of beans by brand $x$ that is sold at the same prize as the original can $x$–the share of $x$ increases to 33% of total sales (while sales of $x'$ are negligible).

From the body of research we summarize above it is clear that human decision-making is often not reference independent. However, reference independence can typically be considered a *desired* property of the decision-making process of artificially intelligent agents, as we have demonstrated above, in the example that is visualized by Figure \[fig:examples\]. Yet, when evaluating artificially intelligence systems, economic rationality in general, and reference dependence in particular, is typically not considered. Instead, research results are commonly evaluated based on traditional performance criteria like accuracy and computational complexity. In this context, a group of well-established artificial intelligence researchers advocates for a paradigm shift in the evaluation of  [@Rahwan2019MachineB] that can be considered a continuation of the cross-disciplinary work of Herbert Simon, who initially coined the term *bounded rationality* [@10.2307/1884852]. In the spirit of Simon, but also of Kahneman, who uses empirical methods to systematically specify the *boundaries* of human rationality, this paper presents an application of the study of economic rationality to the area of formal argumentation. As we study–in 