On the relationship between personal experience, affect and risk perception: The case of climate change.

Examining the conceptual relationship between personal experience, affect, and risk perception is crucial in improving our understanding of how emotional and cognitive process mechanisms shape public perceptions of climate change. This study is the first to investigate the interrelated nature of these variables by contrasting three prominent social-psychological theories. In the first model, affect is viewed as a fast and associative information processing heuristic that guides perceptions of risk. In the second model, affect is seen as flowing from cognitive appraisals (i.e., affect is thought of as a post-cognitive process). Lastly, a third, dual-process model is advanced that integrates aspects from both theoretical perspectives. Four structural equation models were tested on a national sample (N = 808) of British respondents. Results initially provide support for the "cognitive" model, where personal experience with extreme weather is best conceptualized as a predictor of climate change risk perception and, in turn, risk perception a predictor of affect. Yet, closer examination strongly indicates that at the same time, risk perception and affect reciprocally influence each other in a stable feedback system. It is therefore concluded that both theoretical claims are valid and that a dual-process perspective provides a superior fit to the data. Implications for theory and risk communication are discussed. © 2014 The Authors. European Journal of Social Psychology published by John Wiley & Sons, Ltd

Causal Inference and Data-Fusion in Econometrics

Learning about cause and effect is arguably the main goal in applied econometrics. In practice, the validity of these causal inferences is contingent on a number of critical assumptions regarding the type of data that has been collected and the substantive knowledge that is available. For instance, unobserved confounding factors threaten the internal validity of estimates, data availability is often limited to non-random, selection-biased samples, causal effects need to be learned from surrogate experiments with imperfect compliance, and causal knowledge has to be extrapolated across structurally heterogeneous populations. A powerful causal inference framework is required to tackle these challenges, which plague most data analysis to varying degrees. Building on the structural approach to causality introduced by Haavelmo (1943) and the graph-theoretic framework proposed by Pearl (1995), the artificial intelligence (AI) literature has developed a wide array of techniques for causal learning that allow to leverage information from various imperfect, heterogeneous, and biased data sources (Bareinboim and Pearl, 2016). In this paper, we discuss recent advances in this literature that have the potential to contribute to econometric methodology along three dimensions. First, they provide a unified and comprehensive framework for causal inference, in which the aforementioned problems can be addressed in full generality. Second, due to their origin in AI, they come together with sound, efficient, and complete algorithmic criteria for automatization of the corresponding identification task. And third, because of the nonparametric description of structural models that graph-theoretic approaches build on, they combine the strengths of both structural econometrics as well as the potential outcomes framework, and thus offer a perfect middle ground between these two competing literature streams.Comment: Abstract change

Doctor of Philosophy

dissertationTrusted computing base (TCB) of a computer system comprises components that must be trusted in order to support its security policy. Research communities have identified the well-known minimal TCB principle, namely, the TCB of a system should be as small as possible, so that it can be thoroughly examined and verified. This dissertation is an experiment showing how small the TCB for an isolation service is based on software fault isolation (SFI) for small multitasking embedded systems. The TCB achieved by this dissertation includes just the formal definitions of isolation properties, instruction semantics, program logic, and a proof assistant, besides hardware. There is not a compiler, an assembler, a verifier, a rewriter, or an operating system in the TCB. To the best of my knowledge, this is the smallest TCB that has ever been shown for guaranteeing nontrivial properties of real binary programs on real hardware. This is accomplished by combining SFI techniques and high-confidence formal verification. An SFI implementation inserts dynamic checks before dangerous operations, and these checks provide necessary invariants needed by the formal verification to prove theorems about the isolation properties of ARM binary programs. The high-confidence assurance of the formal verification comes from two facts. First, the verification is based on an existing realistic semantics of the ARM ISA that is independently developed by Cambridge researchers. Second, the verification is conducted in a higher-order proof assistant-the HOL theorem prover, which mechanically checks every verification step by rigorous logic. In addition, the entire verification process, including both specification generation and verification, is automatic. To support proof automation, a novel program logic has been designed, and an automatic reasoning framework for verifying shallow safety properties has been developed. The program logic integrates Hoare-style reasoning and Floyd's inductive assertion reasoning together in a small set of definitions, which overcomes shortcomings of Hoare logic and facilitates proof automation. All inference rules of the logic are proven based on the instruction semantics and the logic definitions. The framework leverages abstract interpretation to automatically find function specifications required by the program logic. The results of the abstract interpretation are used to construct the function specifications automatically, and the specifications are proven without human interaction by utilizing intermediate theorems generated during the abstract interpretation. All these work in concert to create the very small TCB

Workshop on Database Programming Languages

These are the revised proceedings of the Workshop on Database Programming Languages held at Roscoff, Finistère, France in September of 1987. The last few years have seen an enormous activity in the development of new programming languages and new programming environments for databases. The purpose of the workshop was to bring together researchers from both databases and programming languages to discuss recent developments in the two areas in the hope of overcoming some of the obstacles that appear to prevent the construction of a uniform database programming environment. The workshop, which follows a previous workshop held in Appin, Scotland in 1985, was extremely successful. The organizers were delighted with both the quality and volume of the submissions for this meeting, and it was regrettable that more papers could not be accepted. Both the stimulating discussions and the excellent food and scenery of the Brittany coast made the meeting thoroughly enjoyable. There were three main foci for this workshop: the type systems suitable for databases (especially object-oriented and complex-object databases,) the representation and manipulation of persistent structures, and extensions to deductive databases that allow for more general and flexible programming. Many of the papers describe recent results, or work in progress, and are indicative of the latest research trends in database programming languages. The organizers are extremely grateful for the financial support given by CRAI (Italy), Altaïr (France) and AT&T (USA). We would also like to acknowledge the organizational help provided by Florence Deshors, Hélène Gans and Pauline Turcaud of Altaïr, and by Karen Carter of the University of Pennsylvania

State-of-the-art on evolution and reactivity

This report starts by, in Chapter 1, outlining aspects of querying and updating resources on the Web and on the Semantic Web, including the development of query and update languages to be carried out within the Rewerse project. From this outline, it becomes clear that several existing research areas and topics are of interest for this work in Rewerse. In the remainder of this report we further present state of the art surveys in a selection of such areas and topics. More precisely: in Chapter 2 we give an overview of logics for reasoning about state change and updates; Chapter 3 is devoted to briefly describing existing update languages for the Web, and also for updating logic programs; in Chapter 4 event-condition-action rules, both in the context of active database systems and in the context of semistructured data, are surveyed; in Chapter 5 we give an overview of some relevant rule-based agents frameworks