The project aims to bring together researchers who employ a variety of different mathematical and scientific techniques in epistemology. These researchers are motivated by the same questions about belief and knowledge that drive traditional or mainstream epistemologists. However, they distinguish themselves by the use of three research methods:
(i) constructing formal models for deriving analytical results;
(ii) testing such models empirically by means of psychological experiments;
(iii) using computer simulations for studying the behaviour of (groups of) epistemic agents.
These methods clarify the nature of epistemic problems, allow us to formulate those problems in a way that makes them amenable to empirical testing, and they lead to precise and often definite results. For instance: experiments in epistemology may refute an intuitively plausible philosophical hypothesis; mathematical proofs or simulations may demonstrate surprising properties of a familiar system; formulating a philosophical argument using formal methods may reveal hidden assumptions in the argument. Thus, the scientific approach greatly enhances epistemological research.
By making practically applicable contributions to our understanding of belief and knowledge, and by the manifold links to other academic disciplines, the scientific approach greatly increases the general relevance of epistemological research. World-class research in these topics requires the technical and mathematical expertise of a variety of researchers, as well as sustained collective thinking about the problems, which can be achieved only by a network of this sort. For these reasons, the proposed network is a timely, innovative, and valuable endeavour that offers unparalled opportunities for the sort of collaborative effort required.
A central concept of epistemology is rational belief, which can be discussed and studied from an individual, social, or institutional perspective:
The network tackles two central, but badly understood questions in individual epistemology: First, how should we revise a partial belief when learning conditional information? Due to considerable disagreement about how conditionals should be represented and processed, there is a lot to gain from the introduction of formal and empirical methods (Douven and Romeijn 2011; Pfeifer 2012; Hartmann and Rafiee Rad 2013).
Second, how should we reason explanatorily? When is an Inference to the Best Explanation (IBE) valid? Network members have recently developed probabilistic models for answering these questions (Schupbach and Sprenger 2011; Crupi and Tentori 2012). We will refine the models by means of (i) relating them to probabilistic models of causal explanation, confirmation and coherence (Olsson 2005; Woodward 2003); (ii) empirically testing their predictions; (iii) applying the accuracy-based approach to belief revision pursued by Leitgeb and Pettigrew 2010. For designing suitable experiments, we rely on the network members’ expertise in bridging epistemological and psychological research (e.g., Crupi, Tentori and Gonzalez 2007; Douven and Verbrugge 2010).
The question of how we should react to disagreement with our peers is not only a central question of traditional epistemology: it can be naturally addressed by the scientific approach. Our strategy is twofold: First, we analyze the rationality of disagreement resolution procedures by using the Laputa environment developed at Lund for simulating social belief dynamics (Olsson 2011, 2013; Vallinder and Olsson 2012). Second, we apply these results to models of reaching a consensus that the network members developed over the last years (e.g., Douven and Riegler 2010; Martini, Sprenger and Colyvan 2013; Zollman 2012). We will then better understand the conditions where consensual disagreement resolution is epistemically successful.
The division of scientific labor has become a central topic of applied (social) epistemology. How does the assignment of individual epistemic tasks affect the progress of the group knowledge? Thanks to the publications of several network members, agent-based simulations have emerged as a forceful tool for studying those questions (de Langhe 2010, 2013; Mayo-Wilson 2013; Zollman 2007; Zollman, Mayo-Wilson and Danks 2012). However, the diversity of these models prompts urgent methodological questions: Should the models be driven by competition or cooperation? Are rigorous mathematical results more valuable than simulation outcomes?
By answering these questions, we will move toward the next generation of socio-epistemic simulation models.
As the last fifty years of research in these areas has shown, each of these three broad topics admits of detailed and illuminating study using formal and scientific techniques. However, as these techniques become more sophisticated so it is more difficult for researchers unfamiliar with them to enter that research area. One of the aims of the network is to facilitate interaction between researchers who are knowledgeable about different formal techniques so that each can enter the others’ research area to share insights from their own.