  Successful design of human-in-the-loop control systems 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.
