@misc{BorckGohl2022,
  author    = {Borck, Rainald and Gohl, Niklas},
  title     = {Steigende Mieten?},
  series = {{\"O}konomenstimme},
  journal   = {{\"O}konomenstimme},
  publisher = {KOF Konjunkturforschungsstelle der ETH Z{\"u}rich},
  address   = {Z{\"u}rich},
  year      = {2022},
  abstract  = {Vor dem Hintergrund rasant steigender Mieten in deutschen Großst{\"a}dten untersuchen wir in einer neuen Studie die Auswirkungen von Gentrifizierung sowie von politischen Gegenmaßnahmen auf unterschiedliche Einkommensgruppen anhand eines quantitativen Modells f{\"u}r Berlin. Wir finden, dass eine Mietpreisbindung (wie der „Mietendeckel") allen Haushalten, vor allem aber den {\"a}rmeren Haushalten, schadet. Andere Maßnahmen wie Neubau oder direkte Subventionen schneiden besser ab.},
  language  = {de}
}
@misc{DebreDijkstra2021,
  author    = {Debre, Maria Josepha and Dijkstra, Hylke},
  title     = {Immune to COVID?},
  publisher = {London School of Economics and Political Science},
  address   = {London},
  year      = {2021},
  language  = {en}
}
@misc{EweltKnauerSchweringWinkelmann2022,
  author    = {Ewelt-Knauer, Corinna and Schwering, Anja and Winkelmann, Sandra},
  title     = {Doing good by doing bad},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe},
  number    = {3},
  issn      = {1867-5808},
  doi       = {10.25932/publishup-57791},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-577912},
  pages     = {18},
  year      = {2022},
  abstract  = {This study investigates how tone at the top, implemented by top management, and tone at the bottom, in an employee's immediate work environment, determine noncompliance. We focus on the disallowed actions of employees that improve their own and, in turn, the company's performance, referred to as performance-improving noncompliant behavior (PINC behavior). We conduct a survey of German sales employees to investigate specifically how, on the one hand, (1) corporate rules and (2) performance pressure, both implemented by top management, and, on the other hand, (3) others' PINC expectations and (4) others' PINC behavior, both arising from the employee's immediate work environment, influence PINC behavior. When considered in isolation, we find that corporate rules, as top management's main instrument to guide employee behavior, decrease employee PINC behavior. However, this effect is negatively influenced by the employees' immediate work environment when employees are expected to engage in PINC or when others engage in PINC. In contrast, even though top management places great performance pressure on employees, that by itself does not increase PINC behavior. Overall, our study informs practitioners and researchers about whether and how the four determinants increase or decrease employees' PINC behavior, which is important to comprehend triggers and to counteract such misconduct.},
  language  = {en}
}
@article{Abramova2022,
  author    = {Abramova, Olga},
  title     = {No matter what the name, we're all the same?},
  series = {Electronic markets},
  volume    = {32},
  journal   = {Electronic markets},
  publisher = {Springer},
  address   = {Heidelberg},
  issn      = {1019-6781},
  doi       = {10.1007/s12525-021-00505-z},
  pages     = {1419 -- 1446},
  year      = {2022},
  abstract  = {Sharing marketplaces emerged as the new Holy Grail of value creation by enabling exchanges between strangers. Identity reveal, encouraged by platforms, cuts both ways: While inducing pre-transaction confidence, it is suspected of backfiring on the information senders with its discriminative potential. This study employs a discrete choice experiment to explore the role of names as signifiers of discriminative peculiarities and the importance of accompanying cues in peer choices of a ridesharing offer. We quantify users' preferences for quality signals in monetary terms and evidence comparative disadvantage of Middle Eastern descent male names for drivers and co-travelers. It translates into a lower willingness to accept and pay for an offer. Market simulations confirm the robustness of the findings. Further, we discover that females are choosier and include more signifiers of involuntary personal attributes in their decision-making. Price discounts and positive information only partly compensate for the initial disadvantage, and identity concealment is perceived negatively.},
  language  = {en}
}
@misc{Abramova2022,
  author    = {Abramova, Olga},
  title     = {No matter what the name, we're all the same?},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe},
  issn      = {1867-5808},
  doi       = {10.25932/publishup-60064},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-600641},
  pages     = {30},
  year      = {2022},
  abstract  = {Sharing marketplaces emerged as the new Holy Grail of value creation by enabling exchanges between strangers. Identity reveal, encouraged by platforms, cuts both ways: While inducing pre-transaction confidence, it is suspected of backfiring on the information senders with its discriminative potential. This study employs a discrete choice experiment to explore the role of names as signifiers of discriminative peculiarities and the importance of accompanying cues in peer choices of a ridesharing offer. We quantify users' preferences for quality signals in monetary terms and evidence comparative disadvantage of Middle Eastern descent male names for drivers and co-travelers. It translates into a lower willingness to accept and pay for an offer. Market simulations confirm the robustness of the findings. Further, we discover that females are choosier and include more signifiers of involuntary personal attributes in their decision-making. Price discounts and positive information only partly compensate for the initial disadvantage, and identity concealment is perceived negatively.},
  language  = {en}
}
@misc{PanzerBenderGronau2022,
  author    = {Panzer, Marcel and Bender, Benedict and Gronau, Norbert},
  title     = {Neural agent-based production planning and control},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe},
  issn      = {1867-5808},
  doi       = {10.25932/publishup-60477},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-604777},
  pages     = {26},
  year      = {2022},
  abstract  = {Nowadays, production planning and control must cope with mass customization, increased fluctuations in demand, and high competition pressures. Despite prevailing market risks, planning accuracy and increased adaptability in the event of disruptions or failures must be ensured, while simultaneously optimizing key process indicators. To manage that complex task, neural networks that can process large quantities of high-dimensional data in real time have been widely adopted in recent years. Although these are already extensively deployed in production systems, a systematic review of applications and implemented agent embeddings and architectures has not yet been conducted. The main contribution of this paper is to provide researchers and practitioners with an overview of applications and applied embeddings and to motivate further research in neural agent-based production. Findings indicate that neural agents are not only deployed in diverse applications, but are also increasingly implemented in multi-agent environments or in combination with conventional methods — leveraging performances compared to benchmarks and reducing dependence on human experience. This not only implies a more sophisticated focus on distributed production resources, but also broadening the perspective from a local to a global scale. Nevertheless, future research must further increase scalability and reproducibility to guarantee a simplified transfer of results to reality.},
  language  = {en}
}
@article{PanzerBenderGronau2022,
  author    = {Panzer, Marcel and Bender, Benedict and Gronau, Norbert},
  title     = {Neural agent-based production planning and control},
  series = {Journal of Manufacturing Systems},
  volume    = {65},
  journal   = {Journal of Manufacturing Systems},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0278-6125},
  doi       = {10.1016/j.jmsy.2022.10.019},
  pages     = {743 -- 766},
  year      = {2022},
  abstract  = {Nowadays, production planning and control must cope with mass customization, increased fluctuations in demand, and high competition pressures. Despite prevailing market risks, planning accuracy and increased adaptability in the event of disruptions or failures must be ensured, while simultaneously optimizing key process indicators. To manage that complex task, neural networks that can process large quantities of high-dimensional data in real time have been widely adopted in recent years. Although these are already extensively deployed in production systems, a systematic review of applications and implemented agent embeddings and architectures has not yet been conducted. The main contribution of this paper is to provide researchers and practitioners with an overview of applications and applied embeddings and to motivate further research in neural agent-based production. Findings indicate that neural agents are not only deployed in diverse applications, but are also increasingly implemented in multi-agent environments or in combination with conventional methods — leveraging performances compared to benchmarks and reducing dependence on human experience. This not only implies a more sophisticated focus on distributed production resources, but also broadening the perspective from a local to a global scale. Nevertheless, future research must further increase scalability and reproducibility to guarantee a simplified transfer of results to reality.},
  language  = {en}
}
@misc{PanzerBenderGronau2023,
  author    = {Panzer, Marcel and Bender, Benedict and Gronau, Norbert},
  title     = {A deep reinforcement learning based hyper-heuristic for modular production control},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe},
  issn      = {1867-5808},
  doi       = {10.25932/publishup-60564},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-605642},
  pages     = {24},
  year      = {2023},
  abstract  = {In nowadays production, fluctuations in demand, shortening product life-cycles, and highly configurable products require an adaptive and robust control approach to maintain competitiveness. This approach must not only optimise desired production objectives but also cope with unforeseen machine failures, rush orders, and changes in short-term demand. Previous control approaches were often implemented using a single operations layer and a standalone deep learning approach, which may not adequately address the complex organisational demands of modern manufacturing systems. To address this challenge, we propose a hyper-heuristics control model within a semi-heterarchical production system, in which multiple manufacturing and distribution agents are spread across pre-defined modules. The agents employ a deep reinforcement learning algorithm to learn a policy for selecting low-level heuristics in a situation-specific manner, thereby leveraging system performance and adaptability. We tested our approach in simulation and transferred it to a hybrid production environment. By that, we were able to demonstrate its multi-objective optimisation capabilities compared to conventional approaches in terms of mean throughput time, tardiness, and processing of prioritised orders in a multi-layered production system. The modular design is promising in reducing the overall system complexity and facilitates a quick and seamless integration into other scenarios.},
  language  = {en}
}
@article{PanzerBenderGronau2023,
  author    = {Panzer, Marcel and Bender, Benedict and Gronau, Norbert},
  title     = {A deep reinforcement learning based hyper-heuristic for modular production control},
  series = {International journal of production research},
  journal   = {International journal of production research},
  publisher = {Taylor \& Francis},
  address   = {London},
  issn      = {0020-7543},
  doi       = {10.1080/00207543.2023.2233641},
  pages     = {1 -- 22},
  year      = {2023},
  abstract  = {In nowadays production, fluctuations in demand, shortening product life-cycles, and highly configurable products require an adaptive and robust control approach to maintain competitiveness. This approach must not only optimise desired production objectives but also cope with unforeseen machine failures, rush orders, and changes in short-term demand. Previous control approaches were often implemented using a single operations layer and a standalone deep learning approach, which may not adequately address the complex organisational demands of modern manufacturing systems. To address this challenge, we propose a hyper-heuristics control model within a semi-heterarchical production system, in which multiple manufacturing and distribution agents are spread across pre-defined modules. The agents employ a deep reinforcement learning algorithm to learn a policy for selecting low-level heuristics in a situation-specific manner, thereby leveraging system performance and adaptability. We tested our approach in simulation and transferred it to a hybrid production environment. By that, we were able to demonstrate its multi-objective optimisation capabilities compared to conventional approaches in terms of mean throughput time, tardiness, and processing of prioritised orders in a multi-layered production system. The modular design is promising in reducing the overall system complexity and facilitates a quick and seamless integration into other scenarios.},
  language  = {en}
}
@misc{BenlianWienerCrametal.2022,
  author    = {Benlian, Alexander and Wiener, Martin and Cram, W. Alec and Krasnova, Hanna and Maedche, Alexander and Mohlmann, Mareike and Recker, Jan and Remus, Ulrich},
  title     = {Algorithmic management},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Wirtschafts- und Sozialwissenschaftliche Reihe},
  number    = {6},
  issn      = {2363-7005},
  doi       = {10.25932/publishup-60711},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-607112},
  pages     = {17},
  year      = {2022},
  language  = {en}
}