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Exploring the Political Galapagos

Exploring the Political Galapagos. Ideas, Networks and Evolution Stuart Astill, London School of Economics and Institut d’Etudes Politiques de Paris. How is policy really made?. Who am I, why am I asking? Isn’t this a complicated answer? A very brief summary. Four statements.

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Exploring the Political Galapagos

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  1. Exploring the Political Galapagos Ideas, Networks and Evolution Stuart Astill, London School of Economics and Institut d’Etudes Politiques de Paris

  2. How is policy really made? • Who am I, why am I asking? • Isn’t this a complicated answer? • A very brief summary

  3. Four statements • Knowledge is incomplete in policy making • Ideas and aims change over time • Policy makers have multiple sets of conflicting rationales • Policy is made in networks

  4. Agent based thinking • An agent does not have to mean a person • Analytical approaches are good (sometimes…) • Complex systems cannot be solved • Using simulation • Or just think like that to get a new angle

  5. Three strands • Ideational approaches (Goldstein and Keohane 1993, Sabatier [and Jenkins-Smith] 1993 [and 1999], Schonhardt-Bailey ‘International trade and political institutions’ 2001) • Network Analysis (Journal of theoretical politics 10 (4), Peter John 1998, 2000, Knoke 1982 ‘Network Analysis’) • Policy Evolution (Dowding, John, others – see paper)

  6. Environment of ideas • Policy gets made in people’s heads • People are connected by pathways over which ideas move (a ‘network that forms policy’ NFP) • The real world is • Mediated • Forecast • Imagined • What sort of ideas? – a horse – a platonic horse – my version of a horse • Scaled up to a network

  7. Memetic view • Memes – they carry information like genes • Policy viruses • They need to be able to copy ‘fairly well’ • Reproduce from head to head, across the network

  8. Random variation/natural selection • Very, very likely to get shot down • A choice • 1) Actors are deliberately moulding policies according to their ‘rational’ utility maximisation • 2) Policies are flying around being varied inaccurately and undirected, getting wrongly passed on, being only half-baked We must believe (or assume) (2) rather than (1) or the model makes no sense

  9. Specific reasons for randomness • General utility maximising aims are difficult to map onto specific policies • Aims on specific policies do not have known ‘levers’ and we do not know which direction or how hard to pull them • Each policy’s sub-memes can get rearranged creating natural randomness • This IS how policy is made • I have experienced it

  10. Typical Chaotic system • Chaos proves that very simple deterministic models can have random results • Even if policy makers are operating according to simple deterministic rules their actions on policy can have random results • Don’t even bother trying to solve it

  11. Focus on the meme • Imagine you are a meme, trying to get multiplied and passed on through the network of people’s brains • It doesn’t really happen like that • Find out why in my paper, available online • But it makes analysis clearer – it’s a model

  12. Intentions – of course, but in their place • And their place is in the environment not the actor • The bird and the seed • If bird finds seed tasty, bird eats seed, bird excretes seed • SEED IS SUCCESSFUL • We do not try to ask what the bird is thinking • The human and the policy • If Sally finds policy tasty, Sally remembers policy, Sally passes policy • POLICY IS SUCCESSFUL • We do not try to ask what the politician is thinking (thank goodness)

  13. Two stage evolution • Evolution in the real world, not viable • Evolution happens in people’s heads • Then ‘successful policy’ implemented • Things happen in the real world • Which in turn affects people perceptions

  14. Types of ideas – change differently • World views-basic beliefs • Principled beliefs-basic perceptions of the problem • Causal beliefs-ways and means, causes, evaluations These all evolve more or less quickly in the environment of ideas – but we are most interested in the policy meme

  15. Further reflections If we have time: • Ideational concepts and linkage (PM’s idea), co-evolution of all elements • Network analysis – absolutely vital. Very complex to analyse but surprising counterintuitive results. Evolution is defined by terrain. • Evolution – can be limiting but Dawkins’ extended phenotype gives us hope

  16. What is the vision? A policy evolves out of the ‘policy soup’ in an environment defined by co-evolving ideas and defined by the network linkages between actors that are pathways along which ideas can be transmitted. The successful policy in this environment survives according to the rules of Darwinian theory – others perish.

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