The interplay between covariation, temporal, and mechanism information in singular causation judgments

Most psychological studies on causal cognition have focused on how people make predictions from causes to effects or how they assess causal strength for general causal relationships (e.g., “smoking causes cancer”). In the past years, there has been a surge of interest in other types of causal judgments, such as diagnostic inferences or causal selection. Our focus here is on how people assess singular causation relations between cause and effect events that occurred at a particular spatiotemporal location (e.g., “Mary’s having taking this pill caused her sickness.”). The analysis of singular causation has received much attention in philosophy, but relatively few psychological studies have investigated how lay people assess these relations. Based on the power PC model of causal attribution proposed by Cheng and Novick (2005), we have developed and tested a new computational model of singular causation judgments integrating covariation, temporal, and mechanism information. We provide an overview of this research and outline important questions for future research.

Problems with Other Interpretations of Locke’s Account of Identity

This chapter contrasts the kind-dependent interpretation with other interpretations that have dominated the secondary literature on Locke’s account of identity and aims to offer further support for why his approach to questions of identity is best interpreted as kind-dependent. It shows that alternative interpretations are often based on metaphysical assumptions that Locke would be reluctant to endorse. The chapter pays particularly close attention to disputes between defenders of coincidence and Relative Identity interpretations of Locke. The disputes are commonly traced back to a disagreement about the question of how many things exist at a particular spatiotemporal location. Rather than siding with one position, the author’s strategy is to identify problems that arise for both types of interpretations, and to show how the kind-dependent interpretation avoids them. Moreover, she argues that other interpretive options such as four-dimensionalism or mode interpretations are also based on questionable metaphysical assumptions.

Patterns, Information, and Causation

This paper articulates an account of causation as a collection of information-theoretic relationships between patterns instantiated in the causal nexus. I draw on Dennett’s account of real patterns to characterize potential causal relata as patterns with specific identification criteria and noise tolerance levels, and actual causal relata as those patterns instantiated at some spatiotemporal location in the rich causal nexus as originally developed by Salmon. I develop a representation framework using phase space to precisely characterize causal relata, including their degree(s) of counterfactual robustness, causal profiles, causal connectivity, and privileged grain size. By doing so, I show how the philosophical notion of causation can be rendered in a format that is amenable for direct application of mathematical techniques from information theory such that the resulting informational measures are causal informational measures. This account provides a metaphysics of causation that supports interventionist semantics and causal modeling and discovery techniques.

Computer Systems that Learn

Inverse problems are among the most challenging in computational and applied science and have been studied extensively (Bunge, 2006; Hensel, 1991; Kaipio & Somersalo, 2004; Kirsch, 1996; Pizlo, 2001; Sabatier, 2000; Tarantola, 2005; Woodbury, 2002). Although there is no precise definition, the term refers to a wide range of problems that are generally described by saying that their answer is known, but not the question. An obvious example would be “Guessing the intentions of a person from her/his behavior.” In our case: “Guessing a past event from its vestiges.” In archaeology, the main source for inverse problems lies in the fact that archaeologists generally do not know why archaeological observables have the shape, size, texture, composition, and spatiotemporal location they have. Instead, we have sparse and noisy observations or measurements of perceptual properties, and an incomplete knowledge of relational contexts and possible causal processes. From this information, an inverse engineering approach should be used to interpret adequately archaeological observables as the material consequence of some social actions.