“An ethnographic seduction”: how qualitative research and Agent-based models can benefit each other

We provide a general analytical framework for empirically informed agent-based simulations. This methodology provides present-day agent-based models with a sound and proper insight as to the behavior of social agents — an insight that statistical data often fall short of providing at least at a micro level and for hidden and sensitive populations. In the other direction, simulations can provide qualitative researchers in sociology, anthropology and other fields with valuable tools for: (a) testing the consistency and pushing the boundaries, of specific theoretical frameworks; (b) replicating and generalizing results; (c) providing a platform for cross-disciplinary validation of results

Combining quantitative narrative analysis and predictive modeling - an eye tracking study

As a part of a larger interdisciplinary project on Shakespeare sonnets’ reception (Jacobs et al., 2017; Xue et al., 2017), the present study analyzed the eye movement behavior of participants reading three of the 154 sonnets as a function of seven lexical features extracted via Quantitative Narrative Analysis (QNA). Using a machine learning- based predictive modeling approach five ‘surface’ features (word length, orthographic neighborhood density, word frequency, orthographic dissimilarity and sonority score) were detected as important predictors of total reading time and fixation probability in poetry reading. The fact that one phonological feature, i.e., sonority score, also played a role is in line with current theorizing on poetry reading. Our approach opens new ways for future eye movement research on reading poetic texts and other complex literary materials (cf. Jacobs, 2015c)

Climate Science, Development Practice, and Policy Interactions in Dryland Agroecological Systems

The literature on drought, livelihoods, and poverty suggests that dryland residents are especially vulnerable to climate change. However, assessing this vulnerability and sharing lessons between dryland communities on how to reduce vulnerability has proven difficult because of multiple definitions of vulnerability, complexities in quantification, and the temporal and spatial variability inherent in dryland agroecological systems. In this closing editorial, we review how we have addressed these challenges through a series of structured, multiscale, and interdisciplinary vulnerability assessment case studies from drylands in West Africa, southern Africa, Mediterranean Europe, Asia, and Latin America. These case studies adopt a common vulnerability framework but employ different approaches to measuring and assessing vulnerability. By comparing methods and results across these cases, we draw out the following key lessons: (1) Our studies show the utility of using consistent conceptual frameworks for vulnerability assessments even when quite different methodological approaches are taken; (2) Utilizing narratives and scenarios to capture the dynamics of dryland agroecological systems shows that vulnerability to climate change may depend more on access to financial, political, and institutional assets than to exposure to environmental change; (3) Our analysis shows that although the results of quantitative models seem authoritative, they may be treated too literally as predictions of the future by policy makers looking for evidence to support different strategies. In conclusion, we acknowledge there is a healthy tension between bottom-up/ qualitative/place-based approaches and top-down/quantitative/generalizable approaches, and we encourage researchers from different disciplines with different disciplinary languages, to talk, collaborate, and engage effectively with each other and with stakeholders at all levels

Econometrics meets sentiment : an overview of methodology and applications

The advent of massive amounts of textual, audio, and visual data has spurred the development of econometric methodology to transform qualitative sentiment data into quantitative sentiment variables, and to use those variables in an econometric analysis of the relationships between sentiment and other variables. We survey this emerging research field and refer to it as sentometrics, which is a portmanteau of sentiment and econometrics. We provide a synthesis of the relevant methodological approaches, illustrate with empirical results, and discuss useful software

Computational Sociolinguistics: A Survey

Language is a social phenomenon and variation is inherent to its social nature. Recently, there has been a surge of interest within the computational linguistics (CL) community in the social dimension of language. In this article we present a survey of the emerging field of "Computational Sociolinguistics" that reflects this increased interest. We aim to provide a comprehensive overview of CL research on sociolinguistic themes, featuring topics such as the relation between language and social identity, language use in social interaction and multilingual communication. Moreover, we demonstrate the potential for synergy between the research communities involved, by showing how the large-scale data-driven methods that are widely used in CL can complement existing sociolinguistic studies, and how sociolinguistics can inform and challenge the methods and assumptions employed in CL studies. We hope to convey the possible benefits of a closer collaboration between the two communities and conclude with a discussion of open challenges.Comment: To appear in Computational Linguistics. Accepted for publication: 18th February, 201

The lessons from QE and other 'unconventional' monetary policies - evidence from the Bank of England

This paper investigates the effectiveness of the ‘quantitative easing’ policy, as implemented by the Bank of England in March 2009. Similar policies had been previously implemented in Japan, the U.S. and the Eurozone. The effectiveness is measured by the impact of Bank of England policies (including, but not limited to QE) on nominal GDP growth – the declared goal of the policy, according to the Bank of England. Unlike the majority of the literature on the topic, the general-to-specific econometric modeling methodology (a.k.a. the ‘Hendry’ or ‘LSE’ methodology) is employed for this purpose. The empirical analysis indicates that QE as defined and announced in March 2009 had no apparent effect on the UK economy. Meanwhile, it is found that a policy of ‘quantitative easing’ defined in the original sense of the term (Werner, 1994) is supported by empirical evidence: a stable relationship between a lending aggregate (disaggregated M4 lending, i.e. bank credit for GDP transactions) and nominal GDP is found. The findings imply that BoE policy should more directly target the growth of bank credit for GDP-transactions

Automated analysis of feature models: Quo vadis?

Feature models have been used since the 90's to describe software product lines as a way of reusing common parts in a family of software systems. In 2010, a systematic literature review was published summarizing the advances and settling the basis of the area of Automated Analysis of Feature Models (AAFM). From then on, different studies have applied the AAFM in different domains. In this paper, we provide an overview of the evolution of this field since 2010 by performing a systematic mapping study considering 423 primary sources. We found six different variability facets where the AAFM is being applied that define the tendencies: product configuration and derivation; testing and evolution; reverse engineering; multi-model variability-analysis; variability modelling and variability-intensive systems. We also confirmed that there is a lack of industrial evidence in most of the cases. Finally, we present where and when the papers have been published and who are the authors and institutions that are contributing to the field. We observed that the maturity is proven by the increment in the number of journals published along the years as well as the diversity of conferences and workshops where papers are published. We also suggest some synergies with other areas such as cloud or mobile computing among others that can motivate further research in the future.Ministerio de Economía y Competitividad TIN2015-70560-RJunta de Andalucía TIC-186