Real-life processes involve interacting business objects of different types. Object-centric event logs capture the execution of activities in such processes. An important step in the analysis of such logs is the identification of sets of objects which characterize an execution of the process, called a case. Given a case notion, visualizations can be constructed to display the relations between the executed activities and the involved business objects. Depending on the utilized case notion, these visualizations can quickly become excessively complex, impeding human analysis, or may oversimplify the underlying process, inducing flawed insights. To combat these issues, new case notions are needed to reduce complexity while representing relevant structures of the underlying business process correctly. In this paper, we propose continuous measures to quantify how correctly an object-centric case notion adheres to a given log and how complex the resulting visualizations are. These measures allow us to conceptualize the search for new object-centric case notions as a joint optimization problem among the two quality dimensions of correctness and simplicity. As a result, we can provide a new case notion that significantly reduces complexity in comparison to existing techniques, while preserving relevant object interactions. To evaluate our approach, we apply it to a range of real-life logs and find that major complexity reductions can be achieved without causing excessive correctness issues.