About this Research Topic
Transdisciplinary engineering is the emerging approach to build a capacity in engineering to effectively collaborate across academic-industry/government or community boundaries, and to integrate knowledge across academic disciplines, including the social sciences. This is critical to developing effective decision tools in organizations with the resources to affect material change on the environment and society - including at government level and industrial level in manufacturing and product development. We are looking for papers that explore how decision support in engineering settings can be augmented by collaborating across knowledge communities or how engineering analysis can support decisions in non-engineering settings like government policy making or community decisions in the context of sustainability - for clean water, air, low carbon energy, food and agriculture.
Sustainability rests on choices made by a range of actors at industry, policy and community level. These choices are commonly focused on either economic analysis within policy settings or engineering/financial analysis within manufacturing or other industrial firms. These choices are often framed with a narrow array of inputs failing to take account of issues or perspectives offered by other actors that sit outside these perspectives. In policy this can be engineers, in industrial firms this can be wider expertise from social sciences and environmental sciences. Part of the challenge here is how to integrate a wide array of perspective to make decisions manageable and timely and partly it is about how organizations engage with others. Recent advances in this area see roles both for technical support in the use of machine learning and related technologies - but questions often remain about the role of people in these settings. Others will look at participative and co-productive approaches, which themselves can be complex and challenging to implement.
- Transdisciplinarity in engineering contexts - how different disciplines can contribute to decision support
- Use of advance machine learning/AI technologies in non-engineering settings, including in government decision making withing engineering contexts
- The role of engineers as decision support in community level projects exploring the challenges of co-production
- Empirical studies which can be ethnographic in flavor, mixed methods or quantitative experimental designs exploring the impact of decision support mechanisms
- Studies exploring the mixture of digital/computational models for decision-making with different groups - in industry, government and community
Sustainability rests on choices made by a range of actors at industry, policy and community level. These choices are commonly focused on either economic analysis within policy settings or engineering/financial analysis within manufacturing or other industrial firms. These choices are often framed with a narrow array of inputs failing to take account of issues or perspectives offered by other actors that sit outside these perspectives. In policy this can be engineers, in industrial firms this can be wider expertise from social sciences and environmental sciences. Part of the challenge here is how to integrate a wide array of perspective to make decisions manageable and timely and partly it is about how organizations engage with others. Recent advances in this area see roles both for technical support in the use of machine learning and related technologies - but questions often remain about the role of people in these settings. Others will look at participative and co-productive approaches, which themselves can be complex and challenging to implement.
- Transdisciplinarity in engineering contexts - how different disciplines can contribute to decision support
- Use of advance machine learning/AI technologies in non-engineering settings, including in government decision making withing engineering contexts
- The role of engineers as decision support in community level projects exploring the challenges of co-production
- Empirical studies which can be ethnographic in flavor, mixed methods or quantitative experimental designs exploring the impact of decision support mechanisms
- Studies exploring the mixture of digital/computational models for decision-making with different groups - in industry, government and community
Keywords: co-production, digital twins, machine learning, teamwork, public policy, transdisciplinary engineering, product design, industry 4.0/5.0
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