@unpublished{65499,
  abstract     = {{This paper examines whether player-reported workplace quality is associated with team success in the National Football League (NFL). Using panel data for all 32 NFL teams across four seasons (2022-2025), we test whether NFLPA report card rankings-player evaluations of facilities, travel, medical support, coaching, and organizational environment-are related to regular season win percentage. Fixed effects models controlling for player quality, roster composition, injuries, coaching tenure, and past performance reveal a statistically significant within-team association between better player-reported workplace conditions and higher win percentages. However, this relationship does not persist when workplace quality is lagged, suggesting that player evaluations may partly reflect current team performance rather than predict future outcomes. These findings indicate that player evaluations of workplace quality are closely aligned with team success, highlighting the role of perception and short-run performance dynamics in a high-skill labor market setting.}},
  author       = {{Protte, Marius}},
  keywords     = {{NFL team performance, NFLPA report cards, player satisfaction, organizational environment, non-pecuniary compensation}},
  title        = {{{Player-Perceived Workplace Quality and Team Performance: Evidence from NFLPA Report Cards}}},
  year         = {{2026}},
}

@article{61339,
  author       = {{Protte, Marius and Djawadi, Behnud Mir}},
  journal      = {{Frontiers in Behavioral Economics}},
  keywords     = {{cheating, human-machine interaction, ambiguity, verification process, algorithm aversion, algorithm appreciation}},
  pages        = {{1645749}},
  title        = {{{Human vs. Algorithmic Auditors: The Impact of Entity Type and Ambiguity on Human Dishonesty}}},
  doi          = {{10.3389/frbhe.2025.1645749}},
  volume       = {{4}},
  year         = {{2025}},
}

@inbook{48387,
  author       = {{Lebedeva, Anastasia and Protte, Marius and van Straaten, Dirk and Fahr, René}},
  booktitle    = {{Advances in Information and Communication}},
  location     = {{Berlin}},
  pages        = {{178–204}},
  publisher    = {{Springer, Cham}},
  title        = {{{Involvement of domain experts in the AI training does not affect adherence – An AutoML study}}},
  doi          = {{https://doi.org/10.1007/978-3-031-53960-2_13}},
  volume       = {{919}},
  year         = {{2024}},
}

@article{21369,
  abstract     = {{Successful design of human-in-the-loop control sys- tems requires appropriate models for human decision makers. Whilst most paradigms adopted in the control systems literature hide the (limited) decision capability of humans, in behavioral economics individual decision making and optimization processes are well-known to be affected by perceptual and behavioral biases. Our goal is to enrich control engineering with some insights from behavioral economics research through exposing such biases in control-relevant settings.
This paper addresses the following two key questions:
1) How do behavioral biases affect decision making?
2) What is the role played by feedback in human-in-the-loop control systems?
Our experimental framework shows how individuals behave when faced with the task of piloting an UAV under risk and uncertainty, paralleling a real-world decision-making scenario. Our findings support the notion of humans in Cyberphysical Systems underlying behavioral biases regardless of – or even because of – receiving immediate outcome feedback. We observe substantial shares of drone controllers to act inefficiently through either flying excessively (overconfident) or overly conservatively (underconfident). Furthermore, we observe human-controllers to self-servingly misinterpret random sequences through being subject to a “hot hand fallacy”. We advise control engineers to mind the human component in order not to compromise technological accomplishments through human issues.}},
  author       = {{Protte, Marius and Fahr, René and Quevedo, Daniel E.}},
  journal      = {{IEEE Control Systems Magazine}},
  number       = {{6}},
  pages        = {{57 -- 76}},
  publisher    = {{IEEE}},
  title        = {{{Behavioral Economics for Human-in-the-loop Control Systems Design: Overconfidence and the hot hand fallacy}}},
  doi          = {{10.1109/MCS.2020.3019723}},
  volume       = {{40}},
  year         = {{2020}},
}

@misc{21371,
  author       = {{Protte, Marius}},
  title        = {{{The effect of organizational support on whistleblowing behavior - An experimental analysis}}},
  year         = {{2019}},
}