Economics of intelligent selection of wireless access networks in a market-based framework : a game-theoretic approach

The Digital Marketplace is a market-based framework where network operators offer communications services with competition at the call level. It strives to address a tussle between the actors involved in a heterogeneous wireless access network. However, as with any market-like institution, it is vital to analyze the Digital Marketplace from the strategic perspective to ensure that all shortcomings are removed prior to implementation. In this paper, we analyze the selling mechanism proposed in the Digital Marketplace. The mechanism is based on a procurement first-price sealed-bid auction where the network operators represent the sellers/bidders, and the end-user of a wireless service is the buyer. However, this auction format is somewhat unusual as the winning bid is a composition of both the network operator’s monetary bid and their reputation rating. We create a simple economic model of the auction, and we show that it is mathematically intractable to derive the equilibrium bidding behavior when there are N network operators, and we make only generic assumptions about the structure of the bidding strategies. We then move on to consider a scenario with only two network operators, and assume that network operators use bidding strategies which are linear functions of their costs. This results in the derivation of the equilibrium bidding behavior in that scenario

What do Bayesian methods offer population forecasters?

The Bayesian approach has a number of attractive properties for probabilistic forecasting. In this paper, we apply Bayesian time series models to obtain future population estimates with uncertainty for England and Wales. To account for heterogeneity found in the historical data, we add parameters to represent the stochastic volatility in the error terms. Uncertainty in model choice is incorporated through Bayesian model averaging techniques. The resulting predictive distributions from Bayesian forecasting models have two main advantages over those obtained using traditional stochastic models. Firstly, data and uncertainties in the parameters and model choice are explicitly included using probability distributions. As a result, more realistic probabilistic population forecasts can be obtained. Second, Bayesian models formally allow the incorporation of expert opinion, including uncertainty, into the forecast. Our results are discussed in relation to classical time series methods and existing cohort component projections. This paper demonstrates the flexibility of the Bayesian approach to simple population forecasting and provides insights into further developments of more complicated population models that include, for example, components of demographic change

Integrated Modelling of European Migration: Background, specification and results

The aims of this paper are to present the background and specification of the Integrated Modelling of European Migration (IMEM) model. Currently, international migration data are collected by individual countries with separate collection systems and designs. This creates problems when attempting to understand or predict population movements between countries as the reported data are inconsistent in terms of their availability, definitions and quality. Rather than wait for countries to harmonise their migration data collection and reporting systems, we propose a model to overcome the limitations of the various data sources. In particular, we propose a Bayesian model for harmonising and correcting the inadequacies in the available data and for estimating the completely missing flows. The focus is on estimating recent international migration flows amongst countries in the European Union (EU) and European Free Trade Association (EFTA) from 2002 to 2008, using data collected by Eurostat and other national and international institutions. We also include additional information provided by experts on the effects of undercount, measurement and accuracy. The methodology is integrated and capable of providing a synthetic data base with measures of uncertainty for international migration flows and other model parameters.

Improved Constraints on Dark Matter Annihilations Around Primordial Black Holes

Cosmology may give rise to appreciable populations of both particle dark matter and primordial black holes (PBH) with the combined mass density providing the observationally inferred value $\Omega_{\rm DM}\approx0.26$. However, previous studies have highlighted that scenarios with both particle dark matter and PBH are strongly excluded by $\gamma$-ray limits for particle dark matter with a velocity independent thermal cross section $\langle\sigma v\rangle\sim3\times10^{-26}{\rm cm}^3/{\rm s}$, as is the case for classic WIMP dark matter. Here we extend these existing studies on $s$-wave annihilating particle dark matter to ascertain the limits from diffuse $\gamma$-rays on velocity dependent annihilations which are $p$-wave with $\langle\sigma v \rangle\propto v^2$ or $d$-wave with $\langle\sigma v \rangle\propto v^4$, which we find to be considerably less constraining. Furthermore, we highlight that even if the freeze-out process is $p$-wave it is relatively common for (loop/phase-space) suppressed $s$-wave processes to actually provide the leading contributions to the experimentally constrained $\gamma$-ray flux from the PBH halo. This work also utilyses a refined treatment of the PBH dark matter density profile and outlines an improved application of extra-galactic $\gamma$-ray bounds.Comment: 37 pages, 11 Figure

Energy Efficiency of Quantum Statevector Simulation at Scale

Classical simulations are essential for the development of quantum computing, and their exponential scaling can easily fill any modern supercomputer. In this paper we consider the performance and energy consumption of large Quantum Fourier Transform (QFT) simulations run on ARCHER2, the UK's National Supercomputing Service, with QuEST toolkit. We take into account CPU clock frequency and node memory size, and use cache-blocking to rearrange the circuit, which minimises communications. We find that using 2.00GHz instead of 2.25GHz can save as much as 25% of energy at 5% increase in runtime. Higher node memory also has the potential to be more efficient, and cost the user fewer CUs, but at higher runtime penalty. Finally, we present a cache-blocking QFT circuit, which halves the required communication. All our optimisations combined result in 40% faster simulations and 35% energy savings in 44 qubit simulations on 4,096 ARCHER2 nodes.Comment: 4 pages, 5 figures. Submitted to Sustainable Supercomputing at SC2

Augmenting migration statistics with expert knowledge

International migration statistics vary considerably from one country to another in terms of measurement, quality and coverage. Furthermore, immigration tend to be captured more accurately than emigration. In this paper, we first describe the need to augment reported flows of international migration with knowledge gained from experts on the measurement of migration statistics, obtained from a multi-stage Delphi survey. Second, we present our methodology for translating this information into prior distributions for input into the Integrated Modelling of European Migration (IMEM) model, which is designed to estimate migration flows amongst countries in the European Union (EU) and European Free Trade Association (EFTA), by using recent data collected by Eurostat and other national and international institutions. The IMEM model is capable of providing a synthetic data base with measures of uncertainty for international migration flows and other model parameters.