5.2 State of the art: from modeling to experimentation
The KEMs described here are categorised from research on large-scale and complex systems, up to the impact of specific interventions on the individual. Research is done through models of reality, field research and observations in specific contexts. They provide insight into the effect of interventions and offer a perspective on how people deal with their (new) reality. In addition, we describe methods that provide insight into the way in which the individual experiences his or her daily life. Of course, science still makes extensive use of laboratories in which humans are observed while they are subjected to behavioural experiments. In transitions, however, behaviour is more complex due to relationships between individuals and dependence on environmental factors. There is therefore a need for environments that are more open and therefore less controllable. In addition to the categorisation of complex systems versus the individual, a distinction can also be made based on the time dimension. Models sometimes try to imagine a new reality, for example in a virtual world. Research is also being done into the future in the present. By means of exhibitions and the creation of prototypes that can be experienced, visions are presented that give people a critical / different view of reality.
In the design and engineering sciences, transitions are often addressed by research by design or by design research. In this process interventions are conceived, executed, analysed and reflected on. In their book on design research in practice, Koskinen et al. (2011) distinguish the lab, the field and the storefront as research domains with their variety of underlying theories and methods. The model of reality is not addressed in this, because the engineering sciences are often concerned with artifacts. Given shifts in the design domain to networked systems, data-driven methods and social design, not only analysis but also synthesis will become increasingly important for this approach. The artifact will no longer only be seen as a separate element, but as a connector between the stakeholders in an environment.
Virtual environments When systems have a major social impact, they cannot be easily regulated. In addition, interventions in this type of system are often irresponsible or too expensive. Examples are safety-critical systems. In these cases, one can decide to model the system. Based on a broad knowledge and experience of the factors that influence a system, models can be created that simulate reality. These KEMs are therefore mainly focused on simulation. An example of this is Digital Twins. These are digital replicas of living or non-living physical entities, where the digital replica adapts based on data from the physical world (El Saddik, 2018). The digital replica can be used as a test environment for monitoring. For example, when maintenance is necessary in complex infrastructural or industrial installations, to research processes. One can also try to predict the possible impact of interventions by adjusting certain variables in the models.
Another context where models of reality are applied is in economics, where the impact of interventions is studied by means of, for example, Equilibrium Analysis. Furthermore, Sandboxes offer isolated digital environments in which developers can create and test new concepts without interfering with other (critical) parts of a project. There are also virtual environments that can actually be experienced by people, the so-called Virtual Reality. These virtual environments offer the control of laboratories, but can also simulate complex processes such as an industrial production line or airport traffic control. In virtual environments, for example, using methods such as Serious Gaming (e.g. Mayer et al., 2014), collaboration can be studied and training courses can be realised. The limitation of these virtual environments, however, is that to date they mainly serve the visual and auditory channel and still to a very limited extent the other senses.
Everyday life Today's connected and data-driven systems and the link with artificial intelligence make it increasingly easy to observe human behaviour in daily life. Methods such as Crowdsourcing, in which both sensor data and other user information are acquired via, for example, the mobile phone, offer a view that interferes minimally with daily life. From software development, the method is Perpetual Beta, in which the implementation of systems is always in a test phase, and developers make continuous interventions. It allows early design iterations to be implemented in the real world and uses online channels to gain feedback from users and improve designs. Perpetual Beta is used, for example, in urban development (Fredericks et al., 2019).
In addition to acquiring data by mobile phones, use can also be made of so-called Technology Probes, specially designed artefacts with sensors and possibly actuators that are connected to the internet and can therefore exchange data from the environment. This allows the creation of experimental environments in physical and / or virtual environments that are part of society. These so-called Experiential Design Landscapes serve as a playground for in-situ design research by multi-stakeholder teams (Peeters & Megens, 2014). Existing products and services can also provide contextual data from the environment. This is already widely used in the business community for the development of new products and services. At Philips Design, for example, the Data-enabled Design method is used, in which sensor data from physical and digital products is combined with qualitative data from users. In this way, designers obtain detailed and nuanced contextual, behavioural and experiential insights from daily life (Van Kollenburg & Bogers, 2019).
In daily life there are also various experimental environments in which large, possibly more homogeneous, target groups come together, the so-called Pilot Grounds, Field Labs and Living Labs. User-oriented methods are used in these environments and open innovation is often stimulated. They are used to observe and measure, build and validate prototypes, and address complex challenges in as many real-world situations as possible. Many labs are linked to so-called Smart City initiatives around Amsterdam, Rotterdam and Eindhoven in particular. The environments are linked to daily activities. However, they offer more control than everyday life because they are often bound by environment or time. In City Labs, citizens, researchers, students, technologists, businesses, NGOs, entrepreneurs, teachers and policymakers come together (e.g. Scholl & Kemp, 2016). An example of this is NEMO Kennislink, where, in the context of the Science Museum, co-creation is used to develop solutions for the future of the Amsterdam metropolitan region. In addition to cities, regional applications can also be considered: in the Brainport region, for example, a stretch of motorway and several streets have been brought together in the Helmond Smart Mobility Living Lab where traffic research can be carried out.
Living Labs can also be linked to specific target groups, such as athletes, by turning a sports complex into an experimental environment. Or doctors, nurses and patients in a hospital. They can also be temporary, such as festivals. Prototypes can be tested and experienced during festivals and a lot of data can be generated or feedback can be obtained in a short period of time. A festival is seen as a temporary mini-society with challenges in areas such as energy, waste, logistics, water and food. Innofest, for example, is an organisation that offers entrepreneurs the opportunity to conduct research at various festivals. On the other hand, during the GLOW festival in Eindhoven, researchers observed how light can influence the routing of large groups for crowd management (Corbetta et al., 2018). Finally, Policy Labs (Olejniczak et al., 2019) are environments where government and citizens come together to explore innovative ideas. People hope to achieve social impact through citizen participation and a changing government culture. However, there is limited coherence between the methods used in the different labs. The effectiveness of these environments on policy change has not yet been sufficiently proven and - due to their short existence - it is not known whether these labs are sustainable. However, the advantages are that the feasibility and scalability of initiatives can be tested in a short time and in a relatively flexible manner and that they offer a safe environment for co-design and participation.
Workshops / manufacturing environments In these environments, making is central. This can focus on the here and now, by enabling people to create. In the Maker Movement (Dougherty, 2012), people in environments such as Fab Labs can create artifacts. To this end, knowledge is shared about, for example, production processes, models and software code. This may also lead to new research initiatives such as Citizen Science (Irwin, 1995). By enabling a large group of people to develop specific products to carry out measurements, such as an air quality meter, public data can be generated on a large scale. By linking the various measurements, the action perspective of the research community and thus knowledge production is increased through public involvement. Workplaces also promote bottom-up initiatives and encourage self-determination by bringing together cultural and economic practices.
Making can also be used in the arts, design and science for a critical reflection on technology in society, so-called Critical Making is based on Critical Design (Dunne, 1999). Speculative Making of Art Science creates a hybrid form of art and science, both of which have a unique ability to shape our understanding of the world. The collaboration provides new insights for both and leads to new hybrid forms of knowledge and presentation. Artistic research offers room for subjectivity that can lead to generically valid principles through the use of performative and speculative research methods. This type of research is often linked to the aforementioned Showroom Approach (Koskinen et al., 2011). The experimental environments that are linked to this are exhibitions and museums or Future Labs.
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