Distance Decay in International Trade Patterns - a Meta-analysis

Trade costs remain an important barrier to international trade in today’s globalizing economy. Despite the popular discussion on the “death of distance”, distance is still an important source of trade costs and continues to have an irrevocable impact on the patterns of international trade. The literature identifies various factors that can explain the importance of geographical proximity for bilateral trade. First, transport costs and costs of timeliness increase with distance. Moreover, psychic distance increases as well. Because of cultural unfamiliarity and information costs, traders have less knowledge of distant markets. Empirical estimates of the distance effect in trade abound. The evidence indicates that distance still matters for trade. However, differences in estimated effects across the literature make generalizations about the distance effect and its development over time more difficult. This paper performs a meta-analysis of existing empirical studies of bilateral trade, in order to contribute to our understanding of distance decay in trade. Meta-analysis is a statistical analysis of a set of existing empirical results in a specific research area, in order to integrate the findings. It constitutes a quantitative survey of the literature that explicitly addresses the causes of cross-study variation in empirical outcomes. To perform the meta-analysis, a sample of gravity studies was constructed that is as representative as possible. For this purpose, a literature search has been conducted on the Internet, using the Econlit database. Using the search string “trade and/or distance, and gravity, in all fields”, a list of 214 applicable studies has been identified. From this list, 30 studies were randomly selected into the meta-analysis sample. The paper focuses on two key issues. First, it investigates cross-estimate variation in the distance effect according to differences in, e.g., time period concerned, data type used, or empirical specification and estimation method used. Then, we analyse whether the impact of distance has declined over time.

Plasticity of Sorghum Biomass and Inflorescence Traits in Response to Nitrogen Application

Nitrogen is an essential nutrient required for growth and development in plants. Insufficient nitrogen availability can reduce vegetative growth and grain yield. However, nitrogen is a costly input for farmers, is energy intensive to manufacture, and runoff of excess nitrogen fertilizer impacts water quality. Compared to its close relative, maize, sorghum has much greater resilience to nitrogen and water deficit, and heat stress, allowing sorghum to be grown with fewer inputs and on marginal land. Variation in total biomass accumulation and grain yield between sorghum accessions, as well as between nitrogen conditions, can be largely explained by differences in vegetative growth and inflorescence architecture traits. Previous genome-wide association studies (GWAS) in sorghum have identified genetic markers associated with genes known to play roles in controlling growth and development. However, these studies have typically been conducted using field trials with “optimal” nitrogen application conditions. A set of 345 diverse inbred lines from the Sorghum Association Panel (SAP) were grown under both standard nitrogen application (N+) and no nitrogen application (N-) treatments, and a range of biomass and inflorescence-related traits were phenotyped, including plant height, lower and upper stem diameter, rachis length, lower and upper rachis diameter, and primary branch number. Stem volume, an approximation of biomass, was calculated from the directly measured traits. Stem volume was, on average, 10.48% higher for genotypes in nitrogen fertilized blocks, than for genetically identical plants in no nitrogen application blocks. Within individual treatment conditions, between 58.1% and 90.7% of the total variation for the measured and calculated traits could be explained by genetic factors. Genome-wide association studies were conducted to identify genetic markers associated with these traits in order to better understand the genetic factors involved in nitrogen stress response for potential use in breeding improved sorghum varieties. Co-Advisers: Brandi Sigmon and James C. Schanbl

Ordered Preference Elicitation Strategies for Supporting Multi-Objective Decision Making

In multi-objective decision planning and learning, much attention is paid to producing optimal solution sets that contain an optimal policy for every possible user preference profile. We argue that the step that follows, i.e, determining which policy to execute by maximising the user's intrinsic utility function over this (possibly infinite) set, is under-studied. This paper aims to fill this gap. We build on previous work on Gaussian processes and pairwise comparisons for preference modelling, extend it to the multi-objective decision support scenario, and propose new ordered preference elicitation strategies based on ranking and clustering. Our main contribution is an in-depth evaluation of these strategies using computer and human-based experiments. We show that our proposed elicitation strategies outperform the currently used pairwise methods, and found that users prefer ranking most. Our experiments further show that utilising monotonicity information in GPs by using a linear prior mean at the start and virtual comparisons to the nadir and ideal points, increases performance. We demonstrate our decision support framework in a real-world study on traffic regulation, conducted with the city of Amsterdam.Comment: AAMAS 2018, Source code at https://github.com/lmzintgraf/gp_pref_elici

Decomposition of Natural Catastrophe Risks: Insurability Using Parametric CAT Bonds

status: publishe

Lingualyzer: A computational linguistic tool for multilingual and multidimensional text analysis

Most natural language models and tools are restricted to one language, typically English. For researchers in the behavioral sciences investigating languages other than English, and for those researchers who would like to make cross-linguistic comparisons, hardly any computational linguistic tools exist, particularly none for those researchers who lack deep computational linguistic knowledge or programming skills. Yet, for interdisciplinary researchers in a variety of fields, ranging from psycholinguistics, social psychology, cognitive psychology, education, to literary studies, there certainly is a need for such a cross-linguistic tool. In the current paper, we present Lingualyzer (https://lingualyzer.com), an easily accessible tool that analyzes text at three different text levels (sentence, paragraph, document), which includes 351 multidimensional linguistic measures that are available in 41 different languages. This paper gives an overview of Lingualyzer, categorizes its hundreds of measures, demonstrates how it distinguishes itself from other text quantification tools, explains how it can be used, and provides validations. Lingualyzer is freely accessible for scientific purposes using an intuitive and easy-to-use interface

Zipf’s law revisited: Spoken dialog, linguistic units, parameters, and the principle of least effort

The ubiquitous inverse relationship between word frequency and word rank is commonly known as Zipf’s law. The theoretical underpinning of this law states that the inverse relationship yields decreased effort in both the speaker and hearer, the so-called principle of least effort. Most research has focused on showing an inverse relationship only for written monolog, only for frequencies and ranks of one linguistic unit, generally word unigrams, with strong correlations of the power law to the observed frequency distributions, with limited to no attention to psychological mechanisms such as the principle of least effort. The current paper extends the existing findings, by not focusing on written monolog but on a more fundamental form of communication, spoken dialog, by not only investigating word unigrams but also units quantified on syntactic, pragmatic, utterance, and nonverbal communicative levels by showing that the adequacy of Zipf’s formula seems ubiquitous, but the exponent of the power law curve is not, and by placing these findings in the context of Zipf’s principle of least effort through redefining effort in terms of cognitive resources available for communication. Our findings show that Zipf’s law also applies to a more natural form of communication—that of spoken dialog, that it applies to a range of linguistic units beyond word unigrams, that the general good fit of Zipf’s law needs to be revisited in light of the parameters of the formula, and that the principle of least effort is a useful theoretical framework for the findings of Zipf’s law

Surface and Contextual Linguistic Cues in Dialog Act Classification: A Cognitive Science View

What role do linguistic cues on a surface and contextual level have in identifying the intention behind an utterance? Drawing on the wealth of studies and corpora from the computational task of dialog act classification, we studied this question from a cognitive science perspective. We first reviewed the role of linguistic cues in dialog act classification studies that evaluated model performance on three of the most commonly used English dialog act corpora. Findings show that frequency‐based, machine learning, and deep learning methods all yield similar performance. Classification accuracies, moreover, generally do not explain which specific cues yield high performance. Using a cognitive science approach, in two analyses, we systematically investigated the role of cues in the surface structure of the utterance and cues of the surrounding context individually and combined. By comparing the explained variance, rather than the prediction accuracy of these cues in a logistic regression model, we found that (1) while surface and contextual linguistic cues can complement each other, surface linguistic cues form the backbone in human dialog act identification, (2) with word frequency statistics being particularly important for the dialog act, and (3) the similar trends across corpora, despite differences in the type of dialog, corpus setup, and dialog act tagset. The importance of surface linguistic cues in dialog act classification sheds light on how both computers and humans take advantage of these cues in speech act recognition

A realistic, multimodal virtual agent for the healthcare domain

We introduce an interactive embodied conversational agent for deployment in the healthcare sector. The agent is operated by a software architecture that integrates speech recognition, dialog management, and speech synthesis, and is embodied by a virtual human face developed using photogrammetry techniques. These features together allow for real-time, face-to-face interactions with human users. Although the developed software architecture is domain-independent and highly customizable, the virtual agent will initially be applied to healtcare domain. Here we give an overview of the different components of the architecture

Architecture handbook

2002 handbook for the Faculty of Architectur