**Author(s):** White, C. J. M.; Muthukrishna, M. We tested whether sharing socioeconomic status predicts cultural similarity between residents of different nations, such as whether elites share in a “global culture” or whether certain cultural variants are characteristic of low SES. We used the cultural fixation index to examine cultural distance between members of socioeconomic groups living in 88 countries, according to the set of cultural beliefs, values, and behaviors assessed by the World Values Survey (2005 – 2019). Overall, within-country cultural distances between SES-groups (defined by education, income, or self-reported status) were small, and between-country cultural distances were no more similar if people shared the same SES. However, highly educated respondents were consistently more culturally similar to people in the USA. This pattern was not found when SES was defined by income or self-reported status, and education did not predict cultural similarities to other global superpowers (e.g., China or Russia). Results show the global spread of Western values through formal education and imply that cross-cultural studies recruiting college-educated samples are likely to under-represent the world’s cultural variation.

Higher education predicts worldwide cultural similarity to the United States

**Author(s):** Kaše, Vojtěch; Glomb, Tomáš Introduction
In this paper, we offer a diachronic computational text analysis of a large corpus of ancient Greek inscriptions, especially focusing on changes in their religious and moral content. Comparing our results with proxy data on economic development of the Ancient Mediterranean, we suggest that our analysis offers important insights into the cultural evolutionary dynamics of religions in general.

Materials and Methods
The paper is based on PHI, a comprehensive dataset of ancient Greek inscriptions. In total, it contains 218,162 inscriptions and 4,419,743 words. We analyze semantic trends in the inscriptions during the period from the 8th c. BCE to the 4th c. CE using the methods of distributional semantic modeling (word-embeddings).

Results and Discussion
Our results indicate a high extent of fluctuations in the moral load of inscriptions over time. However, our attempt to statistically compare our results with other data concerning the economic and demographic development of the Ancient Mediterranean reveals clear methodological limitations of our approach.

The Cultural Evolution of Graeco-Roman Religions in the Light of Epigraphic Evidence: A computational approach

**Author(s):** Claessens, S; Atkinson, Q. D. INTRODUCTION: Cross-national analyses test hypotheses about drivers of global variation in national outcomes. However, since nations are connected via spatial proximity and cultural ancestry, cross-national analyses often violate assumptions of non-independence, inflating false positive rates. Here, we test whether cross-national research in economics and psychology sufficiently accounts for non-independence. METHODS: We conducted a systematic literature review, a simulation study, and reanalyses of 12 previous studies to determine whether previous cross-national analyses sufficiently account for non-independence. RESULTS: Our literature review revealed that controls for non-independence are rare in economics and psychology. When studies do include controls for non-independence, our simulations suggest that commonly used methods continue to produce false positives. In addition, we show that half of our reanalyses are no longer significant after controlling for non-independence using global proximity matrices. DISCUSSION: Existing cross-national analyses must be treated with caution. We urge social scientists to sufficiently control for non-independence in cross-national research.

The non-independence of nations and why it matters

The purpose of the present study is to investigate how diversification in the tourism market affects destination competitiveness across member-states of the Eurozone economic space. We conceptualize destination competitiveness in terms of tourism direct GDP whereas we use two discrete and profitable tourism segments, namely business and leisure tourism spending to define market diversification. The methodological approach was based on annual balanced panel data obtained from World Travel and Tourism Council (WTTC) over the period 1996–2019. We used second-generation panel tests to detect potential cointegrating relationships and causalities across countries under consideration. Research findings revealed two bi-directional causalities. The first identified causality indicates that business tourism spending Granger causes direct Gross Domestic Product (GDP) directly generated in the tourism sector and vice versa. An increase (decrease) in business tourism spending will cause an increase (decrease) in tourism direct GDP. The second bi-directional causality suggests that changes to leisure tourism spending will cause changes to tourism direct GDP in the same direction and vice versa. Based on results practical implications arise indicating that the enhancement of business and leisure tourism (e.g., investments and innovations), will increase destination competitiveness.

How competitive is the tourism market in the eurozone economic space?

Research in financial economics utilizing natural language processing has modeled how U.S. companies compete across product markets, how they innovate, and how supply chains are organized. This presentation will summarize these models, how they have evolved to the present day, and new work in progress. A key technical foundation is that these concepts of market structure, which were developed by economic theorists, have relatedness and overlap as their very intellectual foundation. Hence NLP methods (new and old) that focus on textual relatedness make up an ideal toolkit for analyzing these economic concepts, which are also central to many applications aimed at understanding and predicting business success.
One of the most compelling properties of an NLP approach is that market structures can be updated and redrawn every year at low cost as textual corpora are updated. This observation indicates the use of NLP to measure innovation. This has led to many insights on how companies manage growth, whether and when they go public, and how they manage the risk of disruption. This work continues to evolve with a focus on innovation, disaggregating complex product portfolios, and to build NLP measures for private firms through their websites. Among other economic findings, this work reveals that U.S. companies are becoming increasingly complex and broad in their product offerings, a result that is not seen using standard numerical databases in the public domain. Work using these same methods has also been deployed in related settings such as modeling systemic risk.

Using NLP to Model Product Market Boundaries, Market Structure, and Innovation

Digital technologies have been penetrating every aspect of business. Such pervasive deployment of digital technologies enables organisations to reinvent themselves in defining and conducting business. Leveraging the value of information as the key resource, through digital innovation such as digitisation and servitisation of products and services, becomes necessary for a business to survive and to remain competitive. Business ecosystems of the organisations and their partners have been formed through digital connectivity and digital platform which offer clearly strategic advantages and competitiveness. Interconnectivities between entities in the ecosystem are realised through multiple flows such as goods, finance and information. For a business organisation to gain competitive advantages, a successful transformation of the organisation in many dimensions are essential to allow value co-creation with other members in the ecosystem. These dimensions include mindset, culture, values, leadership, structure, process and IT systems. This keynote will discuss the notions of digital innovation and transformation, and the prerequisites and readiness for the transformation towards business ecosystems. By examining the current practice of successful examples, key findings will lead to the principles and models of organisational transformation for value co-creation and optimising benefits in the business ecosystems. This keynote will hopefully inspire practitioners and researchers to benefit from existing theoretical lenses and methods, and to derive their own guidelines and models to support organisations in digital transformation.

Digital Innovation and Transformation to Business Ecosystems

In this poster, we describe the results of team LIORI at the FinCausal 2020 Shared task held as a part of the 1st Joint Workshop on Financial Narrative Processing and MultiLingual Financial Summarisation.

LIORI at FinCausal 2020, Tasks 1 & 2

The work proposes a combination of linguistic features with features derived from pre-trained BERT for training a supervised classifier for the task of Causality Detection. The Linguistic features help to inject knowledge about the semantic and syntactic structure of the input sentences. Experiments on the FinCausal Shared Task1 datasets indicate that the combination of Linguistic features with BERT improves overall performance for causality detection. The proposed system achieves a weighted average F1 score of 0.952 on the post-evaluation dataset.

A Knowledge Induced Neural Net for Causality Detection

In our research work, we represent the content of the sentence in graphical form after extracting triples from the sentences. This poster gives an overview of novel methods to generate an extractive summary by scoring the triples. It also discusses the methods to generate extractive summary after performing binary classification using ML-based and DNN based approaches by utilizing features extracted from the triples. The poster concludes by highlighting the finding of our experiments.

Knowledge Graph and Deep Neural Network for Extractive Text Summarization by Utilizing Triples

The rapid growth of textual data published everyday on the web has prompted the need for automatic text summarization methodologies. In the financial domain, quoted companies are required to periodically publish a report in textual form.
Depending on the reader, those reports may contain redundant or irrelevant information. The proposed task aims at highlighting business-relevant information while analyzing the annual reports.
The proposed methodology exploit the advancements in the Natural Language Understanding field to create a fine-tuned architecture able to specialize the general knowledge towards the financial domain.

End-to-end Training For Financial Report Summarization

In order to provide an explanation of machine learning models, causality detection attracts lots of attention in the artificial intelligence research community. In this paper, we explore the cause-effect detection in financial news and propose an approach, which combines the BIO scheme with the Viterbi decoder for addressing this challenge. Our approach is ranked the first in the official run of cause-effect detection (Task 2) of the FinCausal-2020 shared task. We not only report the implementation details and ablation analysis in this paper but also publish our code
for academic usage.

Improving Causality Detection Using Viterbi Decoder

This poster describes our system developed for the sub-task 1 of the FinCausal shared task in the FNP-FNS workshop held in conjunction with COLING-2020. The system classifies whether a financial news text segment contains causality or not. To address this task, we fine-tune and ensemble the generic and domain-specific BERT language models pre-trained on financial text corpora. The task data is highly imbalanced with the majority \textit{non-causal} class; therefore, we train the models using strategies such as under-sampling, cost-sensitive learning, and data augmentation. Our best system achieves a weighted F1-score of $96.98$ securing $4^{th}$ position on the evaluation leaderboard. The code is available at \url{https://github.com/sarthakTUM/fincausal}

Underline digital video library

Higher education predicts worldwide cultural similarity to the United States

The Cultural Evolution of Graeco-Roman Religions in the Light of Epigraphic Evidence: A computational approach

The non-independence of nations and why it matters

How competitive is the tourism market in the eurozone economic space?

Using NLP to Model Product Market Boundaries, Market Structure, and Innovation

Digital Innovation and Transformation to Business Ecosystems

LIORI at FinCausal 2020, Tasks 1 & 2

A Knowledge Induced Neural Net for Causality Detection

Knowledge Graph and Deep Neural Network for Extractive Text Summarization by Utilizing Triples

End-to-end Training For Financial Report Summarization

Improving Causality Detection Using Viterbi Decoder

Mixture of BERTs for Causal Sentence Identification in Financial Texts

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