I compare two prominent approaches to knowledge of metaphysical modality, the more traditional approach via conceiving viz. imagining a scenario and a more recent approach via counterfactual reasoning. In particular, Timothy Williamson has claimed that the proper context for a modal exercise of imagination is a counterfactual supposition. I critically assess this claim, arguing that a purely conceivability/imaginability-based approach has a key advantage compared to a counterfactual-based one. It can take on board Williamson’s insights about the structure of modal imagination (...) while avoiding aspects of counterfactual reasoning which are orthogonal to figuring out metaphysical modality. In assessing whether A is possible, we creatively devise test scenarios, psychologically and metaphysically apt A-scenarios, which manifest the relevant metaphysical requirements and test them for their compatibility with A. In this exercise, imagination is subject to implicit constraints as Williamson has it, but it is not bound to drawing consequences from minimally altering actuality such as to make room for A. (shrink)

This article assesses how people reason with categories whose features are related in causal cycles. Whereas models based on causal graphical models have enjoyed success modeling category-based judgments as well as a number of other cognitive phenomena, CGMs are only able to represent causal structures that are acyclic. A number of new formalisms that allow cycles are introduced and evaluated. Dynamic Bayesian networks represent cycles by unfolding them over time. Chain graphs augment CGMs by allowing the presence of undirected links (...) that model feedback relations between variables. Unfolded chain graphs are chain graphs that unfold over time. An existing model of causal cycles is also evaluated. Four experiments in which subjects reason about categories with cyclically related features provided evidence against DBNs and alpha centrality and for the two types of chain graphs. Chain graphs—a mechanism for representing the equilibrium distribution of a dynamic system—may thus be good candidates for modeling how people reason causally with complex systems. Applications of chain graphs to areas of cognition other than category-based judgments are discussed. (shrink)

What is data? That question is the fundamental investigation of this dissertation. I have developed a methodology from social-scientific processes to explore how different people understand the concept of data, rather than to rely on my own philosophical intuitions or thought experiments about the “nature” of data. The evidence I have gathered as to different individuals' constructions of data can be used to inform further inquiry of data and the design of information systems. My research demonstrates that people have different (...) constructions of data. The methodology of the SDFN, created for this dissertation, has proven able to probe those understandings. The SDFN, loosely based on a DFD and combined with ideas from SNA, provides a way of discovering practical definitions of hard-to-operationalize terms like data. The process of repeatedly categorizing various items as data allows the methodology to explore how participants actually use the term, rather than relying on theoretical dictionary-based definitions. Analysis of the interviews found three different constructions of data: data as communications, a container for meaning; data as subjective observations, sense-impressions filtered by knowledge; and data as objective facts, measurements revealing the relationships of reality. (shrink)

This paper provides a computational characterization of coherence that applies to a wide range of philosophical problems and psychological phenomena. Maximizing coherence is a matter of maximizing satisfaction of a set of positive and negative constraints. After comparing five algorithms for maximizing coherence, we show how our characterization of coherence overcomes traditional philosophical objections about circularity and truth.

Discussion is frequently observed in democratic politics, but change in view is rarely observed. Call this the ‘unchanging minds hypothesis’. I assume that a given belief or desire is not isolated, but, rather, is located in a network structure of attitudes, such that persuasion sufficient to change an attitude in isolation is not sufficient to change the attitude as supported by its network. The network structure of attitudes explains why the unchanging minds hypothesis seems to be true, and why it (...) is false: due to the network, the effects of deliberative persuasion are typically latent, indirect, delayed, or disguised. Finally, I connect up the coherence account of attitudes to several topics in recent political and democratic theory. Key Words: deliberation • democracy • persuasion • change in view • coherence. (shrink)

ABSTRACTHigh-level judgement and decision-making tasks display dynamic bidirectional relationships in which salient cues determine how responses are evaluated by decision-makers, and these responses in turn determine the cues that are considered. In this paper, we propose Kosko's bidirectional associative memory network, a minimal two-layer recurrent neural network, as a mathematically tractable toy model with which the properties of existing bidirectional models, and the behavioural implications of these properties, can be studied. We first derive results regarding the dynamics of the BAM (...) network, and then show how these results can be used to provide an analytically sound explanation for a number of important findings, including coherence shifts in judgement and choice, anchoring effects, and reference point effects. (shrink)

This article interprets emotional change as a transition in a complex dynamical sys- tem. We argue that the appropriate kind of dynamical system is one that extends recent work on how neural networks can perform parallel constraint satisfaction. Parallel processes that integrate both cognitive and affective constraints can give rise to states that we call emotional gestalts, and transitions can be understood as emotional ges- talt shifts. We describe computational models that simulate such phenomena in ways that show how dynamical (...) and gestalt metaphors can be given a concrete realization. (shrink)