Weisberg and Muldoon’s epistemic landscape model (ELM) has been one of the most significant contributions to the use of agent-based models in philosophy. The model provides an innovative approach to establishing the optimal distribution of cognitive labor in scientific communities, using an epistemic landscape. In the paper, we provide a critical examination of ELM. First, we show that the computing mechanism for ELM is correct insofar as we are able to replicate the results using another programming language. Second, we show (...) that small changes in the rules that determine the behavior of individual agents can lead to important changes in simulation results. Accordingly, we claim that ELM results are robust with respect to the computing mechanism, but not necessarily across parameter space. We conclude by reflecting on the possible lessons to be gained from ELM as a class of simulations or cluster of models. (shrink)

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. (shrink)