Static species distribution models in the marine realm: The case of baleen whales in the Southern Ocean

Aim Information on the spatio-temporal distribution of marine species is essential for developing proactive management strategies. However, sufficient information is seldom available at large spatial scales, particularly in polar areas. The Southern Ocean (SO) represents a critical habitat for various species, particularly migratory baleen whales. Still, the SO’s remoteness and sea ice coverage disallow obtaining sufficient information on baleen whale distribution and niche preference. Here, we used presence-only species distribution models to predict the circumantarctic habitat suitability of baleen whales and identify important predictors affecting their distribution. Location The Southern Ocean (SO). Methods We used Maxent to model habitat suitability for Antarctic minke, Antarctic blue, fin and humpback whales. Our models employ extensive circumantarctic data and carefully prepared predictors describing the SO’s environment and two spatial sampling bias correction options. Species-specific spatial-block cross-validation was used to optimize model complexity and for spatially independent model evaluation. Results Model performance was high on cross-validation, with generally little predicted uncertainty. The most important predictors were derived from sea ice, particularly seasonal mean and variability of sea ice concentration and distance to the sea ice edge. Main conclusions Our models support the usefulness of presence-only models as a cost-effective tool in the marine realm, particularly for studying the migratory whales’ distribution. However, we found discrepancies between our results and (within) results of similar studies, mainly due to using different species data quality and quantity, different study area extent and methodological reasons. We further highlight the limitations of implementing static distribution models in the highly dynamic marine realm. Dynamic models, which relate species information to environmental conditions contemporaneous to species occurrences, can predict near-real-time habitat suitability, necessary for dynamic management. Nevertheless, obtaining sufficient species and environmental predictors at high spatio-temporal resolution, necessary for dynamic models, can be challenging from polar regions

Relation entry, exit and productivity: an overview of recent theoretical and empirical literature

This document provides a review of recent theoretical and empirical literature on the relationship between entry, exit and productivity. Decomposition methods show that entry and exit considerably contribute to productivity growth, but are unable to shed any light on the ultimate sources of productivity growth. However, the theories discussed do provide options for effective policy instruments. We argue that productivity or welfare should be the aim of policy and not the number of entrants, the intensity of competition or the amount of innovation expenditures. Taking a welfare approach, we address market failures with respect to entry. The most eminent market failure is market power of dominant incumbents. Lowering institutional entry barriers economy-wide is a promising policy option for further consideration. Whether such a policy measure actually improves social welfare depends also on the extent of other failures. Therefore, an ex ante cost-benefit analysis needs to precede intervention.

Learning the Designer's Preferences to Drive Evolution

This paper presents the Designer Preference Model, a data-driven solution that pursues to learn from user generated data in a Quality-Diversity Mixed-Initiative Co-Creativity (QD MI-CC) tool, with the aims of modelling the user's design style to better assess the tool's procedurally generated content with respect to that user's preferences. Through this approach, we aim for increasing the user's agency over the generated content in a way that neither stalls the user-tool reciprocal stimuli loop nor fatigues the user with periodical suggestion handpicking. We describe the details of this novel solution, as well as its implementation in the MI-CC tool the Evolutionary Dungeon Designer. We present and discuss our findings out of the initial tests carried out, spotting the open challenges for this combined line of research that integrates MI-CC with Procedural Content Generation through Machine Learning.Comment: 16 pages, Accepted and to appear in proceedings of the 23rd European Conference on the Applications of Evolutionary and bio-inspired Computation, EvoApplications 202

Ecological IVIS design : using EID to develop a novel in-vehicle information system

New in-vehicle information systems (IVIS) are emerging which purport to encourage more environment friendly or ‘green’ driving. Meanwhile, wider concerns about road safety and in-car distractions remain. The ‘Foot-LITE’ project is an effort to balance these issues, aimed at achieving safer and greener driving through real-time driving information, presented via an in-vehicle interface which facilitates the desired behaviours while avoiding negative consequences. One way of achieving this is to use ecological interface design (EID) techniques. This article presents part of the formative human-centred design process for developing the in-car display through a series of rapid prototyping studies comparing EID against conventional interface design principles. We focus primarily on the visual display, although some development of an ecological auditory display is also presented. The results of feedback from potential users as well as subject matter experts are discussed with respect to implications for future interface design in this field

Global Trade Models and Economic Policy Analyses: Relevance, Risks and Repercussions for Africa

Computable general equilibrium (CGE) models are widely used for trade policy analyses and recommendations. Simulation results from these models have also been used as a basis for offering advice to African countries on what positions to take in multilateral trade negotiations. There is however increasing discomfort with the use of these models for policy recommendations, especially in Africa. In this paper we compare the results of several CGE studies that examined the impact of potential Doha Round reforms on Africa and demonstrate that the results differ drastically both in terms of magnitude and direction. Part of the discrepancies in results can be explained by differences in database, model structure, and choice of parameters. Others are, however, difficult to explain because several studies either do not report key assumptions made or do not provide a clear description of how their framework differs from those in the literature. We also show that the modelling approach and the database used in most CGE studies do not take account of key features of African economies that have serious implications for the impact of trade reforms on Africa. Finally, we outline potential consequences of the misuse of CGE models for policy evaluation and suggest pitfalls to avoid if CGE model results are to be taken seriously by policy makers in Africa.Trade Reforms; CGE Models; Doha Round; Africa

Regret in Dynamic Decision Problems

The paper proposes a framework to extend regret theory to dynamic contexts. The key idea is to conceive of a dynamic decision problem with regret as an intra-personal game in which the agent forms conjectures about the behaviour of the various counterfactual selves that he could have been. We derive behavioural implications in situations in which payoffs are correlated across either time or contingencies. In the first case, regret might lead to excess conservatism or a tendency to make up for missed opportunities. In the second case, behaviour is shaped by the agent’s self-conception. We relate our results to empirical evidence

21st century trade agreements: implications for long-run development policy

This repository item contains a single issue of The Pardee Papers, a series papers that began publishing in 2008 by the Boston University Frederick S. Pardee Center for the Study of the Longer-Range Future. The Pardee Papers series features working papers by Pardee Center Fellows and other invited authors. Papers in this series explore current and future challenges by anticipating the pathways to human progress, human development, and human well-being. This series includes papers on a wide range of topics, with a special emphasis on interdisciplinary perspectives and a development orientation.This paper examines the extent to which the emerging world trading regime leaves nations the “policy space” to deploy effective policy for long-run diversification and development and the extent to which there is a convergence of such policy space under global and regional trade regimes. We examine the economic theory of trade and long-run growth and underscore the fact that traditional theories lose luster in the presence of the need for long-run dynamic comparative advantages and when market failures are rife. We then review a “toolbox” of policies that have been deployed by developed and developing countries past and present to kick-start diversity and development with the hope of achieving longrun growth. Next, we examine the extent to which rules under the World Trade Organization (WTO), trade agreements between the European Union (EU) and developing countries, trade agreements between the United States (US) and developing countries, and those among developing countries (South-South, or S-S, agreements) allow for the use of such policies. We demonstrate that there is a great divergence among trade regimes over this question. While S-S agreements provide ample policy space for industrial development, the WTO and EU agreements largely represent the middle of the spectrum in terms of constraining policy space choices. On the far end, opposite S-S agreements, US agreements place considerably more constraints by binding parties both broadly and deeply in their trade commitments. Rachel Denae Thrasher holds a master’s degree in International Relations and a law degree, both from Boston University, and she is a Research Fellow at the Frederick S. Pardee Center for the Study of the Longer-Range Future. Her recent research has focused on policy issues related to regional trade agreements, multilateral environmental agreements (MEAs) and on global forests governance. Kevin P. Gallagher is an Assistant Professor in the Department of International Relations and Research Fellow at the Frederick S. Pardee Center for the Study of the Longer-Range Future, both at Boston University. He is also a fellow at the Global Development and Environment Institute at Tufts University. He has written extensively on trade and global development. Also see related publication The Future of the WTO, by Kevin Gallagher

Estimating Risk Preferences in the Field

We survey the literature on estimating risk preferences using field data. We concentrate our attention on studies in which risk preferences are the focal object and estimating their structure is the core enterprise. We review a number of models of risk preferences—including both expected utility (EU) theory and non-EU models—that have been estimated using field data, and we highlight issues related to identification and estimation of such models using field data. We then survey the literature, giving separate treatment to research that uses individual-level data (e.g., property insurance data) and research that uses aggregate data (e.g., betting market data). We conclude by discussing directions for future research