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Between 2011 and 2014 the European Non-Equilibrium Social Science Project (NESS) investigated the place of equilibrium in the social sciences and policy. Orthodox economics is based on an equilibrium view of how the economy functions and does not offer a complete description of how the world operates. However, mainstream economics is not an empty box. Its fundamental insight, that people respond to incentives, may be the only universal law of behaviour in the social sciences. Only economics has used equilibrium as a primary driver of system behaviour, but economics has become much more empirical at the microlevel over the past two decades. This is due to two factors: advances in statistical theory enabling better estimates of policy consequences at the microlevel, and the rise of behavioural economics which looks at how people, firms and governments really do behave in practice. In this context, this chapter briefly reviews the contributions of this book across the social sciences and ends with a discussion of the research themes that act as a roadmap for further research. These include: realistic models of agent behaviour; multilevel systems; policy informatics; narratives and decision making under uncertainty; and validation of agent-based complex systems models.

Economics is by no means an empty box. For example, it offers what is possibly the only general law of behaviour in the social sciences, namely that agents react to incentives. Over the past two decades or so, at the micro level, the level of individual agent decision making, the discipline has made progress. Developments in the econometric theory of the analysis of large longitudinal data bases and the rise of behavioural and experimental economics have made the discipline much more empirical. However, at the macro level, economics has, if anything, gone backwards. The main intellectual effort since the 1980s has been to import the concept of equilibrium into macroeconomics. It is no surprise that policymakers during the financial crisis of the late 2000s found the mainstream economic models to be of little or no help at all. In the 1950s, there was an active debate about the computational limits which agents faced when making decisions. The polymath Nobel Laureate Herbert Simon was prominent in arguing that the rational model of choice, the core model of economic theory, was not realistic in many situations because of these limits. Even after the event, it may not be possible to determine what the optimal decision would have been at any given time. The world is in general too complex. Mainstream economics gradually lost sight of this fundamental challenge to one of its key assumptions. The rise of cyber society and Big Data mean that Simon’s challenge is more relevant than ever. Looking to the future, new models of ‘rational’ agent behaviour are required which are better suited to the cyber society of the twenty-first century. Key areas of research include: agent decision making rules; heuristics to identify decision types in any given context; network percolation of imitation, incentives, ideas, beliefs, influence and sentiments; networks evolution; the policy implications of different modes of behaviour; fundamental theory and tools to operationalise narrative dynamics; computational theories of narratives, including Big Data; tools for processing narratives and sentiment; and predicting the emergence of narratives.

Economics can benefit through adopting various ideas from social psychology. Social and economic processes can be analysed at different levels: the microlevel (individuals), mesolevel (system structures), and macrolevel (whole socioeconomic system). Contrary to classic economic models, when making decisions, people do not consider all available information at the microlevel—this is not possible. Decisions may have many competing dimensions and there may be no single optimum. Whereas in traditional economy the main difference between the levels is the degree of aggregation, studies how individuals cooperatively create, change and maintain their understanding of the world. Meanings arise as a result of coordinated action of humans who interpret their world by building models of it and how it functions. A natural way of acquiring meanings and conveying them to others is through —stories that have a beginning, a body, and an end. Narratives exist at all levels of social reality. They provide the structure by which an individual can understand the world, with their roles in narratives individuals suggesting how to behave. Interacting individuals socially construct narratives bottom-up. Group narratives emerge from integration of stories describing individual experiences of actors. Shared narratives allow actors to find commonality in their experiences, find coherence in the flow of events and allow them to coordinate in common actions. At the macrolevel narratives define the system and its common culture. Sometimes narration may have more impact on an economy than hard data. Even the choice of which facts we refer to and those we do not may determine the leading narrative and hence the behaviour of people. Socio-economic processes can and should be analysed in line with narratives linking individuals, organisations and societies to better understand what is happening in the whole economic system.

This chapter addresses the relationship between sociology and Non-Equilibrium Social Science (NESS). Sociology is a multiparadigmatic discipline with significant disagreement regarding its goals and status as a scientific discipline. Different theories and methods coexist temporally and geographically. However, it has always aimed at identifying the main factors that explain the temporal stability of norms, institutions and individuals’ practices; and the dynamics of institutional change and the conflicts brought about by power relations, economic and cultural inequality and class struggle. Sociologists considered equilibrium could not sufficiently explain the constitutive, maintaining and dissolving dynamics of society as a whole. As a move from the formal apparatus for the study of equilibrium, NESS does not imply a major shift from traditional sociological theory. Complex features have long been articulated in sociological theorization, and sociology embraces the complexity principles of NESS through its growing attention to complex adaptive systems and non-equilibrium sciences, with human societies seen as highly complex, path-dependent, far-from equilibrium, and self-organising systems. In particular, Agent-Based Modelling provides a more coherent inclusion of NESS and complexity principles into sociology. Agent-based sociology uses data and statistics to gauge the ‘generative sufficiency’ of a given microspecification by testing the agreement between ‘real-world’ and computer generated macrostructures. When the model cannot generate the outcome to be explained, the microspecification is not a viable candidate explanation. The separation between the explanatory and pragmatic aspects of social science has led sociologists to be highly critical about the implementation of social science in policy. However, ABM allows systematic exploration of the consequences of modelling assumptions and makes it possible to model much more complex phenomena than previously. ABM has proved particularly useful in representing socio-technical and socio-ecological systems, with the potential to be of use in policy. ABM offers formalized knowledge that can appear familiar to policymakers versed in the methods and language of economics, with the prospect of sociology becoming more influential in policy.

Geography makes little use of the concept of equilibrium. Unlike economics, geographical inquiry is based on the recognition of differences and asymmetries among regions and civilisations. In this it does not refer to general mechanisms that would be equivalent to the market for fixing prices and equilibrating supply and demand. Early geographers searched for explanations to the great variety of landscapes and ways of life that were observed all over the planet. Modern geographers study both the ‘vertical’ interactions between societies and their local milieu and the ‘horizontal’ interactions between cities and regions. This involves two opposing causes of territorial inequalities, spatial diffusion of innovation and urban transition. Whereas diffusion of innovation alone might result in homogeneity, combined with the dynamics of city formation the result is increasing heterogeneity and inequality. The phenomenon of increasing returns with city size is explained by higher population densities and connections multiplying the probability of productive interactions, as well as by adaptive valuation of accumulated assets. While there may be great wealth, in some large urban agglomerations large informal settlements of slums and shanties are still expanding. Global societal evolution is an open process with no fixed asymptotic point in the future: there is no final equilibrium state to reach for the world. Open evolution may hamper the quality of predictions that can be made about the future, but geographical knowledge of past dynamics may help to make forecasts more certain. Powerful analytical tools have been developed in the last five or six decades that greatly improve the quality of geographical work and its ability to provide stakeholders and decision makers with clearer insights for exploring possible territorial futures. Geographical Information Systems are now universally used in all kind of administrations dealing with localised services. Detailed geographical information from many data sources enables a shift from a macro-static view to a micro-macro dynamical view that is necessary for management and planning policies in a non-linear world. As a science geography remains deliberately far from equilibrium.

Our perceptions of cities until quite recently were that they were largely stable in spatial structure over long periods of time, decades, even centuries, and that this suggested that they were in equilibrium. Cities appeared similar from generation to generation and although there were superficial changes due to fashion and technology, their overall structures were unchanging. To a large extent, this view of cities in equilibrium is borne of thinking about them physically but as soon as we unpack their dynamics, we realise that this a superficial perception. Cities are always in disequilibrium. They are in fact far-from-equilibrium being maintained through a tension of many countervailing forces that break down and build up on many different spatial and temporal scales, thus all coalescing in strong volatility and heterogeneity in urban form and function. Here we first review the concept of equilibrium and dynamics, and then we introduce ideas about discontinuity drawing on ideas from catastrophe and chaos theory. We argue that we should think of cities as being far-from-equilibrium structures and allude to ideas about innovation and technological change that condition their dynamic entirely. Our conclusion is that what happens in cities is increasingly disconnected from their physical form and this is particularly the case in the contemporary world where changes to the built environment are ever out-of-sync with changes in human behaviours, activity locations, patterns of movement, and globalisation.

Do the complex dynamics of international relations resemble the long-term evolution observed in living systems? This chapter will try to identify the mechanisms associated with those dynamics, and to determine if the science of complex adaptive systems can aid in the understanding of international development.It tries to address the weaknesses of current theories of international political economy to adequately explain global diversity and queries its empirical and theoretical limitations. Providing insight on the mechanisms by which divergence is a response to heightened interconnectivity, complexity theory offers a way to overcome the limitations of conventional political economy analysis.We find that at a qualitative level the dynamics of the international system resemble known aspects of biological behaviour, speciation and intermittent behaviour. The next frontier for the study of social development is to find quantitative measures that define these processes.

Systems theory is fundamental to understanding the dynamics of the complex social systems of concern to policy makers. A is defined as: (1) an assembly of components, connected together in an organised way; (2) the components are affected by being in the system and the behaviour of the systems is changed if they leave it; (3) the organised assembly of components does something; and (4) the assembly has been identified as being of particular interest. is central to system behaviour at all levels, and can be responsible for systems behaving in complex and unpredictable ways. Systems can be represented by and there is a growing literature that shows how the behaviour of individuals is highly dependent on their social networks. This includes copying or following the advice of others when making decisions. Network theory gives insights into social phenomena such as the spread of information and the way people form social groups which then constrain their behaviour. It is emerging as a powerful way of examining the dynamics of social systems. Most systems relevant to policy have many levels, from the individual to local and national and international organisations and institutions. In many social systems the micro, meso and macrolevel dynamics are coupled, meaning that they cannot be studied or modified in isolation. Systems and network science allow computer simulations to be used to investigate possible system behaviour. This science can be made available to policy makers through which involves computer-based simulation, data, visualisation, and interactive interfaces. The future of science-based policy making is seen to be through Global Systems Science which combines complex systems science and policy informatics to inform policy makers and facilitate citizen engagement. In this context, systems theory and network science are fundamental for modelling far-from-equilibrium systems for policy purposes.

This chapter aims to discuss certain limitations of the dominant equilibrium thinking in policy and explore more complexity-friendly alternatives. If societies and markets are viewed as complex, non-equilibrium systems, understanding nonlinear, adaptive and evolving patterns emerging from agent behaviour in network structures is fundamental for policy purposes. This requires improved realism of the behavioural and social foundations on which policies are based. We must also reconsider the mantra of incentivisation, institutional design and the top-down regulation that typically dominates conventional policy. Recent cases of financial market regulation and health policies can help us to understand the importance of looking at the subtle ways in which people or organisations behave when exposed to social influence, and pre-existing social norms and network externalities. Changing the current policy narrative and exploring complexity-friendly concepts, instruments and methods requires a shift of focus of policy-making from forecast and prediction of system equilibrium in order to understand and manage complex social systems better.

In this chapter we outline a novel theory of the consumer market, in which information plays the key role. Consumers know only part of the available business offers and cannot ascertain the quality of the products they desire and businesses have even less knowledge of what consumers desire. In the market consumers and businesses must find a match with severely deficient information. Instead of optimisation under the constraints, our theory focuses on how the information constraints can be gradually reduced. We show that upon constraint-reduction we do not come closer to the full information limit typically portrayed in mainstream economics; rather both consumer wants and business offers expand with concomitant new information deficiencies. Therefore the consumer market is always in non-equilibrium and information will always be deficient. We argue that in the dynamic pursuit to reduce information constraints wealth is created and this is the main driving force that powers economic growth.