Scientific institutions and mechanisms

Descriptive and normative

August 24, 2021 — December 8, 2023

adaptive
bounded compute
collective knowledge
distributed
economics
how do science
incentive mechanisms
institutions
learning
mind
networks
sociology
standards
swarm
Figure 1

On heuristic mechanism and institutional design for communities of scientific practice for the common property resource that is human knowledge. Sociology of science, in other words. 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.

A place to file questions like this, in other words (O’Connor and Wu 2021):

Diversity of practice is widely recognized 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 modeling 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 Mechanism design for science

  • Accelerating science through evolvable institutions

    The biggest red flag is with our institutions of science. Institutions affect all the other factors, especially the management of money and talent. And today, many in the metascience community have concerns about our institutions. Common criticisms include:

    • Speed. It can easily take 12–18 months to get a grant (if you’re lucky)
    • Overhead. Researchers typically spend 30–50% of their time on grants
    • Patience. Researchers feel they need to show results regularly and can’t pursue a path that might take many years to get to an outcome
    • Risk tolerance. Grant funding favors conservative, incremental proposals rather than bold, “high-risk, high-reward” programs (despite efforts to the contrary)
    • Consensus. A field can converge on a hypothesis and prune alternate branches of study too quickly
    • Researcher age. The trend over time is for grant money to go to older, more established researchers
    • Freedom. Scientists lack the freedom to direct their research fully autonomously; grant funding has too many strings attached

    Now, as a former tech founder, I can’t help but notice that most of these problems seem much alleviated in the world of for-profit VC funding. Raising VC money is relatively quick (typically a round comes together in a few months rather than a year or more). As a founder/CEO, I spent about 10–15% of my time fundraising, not 30–50%. VCs make bold bets, actively seek contrarian positions, and back young upstarts. They mostly give founders autonomy, perhaps taking a board seat for governance, and only firing the CEO for very bad performance. (The only concern listed above that startup founders might also complain about is patience: if your money runs out, you’d better have progress to show for it, or you’re going to have a bad time raising the next round.)

    I don’t think the VC world does better on these points because VCs are smarter, wiser, or better people than science funders—they’re not. Rather, VCs:

    • Compete for deals (and really don’t want to miss good deals)
    • Succeed or fail in the long run based on the performance of their portfolio
    • See those outcomes within a matter of ~5–10 years

    In short, VCs are subject to evolutionary pressure. They can’t get stuck in obviously bad equilibria because if they do they will get out-competed and lose market power.

  • A Guide to DeSci, the Latest Web3 Movement

2 Incoming

Figure 3

Smaldino and O’Connor (2020):

Why do bad methods persist in some academic disciplines, even when they have been clearly rejected in others? What factors allow good methodological advances to spread across disciplines? In this paper, we investigate some key features determining the success and failure of methodological spread between the sciences. We introduce a formal model that considers factors like methodological competence and reviewer bias towards one’s own methods. We show how self-preferential biases can protect poor methodology within scientific communities, and lack of reviewer competence can contribute to failures to adopt better methods. We then use a second model to further argue that input from outside disciplines, especially in the form of peer review and other credit assignment mechanisms, can help break down barriers to methodological improvement. This work therefore presents an underappreciated benefit of interdisciplinarity.

On the origin of psychological research practices, with special regard to self-reported nostril width:

when does a certain practice–e.g., a study design, a way to collect data, a particular statistical approach–”succeed” and start to dominate journals?

It must be capable of surviving a multi-stage selection procedure:

  1. Implementation must be sufficiently affordable so that researchers can actually give it a shot
  2. Once the authors have added it to a manuscript, it must be retained until submission
  3. The resulting manuscript must enter the peer-review process and survive it (without the implementation of the practice getting dropped on the way)
  4. The resulting publication needs to attract enough attention post-publication so that readers will feel inspired to implement it themselves, fueling the eternally turning wheel of Samsara publication-oriented science

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 science community are neat. 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 actual 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 (2023):

Du Bois took quite the opposite route from trying to introduce lotteries, with their embrace of chance randomization. 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

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