This special issue explores foundational questions in behavioral economics and behavioral public policy, drawing on the work of Mario Rizzo, a critical voice in the debate on behavioral paternalism. Behavioral economics has offered significant insights into decision-making, often challenging traditional economic models. However, it has also introduced normative frameworks into policy analysis, such as preference purification, that critics argue oversimplify human decision-making and risk imposing external values. Contributions to this issue examine themes including the tension between standard rationality and inclusive rationality, the epistemological limitations of paternalistic interventions, and the role of tacit knowledge and dynamic learning in policymaking. By engaging perspectives from economics, psychology, philosophy, and law, the issue discusses process-based approaches to policy analysis that respect individual agency and accommodate uncertainty. It also highlights the political economy dimensions of behavioral public policy, emphasizing the need for institutional reforms that enable learning and systematic change rather than narrowly focusing on individual cognitive biases. This issue serves as both a tribute to Mario Rizzo’s intellectual contributions and a call for a deeper reflection on the methodological and normative foundations of behavioral public policy.

The additive manufacturing of parts made from close-to-production materials poses a great challenge. One example are highly viscous silicones, as used in injection moulding. For small production quantities, the manufacturing of injection moulds is uneconomical. This paper presents tensile specimens printed with an in-house developed dispensing system, which are analysed for air cavities (micro-CT scans) and mechanical properties. Based on the results, advice for the design and slicing parameters of parts using high-viscosity silicones in AM by means of material extrusion are developed.

Design hackathons offer a unique research opportunity to study time-pressured collaborative design. At the same time, research on design hackathons faces unique methodological challenges, prompting the exploration of new research approaches. This paper proposes a new data-collection framework that leverages a virtual format of hackathon events and enables a deeper insight into hackathon dynamics. The framework applicability is presented through a case study of the IDEA challenge hackathon, in which different intrusive and non-intrusive data collection approaches were used.

This study presents a coding scheme for design protocols that is derived from Triple Process Theory postulating the existence of three categories of cognitive processes: spontaneous, deliberate, and metacognitive. We applied the coding scheme to think-aloud protocols of designers engaged in an open design task. Results show that all three types occur during designing. The scheme we propose has the potential to ground accounts of Triple Process Theory for design in empirical studies. We explore the relation between design sessions outcomes and shifts between cognitive processes.

The process of generating FMEA following document-centric approach is tedious and susceptible to human error. This paper presents preliminary methodology for robotic manufacturing process modelling in MBSE environment with a scope of automating multiple steps of the modelling process using ontology. This is followed by the reasoning towards automatic generation of process FMEA from the MBSE model. The proposed methodology allows to establish robust and self-synchronising links between process-relevant information, reduce the likelihood of human error, and scale down time expenses.

As current vehicle development processes in the automotive industry are highly distributed, the interaction between design teams is limited. In this paper we use a simulation in order to investigate how the rate of design team interaction affects the solution quality and development cost. Results show, that in case of no limiting constraints, a low rate of interaction yields the best results regarding solution quality and development cost. If design activities are affected by constraints, however, the rate of interaction is subject to a conflict between solution quality and development cost.

Modern products are often developed in local distributed teams involving various engineering domains. As a result, product development processes are characterized by a high degree of complexity and individuality. However, the project context is often not integrated into the project planning, which can lead to uncertainties in the processes. In addition, reflection does not take place adequately in process execution. Therefore, this paper presents a concept for agile process design that enables reducing uncertainties based on context-specific reflections and adapting the processes.

Though many engineering schools are integrating sustainability in the curriculum, most are still struggling, especially with social sustainability. The aim with this study was to test a process created to help students work systematically with these issues. Results show that students in the study did indeed benefit from structured process support as their general knowledge level of social sustainability issues as well as their understanding of how to integrate them in a product assessment was very low. Reflections from teaching staff on how to further develop such a process are also shared.

The problem at hand is that vast amount of data on industrial changes is captured and stored; yet the present challenge is to systematically retrieve and use them in a purposeful way. This paper presents an industrial case study where complex product development processes are modeled using the design structure matrix (DSM) to analyze engineering change requests sequences. Engineering change requests are documents used to initiate a change process to enhance a product. Due to the amount of changes made in different projects, engineers want to be able to analyze these change processes to identify patterns and propose the best practices. The previous work has not specifically explored modeling engineering change requests in a DSM to holistically analyze sequences. This case study analyzes engineering change request sequences from four recent industrial product development projects and compares patterns among them. In the end, this research can help to identify and guide process improvement work within projects.

Irrigation water is produced within the irrigation subprocess of a farm. Water supply is identified for effective field water, which sets the upper bound on water available for plant use. Georgescu-Roegen process analysis concepts are merged with the neoclassical theory of cost as the underlying framework. The approach is illustrated for a permanent overhead system used in a Florida citrus grove. The marginal cost for the 2.54 centimeters application depth dominates all other depths for the higher water levels. Process analysis is an important analytical tool for increasing understanding of the features of irrigation water supply.

The data storage capacities of modern process automation systems have grown rapidly. Nowadays, the systems are able to frequently carry out even hundreds of measurements in parallel and store them in databases. However, these data are still rarely used in the analysis of processes. In this article, preparation of the raw data for further analysis is considered using feature extraction from signals by piecewise linear modeling. Prior to modeling, a preprocessing phase that removes some artifacts from the data is suggested. Because optimal models are computationally infeasible, fast heuristic algorithms must be utilized. Outlines for the optimal and some fast heuristic algorithms with modifications required by the preprocessing are given. In order to illustrate utilization of the features, a process diagnostics framework is presented. Among a large number of signals, the procedure finds the ones that best explain the observed short-term fluctuations in one signal. In the experiments, the piecewise linear modeling algorithms are compared using a massive data set from an operational paper machine. The use of piecewise linear representations in the analysis of changes in one real process measurement signal is demonstrated.