@article{AdnanSrsicVenticichetal.2020, author = {Adnan, Hassan Sami and Srsic, Amanda and Venticich, Pete Milos and Townend, David M.R.}, title = {Using AI for mental health analysis and prediction in school surveys}, series = {European journal of public health}, volume = {30}, journal = {European journal of public health}, publisher = {Oxford Univ. Press}, address = {Oxford [u.a.]}, issn = {1101-1262}, doi = {10.1093/eurpub/ckaa165.336}, pages = {V125 -- V125}, year = {2020}, abstract = {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.}, language = {en} } @techreport{Andres2024, type = {Working Paper}, author = {Andres, Maximilian}, title = {Equilibrium selection in infinitely repeated games with communication}, series = {CEPA Discussion Papers}, journal = {CEPA Discussion Papers}, number = {75}, issn = {2628-653X}, doi = {10.25932/publishup-63180}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-631800}, pages = {38}, year = {2024}, abstract = {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.}, language = {en} } @techreport{AndresBruttel2024, type = {Working Paper}, author = {Andres, Maximilian and Bruttel, Lisa}, title = {Communicating Cartel Intentions}, series = {CEPA Discussion Papers}, journal = {CEPA Discussion Papers}, number = {77}, issn = {2628-653X}, doi = {10.25932/publishup-63846}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-638469}, pages = {36}, year = {2024}, abstract = {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.}, language = {en} } @article{AndresBruttelFriedrichsen2022, author = {Andres, Maximilian and Bruttel, Lisa and Friedrichsen, Jana}, title = {How communication makes the difference between a cartel and tacit collusion}, series = {European economic review}, volume = {152}, journal = {European economic review}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0014-2921}, doi = {10.1016/j.euroecorev.2022.104331}, pages = {1 -- 18}, year = {2022}, abstract = {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.}, language = {en} } @techreport{AndresBruttelFriedrichsen2020, type = {Working Paper}, author = {Andres, Maximilian and Bruttel, Lisa Verena and Friedrichsen, Jana}, title = {Choosing between explicit cartel formation and tacit collusion - An experiment}, series = {CEPA Discussion Papers}, journal = {CEPA Discussion Papers}, number = {19}, issn = {2628-653X}, doi = {10.25932/publishup-47388}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-473885}, pages = {55}, year = {2020}, abstract = {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.}, language = {en} } @techreport{AndresBruttelFriedrichsen2022, type = {Working Paper}, author = {Andres, Maximilian and Bruttel, Lisa Verena and Friedrichsen, Jana}, title = {How communication makes the difference between a cartel and tacit collusion}, series = {CEPA Discussion Papers}, journal = {CEPA Discussion Papers}, issn = {2628-653X}, doi = {10.25932/publishup-56223}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-562234}, pages = {67}, year = {2022}, abstract = {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.}, language = {en} } @article{Ayzel2021, author = {Ayzel, Georgy}, title = {Deep neural networks in hydrology}, series = {Vestnik of Saint Petersburg University. Earth Sciences}, volume = {66}, journal = {Vestnik of Saint Petersburg University. Earth Sciences}, number = {1}, publisher = {Univ. Press}, address = {St. Petersburg}, issn = {2541-9668}, doi = {10.21638/spbu07.2021.101}, pages = {5 -- 18}, year = {2021}, abstract = {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.}, language = {en} } @misc{AyzelIzhitskiy2019, author = {Ayzel, Georgy and Izhitskiy, Alexander}, title = {Climate change impact assessment on freshwater inflow into the Small Aral Sea}, series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {1071}, issn = {1866-8372}, doi = {10.25932/publishup-47279}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-472794}, pages = {21}, year = {2019}, abstract = {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.}, language = {en} } @article{AyzelIzhitskiy2019, author = {Ayzel, Georgy and Izhitskiy, Alexander}, title = {Climate Change Impact Assessment on Freshwater Inflow into the Small Aral Sea}, series = {Water}, volume = {11}, journal = {Water}, number = {11}, publisher = {MDPI}, address = {Basel}, issn = {2073-4441}, doi = {10.3390/w11112377}, pages = {19}, year = {2019}, abstract = {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.}, language = {en} } @article{BaumgartBoosEckstein2023, author = {Baumgart, Lene and Boos, Pauline and Eckstein, Bernd}, title = {Datafication and algorithmic contingency}, series = {Work organisation, labour \& globalisation}, volume = {17}, journal = {Work organisation, labour \& globalisation}, number = {1}, publisher = {Pluto Journals}, address = {London}, issn = {1745-641X}, doi = {10.13169/workorgalaboglob.17.1.0061}, pages = {61 -- 73}, year = {2023}, abstract = {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).}, language = {en} }