Convergence in Finite Cournot Oligopoly with Social and Individual Learning

Convergence to Nash equilibrium in Cournot oligopoly is a problem that recurrently arises as a subject of study in economics. The development of evolutionary game theory has provided an equilibrium concept more directly connected with adjustment dynamics and the evolutionary stability of the equilibria of the Cournot game has been studied by several articles. Several articles show that the Walrasian equilibrium is the stable evolutionary solution of the Cournot game. Vriend (2000) proposes to use genetic algorithm for studying learning dynamics in this game and obtains convergence to Cournot equilibrium with individual learning. We show in this article how social learning gives rise to Walras equilibrium and why, in a general setup, individual learning can effectively yield convergence to Cournot instead of Walras equilibrium. We illustrate these general results by computational experiments.Cournot oligopoly; Learning; Evolution; Selection; Evolutionary stability; Nash equilibrium; Genetic algorithms

Social and Technological Efficiency of Patent Systems

This article develops an evolutionary model of industry dynamics in order to carry out a richer theoretical analysis of the consequences of a stronger patent system. The first results obtained in our article are rather consistent with the anti-patent arguments and they do not favour the case for a stronger patent system: higher social welfare and technical progress are observed in our model in industries with milder patent systems (lower patent height and patent life).Innovation, technical progress, patent system, Intellectual property rights,

Sulfonated sporopollenin as an efficient and recyclable heterogeneous catalyst for dehydration of D-xylose and xylan into furfural

The natural acidity of sporopollenin, the biopolymer coating the outer walls of pollen grains, was enhanced by the sulfonation of its surface. Modified sporopollenin displaying sulfonic acid groups has been prepared, characterized by elemental analysis, SEM, EDX, FTIR and XPS and tested as a heterogeneous catalyst in the dehydration of D-xylose and xylan to produce furfural. The optimal reaction conditions involve 10 wt % of sulfonated sporopollenin in the presence of 1.5 mmol of NaCl in a biphasic water-CPME system. When heated at 190 °C, the reaction affords furfural in a yield of 69% after 40 min under microwave irradiation. The time dependence of the dehydration and influence of temperature, pentose loading and positive effect of chloride ions on the reaction rate are reported. It was found that the catalytic system, recharged with the pentose and solvent, could be recycled ten times without loss of performance. The transformation of xylan into furfural at 190 °C for 50 min gave furfural in a yield of 37%

The Hunterian Museum will continue to display Byrne’s skeleton: poor arguments for a sad decision

Peer reviewe

Bury the ‘Irish Giant’: a rapid response to some positive rapid responses

Doyal, Len and Thomas L Muinzer, ‘Bury the “Irish Giant”: a rapid response to some positive rapid responses’ 1 February 2012, http://www.bmj.com/rapid-response/2012/02/01/re-should-skeleton-%E2%80%9C-irish-giant%E2%80%9D-be-buried-seaPeer reviewe

Bury the ‘Irish Giant’: a rapid response to some critical rapid responses

Peer reviewe

Dissociation between Cervical Mucus and Urinary Hormones during the Postpartum Return of Fertility in Breastfeeding Women

Identifying the return of fertility with cervical mucus observations is challenging during the postpartum period. Use of urinary measurements of estrogen and progesterone can assist in understanding the return to fertility during this period. The purposes of this study were to describe the postpartum return of fertility by an analysis of total estrogen (TE) and pregnanediol glucuronide (PDG) profiles and to correlate these profiles with cervical mucus observations. Twenty-six participants collected urine samples during the postpartum period and recorded mucus scores. TE and PDG hormones were analyzed and compared with mucus scores. During amenorrhea, mucus reflected TE changes in only 35 percent of women; after amenorrhea, typical mucus patterns were seen in 33 percent of cycles. We concluded that postpartum mucus and hormone profiles are significantly dissociated but that monitoring urinary hormones may assist in identifying the return of fertility. We also identified different hormonal patterns in the return to fertility. The postpartum period is a challenging time for identifying the return of fertility. The purposes of this study were to describe the hormonal patterns during the return of fertility and to correlate these patterns with cervical mucus observations. Twenty-six postpartum women collected urine samples and recorded mucus scores. Urinary estrogen and progesterone hormones were analyzed and compared with mucus scores. Before the return of menses, mucus reflected hormonal changes in only 35 percent women and after first menses in 33 percent of cycles. We found that hormone profiles do not correlate well with mucus observations during the postpartum return of fertility

Embedding Population Dynamics Models in Inference

Increasing pressures on the environment are generating an ever-increasing need to manage animal and plant populations sustainably, and to protect and rebuild endangered populations. Effective management requires reliable mathematical models, so that the effects of management action can be predicted, and the uncertainty in these predictions quantified. These models must be able to predict the response of populations to anthropogenic change, while handling the major sources of uncertainty. We describe a simple ``building block'' approach to formulating discrete-time models. We show how to estimate the parameters of such models from time series of data, and how to quantify uncertainty in those estimates and in numbers of individuals of different types in populations, using computer-intensive Bayesian methods. We also discuss advantages and pitfalls of the approach, and give an example using the British grey seal population.Comment: Published at http://dx.doi.org/10.1214/088342306000000673 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Spirometry use: detection of chronic obstructive pulmonary disease in the primary care setting

Thomas A Barnes1, Len Fromer21Department of Cardiopulmonary Sciences, Northeastern University, Boston, MA, USA; 2David Geffen School of Medicine at UCLA, Los Angeles, CA, USAObjective: To describe a practical method for family practitioners to stage chronic obstructive pulmonary disease (COPD) by the use of office spirometry.Methods: This is a review of the lessons learned from evaluations of the use of office spirometry in the primary care setting to identify best practices using the most recent published evaluations of office spirometry and the analysis of preliminary data from a recent spirometry mass screening project. A mass screening study by the American Association for Respiratory Care and the COPD Foundation was used to identify the most effective way for general practitioners to implement office spirometry in order to stage COPD.Results: A simple three-step method is described to identify people with a high pre-test probability in an attempt to detect moderate to severe COPD: COPD questionnaire, measurement of peak expiratory flow, and office spirometry. Clinical practice guidelines exist for office spirometry basics for safety, use of electronic peak flow devices, and portable spirometers.Conclusion: Spirometry can be undertaken in primary care offices with acceptable levels of technical expertise. Using office spirometry, primary care physicians can diagnose the presence and severity of COPD. Spirometry can guide therapies for COPD and predict outcomes when used in general practice.Keywords: chronic obstructive pulmonary disease, spirometry, family practice, primary care physicia

Faster inference from state space models via GPU computing

Funding: C.F.-J. is funded via a doctoral scholarship from the University of St Andrews, School of Mathematics and Statistics.Inexpensive Graphics Processing Units (GPUs) offer the potential to greatly speed up computation by employing their massively parallel architecture to perform arithmetic operations more efficiently. Population dynamics models are important tools in ecology and conservation. Modern Bayesian approaches allow biologically realistic models to be constructed and fitted to multiple data sources in an integrated modelling framework based on a class of statistical models called state space models. However, model fitting is often slow, requiring hours to weeks of computation. We demonstrate the benefits of GPU computing using a model for the population dynamics of British grey seals, fitted with a particle Markov chain Monte Carlo algorithm. Speed-ups of two orders of magnitude were obtained for estimations of the log-likelihood, compared to a traditional ‘CPU-only’ implementation, allowing for an accurate method of inference to be used where this was previously too computationally expensive to be viable. GPU computing has enormous potential, but one barrier to further adoption is a steep learning curve, due to GPUs' unique hardware architecture. We provide a detailed description of hardware and software setup, and our case study provides a template for other similar applications. We also provide a detailed tutorial-style description of GPU hardware architectures, and examples of important GPU-specific programming practices.Publisher PDFPeer reviewe