In this paper, we focus on the propensity toward identifying natural kinds with successful scientific categories in contemporary discussions of natural kinds within the philosophy of science. Success in this case is understood as the fulfillment of epistemic interests or goals in a given field of scientific research. The prevailing view is that, in order to have a theory of natural kinds that successfully captures current scientific practice, the relevant epistemic interests are the current interests of scientists working in a (...) particular field. In contrast, we concur with the Franklin-Hall (2015) that a wider range of possible interests needs to be taken into account, in order to avoid making natural kinds too dependent on the current stage of research. But unlike Franklin-Hall who suggests looking for an intersection between the interests and goals of current epistemic agents and our neighboring epistemic agents (the categorical bottleneck view), we advocate the view that all these different interests and goals need to be taken into account, and thus our view takes into account a much wider range of categories. (shrink)

How should a machine intelligence perform unsupervised structure discovery over streams of sensory input? One approach to this problem is to cast it as an apperception task [1]. Here, the task is to construct an explicit interpretable theory that both explains the sensory sequence and also satisfies a set of unity conditions, designed to ensure that the constituents of the theory are connected in a relational structure. However, the original formulation of the apperception task had one fundamental limitation: it assumed (...) the raw sensory input had already been parsed using a set of discrete categories, so that all the system had to do was receive this already-digested symbolic input, and make sense of it. But what if we don't have access to pre-parsed input? What if our sensory sequence is raw unprocessed information? The central contribution of this paper is a neuro-symbolic framework for distilling interpretable theories out of streams of raw, unprocessed sensory experience. First, we extend the definition of the apperception task to include ambiguous (but still symbolic) input: sequences of sets of disjunctions. Next, we use a neural network to map raw sensory input to disjunctive input. Our binary neural network is encoded as a logic program, so the weights of the network and the rules of the theory can be solved jointly as a single SAT problem. This way, we are able to jointly learn how to perceive (mapping raw sensory information to concepts) and apperceive (combining concepts into declarative rules). (shrink)

From the local level to international politics, deliberation helps to increase mutual understanding and trust, in order to arrive at political decisions of high epistemic value and legitimacy. This book gives deliberation a dynamic dimension, analysing how levels of deliberation rise and fall in group discussions, and introducing the concept of 'deliberative transformative moments' and how they can be applied to deeply divided societies, where deliberation is most needed but also most difficult to work. Discussions between ex-guerrillas and ex-paramilitaries in (...) Colombia, Serbs and Bosnjaks in Bosnia-Herzegovina, and police officers and locals in Brazilian favelas are used as case studies, with participants addressing how peace can be attained in their countries. Allowing access to the records and transcripts of the discussions opens an opportunity for practitioners of conflict resolution to apply this research to their work in trouble spots of the world, creating a link between the theory and practice of deliberation. (shrink)

In group discussions of any kind there tends to be an up and down in the level of deliberation. To capture this dynamic we coined the concept of Deliberative Transformative Moments (DTM). In deeply divided societies deliberation is particularly important in order to arrive at peace and stability, but deliberation is also very difficult to be attained. Therefore, we wanted to learn about the conditions that in group discussions across the deep divisions of such societies help deliberation. We organized such (...) group discussions between ex-guerrillas and ex-paramilitaries in Colombia, Serbs and Bosnjaks in Srebrenica, and poor residents and local police officers in the favelas (slums) of Brazil. We could identify factors that help to transform discussions from low to high deliberation and risk transformations in the opposite direction. We could also identify factors that help to keep a discussion at a high level of deliberation, and, in a next step, we could determine to what extent long sequences of deliberation had a positive impact on the outcomes of the discussions. Finally, we show how our research results can have a long term effect if it is used in schools of such deeply divided societies. (shrink)