Actual causation and the art of modeling

We look more carefully at the modeling of causality using structural equations. It is clear that the structural equations can have a major impact on the conclusions we draw about causality. In particular, the choice of variables and their values can also have a significant impact on causality. These choices are, to some extent, subjective. We consider what counts as an appropriate choice. More generally, we consider what makes a model an appropriate model, especially if we want to take defaults into account, as was argued is necessary in recent work.Comment: In Heuristics, Probability and Causality: A Tribute to Judea Pearl (editors, R. Dechter, H. Geffner, and J. Y. Halpern), College Publications, 2010, pp. 383-40

Justification and Explanation in Mathematics and Morality

In his influential book, The Nature of Morality, Gilbert Harman writes: “In explaining the observations that support a physical theory, scientists typically appeal to mathematical principles. On the other hand, one never seems to need to appeal in this way to moral principles.” What is the epistemological relevance of this contrast, if genuine? This chapter argues that ethicists and philosophers of mathematics have misunderstood it. They have confused what the chapter calls the justificatory challenge for realism about an area, D—the challenge to justify our D-beliefs—with the reliability challenge for D-realism—the challenge to explain the reliability of our D-beliefs. Harman’s contrast is relevant to the first, but not, evidently, to the second. One upshot of the discussion is that genealogical debunking arguments are fallacious. Another is that indispensability considerations cannot answer the Benacerraf–Field challenge for mathematical realism

Epistemic Mentalizing and Causal Cognition Across Agents and Objects

This dissertation examines mentalizing abilities, causal reasoning, and the interactions thereof. Minds are so much more than false beliefs, yet much of the existing research on mentalizing has placed a disproportionately large emphasis on this one aspect of mental life. The first aim of this dissertation is to examine whether representing others’ knowledge states relies on more fundamentally basic cognitive processes than representations of their mere beliefs. Using a mixture of behavioral and brain measures across five experiments, I find evidence that we can represent others\u27 knowledge quicker and using fewer neural resources than when representing others’ beliefs. To be considered a representation of knowledge rather than belief, both mentalizer and mentalizee must accept the propositional content being represented as factive (Kiparsky & Kiparsky, 2014; Williamson, 2002). As such, my results suggest that representing the mental states of others may be cognitively easier when the content of which does not need to be decoupled from one’s own existing view of reality. Our perception of other minds can influence how we attribute causality for certain events. The second aim of this dissertation is to explore how perceptions of agency and prescriptive social norms influence our intuitions of how agents and objects cause events in the world. Using physics simulations and 3D anthropomorphic stimuli, the results of three experiments show that agency, itself, does not make agents more causal to an outcome than objects. Instead, causal judgments about agents and objects differ as a function of the counterfactuals they respectively afford. Furthermore, I show that what distinguishes the counterfactuals we use to make causal attributions to agents and objects are the prescriptions we hold for how they should behave. Why do we say a fire occurred because of a lightning strike, rather than the necessary presence of oxygen? The answer involves our normative expectations of the probability of lightning strikes and the relative ubiquity of oxygen (Icard et al., 2017). The third aim of this dissertation explores the gradation of causal judgments across multiple contributing events that each vary in their statistical probability. I contribute to ongoing theoretical debates about how humans select causes in experimental philosophy and cognitive science by introducing a publicly available dataset containing 47,970 causal attribution ratings collected from 1,599 adult participants and structured around four novel configurations of causal relationships. By quantitatively manipulating the influence of normality, I systematically explore the continuous space of a causal event’s probability from unlikely to certain. It is my hope that this benchmark dataset may serve as a growing testbed for diverging theoretical models proposing to characterize how humans answer the question: Why

Historically Informed Prediction That Will Not Lead to Historicism: A Theory of Counterfactual Counstruction.

This paper will argue for the use of history in prediction making, specifically in counterfactual predictions. A theory of constraints will be proposed to confidently choose between more or less plausible counterfactual predictions

The Economic Impact of Continuing Operations of the UConn Health Center (Third Report)

University of Connecticut, Health Center, economic impact

Perpetual requirements engineering

This dissertation attempts to make a contribution within the fields of distributed systems, security, and formal verification. We provide a way to formally assess the impact of a given change in three different contexts. We have developed a logic based on Lewis’s Counterfactual Logic. First we show how our approach is applied to a standard sequential programming setting. Then, we show how a modified version of the logic can be used in the context of reactive systems and sensor networks. Last but not least we show how this logic can be used in the context of security systems. Traditionally, change impact analysis has been viewed as an area in traditional software engineering. Software artifacts (source code, usually) are modified in response to a change in user requirements. Aside from making sure that the changes are inherently correct (testing and verification), programmers (software engineers) need to make sure that the introduced changes are coherent with those parts of the systems that were not affected by the artifact modification. The latter is generally achieved by establishing a dependency relation between software artifacts. In rough lines, the process of change management consists of projecting the transitive closure of the this dependency relation based on the set of artifacts that have actually changed and assessing how the related artifacts changed. The latter description of the traditional change management process generally occurs after the affected artifacts are changed. Undesired secondary effects are usually found during the testing phase after the changes have been incorporated. In cases when there is certain level of criticality, there is always a division between production and development environments. Change management (either automatic, tool driven, or completely manually done) can introduce extraneous defects into any of the changed software life-cycle artifacts. The testing phase tries to eradicate a relatively large portion of the undesired defects introduced by change. However, traditional testing techniques are limited by their coverage strength. Therefore, even when maximum coverage is guaranteed there is always the non-zero probability of having secondary effects prior to a change

The Role of Stories in Civil Jury Judgments

A brief review of psychological theories of juror decision making is followed by an introduction to explanation-based theories of judgment. Prior empirical studies of explanation-based processes in juror decision making are then reviewed. An original empirical study of jurors\u27 judgments concerning liability for punitive damages is presented to illustrate the explanation-based approach to civil decisions