The ethical considerations of modelling humans should thus always be considered carefully.
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Developing a computational model requires strong critical thinking and rigour. It can thus be more conducive to removing ideas than to creating new ones. Could it stifle the generative, creative thinking that is central to design? Two approaches to avoiding this shortcoming are to carefully think of the phase in which to integrate the use of a model and to leverage intuition and ideas from the designers and stakeholders as inputs into the model. Data analysis and modelling can be time consuming and require specialised skills, so they can be cost intensive.
Budget or planning may therefore motivate their exclusion. What may address this issue is the development of interfaces and platforms enabling the adaptation of existing models to new situations. As an illustration of such solutions, the platform Kumu offers a user-friendly interface for network analysis. Finally, designers often question whether such models would support the engagement of stakeholders, as they can come across as dry and complicated. Participatory modelling experiments demonstrate that stakeholder engagement can be an integral part of the modelling process Schmitt Olabisi et al.
Finally, some powerful computational models rely on very large datasets from online use, such as Facebook or Twitter data Conte et al. A design problem however does not start with a dataset, but with a problem to solve. As a result, not every systemic problem will possess such a dataset. Design by definition takes place at an early stage of intervention, before the project itself has delivered data. Are computational models still relevant in these contexts? Here are a few responses to this concern. First, many designers may underestimate the amount of data available today, when leveraging online media and advanced data analysis techniques e.
Second, much can already be learnt form models based on limited data, complemented with plausible assumptions. Uncertain data can also be treated as the source of multiple scenarios Kwakkel Finally, there is an opportunity to approach models in a lean, iterative manner: a first model is built based on theory and hypotheses, which can already help to explore and refine the assumptions of the stakeholders and designers; such a model will in turn inform which data to gather throughout the project, so that more and more refined versions can be developed iteratively. As the discussion above suggests, there is an opportunity in expanding current design methods with computational models, provided the following considerations:.
The next steps in demonstrating this potential is to build case studies of design projects leveraging computational models. Adequate cases would concern issues affected by social complexity, which means that the interactions between individuals play a key a role in outcomes. Ideally, data sets should be available, either from the start of the project or through its development. Finally, such projects will require stakeholders that are curious and willing to experiment with new approaches.
This paper showed that despite the fact that much of complexity science is based on quantitative, computational models, the literature on design concerned with complex systems refers nearly exclusively to qualitative approaches. It explored some of the key questions that may be motivating this reluctance to leverage computational models of social systems, deducted a set of guiding principles for their introduction in design for sustainability, and proposed next steps to this endeavour.
Computational models have repeatedly proved their power to shed light on complex social dynamics of importance to sustainability. It is time to explore their application to the field of a design to enable the transition towards sustainable societies. Axtell, R. Agent-based modeling and industrial ecology. Journal of Industrial Ecology, 5 4 , Disentangling intangible social—ecological systems. Global Environmental Change, 22 2 , Boulton, J. Embracing complexity: Strategic perspectives for an age of turbulence. OUP Oxford.
Circle Economy, Policy Levers for a Low-Carbon Economy. Click NL, Knowledge and Innovation Agenda. Conte, R. Manifesto of computational social science.
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