Merton. 1968. The Matthew Effect in Science.” Science.
———. 1988. The Matthew Effect in Science, II: Cumulative Advantage and the Symbolism of Intellectual Property.” Isis.
Nissen, Magidson, Gross, et al. 2016. Publication Bias and the Canonization of False Facts.” arXiv:1609.00494 [Physics, Stat].
O’Connor. 2017. Evolving to Generalize: Trading Precision for Speed.” British Journal for the Philosophy of Science.
O’Connor, and Bruner. 2019. Dynamics and Diversity in Epistemic Communities.” Erkenntnis.
O’Connor, and Weatherall. 2017. Scientific Polarization.” European Journal for Philosophy of Science.
———. 2019. The Misinformation Age: How False Beliefs Spread.
O’Connor, and Wu. 2021. How Should We Promote Transient Diversity in Science?
Osborne. 2022. Science Education in an Age of Misinformation.”
Rekdal. 2014. Academic Urban Legends.” Social Studies of Science.
Robbins. 1932. An Essay on the Nature and Significance of Economic Science.
Ross, Glennon, Murciano-Goroff, et al. 2022. Women Are Credited Less in Science Than Men.” Nature.
Rubin, and O’Connor. 2018. Discrimination and Collaboration in Science.” Philosophy of Science.
Rzhetsky, Foster, Foster, et al. 2015. Choosing Experiments to Accelerate Collective Discovery.” Proceedings of the National Academy of Sciences.
Smaldino, and O’Connor. 2020. Interdisciplinarity Can Aid the Spread of Better Methods Between Scientific Communities.”
Smith, Sørensen, and Tian. 2021. Informational Herding, Optimal Experimentation, and Contrarianism.” The Review of Economic Studies.
Spranzi. 2004. Galileo and the Mountains of the Moon: Analogical Reasoning, Models and Metaphors in Scientific Discovery.” Journal of Cognition and Culture.
Stove. 1982. Popper and After: Four Modern Irrationalists.
Suppes. 2002. Representation and Invariance of Scientific Structures.
Thagard. 1993. “Societies of Minds: Science as Distributed Computing.” Studies in History and Philosophy of Modern Physics.
———. 1994. “Mind, Society, and the Growth of Knowledge.” Philosophy of Science.
———. 1997. “Collaborative Knowledge.” Noûs.
———. 2005. “How to Be a Successful Scientist.” Scientific and Technological Thinking.
———. 2007. Coherence, Truth, and the Development of Scientific Knowledge.” Philosophy of Science.
Thagard, and Litt. 2008. “Models of Scientific Explanation.” In The Cambridge Handbook of Computational Psychology.
Thagard, and Zhu. 2003. “Acupuncture, Incommensurability, and Conceptual Change.” Intentional Conceptual Change.
The Importance of Frontier Knowledge for the Generation of Ideas.” 2018. CEPR.
Thurner, and Hanel. 2010. “Peer-Review in a World with Rational Scientists: Toward Selection of the Average.”
Valente, and Rogers. 1995. The Origins and Development of the Diffusion of Innovations Paradigm as an Example of Scientific Growth.” Science Communication.
Vazire. 2017. Our Obsession with Eminence Warps Research.” Nature News.
Wagenmakers, Sarafoglou, and Aczel. 2022. One Statistical Analysis Must Not Rule Them All.” Nature.
Weisbuch, Deffuant, Amblard, et al. 2002. Meet, Discuss, and Segregate! Complexity.
Weng, Flammini, Vespignani, et al. 2012. Competition Among Memes in a World with Limited Attention.” Scientific Reports.
Wible. 1998. Economics of Science.
Williams. 2022. The Marketplace of Rationalizations.” Economics & Philosophy.
Woodley, and Pratt. 2020. The CSCCE Community Participation Model – A Framework to Describe Member Engagement and Information Flow in STEM Communities.”
Wu, O’Connor, and Smaldino. 2022. The Cultural Evolution of Science.”
Yarkoni. 2019. The Generalizability Crisis.” Preprint.
Zimmer. 2020. How You Should Read Coronavirus Studies, or Any Science Paper.” The New York Times.