Time reversal in classical electromagnetism

Richard Feynman has claimed that anti-particles are nothing but particles `propagating backwards in time'; that time reversing a particle state always turns it into the corresponding anti-particle state. According to standard quantum eld theory textbooks this is not so: time reversal does not turn particles into anti-particles. Feynman's view is interesting because, in particular, it suggests a nonstandard, and possibly illuminating, interpretation of the CPT theorem. In this paper, we explore a classical analog of Feynman's view, in the context of the recent debate between David Albert and David Malament over time reversal in classical electromagnetism

Bayesianism, Infinite Decisions, and Binding

We pose and resolve several vexing decision theoretic puzzles. Some are variants of existing puzzles, such as ‘Trumped’ (Arntzenius and McCarthy 1997), ‘Rouble trouble’ (Arntzenius and Barrett 1999), ‘The airtight Dutch book’ (McGee 1999), and ‘The two envelopes puzzle’ (Broome 1995). Others are new. A unified resolution of the puzzles shows that Dutch book arguments have no force in infinite cases. It thereby provides evidence that reasonable utility functions may be unbounded and that reasonable credence functions need not be countably additive. The resolution also shows that when infinitely many decisions are involved, the difference between making the decisions simultaneously and making them sequentially can be the difference between riches and ruin. Finally, the resolution reveals a new way in which the ability to make binding commitments can save perfectly rational agents from sure losses

No Regrets

I argue that standard decision theories, namely causal decision theory and evidential decision theory, are incoherent. I devise a new decision theory, from which standard game theory will follow as a corollary

No Regrets

Edith Piaf is famous for her chanson “Non, je ne regrette rien”. I suggest that rational people should not violate Piaf’s ‘No Regrets’ maxim; a rational person should not be able to fore-see that she will regret her decisions. In section 2 I formulate a principle, Desire Reflection, which is a version of Piaf’s maxim. In section 3 I argue that standard evidential decision theory violates this principle. In section 4 I argue that standard causal decison theory does not violate it. In section 5 I discuss whether a couple of variations on these standard decision theories satisfy Desire Reflection. In section 6 I make a suggestion for how causal decision theorists should pick what they consider to be the relevant causal situations. In section 7 I discuss the ‘If you’re so smart, why ain’t cha rich’ objection to causal decision theory, and dismiss it. In section 8 I discuss a more serious problem for causal decision theory, namely ‘Decision Instability’, and argue that it is a real problem. In section 9 I develop deliberational decision theory in order to escape Decision Instability. In section 10 I discuss the connection between deliberational decision theory and game theory. I end with some conclusions

Self-locating Priors and Cosmological Measures

We develop a Bayesian framework for thinking about the way evidence about the here and now can bear on hypotheses about the qualitative character of the world as a whole, including hypotheses according to which the total population of the world is infinite. We show how this framework makes sense of the practice cosmologists have recently adopted in their reasoning about such hypotheses

Deconstructing Datalog

The deductive query language Datalog has found a wide array of uses, including static analy- sis (Smaragdakis and Bravenboer, 2010), business analytics (Aref et al., 2015), and distributed programming (Alvaro et al., 2010, 2011). Datalog is high-level and declarative, but simple and well-studied enough to admit efficient implementation strategies. For example, Whaley et al. found they could replace a hand-tuned C implementation of context-sensitive pointer analysis with a comparably-performing Datalog program that was 100x smaller (Whaley and Lam, 2004; Whaley et al., 2005). However, Datalog’s semantics are not stable under extensions. For instance, adding arithmetic operations breaks Datalog’s termination guarantee. Despite this, nearly all practical implementations extend Datalog beyond its theoretical core to add niceties such as arithmetic, datatypes, aggregations, and so on. Moreover, pure Datalog cannot abstract over repeated code: one may express a static analysis over a particular program, but to express the same analysis over multiple programs, one must duplicate the analysis code for each program analyzed. This thesis deconstructs Datalog from a categorical and type theoretic perspective to determine what makes it tick. Datalog’s semantic guarantees are provided by brute syntactic restrictions, such as stratification and the absence of function symbols. In place of these, we find compositional semantic properties such as monotonicity, which we capture using types. We show that this permits integrating Datalog’s features with those of typed functional languages, such as algebraic data types and higher order functions. In particular, this thesis makes the following contributions: 1. We define and expound the semantics and metatheory of Datafun, a pure and total higher-order typed functional language capturing the essence of Datalog. Where Data- log has predicates defined by a restricted class of Horn clauses, Datafun has finite sets and set comprehensions; Datalog’s bottom-up recursive queries become iterative fixed points; and Datalog’s stratification condition becomes a matter of tracking monotonicity with types. 2. We show how to generalize seminaïve evaluation to handle higher-order functions. Seminaïve evaluation is a technique from the Datalog literature which improves the performance of Datalog’s most distinctive feature: recursive queries. These are com- puted iteratively, and under a naïve evaluation strategy, each iteration recomputes all previous values. Seminaïve evaluation computes a safe approximation of the difference between iterations. This can asymptotically improve the performance of Datalog queries. Seminaïve evaluation is defined partly as a program transformation and partly as a modified iteration strategy, and takes advantage of the first-order nature of Datalog. We extend this transformation to handle higher-order programs written in Datafun. 3. In the process of generalizing seminaïve evaluation, we uncover a theory of incremental, monotone, higher-order computation, in which values change over time by growing larger, and programs respond incrementally to these increases

Kiri P. D. Longolius`ele, Amstelaedami

http://tartu.ester.ee/record=b1885827~S1*es

3 kirja G. Corte`le, Noviomagi

http://tartu.ester.ee/record=b1885820~S1*es

Time--The Emotional Asymmetry

A person is future-biased when she would rather, other things being equal, that bad things be in the past than in the future, and that good things be the future than in the past. Most of us are, at least to some degree, future-biased. Consider: Your Past or Future Pain You wake up with an aching jaw. What is going on? You remember that you were scheduled to have your wisdom teeth removed, painfully but safely, under a weak local anesthetic, on Thursday. But has that happened yet? In your groggy condition you are not sure. It could be Friday morning. The ache in your jaw would then be an after-effect of the operation. And it could be Thursday morning. The ache in your jaw would then be the distress of your impacted teeth