A semantical approach to equilibria and rationality

Game theoretic equilibria are mathematical expressions of rationality. Rational agents are used to model not only humans and their software representatives, but also organisms, populations, species and genes, interacting with each other and with the environment. Rational behaviors are achieved not only through conscious reasoning, but also through spontaneous stabilization at equilibrium points. Formal theories of rationality are usually guided by informal intuitions, which are acquired by observing some concrete economic, biological, or network processes. Treating such processes as instances of computation, we reconstruct and refine some basic notions of equilibrium and rationality from the some basic structures of computation. It is, of course, well known that equilibria arise as fixed points; the point is that semantics of computation of fixed points seems to be providing novel methods, algebraic and coalgebraic, for reasoning about them.Comment: 18 pages; Proceedings of CALCO 200

Handlers of Algebraic Effects

Abstract. We present an algebraic treatment of exception handlers and, more generally, introduce handlers for other computational effects representable by an algebraic theory. These include nondeterminism, interactive input/output, concurrency, state, time, and their combinations; in all cases the computation monad is the free-model monad of the theory. Each such handler corresponds to a model of the theory for the effects at hand. The handling construct, which applies a handler to a computation, is based on the one introduced by Benton and Kennedy, and is interpreted using the homomorphism induced by the universal property of the free model. This general construct can be used to describe previously unrelated concepts from both theory and practice.

'The brede of good & strong Horsis': zooarchaeological evidence for size change in horses from early modern London

Almost 200 horse bone measurements from 38 sites excavated across the city of London, dating to the period AD 1220–1900 were analysed. Results identified three main phases of size change: a reduction in size in the mid 14th to 15th century, and size increases in the mid 15th to 16th century and the 17th century. The decline in size testifies to the disruption of horse breeding in the wake of the Black Death, whilst the increases reflect purposeful attempts to increase the size of horses in England through a combination of regulated breeding and the importation of new bloodlines

ARIA 2016: Care pathways implementing emerging technologies for predictive medicine in rhinitis and asthma across the life cycle

The Allergic Rhinitis and its Impact on Asthma (ARIA) initiative commenced during a World Health Organization workshop in 1999. The initial goals were (1) to propose a new allergic rhinitis classification, (2) to promote the concept of multi-morbidity in asthma a

Open data from the third observing run of LIGO, Virgo, KAGRA, and GEO

The global network of gravitational-wave observatories now includes five detectors, namely LIGO Hanford, LIGO Livingston, Virgo, KAGRA, and GEO 600. These detectors collected data during their third observing run, O3, composed of three phases: O3a starting in 2019 April and lasting six months, O3b starting in 2019 November and lasting five months, and O3GK starting in 2020 April and lasting two weeks. In this paper we describe these data and various other science products that can be freely accessed through the Gravitational Wave Open Science Center at https://gwosc.org. The main data set, consisting of the gravitational-wave strain time series that contains the astrophysical signals, is released together with supporting data useful for their analysis and documentation, tutorials, as well as analysis software packages

Spectral discontinuity design: Interrupted time series with spectral mixture kernels

Quasi-experimental designs allow researchers to determine the effect of a treatment, even when randomized controlled trials are infeasible. A prominent example is interrupted time series (ITS) design, in which the effect of an intervention is determined by comparing the extrapolation of a model trained on data acquired up to moment of intervention, with the interpolation by a model trained on data up to the intervention. Typical approaches for ITS use (segmented) linear regression, and consequently ignore many of the spectral features of time series data. In this paper, we propose a Bayesian nonparametric approach to ITS, that uses Gaussian process regression and the spectral mixture kernel. This approach can capture more structure of the time series than traditional methods like linear regression or AR(I)MA models, which improves the extrapolation performance, and hence the accuracy of causal inference. We demonstrate our approach in simulations, and use it to determine the causal effect of Kundalini yoga meditation on heart rate oscillations. We show that our approach is able to detect the causal effect of interventions that alter the spectral features of these time series