Epistemic diversity and industrial selection bias
Bibliographic Data
| ID | 10814799 |
|---|---|
| Authors | Manuela Fernández-Pinto (0000-0002-2318-1284, Universidad de los Andes, corresponding author), Daniel Fernández Pinto (corresponding author) |
| Year | 2023 |
| Volume | 201 |
| Issue | 5 |
| Publication date | 2023-05-15 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Synthese (JOURNAL) |
| Journal identifiers | ISSN: 0039-7857 • E-ISSN: 1573-0964 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s11229-023-04158-7 |
| OpenAlex | W4382809478 |
| Language | EN |
| Citations received | 4 |
| References cited | 27 |
Philosophers of science have argued that epistemic diversity is an asset for the production of scientific knowledge, guarding against the effects of biases, among other advantages. The growing privatization of scientific research, on the contrary, has raised important concerns for philosophers of science, especially with respect to the growing sources of biases in research that it seems to promote. Recently, Holman and Bruner (2017) have shown, using a modified version of Zollman (2010) social network model, that an industrial selection bias can emerge in a scientific community, without corrupting any individual scientist, if the community is epistemically diverse. In this paper, we examine the strength of industrial selection using a reinforcement learning model, which simulates the process of industrial decision-making when allocating funding to scientific projects. Contrary to Holman and Bruner’s model, in which the probability of success of the agents when performing an action is given a priori, in our model the industry learns about the success rate of individual scientists and updates the probability of success on each round. The results of our simulations show that even without previous knowledge of the probability of success of an individual scientist, the industry is still able to disrupt scientific consensus. In fact, the more epistemically diverse the scientific community, the easier it is for the industry to move scientific consensus to the opposite conclusion. Interestingly, our model also shows that having a random funding agent seems to effectively counteract industrial selection bias. Accordingly, we consider the random allocation of funding for research projects as a strategy to counteract industrial selection bias, avoiding commercial exploitation of epistemically diverse communities
Asset (computer security) · Diversity (politics) · Economics · Epistemic community · Epistemology · Management science · Metaphysics · Philosophy of language · Philosophy of science · Political science · Positive economics · Selection (genetic algorithm) · Sociology · Artificial Intelligence · Computer Science · Law · Pharmaceutical Economics and Policy · Pharmaceutical industry and healthcare · Philosophy · scientometrics and bibliometrics research
The Epistemic Benefit of Transient Diversity
Scope and Impact of Financial Conflicts of Interest in Biomedical Research
Pharmaceutical industry sponsorship and research outcome and quality
Learning from Neighbours
The science question in feminism
Golden Holocaust
Social Empiricism
Peer Review or Lottery? A Critical Analysis of Two Different Forms of Decision-making Mechanisms for Allocation of Research Grants
Experimentation by Industrial Selection
Democratizing Strategies for Industry-Funded Medical Research
Why Gender Is a Relevant Factor in the Social Epistemology of Scientific Inquiry
The Fate of Knowledge
Primate Visions
| Unique citing works | 4 |
|---|---|
| Citations per year | 1,33 |
| Citation span | 2023 - 2026 (4) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 3 |