Postgraduate Course: Making science relevant to policy and decision-making (VESC11260)
Course Outline
| School | Royal (Dick) School of Veterinary Studies |
College | College of Medicine and Veterinary Medicine |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
| Course type | Online Distance Learning |
Availability | Available to all students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | Science is often used to support decisions that have profound economic, social and environmental impacts. Good decision-making follows from having clear preferences for what is to be achieved (policy aims) and using science to evaluate potential means of reaching those aims (policy instruments). This "policy-led" decision-making is in contrast to often inefficient and ineffective "data-led" decision-making that attempts to infer the correct policy aims from scientific analysis. In other words, when making decisions, science should follow policy, not vice versa. This course will provide a practical guide to implementing policy-led science for decision-making, and will examine the benefits and potential drawbacks of following such an approach in the public, private and charitable sectors. An emphasis on translating policy aims into tractable scientific questions is a particularly distinctive, and possibly unique, feature among the University's courses on science and policy. |
| Course description |
The course compares two common concepts of the growth objective knowledge: empiricism and critical rationalism. Empiricism lies behind attempts to reach objectively correct decisions through disinterested collection and analysis of data, whereas critical rationalism focuses on defining aims of decisions and testing hypotheses about how best to reach those aims. The course explores the implications of these methods for optimising scientific support for decision-making.
A summary of the course is given below.
- Views on how objective knowledge increases: an introduction to empiricism and critical rationalism and their application to decision-making
- Formulating and testing hypotheses relevant to decision-making: translating policy aims into scientific hypotheses and efficient and effective ways to test those hypotheses
- The process of decision-making: defining and balancing opportunity and risk; communicating decisions; evaluating decisions
- Scientists' contributions to decision-making: the roles of scientific advisory committees and their members
- Regulatory decision-making: why certain products or processes are regulated, the principles of good regulation and how science helps and often hinders regulatory decision-making
- Decision-making in businesses: decision-making in business follows the same principles as regulatory decision-making, companies have policy aims, defending decisions and acknowledging products are not perfect without being defensive
- Monitoring and evaluating decisions: no decision is perfect, risk management, testing that decisions are having the desired effect, general surveillance versus targeted monitoring
- Complex models, big data and decision-making: complexity is not necessarily useful in decision-making, more data may make decisions more controversial, big data cannot discover the "right decision"
- Case study (e.g. environmental risk assessment of GM crops in the EU) - use a historical example of scientific support for decision-making to synthesise the ideas from weeks 1-8
- Science and decision-making in the media: take a current example of a controversial topic where science is being used to support decision-making; examine how the media and pressure groups portray the role of science; how do scientists react to this portrayal
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Information for Visiting Students
| Pre-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
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| Academic year 2022/23, Available to all students (SV1)
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Quota: 41 |
| Course Start |
Semester 2 |
| Course Start Date |
16/01/2023 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 18,
Seminar/Tutorial Hours 20,
Formative Assessment Hours 10,
Summative Assessment Hours 46,
Revision Session Hours 2,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
100 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
Formative Assessment: A short written report analysing the concepts of policy-led and data-led decision-making.
Students will be asked to choose an example of decision-making in everyday life (e.g., choosing what to have for lunch). The report should explain how the decision could be approached using policy-led and data-led methods.
Summative Assessment: Using scientific advice in decision-making.
These exercises will ask students to examine the relationship between decision-makers and their scientific advisors.
i. Planning: The student should assume the role of a decision-maker and write a memorandum to his or her scientific advisors requesting critical evaluation of options to deal with a newly arisen situation that may require intervention. (20%)
ii. Assessment of options: The student should assume the role of the leader of a scientific advisory committee and create a short presentation that briefs a decision-maker about options to solve a problem or realise an opportunity; the presentation should outline a) the committee's understanding of the task they were given and b) the committee's analysis of options available to the decision-maker [the situation may be the same as or different from exercise (i)]. (30%)
iii. Decision-making: the student should assume the role of the leader of a committee of enquiry and write a report that critically evaluates the provision and use of scientific advice provided to decision-makers in the situation under review (the situation may be the same as or different from exercises (i) and (ii)]. (50%)
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| Feedback |
Formative Assessment: Students will be advised on their understanding of the difference between policy-led and data-led approaches to decision-making and their ability to recognise how decisions may be made using existing data and when additional data may be required.
Summative assessment: Students will be advised on their understanding of what comprises a fruitful relationship between decision-makers and their scientific advisors, including the need for dialogue to scope the problem and recognising that good advice comprises options with likely results, not necessarily a recommendation to take a particular course of action.
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| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- critically evaluate the limitations of science in support of decision-making
- critically analyse the contributions of policy uncertainty and scientific uncertainty to controversial or difficult decisions
- demonstrate a critical understanding in order to translate policy aims into decision-making criteria and testable hypotheses
- design monitoring, evaluation and communication plans to support decisions
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Reading List
Barrowman N (2018) Why data is never raw. The New Atlantis 129-135
Hall SS (2011) At fault? Nature 477: 264-269
Holz P and Odag Ö (2020) Popper was not a positivist: why critical rationalism could be an epistemology for qualitative as well as quantitative social scientific research. Qualitative Research in Psychology 17: 541-564
Mazzocchi F (2015) Could big data be the end of theory? EMBO Reports 16: 1250-1255
Justin Parkhurst (2017) The Politics of Evidence: From Evidence-Based Policy to the Good Governance of Evidence. Routledge
Roger Pielke (2007) The Honest Broker: Making Sense of Science in Policy and Politics. Cambridge University Press
Popper K (2002) Science: conjectures and refutations. In: Conjectures and Refutations. Routledge
Rudner R (1953) The scientist qua scientist makes value judgements. Philosophy of Science 20: 1-6
Sarewitz, D (2004) How science makes environmental controversies worse. Environmental Science & Policy 7: 385-403
Sarewitz D (2014) Monitor use of electronic cigarettes to assess risk. Nature 512: 349
Schenkel R (2011) The challenge of feeding scientific evidence into policy-making. Science 330: 1749-1751
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Decision-making,policy,risk,opportunity,regulation,business,communication,big data,complexity |
Contacts
| Course organiser | Prof Alan Raybould
Tel: (0131 6)50 6374
Email: |
Course secretary | Miss Emma Durie
Tel: (0131 6)50 6096
Email: |
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