Epistemic versus bargaining-driven policy learning

There is an excellent article by Professor Claire Dunlop called “The irony of epistemic learning: epistemic communities, policy learning and the case of Europe’s hormones saga” (Open Access). It uses the language of ‘policy learning’ rather than ‘evidence based policymaking’, but these descriptions are closely related. I describe it below, in the form I’ll use in the 2nd ed of Understanding Public Policy (it will be Box 12.2).

Dunlop (2017c) uses a case study – EU policy on the supply of growth hormones to cattle – to describe the ‘irony of epistemic learning’. It occurs in two initial steps.

First, a period of epistemic learning allowed scientists to teach policymakers the key facts on a newly emerging policy issue. The scientists, trusted to assess risk, engaged in the usual processes associated with scientific work: gathering evidence to reduce uncertainty, but always expressing the need to produce continuous research to address inevitable uncertainty in some cases.  The ‘Lamming’ committee of experts commissioned and analysed scientific evidence comprehensively before reporting (a) that the use of ‘naturally occurring’ hormones in livestock was low risk for human consumers if administered according to regulations and guidance, but (b) it wanted more time  to analyse the carcinogenic effects of two ‘synthetic compounds’ (2017c: 224).

Second, a period of bargaining changed the context. EU officials (in DG Agriculture) responded to European Parliament concerns, fuelled by campaigning from consumer groups, which focused on uncertainty and worst-case scenarios. Officials suspended the committee’s deliberations before it was due to report and banned the use of growth hormones in the EU (and the importation of relevant meat).

The irony is two-fold.

First, it results from the combination of processes: scientists, operating in epistemic mode, described low risk but some uncertainty; and policymakers, operating in bargaining mode, used this sense of uncertainty to reject scientific advice.

Second, scientists were there to help policymakers learn about the evidence, but were themselves unable to learn about how to communicate and form wider networks within a political system characterised by periods of bargaining-driven policy learning.

Managing expectations about the use of evidence in policy

Notes for the #transformURE event hosted by Nuffield, 25^th September 2018

I like to think that I can talk with authority on two topics that, much like a bottle of Pepsi and a pack of Mentos, you should generally keep separate:

1. When talking at events on the use of evidence in policy, I say that you need to understand the nature of policy and policymaking to understand the role of evidence in it.
2. When talking with students, we begin with the classic questions ‘what is policy?’ and ‘what is the policy process’, and I declare that we don’t know the answer. We define policy to show the problems with all definitions of policy, and we discuss many models and theories that only capture one part of the process. There is no ‘general theory’ of policymaking.

The problem, when you put together those statements, is that you need to understand the role of evidence within a policy process that we don’t really understand.

It’s an OK conclusion if you just want to declare that the world is complicated, but not if you seek ways to change it or operate more effectively within it.

Put less gloomily:

• We have ways to understand key parts of the policy process. They are not ready-made to help us understand evidence use, but we can use them intelligently.
• Most policy theories exist to explain policy dynamics, not to help us adapt effectively to them, but we can derive general lessons with often-profound implications.

Put even less gloomily, it is not too difficult to extract/ synthesise key insights from policy theories, explain their relevance, and use them to inform discussions about how to promote your preferred form of evidence use.

The only remaining problem is that, although the resultant advice looks quite straightforward, it is far easier said than done. The proposed actions are more akin to the Labours of Hercules than [PAC: insert reference to something easier].

They include:

1. Find out where the ‘action’ is, so that you can find the right audience for your evidence. Why? There are many policymakers and influencers spread across many levels and types of government.
2. Learn and follow the ‘rules of the game’. Why? Each policymaking venue has its own rules of engagement and evidence gathering, and the rules are often informal and unwritten.
3. Gain access to ‘policy networks’. Why? Most policy is processed at a low level of government, beyond the public spotlight, between relatively small groups of policymakers and influencers. They build up trust as they work together, learning who is reliable and authoritative, and converging on how to use evidence to understand the nature and solution to policy problems.
4. Learn the language. Why? Each venue has its own language to reflect dominant ideas, beliefs, or ways to understand a policy problem. In some arenas, there is a strong respect for a ‘hierarchy’ of evidence. In others, they key reference point may be value for money. In some cases, the language reflects the closing-off of some policy solutions (such as redistributing resources from one activity to another).
5. Exploit windows of opportunity. Why? Events, and changes in socioeconomic conditions, often prompt shifts of attention to policy issues. ‘Policy entrepreneurs’ lie in wait for the right time to exploit a shift in the motive and opportunity of a policymaker to pay attention to and try to solve a problem.

So far so good, until you consider the effort it would take to achieve any of these things: you may need to devote the best part of your career to these tasks with no guarantee of success.

Put more positively, it is better to be equipped with these insights, and to appreciate the limits to our actions, than to think we can use top tips to achieve ‘research impact’ in a more straightforward way.

Kathryn Oliver and I describe these ‘how to’ tips in this post and, in this article in Political Studies Review, use a wider focus on policymaking environments to produce a more realistic sense of what individual researchers – and research-producing organisations – could achieve.

There is some sensible-enough advice out there for individuals – produce good evidence, communicate it well, form relationships with policymakers, be available, and so on – but I would exercise caution when it begins to recommend being ‘entrepreneurial’. The opportunities to be entrepreneurial are not shared equally, most entrepreneurs fail, and we can likely better explain their success with reference to their environment than their skill.

Emotion and reason in politics: the rational/ irrational distinction

In ‘How to communicate effectively with policymakers’, Richard Kwiatkowski and I use the distinction between ‘rational’ and ‘irrational’ cognitive shortcuts ‘provocatively’. I sort of wish we had been more direct, because I have come to realise that:

1. My attempts to communicate with sarcasm and facial gestures may only ever appeal to a niche audience, and
2. even if you use the scare quotes – around a word like ‘irrational’ – to denote the word’s questionable use, it’s not always clear what I’m questioning, because
3. you need to know the story behind someone’s discussion to know what they are questioning.*

So, here are some of the reference points I’m using when I tell a story about ‘irrationality’:

1. I’m often invited to be the type of guest speaker that challenges the audience, it is usually a scientific audience, and the topic is usually evidence based policymaking.

So, when I say ‘irrational’, I’m speaking to (some) scientists who think of themselves as rational and policymakers as irrational, and use this problematic distinction to complain about policy-based evidence, post-truth politics, and perhaps even the irrationality of voters for Brexit. Action based on this way of thinking would be counterproductive. In that context, I use the word ‘irrational’ as a way into some more nuanced discussions including:

• all humans combine cognition and emotion to make choices; and,
• emotions are one of many sources of ‘fast and frugal heuristics’ that help us make some decisions very quickly and often very well.

In other words, it is silly to complain that some people are irrational, when we are all making choices this way, and such decision-making is often a good thing.

2. This focus on scientific rationality is part of a wider discussion of what counts as good evidence or valuable knowledge. Examples include:

• Policy debates on the value of bringing together many people with different knowledge claims – such as through user and practitioner experience – to ‘co-produce’ evidence.
• Wider debates on the ‘decolonization of knowledge’ in which narrow ‘Western’ scientific principles help exclude the voices of many populations by undermining their claims to knowledge.

3. A focus on rationality versus irrationality is still used to maintain sexist and racist caricatures or stereotypes, and therefore dismiss people based on a misrepresentation of their behaviour.

I thought that, by now, we’d be done with dismissing women as emotional or hysterical, but apparently not. Indeed, as some recent racist and sexist coverage of Serena Williams demonstrates, the idea that black women are not rational is still tolerated in mainstream discussion.

4. Part of the reason that we can only conclude that people combine cognition and emotion, without being able to separate their effects in a satisfying way, is that the distinction is problematic.

It is difficult to demonstrate empirically. It is also difficult to assign some behaviours to one camp or the other, such as when we consider moral reasoning based on values and logic.

To sum up, I’ve been using the rational/irrational distinction explicitly to make a simple point that is relevant to the study of politics and policymaking:

• All people use cognitive shortcuts to help them ignore almost all information about the world, to help them make decisions efficiently.
• If you don’t understand and act on this simple insight, you’ll waste your time by trying to argue someone into submission or giving them a 500-page irrelevant report when they are looking for one page written in a way that makes sense to them.

Most of the rest has been mostly implicit, and communicated non-verbally, which is great when you want to keep a presentation brief and light, but not if you want to acknowledge nuance and more serious issues.

 

 

 

 

*which is why I’m increasingly interested in Riker’s idea of heresthetics, in which the starting point of a story is crucial. We can come to very different conclusions about a problem and its solution by choosing different starting points, to accentuate one aspect of a problem and downplay another, even when our beliefs and preferences remain basically the same.

 

 

Some rubbish cultural references for lecturers

Reblogged to coincide with all those tweets from lecturers realizing that they are getting old and their cultural references are ancient or inappropriate.

Paul Cairney: Politics & Public Policy

As an ageing lecturer, I often find that my cultural references generally fall flat with late teenage/ early 20s students. Still, I persevere because I forget which ones are completely pancake, which ones still work if you explain them a little bit, and which ones work again because there has been a film remake. So, here is a repository to help me remember:

Things that still just about work

The Matrix seems to work for just about everything, which means that it works for nothing (‘I remember we talked about it, but what was the point again?’)

A JFK scene sort of works as a vague reference to ‘the whole system’ (but undermines regression analysis of discrete variables)

Mr Robot might work – eventually – if you don’t have to subscribe to Amazon to get it

Things that don’t work

Don’t refer to a ‘sliding doors moment‘ in British politics unless you…

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