Research programs regularly compete to achieve the same goal, such as the discovery of the structure of DNA or the construction of a TEA laser. The more the competing programs share information, the faster the goal is likely to be reached, to society’s benefit. But the “priority rule”-the scientific norm according to which the first program to reach the goal in question must receive all the credit for the achievement-provides a powerful disincentive for programs to share information. How, then, is (...) the clash between social and individual interest resolved in scientific practice? This chapter investigates what Robert Merton called science’s “communist” norm, which mandates universal sharing of knowledge, and uses mathematical models of discovery to argue that a communist regime may be on the whole advantageous and fair to all parties, and so might be implemented by a social contract that all scientists would be willing to sign. (shrink)

Philosophers have recently questioned the methodological status of agent-based modeling. Meanwhile, this methodology has been central to various studies of the COVID-19 pandemic. Few agent-based COVID-19 models are accessible to philosophers for inspection or experimentation. We make available a package for modeling the COVID-19 pandemic and similar pandemics and give an impression of what can be achieved with it. In particular, it is shown that by coupling an agent-based model to a standard optimizer we are able to identify strategies for (...) implementing non-pharmacological interventions that flexibly lower or raise social activity, depending on how the outbreak develops, while balancing various desiderata that cannot be fully satisfied together. The simulation outcomes to be presented testify to the power of agent-based modeling and thereby help to push back against the recent philosophical critique of this methodology. (shrink)

The main issue in the epistemology of peer disagreement is whether known disagreement among those who are in symmetrical epistemic positions undermines the rationality of their maintaining their respective views. Douven and Kelp have argued convincingly that this problem is best understood as being about how to respond to peer disagreement repeatedly over time, and that this diachronic issue can be best approached through computer simulation. However, Douven and Kelp’s favored simulation framework cannot naturally handle Christensen’s famous Mental Math example. (...) As a remedy, I introduce an alternative simulation framework, Laputa, inspired by Alvin Goldman’s seminal work on veritistic social epistemology. I show that Christensen’s conciliatory response, reasonably reconstructed and supplemented, gives rise to an increase in epistemic value only if the peers continue to recheck their mental math; else the peers might as well be steadfast. On a meta-level, the study illustrates the power of Goldman’s approach when combined with simulation techniques for handling the computational issues involved. (shrink)

Social epistemology studies knowledge and justified belief acquisition through organized group cooperation. To do this, the way such group cooperation is structured has to be modeled. The obvious way of modeling a group structure is with a directed graph; unfortunately, most types of social cooperation directed at epistemological aims are variably implementable, including in their structural expression. Furthermore, the frequency with which a practice is implemented in a certain way can vary with topology. This entails that the topology of social (...) practices directed toward epistemological ends has to be modeled by a set of directed graphs, or their equivalent, together with a probability distribution over that set. In theory, this is eminently possible; however, there are considerable practical obstacles to the specification of a practice’s topology in this way. This paper examines these practical difficulties and concludes that todays sampling protocols are either far too slow to handle pratices with 10 or more participants, or else prone to produce misleading evaluations. (shrink)