TOWARDS INSTITUTIONAL INFRASTRUCTURES FOR E-SCIENCE: The Scope of the Challenge

The three-fold purpose of this Report to the Joint Information Systems Committee (JISC) of the Research Councils (UK) is to: • articulate the nature and significance of the non-technological issues that will bear on the practical effectiveness of the hardware and software infrastructures that are being created to enable collaborations in e- Science; • characterise succinctly the fundamental sources of the organisational and institutional challenges that need to be addressed in regard to defining terms, rights and responsibilities of the collaborating parties, and to illustrate these by reference to the limited experience gained to date in regard to intellectual property, liability, privacy, and security and competition policy issues affecting scientific research organisations; and • propose approaches for arriving at institutional mechanisms whose establishment would generate workable, specific arrangements facilitating collaboration in e-Science; and, that also might serve to meet similar needs in other spheres such as e- Learning, e-Government, e-Commerce, e-Healthcare. In carrying out these tasks, the report examines developments in enhanced computer-mediated telecommunication networks and digital information technologies, and recent advances in technologies of collaboration. It considers the economic and legal aspects of scientific collaboration, with attention to interactions between formal contracting and 'private ordering' arrangements that rest upon research community norms. It offers definitions of e-Science, virtual laboratories, collaboratories, and develops a taxonomy of collaborative e-Science activities which is implemented to classify British e-Science pilot projects and contrast these with US collaboratory projects funded during the 1990s. The approach to facilitating inter-organizational participation in collaborative projects rests upon the development of a modular structure of contractual clauses that permit flexibility and experience-based learning.

Equity Within and Between Nations

Income inequality, Food Security and Poverty, International Development, Productivity Analysis,

Has Growth and Convergence of Developing Economies Been Derailed?

This article is the transcript of the plenary lecture that Michael Spence delivered at theRimini Conference in Economics and Finance 2014 (RCEF2014) of RCEA. It has beenmoderately edited so as to enable the transfer from the spoken word to the printed page.RCEA owes special thanks to Angelo Melino for organizing and chairing ProfessorSpence lecture

Designing Institutional Infrastructures for e-Science

The opportunity exists today for unprecedented connections between scientists, information, data, computational services, and instruments through the Internet. A new generation of information and communication infrastructures, including advanced Internet computing and Grid technologies, is beginning to enable much greater direct and shared access to more widely distributed computing resources than previously has been possible.3 The term ‘e-Science’ usually is applied in reference to large scale science that, increasingly, is being carried out through distributed global collaborations enabled by the Internet.4 Such collaborative scientific enterprises typically require access to very extensive data collections, very large scale computing resources, and high performance visualisation of research data and analysis of results by the individual users. The potential for these advances in technology to support new levels of collaborative activity in scientific and engineering, and ultimately in other domains, is a major driving force behind the UK’s Core e-Science Programme.

Statistical issues in ecological simulation models

Complex simulation models are being increasingly used in ecological modelling as a way of trying to understand a system by examining the processes that make up that system. Complex simulation models generally model behaviour of a system through a series of rules or algorithms, rather than describing it in a formal mathematical way and this can be a good way of capturing an ecologist's expertise and intuition. When interpreting outputs from such a model, it is important to allow for uncertainty due to parameter values which may not be known precisely and structural or implementation aspects. This thesis develops and applies a number of new statistical methods for handling uncertainty in such models. For stochastic simulation models with intractable likelihoods, parameter estimation can be done using Approximate Bayesian Computation with Markov Chain Monte Carlo (ABC-MCMC). This method does not mix well in the tails of the distribution. In this thesis we develop a version of ABC-MCMC that treats the random inputs as unknown as well as the unknown model parameters and we show empirically that this improves the efficiency of the ABC-MCMC algorithm on a queuing model and an individual-based model (IBM) of the group-living bird, the woodhoopoe. For models that are expensive to run, inference may be challenging even if the likelihood can be evaluated. We consider a deterministic multi-species size-based marine ecosystem model, with unknown initial states and parameters, and carry out Bayesian inference using a combination of MCMC and optimisation algorithms. Stochastic simulation models, especially IBMs, often have model uncertainty that is down to some seemingly arbitrary choices, for example spatial or temporal scales, the timing of different events or the spatial configuration. Ideally the outputs of the model should be insensitive to these choices. We develop methods for variance-based sensitivity analysis for stochastic models, allowing us to assess the sensitivity of the model outputs to stochasticity of the inputs and to partition out the variance between submodels. This enables us to test the arbitrary choices made by the modeller and thus test the robustness of the model. We demonstrate these methods on two IBMs: the woodhoopoe model and a bird breeding synchrony model

Atmospheric oxidation chemistry and ozone production: Results from SHARP 2009 in Houston, Texas

This study considers whether spikes in nitrate in snow sampled at Summit, Greenland, from August 2000 to August 2002 are related to solar proton events. After identifying tropospheric sources of nitrate on the basis of correlations with sulfate, ammonium, sodium, and calcium, we use the three-dimensional global Whole Atmosphere Community Climate Model (WACCM) to examine unaccounted for nitrate spikes. Model calculations confirm that solar proton events significantly impact HOx, NOx, and O3 levels in the mesosphere and stratosphere during the weeks and months following the major 9 November 2000 solar proton event. However, solar proton event (SPE)-enhanced NOy calculated within the atmospheric column is too small to account for the observed nitrate peaks in surface snow. Instead, our WACCM results suggest that nitrate spikes not readily accounted for by measurement correlations are likely of anthropogenic origin. These results, consistent with other recent studies, imply that nitrate spikes in ice cores are not suitable proxies for individual SPEs and motivate the need to identify alternative proxies

Incentivizing Exploration with Heterogeneous Value of Money

Recently, Frazier et al. proposed a natural model for crowdsourced exploration of different a priori unknown options: a principal is interested in the long-term welfare of a population of agents who arrive one by one in a multi-armed bandit setting. However, each agent is myopic, so in order to incentivize him to explore options with better long-term prospects, the principal must offer the agent money. Frazier et al. showed that a simple class of policies called time-expanded are optimal in the worst case, and characterized their budget-reward tradeoff. The previous work assumed that all agents are equally and uniformly susceptible to financial incentives. In reality, agents may have different utility for money. We therefore extend the model of Frazier et al. to allow agents that have heterogeneous and non-linear utilities for money. The principal is informed of the agent's tradeoff via a signal that could be more or less informative. Our main result is to show that a convex program can be used to derive a signal-dependent time-expanded policy which achieves the best possible Lagrangian reward in the worst case. The worst-case guarantee is matched by so-called "Diamonds in the Rough" instances; the proof that the guarantees match is based on showing that two different convex programs have the same optimal solution for these specific instances. These results also extend to the budgeted case as in Frazier et al. We also show that the optimal policy is monotone with respect to information, i.e., the approximation ratio of the optimal policy improves as the signals become more informative.Comment: WINE 201