| Unit name | Mathematical Modelling for Sustainable Development |
|---|---|
| Unit code | SEMTM0008 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Professor. Champneys |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
EMAT20200 Engineering Mathematics 2 or equivalent Students should have a basic ability to edit code and write scripts in a programming environment such as Python or Matlab. |
| Units you must take alongside this one (co-requisite units) |
None |
| Units you may not take alongside this one |
None |
| School/department | School of Engineering Mathematics and Technology |
| Faculty | Faculty of Engineering |
Why is this unit important
Across the globe, people are becoming acutely aware of the need to use resources sustainably, generate our energy renewably, and to create fair systems that meet everyone’s basic human needs such as water, food, healthcare, education, and justice. The extent to which these aims can be met and the question of how to make interventions towards meeting them both lead to quantitative questions that naturally require mathematical modelling. This unit will introduce a range of mathematical techniques that are relevant to modelling socio-technical systems and will explore their application in the broad context of sustainable development. Relevant techniques could include complex network theory, simulation technologies, data assimilation, operations research, and the theory of emergent phenomena such as tipping points, resilience and synchronicity.
At the same time, the unit will introduce the UN's sustainable development goals and drill down into them from a quantitative perspective. Example questions may include:
How does this unit fit into your programme of study
The unit offers extends previously-studied fundamental mathematical modelling and engineering subjects and specialises in addressing sustainable development goals. The unit will provide the opportunity to put quantitative learning into practice as well as equipping students with a broad overview of sustainable development and the kind of mathematical modelling techniques that are used in the field. Successful completion of this unit could also enable students to go on to work in a policy, logistics or consultancy context, or to go on to further study in a more specific area of sustainable development.
An overview of content
The unit will be divided into four core topics:
(i) Sustainable development Introduction to sustainable development goals, how they came about, and how they are monitored and updated. Through this, we will introduce the theory of network science and recent work that has used such theory to ask whether the sustainable development goals are self-consistent.
(ii) Energy transition Introduction to energy systems, showing the complex relationships between production, transmission, distribution and usage using the UK as an example. Through this we will introduce need to remain in synchrony on a network, the challenge of low inertia, and the need for power storage as we undergo transition to a sustainable, renewable energy future.
(iii) Climate resilience We will introduce the theoretical paradigms behind the use of large-scale climate models to predict the effects of climate change. Through this we will introduce the mathematical theories of data assimilation, uncertainty propagation and emergent phenomena.
(iv) Simulation in the social sciences We will introduce the epistemology of theories in the social sciences and the different philosophical and methodological paradigms of how mathematical models may provide decision support. We will give an overview of the theory and practice of different simulation methods including discrete-event, agent-based and systems dynamics models. Through practical use of these methods, we will explore ethical implications of their use in socio-technical systems.
How will students, personally, be different as a result of the unit
Students coming from an engineering or physical science background will be exposed to some of the subtlety involved with the use of mathematical modelling and other reductionist methodologies in the social sciences. It is rare that students from these backgrounds have to think about responsible innovation when they build a theory or perform computations. While these ideas have received a lot attention in the context of AI and big data, there are equally pressing considerations when performing simulations or presenting the results of a mathematical reductionist approach. Students will experience first-hand the adage attributed to George Box that “All models are wrong, but some models are useful”. At the same time, students will be encouraged to consider sustainable development as a complex socio-technical problem that requires mathematical modelling. Students should gain an appreciation that there is often no one right answer, but rather that there are complex interdependencies between goals, which can sometimes be conflicting.
Learning Outcomes
1. Plan, apply and evaluate - within the context of mathematical modelling - interventions that aim to achieve the UN sustainable development goals.
2. Use appropriate mathematical techniques and theories to analyse and develop models of socio-technical systems.
3. Reflect on ethical and responsible innovation practices in the context of mathematical modelling of socio-technical systems.
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The learning will be via a mixture of formal lectures, computer-based laboratory work and revision sessions.
Each of the four core topics will be delivered in a two-week block.. The teaching will include guest lectures from external speakers with domain expertise and significant practical experience of mathematical modelling on the topic at hand. The lectures will give the students the core knowledge on which to base their final assessments
In addition, there will be two week-long blocks in which students work intensively on the formative modelling exercises. These will be run in computer lab format, supported by TAs who have technical expertise on running the software tools which the students will be using.
Students will also be expected to do private study, both on the example practice questions for the final exam, and on writing up their individual projects. Help will be available for both at the end of the unit in the form of drop-in revision sessions.
Tasks which help you learn and prepare you for summative tasks (formative):
Formative assessments as part of the unit will take the form of modelling exercises, one on each of the pairs of topics (i,ii) and (iii,iv). Dedicated time during the unit will be given to performing each of these exercises. In each case, students will be given guidance on how to select appropriate questions to address, and be given access to appropriate software tools.
In addition, a set of short-answer questions will be provided that prepare students for the written exam. Full specimen solutions to these questions will be made available. Student attempts at these questions will not be formally marked but help and feedback will be available through revision sessions.
Tasks which count towards your unit mark (summative):
There will be two pieces of summative assessment, of equal weighting.
When assessment does not go to plan:
Re-assessment takes the same form as the original summative assessment.
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. SEMTM0008).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
