Tag Archives: Evolution

Policy Concepts in 1000 Words: it’s time for some game theory

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).

Table 7.1 prisoners

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.

See also:

This post is one of four updates to the post Policy Concepts in 1000 Words: Rational Choice and the IAD 

Policy Concepts in 1000 Words: the Institutional Analysis and Development Framework

Policy in 500 Words: the Social-Ecological Systems Framework

Policy in 500 Words: Ecology of Games

See also:

How to Navigate Complex Policy Designs

How can governments better collaborate to address complex problems?

See also this tweet – and many others paying homage to it – to explain the title of the post:

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Policy Concepts in 1000 Words: Evolution

(podcast download)

Evolutionary theory is prevalent in policymaking studies and it can be useful if we overcome some initial barriers. First, ‘evolution’ comes with a lot of baggage when we move from a discussion of animals to people. We can blame ‘social-Darwinism’ for the racist/ sexist idea that some people are more evolved than others.

Second, the word ‘evolution’ is used frequently in daily life, and academic studies, without a clear sense of its meaning. When it is used loosely in everyday language, it refers to a long term, gradual process of change. However, evolution can also refer to quick, dramatic change; the idea of ‘punctuated equilibrium’ is that long spells of stability and gradual change are interrupted by relatively short but profound bursts of instability. When we get into the details of studies, there are other sources of potential confusion about, for example, the nature of evolution (does it refer to advancement as well as change?) and the nature of ‘selection’ (do species simply respond blindly to their environments or help create them?).

This sort of confusion can be found in the study of public policy where evolution can refer to a wide range of things, including:

  • the cumulative, long-term development of policy solutions;
  • major disruptions in the way that policy makers think about, and try to solve, policy problems;
  • the maintenance or radical reform of policy-making institutions;
  • ‘emergent’ behaviour within complex systems
  • the trial-and-error strategies adopted by actors, such as policy entrepreneurs, when adapting to their environment;
  • the coming together of multiple factors to create the conditions for major policy change (which can be a creative, ‘window of opportunity’ style process, or a destructive, failure-related ‘perfect storm’ style process).

The most prominent theories of politics and policymaking draw on references to evolution in different ways. For example:

Multiple Streams Analysis (Kingdon). Although policymaker attention may lurch from one problem to another, problems will not be addressed until policy solutions have evolved sufficiently within a policy community and policymakers have the motive and opportunity to adopt them. ‘Evolution’ and the ‘policy primeval soup’ describe the slow progress of an idea towards acceptability within the policy community.

Punctuated Equilibrium Theory (Baumgartner and Jones). ‘Incremental’ policy change in most cases is accompanied by ‘seismic’ change in a small number of cases – an outcome consistent with ‘power laws’ found in the natural and social worlds. Kingdon’s picture of slow progress producing partial mutations is replaced by Baumgartner and Jones’ fast, disruptive, pure mutation.

Complexity theory. People, institutions and their environments are interacting constantly to produce rather unpredictable outcomes (or outcomes that may ‘emerge’ locally, in the absence of central control). This might be broken down into three steps:

  • Institutions, as sets of rules and norms, represent ways for people to retain certain ideas and encourage particular forms of behaviours.
  • Complex systems represent (partly) a large number of overlapping and often interdependent institutions.
  • New behaviours and rules arise from the interaction between multiple institutions and the actors involved.

In other words, different ‘worlds’ are in constant collision, producing new ways of thinking and behaviour that ‘emerge’ from these interactions. They are then passed down through the generations, but in an imperfect way, allowing new forms of thinking and behaviour to emerge.

To describe these processes as ‘evolutionary’, we should use the language of evolution – variation, selection and retention – to describe and explain outcomes. The idea in the natural world is that certain beings (including humans) want to do at least two things: (1) pass on their genes; (2) cooperate with others to secure resources and share them out to their kith and kin. In the political world, the equivalent is passing on ‘memes’ (as described in the 70s by Richard Dawkins) – the ideas (beliefs, ways of thinking) that we use to understand the world and act within it:

  • ‘Variation’ refers to the different rules adopted by different social groups to foster the collective action required to survive.
  • ‘Selection’ describes the interaction between people and their environments; particular environments may provide an advantage to some groups over others and encourage certain behaviours (or, at least, some groups may respond by adapting their behaviour to their environment).
  • ‘Retention’ describes the ways in which people pass on their genes (memes) to ensure the reproduction of their established rules (we might call them ‘institutions’).

The distinctive aspect of applying evolutionary theory to policymaking relates to the idea of passing on memes through the generations. In nature, we think of passing on genes through the generations as a process that takes hundreds, thousands or millions of years. Passing on memes through the ‘policy generations’ is more like the study of fruit flies (months), viruses or bacteria (days or weeks). Ways of thinking, and emerging behaviour, change constantly as people interact with each other, articulating different beliefs and rules and producing new forms of thinking, rules and behaviour. Big jumps in ways of thinking may be associated with generational shifts, but that can take place, for example, as one generation of scientists retires (as described by Kuhn) or, more quickly still, one generation of experts is replaced (within government circles) by another (as described by Hall).

I have discussed in other ‘1000 words’ posts what happens when theories, derived from cases studies of US politics, are applied to other countries and cases. ‘Evolutionary theory’ is more difficult to track, because it is a body of disparate work, loosely related to work in natural science, applied in a non-coordinated way. The same can be said for studies of complexity theory.

To read more, see ‘What is evolutionary theory and how does it inform policy studies?’ PDF, weblink or Green.

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