Almost. I have done a full draft that I will redraft one more time following external feedback and review (then during copy-editing). I am hoping that you might also read some of it and give me feedback, if only to point out big mistakes before it is too late. To be honest, by this stage, I won’t be adding major new sections or chapters (and I no longer want to read this thing), but please let me know if there are big gaps that I should fill in the third edition.
I have included below the introduction and conclusion (and each chapter should also have its own entry (or multiple entries) in the 1000 Words and 500 Words series) and invite you to get in touch – via email or Twitter DM – if you would like a copy of the whole thing.
The IAD provides a language, and way of thinking, about the ways in which different institutions foster collective action. The language is so complicated that I have cheated by summarising key terms in this box (and describing polycentric governance in a different post) to stay within the 1000 words limit:
Governing the Commons
For me, the best way to understand the IAD is through the lens of Governing the Commons (and the research agenda it inspired), which explains how to rethink ‘tragedies of the commons’ and encourage better management of common pool resources (CPRs).
Ostrom rejects the uncritical use of rational choice games to conclude – too quickly – that disastrous collective action problems are inevitable unless we ‘privatize’ commons or secure major government intervention (which is tricky anyway when global problems require international cooperation). The tragedy of the commons presents a too-bleak view of humanity, in which it would be surprising to find cooperation even when the fate of the world is in human hands.
Alternatively, what if there is evidence that people often work collectively and effectively without major coercion? People are social beings who share information, build trust by becoming known as reliable and predictable, and come together to produce, monitor and enforce rules for the group’s benefit. They produce agreements with each other that could be enforced if necessary.
The IAD helps us analyse these cooperative arrangements. Ostrom describes 8 ‘design principles’ of enduring and effective CPR management shared by many real world examples:
CPRs have clear boundaries. Users know what they are managing, and can identify legitimate users.
The rules suit local conditions. Users know what they (a) are expected to contribute to management and (b) receive from CPRs.
The actors affected by the rules help shape them (at low cost).
CPR monitors are users or accountable to users. They monitor (a) the conduct of users and (b) the state of the CPR. The costs of mutual monitoring are low, and their consequences felt quickly.
The penalties for rule-breaking are low if the choice is a one-off and understandable under the circumstances (to avoid alienating the user). The penalties are high if the choice is part of a pattern which makes other users feel like ‘suckers’, or if rule-breaking would be catastrophic.
Conflict resolution is frequent, rapid and low cost.
Users have the right to self-organise without too much outside interference.
Many projects are connected geographically and at different scales – local, regional, national – in ways that do not undermine individual projects.
These design principles help explain why some communities manage CPRs successfully. They allow users to share the same commitment and expect the long-term benefits to be worthwhile.
However, Ostrom stressed that there is no blueprint – no hard and fast rules – to CPR management. There are three particular complications:
Good management requires high trust to encourage norms of reciprocity. Trust is crucial to minimizing the costs of compliance monitoring and enforcement. Trust may develop when participants communicate regularly, share an understanding of their common interests, reciprocate each other’s cooperation, and have proven reliable in the past.
Design principles are important to developing trust and solidarity, but so are ‘evolutionary’changes to behaviour. Actors have often learned about rule efficacy – to encourage cooperation and punish opportunism – through trial-and-error over a long period, beginning with simple, low-cost operational rules producing quick wins.
Rules, rules on rules, more rules, then even more rules
Institutions contain a large, complicated set of rules that serve many different purposes, and need to be understood and analysed in different ways.
Different purposes include:
how many actors are part of an action situation, and the role they play
what they must/ must not do
who is eligible to participate
who can move from one role to another
who controls membership, and how
how many participants are involved in a choice
what will happen if there is no agreement
how to manage and communicate information
the rewards or sanctions
the range of acceptable actions or outcomes from action.
We also need to analyse the relative costs and simplicity of different rules, and the rules about the other rules, including
‘operational’ rules on day-to-day issues (such as specific payoffs/ sanctions for behaviour)
‘collective choice’ rules about how to make those rules
‘constitutional’ rules on who can decide those rules and who can monitor and enforce, and
‘metaconstitutional’ analysis of how to design these constitutions with reference to the wider political and social context.
The world is too complex to break down into simple pieces
By now, you may be thinking that the IAD – and analysis of resource management – is complicated. This is true, partly because each case study – of the physical conditions and social practices regarding resource management – is different in some way. We can use the IAD to compare experiences, but accept that a profoundly successful scheme in one context may fail miserably in another.
Simplicity versus complexity: the world is complex, but should our analysis follow suit?
Indeed, this is why we need to think about rational choice games and the IAD simultaneously, to understand the analytical trade-offs.
Game theory laboratory experiments – built on simple rules and relatively small numbers of parameters – produce parsimonious analysis and results that we can understand relatively easily.
We may reject simple games as unrealistic, but what if we take this criticism to its extreme?
IAD in-depth field studies embrace complexity to try to understand the key dimensions of each study’s context. When we put them all together, there are too many concepts, variables, global applications, and variations-by-context, to contain in a simple theory.
The IAD addresses this trade off by offering a language to help organize research, encouraging people to learn it then use it to apply many different theories to explain different parts of the whole picture.
In other words, it is OK to reject simple models as unrealistic, but to embrace real-world complexity may require a rather complicated language.
Rational choice theory provides a way of thinking about collective action problems. There is great potential for choices made by individuals to have an adverse societal effect when there is an absence of trust, obligation, or other incentives to cooperate. People may have collective aims that require cooperation, but individual incentives to defect. While the action of one individual makes little difference, the sum total of individual actions may be catastrophic.
Simple ‘games’ provide a way to think about these issues logically, by limiting analysis to very specific situations under rather unrealistic conditions, before we consider possible solutions under more realistic conditions. For example, in simple games we assume that individuals pursue the best means to fulfil their preferences: they are able to act ‘optimally’ by processing all relevant information to rank-order their preferences consistently.
Go with it just now, and then we can consider what to do next.
The ‘prisoner’s dilemma’
Two people are caught red-handed and arrested for a minor crime, placed in separate rooms and invited to confess to a major crime (they both did it and the police know it but can’t prove it). The payoffs are:
If Paul confesses and Linda doesn’t, then Paul walks free and Linda receives a 10 year jail sentence (or vice versa)
If both confess they receive a much higher sentence (8 years) than if neither confesses (1 year).
Also assume that they take no benefit from the shorter sentence of the other person (a non-cooperative game).
It demonstrates a collective action problem: although the best outcome for the group requires that neither confess (both would go to jail for a total of 2 years), the actual outcome is that both confess (16 years). The latter represents the ‘Nash equilibrium’ since neither would be better off by changing their strategy unilaterally. Think of it from an individual’s perspective:
Imagine Paul will confess. Linda knows that if she stays silent, she gets suckered into 10 years. If she confesses, she gets 8.
Imagine Paul will stay silent. Linda knows that if she stays silent, she gets 1 year. If she confesses, she suckers Paul into 10.
The effect of Paul and Linda acting as individuals is that they are worse off collectively. Both ‘defect’ (confess) when they should ‘cooperate’ (stay silent).
The ‘logic of collective action’
Olson argues that, as the membership of an interest group rises, so does:
(a) the belief among individuals that their contribution to the group would make little difference and
(b) their ability to ‘free ride’.
I may applaud the actions of a group, but can – and will try to – enjoy the outcomes without leaving my sofa, paying them, or worrying that they will fail without me or punish me for not getting involved.
The ‘tragedy of the commons’
The scenario is that a group of farmers share a piece of land that can only support so many cattle before deteriorating and becoming useless. Although each farmer recognizes the collective benefit to an overall maximum number of cattle, each calculates that the marginal benefit she takes from one extra cow for herself exceeds the extra cost of over grazing to the group. Individuals place more value on the resources they extract for themselves now than the additional rewards they could all extract in the future.
The tragedy is that if all farmers act on the same calculation then they will destroy the common resource. The group is too large to track individual behaviour, individuals place more value on current over future consumption, and there is low mutual trust, with minimal motive and opportunity to produce and enforce binding agreements
This ‘tragedy’ sums up current anxieties about one of the defining problems of our time: global ‘common pool resources’ are scarce and the world’s population and consumption levels are rising; there is no magic solution; and, collective action is necessary but not guaranteed. We may value sustainable water, air, energy, forests, crops, and fishing stocks, but find it difficult to imagine how our small contribution to consumption will make much difference. As a group we fear climate change and seek to change our ways but, as individuals, contribute to the problem.
Overall, these scenarios suggest that individuals have weak incentives to cooperate even if it is in their interests and they agree to do so. This problem famously prompted Hardin (to recommend ‘mutual coercion, mutually agreed upon’ to ensure collective action.
What happens when there are many connected games?
In real life, it is almost impossible to find such self-contained and one-off games.In many repeated – or connected – games, the players know that thereare wider or longer-term consequences to defection.
In ‘nested games’, the behaviour of individuals often seems weird in one game until we recognise their involvement in a series. It may pay off to act ‘irrationally’ in the short term to support a longer-term strategy, or to lose in one to win in another.
In an ‘ecology of games’, many overlapping games take place at roughly the same time, and players to learn how to play one game while keeping an eye on many others, while some key players encourage a wider set of rules under which all games operate.
Evolutionary game theory explores how behaviour changes over multiple games to reflect factors such as (a) feedback and learning from trial and error, and (b) norms and norm enforcement.
For example, player 2 may pursue a ‘tit-for-tat’ strategy. She cooperates at first, then mimics the other player’s previous choice: defecting, to punish the other player’s defection, or cooperating if the other player cooperated. Knowledge of this strategy could provide player 1 with the incentive to cooperate. Further, norms develop when players enforce and expect sanctions for non-cooperation, foster socialisation to discourage norm violation, and some norms become laws.
In other words, this focus on the rules of repeated games gives us more hope than the tragedy of the commons. Indeed, it underpin Ostrom’s famous analysis of the conditions under which people can govern the commons more effectively.
Something I've been meaning to say about The Tragedy of the Commons. Bear with me for a small thread on why our embrace of Hardin is a stain on environmentalism. tldr: we’ve let a flawed metaphor by a racist ecologist define environmental thinking for a half century. 1/
Many theories in this 1000 words series describe multiple policymaking venues. They encourage us to give up on the idea of an all-knowing, all-powerful national central government. Instead, there are many venues in which to make authoritative choices, each contributing to what we call policy.
The word ‘multi-centric’ (coined by Professor Tanya Heikkila, with me and Dr Matt Wood) does not suggest that every venue is of equal importance or power. Rather, it prompts us not to miss something important by focusing too narrowly on one single (alleged) centre of authority.
To some extent, multi-centric policymaking results from choice. Many federal political systems have constitutions that divide power between executive, legislative, and judicial branches, or give some protection to subnational governments. Many others have become ‘quasi-federal’ more organically, by sharing responsibilities with supranational and subnational governments. In such cases, there is explicit choice to distribute power and share responsibility for making policy (albeit with some competition to assert power or shuffle-off responsibility).
However, for the most part, this series helps explain the necessity of multi-centric policymaking with reference to two concepts:
Bounded rationality. Policymakers are only able to pay attention to – and therefore understand and seek to control – a tiny proportion of their responsibilities.
Both factors combine to provide major limits to single central government control. Elected policymakers deal with bounded rationality by prioritising some issues and, necessarily, delegating responsibility for the rest. Delegation may be inside or outside of central government.
1000 Words theories describing multi-centric governmentdirectly
Multi-level governance describes the sharing of power vertically, between many levels of government, and horizontally, between many governmental, quasi-non-governmental and non-governmental organisations. Many studies focus on the diffusion of power within specific areas like the European Union – highlighting choice – but the term ‘governance’ has a wider connection to the necessity of MLG.
For example, part of MLG’s origin story is previous work to help explain the pervasiveness of policy networks:
Policymakers at the ‘top’ ask bureaucrats to research and process policy on their behalf
Civil servants seek information and advice from actors outside of government
They often form enduring relationships built on factors such as trust.
Such policymaking takes place away from a notional centre – or at least a small core executive – and with limited central attention.
Polycentricity describes (a) ‘many decision centers’ with their own separate authority, (b) ‘operating under an overarching set of rules’, but with (c) a sense of ‘spontaneous order’ in which no single centre controls the rules or outcomes. Polycentric governance describes ‘policymaking centres with overlapping authority; they often work together to make decisions, but may also engage in competition or conflict’.
This work on polycentric governance comes primarily from the Institutional Analysis and Development (IAD) framework that helps compare the effectiveness of institutions designed to foster collective action. For example, Ostrom identifies the conditions under which non-governmental institutions can help manage ‘common pool resources’ effectively, while IAD-inspired studies of municipal governance examine how many ‘centres’ can cooperate as or more effectively than a single central government.
Complexity theory has a less clear origin story, but we can identify key elements of complex systems:
They are greater than the sum of their parts
They amplify or dampen policymaking activity, so the same action can have a maximal or no impact
Small initial choices can produce major long term momentum
There are regularities of behaviour despite the ever-present potential for instability
They exhibit ‘emergence’. Local outcomes seem to defy central direction.
Systems contain many actors interacting with many other actors. They follow and reproduce rules, which help explain long periods of regular behaviour. Or, many actors and rules collide when they interact, producing the potential for many bursts of instability. In each case, the system is too large and unpredictable to be subject to central control.
1000 Words theories describing multi-centric government indirectly
Many other theories in this series describe multi-centric policymaking – or aspects of it – without using this term directly. Examples include:
Punctuated equilibrium theory suggests that (a) policymakers at the ‘centre’ of government could pay attention to, and influence, most issues, but (b) they can only focus on a small number and must ignore the rest. Very few issues reach the ‘macropolitical’ agenda. Multiple policymaking organisations process the rest out of the public spotlight.
Multiple streams analysis turns the notion of a policy cycle on its head, and emphasises serendipity over control. Policy does not change until three things come together at the right ‘window of opportunity’: attention to a problem rises, a feasible solution exists, and policymakers have the motive and opportunity to act. Modern MSA studies show that such windows exist at multiple levels of government.
The advocacy coalition framework describes the interaction between many policymakers and influencers. Coalitions contain actors from many levels and types of government, cooperating and competing within subsystems (see networks). They are surrounded by a wider context – over which no single actor has direct control – that provides the impetus for ‘shocks’ to each coalition.
In such accounts, the emphasis is on high levels of complexity, the potential for instability, and the lack of central control over policymaking and policy outcomes. The policy process is not well described with reference to a small group of policymakers at the heart of government.
Democratic governance is defined by the regular rotation of elected leaders. Amidst the churn, the civil service is expected to act as the repository of received wisdom about past policies, including assessments of what works and what doesn’t. The claim is that to avoid repeating the same mistakes we need to know what happened last time and what were the effects. Institutional memory is thus central to the pragmatic task of governing.
What is institutional memory? And, how is it different to policy learning?
Despite increasing recognition of the role that memory can or should play in the policy process, the concept has defied easy scholarly definition.
In the classic account, institutional memory is the sum total of files, procedures and knowledge held by an organisation. Christopher Pollitt, who has pioneered the study of institutional memory, refers to the accumulated knowledge and experience of staff, technical systems, including electronic databases and various kinds of paper records, the management system, and the norms and values of the organizational culture, when talking about institutional memory. In this view, which is based on the key principles of the new institutionalism, memory is essentially an archive.
The problem with this definition is that it is hard to distinguish the concept from policy learning (see also here). If policy learning is in part about increasing knowledge about policy, including correcting for past mistakes, then we could perhaps conceive of a continuum from learning to memory with an inflection point where one starts and the other stops. But, this is easier to imagine than it is to measure empirically. It also doesn’t acknowledge the forms memories take and the ways memories are contested, suppressed and actively forgotten.
In our recent contribution to this debate (see here and here) we define memories as ‘representations of the past’ that actors draw on to narrate what has been learned when developing and implementing policy. When these narratives are embedded in processes they become ‘institutionalised’. It is this emphasis on embedded narratives that distinguishes institutional memory from policy learning. Institutional memory may facilitate policy learning but equally some memories may prohibit genuine adaptation and innovation. As a result, while there is an obvious affinity between the two concepts it is imperative that they remain distinct avenues of inquiry. Policy learning has unequivocally positive connotations that are echoed in some conceptualisations of institutional memory (i.e. Pollitt). But, equally, memory (at least in a ‘static’ form) can be said to provide administrative agents with an advantage over political principals (think of the satirical Sir Humphrey of Yes Minister fame). The below table seeks to distinguish between these two conceptualisations of institutional memory:
Key debates: Is institutional memory declining?
The scholar who has done the most to advance our understanding of institutional memory in government is Christopher Pollitt. His main contention is that institutional memory has declined over recent decades due to: the high rotation of staff in the civil service, changes in IT systems which prevent proper archiving, regular organisational restructuring, rewarding management skills above all others, and adopting new management ‘fads’ that favour constant change as they become popular. This combination of factors has proven to be a perfect recipe for the loss of institutional memory within organisations. The result is a contempt for the past that leads to repeated policy failure.
We came to a different view. Our argument is that one of the key reasons why institutional memory is said to have declined is that it has been conceptualised in a ‘static’ manner more in keeping with an older way of doing government. This practice has assumed that knowledge on a given topic is held centrally (by government departments) and can be made explicit for the purpose of archiving. But, if government doesn’t actually work this way (see relevant posts on networks here) then we shouldn’t expect it to remember this way either. Instead of static repositories of summative documents holding a singular ‘objective’ memory, we propose a more ‘dynamic’ people-centred conceptualisation that sees institutional memory as a composite of intersubjective memories open to change. This draws to the fore the role of actors as crucial interpreters of memory, combining the documentary record with their own perspectives to create a story about the past. In this view, institutional memory has not declined, it is simply being captured in a fundamentally different way.
Key debates: How can an institution improve how it remembers?
How an institution might improve its memory is intrinsically linked to how memory is defined and whether or not it is actually in decline. If we follow Pollitt’s view that memory is about the archive of accumulated knowledge that is being ignored or deliberately dismantled by managerialism then the answer involves returning to an older way of doing government that placed a higher value on experience. By putting a higher value on the past as a resource institutions would reduce staff turnover, stop regular restructures and changes in IT systems, etc. For those of us who work in an institution where restructuring and IT changes are the norm, this solution has obvious attractions. But, would it actually improve memory? Or would it simply make it easier to preserve the status quo (a process that involves actively forgetting disruptive but generative innovations)?
Our definition, relying as it does on a more dynamic conceptualisation of memory, is sceptical about the need to improve practices of remembering. But, if an institution did want to remember better we would favour increasing the opportunity for actors within an institution to reflect on and narrate the past. One example of this might be a ‘Wikipedia’ model of memory in which the story of a policy, it success and failure, is constructed by those involved, highlighting points of consensus and conjecture.
Corbett J, Grube D, Lovell H, Scott R. “Singular memory or institutional memories? Toward a dynamic approach”. Governance. 2018;00:1–19. https://doi.org/10.1111/gove.12340
Pollitt, C. 2009. “Bureaucracies Remember, Post‐Bureaucratic Organizations Forget?” Public Administration 87 (2): 198-218.
Pollitt, C. 2000. “Institutional Amnesia: A Paradox of the ‘Information Age’?” Prometheus 18 (1): 5-16.
We talk a lot about ‘the policy process’ without really saying what it is. If you are new to policy studies, maybe you think that you’ll learn what it is eventually if you read enough material. This would be a mistake! Instead, when you seek a definition of the policy process, you’ll find two common responses:
Both responses seem inadequate: one avoids giving an answer, and another gives the wrong answer!
However, we can combine elements of each approach to give you just enough of a sense of ‘the policy process’ to continue reading the full ‘1000 words’ series:
1. The beauty of the ‘what is policy?’ question …
… is that we don’t give you an answer. It may seem frustrating at first to fail to find a definitive answer, but eventually you’ll accept this problem! The more important outcome is to use the ‘what is policy?’ question to develop analytical skills, to allow you to define policy in more specific circumstances (such as, what are the key elements of policy in this case study?), and ask more useful and specific questions about policy and policymaking. So, look at the questions we need to ask if we begin with the definition, ‘the sum total of government action, from signals of intent to the final outcomes’: does action include statements of intent? Do we include unintended policy outcomes? Are all policymakers in government? What about the things policymakers choose not to do? And so on.
2. The beauty of the policy cycle approach …
… is that it provides a simple way to imagine policy ‘dynamics’, or events and choices producing a never-ending sequence of other events and choices. Look at the stages model to identify many different tasks within one ‘process’, and to get the sense that policymaking is continuous and often ‘its own cause’. It’s not a good description of what actually happens, but it describes what some might like to happen, and used by many governments to describe what they do. Consequently, we can’t simply ignore it, at least without providing a better description, a better plan, and a better way for governments to justify what they do.
There are more complicated but better ways of describing policymaking dynamics
This picture is the ‘policy process’ equivalent of my definition of public policy. It captures the main elements of the policy process described – albeit in different ways – by most policy theories in this series. I present it here to give you enough of an answer – to ‘what is the policy process?’ – to help you ask more questions.
In the middle is ‘policy choice’
At the heart of most policy theory is ‘bounded rationality’, which describes (a) the cognitive limits of all people, and (b) how policymakers overcome such limits to make decisions (in the absence of NZT). In short, they use ‘rational’ and ‘irrational’ shortcuts to action, but these are provocative terms to prompt further reading (on, for example, ‘evidence-based policymaking’).
‘Rational’ describes goal-oriented activity: people may have limits to their attention and ‘information processing’, but they find systematic ways to respond, by setting goals and producing criteria to find the best information. ‘Irrational’ describes aspects of psychology: people draw on habit, emotions, their ‘gut’ or intuition, well-established beliefs, and their familiarity with information to make often-almost-instant decisions.
Surrounding choice is what we’ll call the ‘policy environment’
Environment is a metaphor we’ll use to describe the combination of key elements of the policy process which (a) I describe separately in further 1000 words posts, and (b) policy theories bring together to produce an overall picture of policy dynamics.
There are 5 or 6 key elements. In the picture are 6, reflecting the way Tanya Heikkila and I describe it (and the fact that I had 7 boxes to fill). In real life, I describe 5 because I have 5 digits on each hand. If you are Count Tyrone Rugen you have more choice.
Policy environments are made up of:
A wide range of actors (which can be individuals and organisations with the ability to deliberate and act) making or influencing policy at many levels and types of government.
Institutions, defined as the rules followed by actors. Some are formal, written down, and easy to identify. Others are informal, reproduced via processes like socialisation, and difficult to spot and describe.
Networks, or the relationships between policymakers and influencers. Some are wide open, competitive, and contain many actors. Others are relatively closed, insulated from external attention, and contain few actors.
Ideas, or the beliefs held and shared by actors. There is often a tendency for certain beliefs or ‘paradigms’ to dominate discussion, constraining or facilitating the progress of new ‘ideas’ as policy solutions.
Context and events. Context describes the policy conditions – including economic, social, demographic, and technological factors – that provide the context for policy choice, and are often outside of the control of policymakers. Events can be routine and predictable, or unpredictable ‘focusing’ events that prompt policymaker attention to lurch at short notice.
This picture is only the beginning of analysis, raising further questions that will make more sense when you read further, including: should policymaker choice be at the centre of this picture? Why are there arrows in the cycle but not in my picture? Should we describe complex policymaking ‘systems’ rather than ‘environments’? How exactly does each element in the ‘policy environment’ or ‘system’ relate to the other?
The answer to the final question can only be found in each theory of the policy process, and each theory describes this relationship in a different way. Let’s not worry about that just now! We’ll return to this issue at the end, when thinking about how to combine the insights of many theories.
Imagine that your audience is a group of scientists who have read everything and are only interested in something new. You need a new theory, method, study, or set of results to get their attention.
Let’s say that audience is a few hundred people, or half a dozen in each subfield. It would be nice to impress them, perhaps with some lovely jargon and in-jokes, but almost no-one else will know or care what you are talking about.
Imagine that your audience is a group of budding scientists, researchers, students, practitioners, or knowledge-aware citizens who are new to the field and only interested in what they can pick up and use (without devoting their life to each subfield). Novelty is no longer your friend. Instead, your best friends are communication, clarity, synthesis, and a constant reminder not to take your knowledge and frame of reference for granted.
Let’s say that audience is a few gazillion people. If you want to impress them, imagine that you are giving them one of the first – if not the first – ways of understanding your topic. Reduce the jargon. Explain your problem and why people should care about how you try to solve it. Clear and descriptive titles. No more in-jokes (just stick with the equivalent of ‘I went to the doctor because a strawberry was growing in my arse, and she gave me some cream for it’).
At least, that’s what I’ve been telling myself lately. As things stand, my most-read post of all time is destined to be on the policy cycle, and most people read it because it’s the first entry on a google search. Most readers of that post may never read anything else I’ve written (over a million words, if I cheat a bit with the calculation). They won’t care that there are a dozen better ways to understand the policy process. I have one shot to make it interesting, to encourage people to read more. The same goes for the half-dozen other concepts (including multiple streams, punctuated equilibrium theory, the Advocacy Coalition Framework) which I explain to students first because I now do well in google search (go on, give it a try!).
I also say this because I didn’t anticipate this outcome when I wrote those posts. Now, a few years on, I’m worried that they are not very good. They were summaries of chapters from Understanding Public Policy, rather than first principles discussions, and lots of people have told me that UPP is a little bit complicated for the casual reader. So, when revising it, I hope to make it better, and by better I mean to appeal to a wider audience without dumping the insights. I have begun by trying to write 500-words posts as, I hope, improvements on the 1000-word versions. However, I am also open to advice on the originals. Which ones work, and which ones don’t? Where are the gaps in exposition? Where are the gaps in content?
These posts introduce you to key concepts in the study of public policy. They are all designed to turn a complex policymaking world into something simple enough to understand. Some of them focus on small parts of the system. Others present ambitious ways to explain the system as a whole. The wide range of concepts should give you a sense of a variety of studies out there, but my aim is to show you that these studies have common themes.