Manuela Fernández Pinto and Daniel Fernández Pinto offer a compelling examination of the role that funding sources play in shaping scientific consensus, focusing specifically on the influence of private industry. Drawing on the work of Holman and Bruner (2017), the authors use a reinforcement learning model, known as a Q-learning model, to explore industrial selection. The central concept of industrial selection posits that, rather than corrupting individual scientists, private industry can subtly steer scientific outcomes towards their interests by selectively funding research. In the authors’ simulation, three different funding scenarios are considered: research funded solely by industry, research funded solely by a random agent, and research jointly funded by industry and a random agent.
Results from the simulations reinforce the effects of industrial selection observed by Holman and Bruner, showing a divergence from correct scientific hypotheses under sole industry funding. When scientists are funded solely by a random agent, the outcomes are closer to the correct hypothesis. Most notably, when funding is a mix of industry and random allocation, the random element appears to counteract, or at least delay, the bias introduced by industry funding. The authors further observe an unexpected and somewhat paradoxical interaction with methodological diversity, a factor traditionally seen as a strength in scientific communities. Industrial funding effectively exploits this diversity to skew consensus towards industry-friendly outcomes.
The authors then introduce a provocative and potentially contentious suggestion based on their simulations. They propose that a random allocation of funding might be a more effective countermeasure against industrial selection bias than the commonly held belief in meritocratic funding systems, which might inadvertently perpetuate industry bias. This suggestion arises from their observation that a random funding agent in the simulation effectively obstructs industrial selection bias. They also consider the merits and drawbacks of a two-stage random allocation system, wherein only research proposals that pass an initial quality assessment are subject to a subsequent funding lottery.
The study raises compelling philosophical considerations on the influence of funding on the direction and integrity of scientific research. It challenges the common narrative that methodological diversity and meritocracy inherently lead to unbiased, high-quality science, suggesting instead that these can be co-opted by industry to push scientific consensus toward commercially advantageous outcomes. It further incites reflection on the ethical implications of allowing commercial interests to potentially manipulate scientific consensus and the responsibility of society to ensure the pursuit of truth in science. The research also ties into broader discussions on the balance between rational decision-making and randomness, and the potential role of randomness as a mitigating factor in decision-making processes rife with bias or undue influence.
Future research could delve deeper into how a random allocation system might be implemented in practice, particularly regarding the initial quality assessment process. It would also be beneficial to explore how such a system could coexist with traditional funding sources, and what percentage of overall funding would need to be randomly allocated to effectively mitigate industrial selection bias. Additionally, more nuanced simulations could help further untangle the complex relationship between methodological diversity and industrial bias, and identify other possible factors that may be manipulated to sway scientific consensus. Ultimately, this research presents a provocative stepping stone for further exploration into the complex and subtle ways commercial interests may influence scientific research, and potential innovative strategies to counteract such influences.
Abstract
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.
Epistemic diversity and industrial selection bias