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What should be meant by a scientific approach to policy and how it might help create a more appropriate way of reaching decisions? Many years in practical policy making show that it is part science and part art. Complex systems science, as a form of story telling, can create links between them. Three aspects of policy making are considered: (1) the nature of proof in science and decision making, and how introducing new science into decision making is an under appreciated problem; (2) optimisation and that, despite optimal solutions not existing, policy makers want a single solution with unintended consequences left for future generations of policy makers; and (3) —other things being equal, and the problem of deciding which things can be left alone and over what time periods. In policy, proof is an argument required by investors, regulators, and other decision makers. Proof must satisfy the beliefs and traditions of the decision maker, even when the former contradict observation and the latter are flawed, e.g. ‘we have been doing it this way for thirty years so it must be right’. In policy, the belief in is particularly strong, within a tradition that focusses on parts and ignores wholes. Arguably, individuals do not always optimise and often make decisions by copying others, opening up new ways of nudging people towards compliance. Also firms may not optimise due to risk aversion, lack of information, or just focussing on survival. Even if optimisation were possible, a narrow view on what is being optimised can lead to missed opportunities, as in the case of the multiple returns of agglomeration. Also, although innovation is a pillar of economic policy, its necessary dynamics are incompatible with equilibrium theories. Equilibrium also suggests that ‘do nothing’ is a policy option for no change, rather than drifting into the unknown. However, ‘doing something’ has the , namely showing the benefits over doing nothing or something else. All investment impacts are on the balance of probabilities. Risk free investment and risk free polices are not possible in a complex world. What matters is to have a strong story, backed up by strong evidence on the main elements of the story. Then take a bet.

Two broad arguments are made. The first is that we are some distance away from having a good understanding of collective action, and until we do, claims about the role and scope of government will be based on crude impressions or ideologies. The second is that complexity science is well placed to make advances in in this area because social systems are inherently complex, as are many collective action problems. Indeed, most political ideologies impacting on public policy have emerged from a comparatively simple, mechanistic view of social systems. It is argued that the economic success of capitalist countries can in part be attributed to people being free to form organisations, which are collective acts, and can be seen as the other side of the coin to Adam Smith’s division of labour. A discussion of what is meant by will develop a broader than normal definition that includes social governance, defined here as all forms of institutions, the role of which is to facilitate, or enable, collective action. Governments are part of our social governance furniture, but have a monopoly over the use of force. The concerning government will be used as the antithesis to the thesis that a primary role of government is to enable collective action, leading to a synthesis of the two. A speculation on the role governments should have in complex social systems collective action precedes consideration of what value complexity science can add in the domain of collective action and government. This could be substantial since complexity science includes concepts and tools which can help advance our understanding. At the very least, the dispassionate science of complexity could provide a fresh perspective on what has been an historically emotive and inconclusive debate.

Context is crucial for understanding social phenomena, but is not being addressed. Contexts can become socially entrenched and acquire their own labels, allowing different social coordination systems to be developed for different kinds of situation. Three ways to avoid context are discussed. Fitting data to mathematical models which ‘explain’ the data using significance tests avoids the problems of context, but may average over different contexts inappropriately. ‘Behavioural foundationalism’, which assumes a generic model of behaviour that is valid across different contexts, avoids the context problem by producing models based on a micro-specification to see if the macro-consequences match the available data, e.g. neo-classical decision theory and some agent-based simulations. A third strategy to avoid the context problem is to retreat into specificity, providing so much detail that the context is unique with no attempt at generalisation. Three ways forward are proposed (1) using data mining techniques to look for models whose output ‘fits’ various target kinds of behaviour, (2) context-dependent simulation modelling, with the memory of the agent being context-sensitive, and context-relevant knowledge and behaviours being applied in decision-making, and (3) combining qualitative and formal approaches, with neither qualitative nor quantitative evidence being ignored. Agent-based modelling can use qualitative evidence to inform the behavioural strategies that people use in given situations. Simulations based on micro-level behaviours can produce numbers for comparison with macro-level quantitative data. This supports experimentation to understand emerging processes, and investigate the coherence of the qualitative assumptions and the quantitative evidence. Explicitly recognising and including context-dependency in formal simulation models allows for a well-founded method for integrating qualitative, quantitative and formal modelling approaches in the social sciences. Then some of the wealth of qualitative ethnographic, observational and interviewing work of the social sciences can enrich formal simulation models directly, and allow the quantitative and the qualitative to be assessed together and against each other. Before the advent of cheap computing power, analytic mathematical models were the only formal models available, but their simplicity ruled out context dependency, leading to a focus on what generic models might tell us. New information and communication technologies have resulted in a lot more data on social phenomena to distinguish different contexts and behaviours. We no longer have to fit generic models due to data paucity and limits to storage and processing, or ignore context or over-simplify what we observe to obtain and use formal models. Addressing context has huge potential for the social sciences, including: better models and understanding of human behaviour; more effective ways of collecting, integrating and analysing data; and the prospect for a well-founded means of integrating the insights from quantitative and qualitative evidence and models.

The vision of Global Systems Science (GSS) is to provide scientific evidence and means to engage into a reflective dialogue to support policy-making and public action and to enable civil society to collectively engage in societal action in response to global challenges like climate change, urbanisation, or social inclusion. GSS has four elements: policy and its implementation, the science of complex systems, policy informatics, and citizen engagement. It aims to give policy makers and citizens a better understanding of the possible behaviours of complex social systems. Policy informatics helps generate and evaluate policy options with computer-based tools and the abundance of data available today. The results they generate are made accessible to everybody—policy makers, citizens—through intuitive user interfaces, animations, visual analytics, gaming, social media, and so on. Examples of Global Systems include epidemics, finance, cities, the Internet, trade systems and more. GSS addresses the question of policies having desirable outcomes, not necessarily optimal outcomes. The underpinning idea of GSS is not to precisely predict but to establish possible and desirable futures and their likelihood. Solving policy problems is a process, often needing the requirements, constraints, and lines of action to be revisited and modified, until the problem is ‘satisficed’, i.e. an acceptable compromise is found between competing objectives and constraints. Thus policy problems and their solutions coevolve much as in a design process. Policy and societal action is as much about attempts to understand objective facts as it is about the narratives that guide our actions. GSS tries to reconcile these apparently contradictory modes of operations. GSS thus provides policy makers and society guidance on their course of action rather than proposing (illusionary) optimal solutions.