Logic and/of Truthmaking

The purpose of this paper is to explore the question of how truthmaker theorists ought to think about their subject in relation to logic. Regarding logic and truthmaking, I defend the view that considerations drawn from advances in modal logic have little bearing on the legitimacy of truthmaker theory. To do so, I respond to objections Timothy Williamson has lodged against truthmaker theory. As for the logic of truthmaking, I show how the project of understanding the logical features of the truthmaking relation has led to an apparent impasse. I offer a new perspective on the logic of truthmaking that both explains the problem and offers a way out

Truth tracking performance of social networks: how connectivity and clustering can make groups less competent

Our beliefs and opinions are shaped by others, making our social networks crucial in determining what we believe to be true. Sometimes this is for the good because our peers help us form a more accurate opinion. Sometimes it is for the worse because we are led astray. In this context, we address via agent-based computer simulations the extent to which patterns of connectivity within our social networks affect the likelihood that initially undecided agents in a network converge on a true opinion following group deliberation. The model incorporates a fine-grained and realistic representation of belief (opinion) and trust, and it allows agents to consult outside information sources. We study a wide range of network structures and provide a detailed statistical analysis concerning the exact contribution of various network metrics to collective competence. Our results highlight and explain the collective risks involved in an overly networked or partitioned society. Specifically, we find that 96% of the variation in collective competence across networks can be attributed to differences in amount of connectivity (average degree) and clustering, which are negatively correlated with collective competence. A study of bandwagon or “group think” effects indicates that both connectivity and clustering increase the probability that the network, wholly or partly, locks into a false opinion. Our work is interestingly related to Gerhard Schurz’s work on meta-induction and can be seen as broadly addressing a practical limitation of his approach

Publish Late, Publish Rarely! : Network Density and Group Performance in Scientific Communication

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