TY - JOUR A1 - Adnan, Hassan Sami A1 - Srsic, Amanda A1 - Venticich, Pete Milos A1 - Townend, David M.R. T1 - Using AI for mental health analysis and prediction in school surveys JF - European journal of public health N2 - Background: Childhood and adolescence are critical stages of life for mental health and well-being. Schools are a key setting for mental health promotion and illness prevention. One in five children and adolescents have a mental disorder, about half of mental disorders beginning before the age of 14. Beneficial and explainable artificial intelligence can replace current paper- based and online approaches to school mental health surveys. This can enhance data acquisition, interoperability, data driven analysis, trust and compliance. This paper presents a model for using chatbots for non-obtrusive data collection and supervised machine learning models for data analysis; and discusses ethical considerations pertaining to the use of these models. Methods: For data acquisition, the proposed model uses chatbots which interact with students. The conversation log acts as the source of raw data for the machine learning. Pre-processing of the data is automated by filtering for keywords and phrases. Existing survey results, obtained through current paper-based data collection methods, are evaluated by domain experts (health professionals). These can be used to create a test dataset to validate the machine learning models. Supervised learning can then be deployed to classify specific behaviour and mental health patterns. Results: We present a model that can be used to improve upon current paper-based data collection and manual data analysis methods. An open-source GitHub repository contains necessary tools and components of this model. Privacy is respected through rigorous observance of confidentiality and data protection requirements. Critical reflection on these ethics and law aspects is included in the project. Conclusions: This model strengthens mental health surveillance in schools. The same tools and components could be applied to other public health data. Future extensions of this model could also incorporate unsupervised learning to find clusters and patterns of unknown effects. KW - ethics KW - artificial intelligence KW - adolescent KW - child KW - confidentiality KW - health personnel KW - mental disorders KW - mental health KW - personal satisfaction KW - privacy KW - school (environment) KW - statutes and laws KW - public health medicine KW - surveillance KW - medical KW - prevention KW - datasets KW - machine learning KW - supervised machine learning KW - data analysis Y1 - 2020 U6 - https://doi.org/10.1093/eurpub/ckaa165.336 SN - 1101-1262 SN - 1464-360X VL - 30 SP - V125 EP - V125 PB - Oxford Univ. Press CY - Oxford [u.a.] ER - TY - RPRT A1 - Andres, Maximilian T1 - Equilibrium selection in infinitely repeated games with communication T2 - CEPA Discussion Papers N2 - The present paper proposes a novel approach for equilibrium selection in the infinitely repeated prisoner’s dilemma where players can communicate before choosing their strategies. This approach yields a critical discount factor that makes different predictions for cooperation than the usually considered sub-game perfect or risk dominance critical discount factors. In laboratory experiments, we find that our factor is useful for predicting cooperation. For payoff changes where the usually considered factors and our factor make different predictions, the observed cooperation is consistent with the predictions based on our factor. T3 - CEPA Discussion Papers - 75 KW - cooperation KW - communication KW - infinitely repeated game KW - machine learning Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-631800 SN - 2628-653X IS - 75 ER - TY - RPRT A1 - Andres, Maximilian A1 - Bruttel, Lisa T1 - Communicating Cartel Intentions T2 - CEPA Discussion Papers N2 - While the economic harm of cartels is caused by their price-increasing effect, sanctioning by courts rather targets at the underlying process of firms reaching a price-fixing agreement. This paper provides experimental evidence on the question whether such sanctioning meets the economic target, i.e., whether evidence of a collusive meeting of the firms and of the content of their communication reliably predicts subsequent prices. We find that already the mere mutual agreement to meet predicts a strong increase in prices. Conversely, express distancing from communication completely nullifies its otherwise price-increasing effect. Using machine learning, we show that communication only increases prices if it is very explicit about how the cartel plans to behave. T3 - CEPA Discussion Papers - 77 KW - cartel KW - collusion KW - communication KW - machine learning KW - experiment Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-638469 SN - 2628-653X IS - 77 ER - TY - JOUR A1 - Andres, Maximilian A1 - Bruttel, Lisa A1 - Friedrichsen, Jana T1 - How communication makes the difference between a cartel and tacit collusion BT - a machine learning approach JF - European economic review N2 - This paper sheds new light on the role of communication for cartel formation. Using machine learning to evaluate free-form chat communication among firms in a laboratory experiment, we identify typical communication patterns for both explicit cartel formation and indirect attempts to collude tacitly. We document that firms are less likely to communicate explicitly about price fixing and more likely to use indirect messages when sanctioning institutions are present. This effect of sanctions on communication reinforces the direct cartel-deterring effect of sanctions as collusion is more difficult to reach and sustain without an explicit agreement. Indirect messages have no, or even a negative, effect on prices. KW - cartel KW - collusion KW - communication KW - machine learning KW - experiment Y1 - 2023 U6 - https://doi.org/10.1016/j.euroecorev.2022.104331 SN - 0014-2921 SN - 1873-572X VL - 152 SP - 1 EP - 18 PB - Elsevier CY - Amsterdam ER - TY - RPRT A1 - Andres, Maximilian A1 - Bruttel, Lisa Verena A1 - Friedrichsen, Jana T1 - Choosing between explicit cartel formation and tacit collusion – An experiment T2 - CEPA Discussion Papers N2 - Numerous studies investigate which sanctioning institutions prevent cartel formation but little is known as to how these sanctions work. We contribute to understanding the inner workings of cartels by studying experimentally the effect of sanctioning institutions on firms’ communication. Using machine learning to organize the chat communication into topics, we find that firms are significantly less likely to communicate explicitly about price fixing when sanctioning institutions are present. At the same time, average prices are lower when communication is less explicit. A mediation analysis suggests that sanctions are effective in hindering cartel formation not only because they introduce a risk of being fined but also by reducing the prevalence of explicit price communication. T3 - CEPA Discussion Papers - 19 KW - cartel KW - collusion KW - communication KW - machine learning KW - experiment Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-473885 SN - 2628-653X IS - 19 ER - TY - RPRT A1 - Andres, Maximilian A1 - Bruttel, Lisa Verena A1 - Friedrichsen, Jana T1 - How communication makes the difference between a cartel and tacit collusion BT - a machine learning approach T2 - CEPA Discussion Papers N2 - This paper sheds new light on the role of communication for cartel formation. Using machine learning to evaluate free-form chat communication among firms in a laboratory experiment, we identify typical communication patterns for both explicit cartel formation and indirect attempts to collude tacitly. We document that firms are less likely to communicate explicitly about price fixing and more likely to use indirect messages when sanctioning institutions are present. This effect of sanctions on communication reinforces the direct cartel-deterring effect of sanctions as collusion is more difficult to reach and sustain without an explicit agreement. Indirect messages have no, or even a negative, effect on prices. T3 - CEPA Discussion Papers - 53 KW - cartel KW - collusion KW - communication KW - machine learning KW - experiment Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-562234 SN - 2628-653X ER - TY - JOUR A1 - Ayzel, Georgy T1 - Deep neural networks in hydrology BT - the new generation of universal and efficient models BT - новое поколение универсальных и эффективных моделей JF - Vestnik of Saint Petersburg University. Earth Sciences N2 - For around a decade, deep learning - the sub-field of machine learning that refers to artificial neural networks comprised of many computational layers - modifies the landscape of statistical model development in many research areas, such as image classification, machine translation, and speech recognition. Geoscientific disciplines in general and the field of hydrology in particular, also do not stand aside from this movement. Recently, the proliferation of modern deep learning-based techniques and methods has been actively gaining popularity for solving a wide range of hydrological problems: modeling and forecasting of river runoff, hydrological model parameters regionalization, assessment of available water resources. identification of the main drivers of the recent change in water balance components. This growing popularity of deep neural networks is primarily due to their high universality and efficiency. The presented qualities, together with the rapidly growing amount of accumulated environmental information, as well as increasing availability of computing facilities and resources, allow us to speak about deep neural networks as a new generation of mathematical models designed to, if not to replace existing solutions, but significantly enrich the field of geophysical processes modeling. This paper provides a brief overview of the current state of the field of development and application of deep neural networks in hydrology. Also in the following study, the qualitative long-term forecast regarding the development of deep learning technology for managing the corresponding hydrological modeling challenges is provided based on the use of "Gartner Hype Curve", which in the general details describes a life cycle of modern technologies. N2 - В течение последнего десятилетия глубокое обучение - область машинного обучения, относящаяся к искусственным нейронным сетям, состоящим из множества вычислительных слоев, - изменяет ландшафт развития статистических моделей во многих областях исследований, таких как классификация изображений, машинный перевод, распознавание речи. Географические науки, а также входящая в их состав область исследования гидрологии суши, не стоят в стороне от этого движения. В последнее время применение современных технологий и методов глубокого обучения активно набирает популярность для решения широкого спектра гидрологических задач: моделирования и прогнозирования речного стока, районирования модельных параметров, оценки располагаемых водных ресурсов, идентификации факторов, влияющих на современные изменения водного режима. Такой рост популярности глубоких нейронных сетей продиктован прежде всего их высокой универсальностью и эффективностью. Представленные качества в совокупности с быстрорастущим количеством накопленной информации о состоянии окружающей среды, а также ростом доступности вычислительных средств и ресурсов, позволяют говорить о глубоких нейронных сетях как о новом поколении математических моделей, призванных если не заменить существующие решения, то значительно обогатить область моделирования геофизических процессов. В данной работе представлен краткий обзор текущего состояния области разработки и применения глубоких нейронных сетей в гидрологии. Также в работе предложен качественный долгосрочный прогноз развития технологии глубокого обучения для решения задач гидрологического моделирования на основе использования «кривой ажиотажа Гартнера», в общих чертах описывающей жизненный цикл современных технологий. T2 - Глубокие нейронные сети в гидрологии KW - deep neural networks KW - deep learning KW - machine learning KW - hydrology KW - modeling KW - глубокие нейронные сети KW - глубокое обучение KW - машинное обучение KW - гидрология KW - моделирование Y1 - 2021 U6 - https://doi.org/10.21638/spbu07.2021.101 SN - 2541-9668 SN - 2587-585X VL - 66 IS - 1 SP - 5 EP - 18 PB - Univ. Press CY - St. Petersburg ER - TY - GEN A1 - Ayzel, Georgy A1 - Izhitskiy, Alexander T1 - Climate change impact assessment on freshwater inflow into the Small Aral Sea T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - During the last few decades, the rapid separation of the Small Aral Sea from the isolated basin has changed its hydrological and ecological conditions tremendously. In the present study, we developed and validated the hybrid model for the Syr Darya River basin based on a combination of state-of-the-art hydrological and machine learning models. Climate change impact on freshwater inflow into the Small Aral Sea for the projection period 2007–2099 has been quantified based on the developed hybrid model and bias corrected and downscaled meteorological projections simulated by four General Circulation Models (GCM) for each of three Representative Concentration Pathway scenarios (RCP). The developed hybrid model reliably simulates freshwater inflow for the historical period with a Nash–Sutcliffe efficiency of 0.72 and a Kling–Gupta efficiency of 0.77. Results of the climate change impact assessment showed that the freshwater inflow projections produced by different GCMs are misleading by providing contradictory results for the projection period. However, we identified that the relative runoff changes are expected to be more pronounced in the case of more aggressive RCP scenarios. The simulated projections of freshwater inflow provide a basis for further assessment of climate change impacts on hydrological and ecological conditions of the Small Aral Sea in the 21st Century. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1071 KW - Small Aral Sea KW - hydrology KW - climate change KW - modeling KW - machine learning Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-472794 SN - 1866-8372 IS - 1071 ER - TY - JOUR A1 - Ayzel, Georgy A1 - Izhitskiy, Alexander T1 - Climate Change Impact Assessment on Freshwater Inflow into the Small Aral Sea JF - Water N2 - During the last few decades, the rapid separation of the Small Aral Sea from the isolated basin has changed its hydrological and ecological conditions tremendously. In the present study, we developed and validated the hybrid model for the Syr Darya River basin based on a combination of state-of-the-art hydrological and machine learning models. Climate change impact on freshwater inflow into the Small Aral Sea for the projection period 2007-2099 has been quantified based on the developed hybrid model and bias corrected and downscaled meteorological projections simulated by four General Circulation Models (GCM) for each of three Representative Concentration Pathway scenarios (RCP). The developed hybrid model reliably simulates freshwater inflow for the historical period with a Nash-Sutcliffe efficiency of 0.72 and a Kling-Gupta efficiency of 0.77. Results of the climate change impact assessment showed that the freshwater inflow projections produced by different GCMs are misleading by providing contradictory results for the projection period. However, we identified that the relative runoff changes are expected to be more pronounced in the case of more aggressive RCP scenarios. The simulated projections of freshwater inflow provide a basis for further assessment of climate change impacts on hydrological and ecological conditions of the Small Aral Sea in the 21st Century. KW - Small Aral Sea KW - hydrology KW - climate change KW - modeling KW - machine learning Y1 - 2019 U6 - https://doi.org/10.3390/w11112377 SN - 2073-4441 VL - 11 IS - 11 PB - MDPI CY - Basel ER - TY - JOUR A1 - Baumgart, Lene A1 - Boos, Pauline A1 - Eckstein, Bernd T1 - Datafication and algorithmic contingency BT - how agile organisations deal with technical systems JF - Work organisation, labour & globalisation N2 - In the context of persistent images of self-perpetuated technologies, we discuss the interplay of digital technologies and organisational dynamics against the backdrop of systems theory. Building on the case of an international corporation that, during an agile reorganisation, introduced an AI-based personnel management platform, we show how technical systems produce a form of algorithmic contingency that subsequently leads to the emergence of formal and informal interaction systems. Using the concept of datafication, we explain how these interactions are barriers to the self-perpetuation of data-based decision-making, making it possible to take into consideration further decision factors and complementing the output of the platform. The research was carried out within the scope of the research project ‘Organisational Implications of Digitalisation: The Development of (Post-)Bureaucratic Organisational Structures in the Context of Digital Transformation’ funded by the German Research Foundation (DFG). KW - digitalisation KW - datafication KW - organisation KW - agile KW - technical system KW - systems theory KW - interaction KW - algorithmic contingency KW - machine learning KW - platform Y1 - 2023 U6 - https://doi.org/10.13169/workorgalaboglob.17.1.0061 SN - 1745-641X SN - 1745-6428 VL - 17 IS - 1 SP - 61 EP - 73 PB - Pluto Journals CY - London ER -