2020/06/03
Introduction
I am an independent researcher with broad interests in the dynamics of human and human-environmental systems, and the characterization, origins and persistence of complexity; I am currently working on ways to increase rigour and transparency in agent-based modelling (Gotts and Polhill 2011, Polhill and Gotts 2017, Polhill et al. 2018, Gotts et al. 2019, Lippe et al. 2019), and on complex behaviour in cellular automata, particularly Conway’s “Game of Life” (Gardner 1970, Berlekamp, Conway and Guy 1982, Gotts 2009a, 2011). Until 2012 I was senior research scientist at the James Hutton Institute in Aberdeen, where I worked on agent-based modelling first of rural land use, later on domestic denergy demand and energy-related behaviours at work. I have begun work on the application of agent-based modelling to historical research (Gotts 2014, 2017) and intend to expand this, focusing on the history of information and communication technologies (ICT) in the broadest sense, perhaps better described as the history of information systems since the social and epistemological context of their use is as important as the technologies themselves, and on large-scale or “macro” history, drawing on the insights of world-systems analysis (Chase-Dunn and Babones 2006, Arrighi 2010); these topics are discussed below. From July 2020 to December 2021 I returned to paid employment as research fellow in agent-based modelling at the University of Leeds School of Geography, to model health care worker movements in a hospital environment in the context of the Covid pandemic (see the paragrpah on the SAFER project under "Recent Research Projects"). I am currently continuing this work as a visiting research fellow.
Complexity and Emergence
Complexity is a slippery concept. There are domains in information theory and computer science where precise concepts referred to as complexity are used and explored (Kolmogorov complexity in information theory, computational or algorithmic complexity in computer science). However, neither bears a close relationship to the everyday sense of the word: in information theory, the Kolmogorov complexity of a symbol sequence of a given length is maximal when the sequence has no discernible pattern; while computational complexity concerns how fast the resources required to answer members of a class of problems increase with problem size. The everyday sense of the term “complexity” seems close to that associated with the idea of complex systems (Arthur et al. 1997, Auyang 1998, Mitchell 2009 Sayama 2015, Gotts et al. 2019, Klein and Hoel 2020) such as organisms, ecosystems, economies and societies – but here again, it is not easy to say exactly what makes a system complex, or more complex than another. I suggest that complex systems typically have the following characteristics:
However, systems sharing these features vary widely in the nature of the complexity they display, with some clearly being more complex than others – having additional kinds of complexity. This is particularly the case when we consider multi-level complex systems, which we define here as systems in which some components – relatively autonomous parts of the system that interact with other components to produce the global dynamics – can themselves be regarded as complex systems. As with complex systems in general, there is a wide range of examples – planetisimals in a developing solar system, firms within an economy, the lipid globules that arise and develop in certain experiments in the areas of synthetic biology and abiogenesis research; and any complex system containing organisms can be so regarded.
Emergence is at least as slippery a concept as complexity. When a collection or arrangement of entities has a property which cannot be predicated of the individual entities, we have an example of what can be called conceptual emergence. For example, temperature is an emergent property of the motion of atoms (Gilbert and Troitzsch 1999, p.10). Many terms of ordinary language refer to emergent properties of this kind: for example, a herd of cows may be evenly distributed or patchily distributed across a field, and a group of people may be homogeneous or heterogeneous in their appearance or opinions. In this sense, emergence is a commonplace and everyday phenomenon.
The subjectivist view sees emergence as located in the eye of the beholder. Emergence is frequently defined in terms of the impossibility of predicting or deducing properties of a system or structure consisting of numerous components from a description of those components. For example (Mihata 1997, p.31):
The concept of emergence is most often used today to refer to the process by which patterns or global-level structures arise from interactive local-level processes. This “structure” or “pattern” cannot be understood or predicted from the behavior or properties of the component units alone.
The problem with this approach is that it makes emergence contingent on the current state of our ability to predict or deduce. Unless it can be shown that some “emergent” feature of the system could never be deduced from some specified kind of description of the system's components, what is “emergent” for me may not be so for you, and what is “emergent” now may cease to be so some time in the future. Gilbert (1996, p.7) gives an interesting example of the instability of emergence defined in this way: Forrest and Miller (1990) report a mapping from classifier systems (Holland 1992, pp.172-182) to the much better-understood Boolean networks (Kauffman 1990). Some of the “emergent” properties of classifier systems have thus, if emergence is defined in subjectivist fashion, become non-emergent. In the light of this, Gilbert (1996) suggests:
emergence [defined in this way] may be neither a stable nor a particularly interesting property of complex systems: what is interesting are the systems' macro properties and the relationship of those macro properties to the micro ones.
The third way of looking at emergence is to regard it as a real, inherent property of certain systems or processes. In this sense, emergence is seen as referring primarily to the way that global phenomena result from multiple local interactions. Auyang (1998) introduces emergent properties as follows:
Large composite systems are variegated and full of surprises. Perhaps their most wonderful is that despite their complexity on the small scale, sometimes they crystallize into large-scale patterns that can be conceptualized rather simply... These salient patterns are the emergent properties of compounds. Emergent properties manifest not so much the material bases of compounds as how the material is organized.
Auyang develops an analysis in terms of different kinds of microexplanations of macrophenomena. This depends on the notion of an independent individual approximation. This is an analysis of a system of interacting individuals which depends on replacing the interactions between specific individuals by an idealisation in which interactions occur only between each individual and an overall situation, which is jointly produced by those individuals but cannot be significantly affected by any one of them. The individuals in the idealisation may not be spatially localised, and may not correspond to those initially considered (for example, theories of electrical conduction make use of quanta of concerted motion such as phonons as well as electrons and ions). Auyang concludes (1998, p.178) that only those macro phenomena that cannot be approximated by independent-individual models should be considered emergent. We can then discover that something we thought was emergent is not, but whether something is emergent is a matter of fact, independent of our knowledge.
Finally, we can distinguish synchronic emergence, describing the relationship between the micro and macro features of a system at a given time, from diachronic emergence, sometimes called “self-organization”, in which new forms of complexity arise within a system over time. The latter is particularly evident in biological evolution, as described for example in Maynard Smith and Szathmáry (1995), Lane (2010), and in human prehistory and history.
- Interacting levels of structure (Klein and Hoel 2020). A complex system has two or more levels of structure, such that a heuristically useful description can be given at each level, and in a description of a given level, the details of lower (more local) levels can largely be ignored. However, there is not absolute separation of levels: there will be circumstances in which local events give rise to a cascade of changes with effects at higher levels.
- Path-dependence (Tekwa et al. 2019). From different starting points – even if only very slightly different – qualitatively different paths may be followed indefinitely: the system is non-ergodic.
- Resilience and phase shifts. Typically, the system has a number of distinct metastable macro-states. In response to external influences, it is likely to be “resilient” – remain in the same macro-state until the external influence passes a threshold, when it will transition relatively quickly to a new macro-state; which new macro-state it enters may depend sensitively on local interactions and thus be hard to predict (Bitterman and Bennett 2016). The set of metastable states may change over time, with novel states appearing, others disappearing.
- Fat-tailed (or leptokurtic) distributions in the size of events (Zurlini et al. 2006). If the distribution of sizes of some class of events is plotted, the number of events in successive size classes will frequently decline more slowly than an exponential distribution, following a power law, for example.
However, systems sharing these features vary widely in the nature of the complexity they display, with some clearly being more complex than others – having additional kinds of complexity. This is particularly the case when we consider multi-level complex systems, which we define here as systems in which some components – relatively autonomous parts of the system that interact with other components to produce the global dynamics – can themselves be regarded as complex systems. As with complex systems in general, there is a wide range of examples – planetisimals in a developing solar system, firms within an economy, the lipid globules that arise and develop in certain experiments in the areas of synthetic biology and abiogenesis research; and any complex system containing organisms can be so regarded.
Emergence is at least as slippery a concept as complexity. When a collection or arrangement of entities has a property which cannot be predicated of the individual entities, we have an example of what can be called conceptual emergence. For example, temperature is an emergent property of the motion of atoms (Gilbert and Troitzsch 1999, p.10). Many terms of ordinary language refer to emergent properties of this kind: for example, a herd of cows may be evenly distributed or patchily distributed across a field, and a group of people may be homogeneous or heterogeneous in their appearance or opinions. In this sense, emergence is a commonplace and everyday phenomenon.
The subjectivist view sees emergence as located in the eye of the beholder. Emergence is frequently defined in terms of the impossibility of predicting or deducing properties of a system or structure consisting of numerous components from a description of those components. For example (Mihata 1997, p.31):
The concept of emergence is most often used today to refer to the process by which patterns or global-level structures arise from interactive local-level processes. This “structure” or “pattern” cannot be understood or predicted from the behavior or properties of the component units alone.
The problem with this approach is that it makes emergence contingent on the current state of our ability to predict or deduce. Unless it can be shown that some “emergent” feature of the system could never be deduced from some specified kind of description of the system's components, what is “emergent” for me may not be so for you, and what is “emergent” now may cease to be so some time in the future. Gilbert (1996, p.7) gives an interesting example of the instability of emergence defined in this way: Forrest and Miller (1990) report a mapping from classifier systems (Holland 1992, pp.172-182) to the much better-understood Boolean networks (Kauffman 1990). Some of the “emergent” properties of classifier systems have thus, if emergence is defined in subjectivist fashion, become non-emergent. In the light of this, Gilbert (1996) suggests:
emergence [defined in this way] may be neither a stable nor a particularly interesting property of complex systems: what is interesting are the systems' macro properties and the relationship of those macro properties to the micro ones.
The third way of looking at emergence is to regard it as a real, inherent property of certain systems or processes. In this sense, emergence is seen as referring primarily to the way that global phenomena result from multiple local interactions. Auyang (1998) introduces emergent properties as follows:
Large composite systems are variegated and full of surprises. Perhaps their most wonderful is that despite their complexity on the small scale, sometimes they crystallize into large-scale patterns that can be conceptualized rather simply... These salient patterns are the emergent properties of compounds. Emergent properties manifest not so much the material bases of compounds as how the material is organized.
Auyang develops an analysis in terms of different kinds of microexplanations of macrophenomena. This depends on the notion of an independent individual approximation. This is an analysis of a system of interacting individuals which depends on replacing the interactions between specific individuals by an idealisation in which interactions occur only between each individual and an overall situation, which is jointly produced by those individuals but cannot be significantly affected by any one of them. The individuals in the idealisation may not be spatially localised, and may not correspond to those initially considered (for example, theories of electrical conduction make use of quanta of concerted motion such as phonons as well as electrons and ions). Auyang concludes (1998, p.178) that only those macro phenomena that cannot be approximated by independent-individual models should be considered emergent. We can then discover that something we thought was emergent is not, but whether something is emergent is a matter of fact, independent of our knowledge.
Finally, we can distinguish synchronic emergence, describing the relationship between the micro and macro features of a system at a given time, from diachronic emergence, sometimes called “self-organization”, in which new forms of complexity arise within a system over time. The latter is particularly evident in biological evolution, as described for example in Maynard Smith and Szathmáry (1995), Lane (2010), and in human prehistory and history.
Cellular Automata and Conway’s “Game of Life”
Cellular automata (CA) are abstract systems, consisting of a (finite or infinite) set of cells, generally arranged in a regular lattice in one, two or more dimensions. A neighbourhood relation is defined on the set of cells, again generally in a regular fashion. For example, the lattice may consist of a two-dimensional grid of squares, with the neighbours of a cell consisting either of the four cells sharing an edge with the cell itself (the “von Neumann neighbourhood”), or of the eight cells that share either an edge or a point with the cell itself (the “Moore neighbourhood”). The lattice itself is generally either finite, and toroidal in topology, or infinite in all directions. Each cell has a finite set of possible states, and in the typical case, the states of all cells are updated simultaneously. The state of a cell at time t+1 then depends, in general deterministically, on the state of the cell itself, and its neighbours, at time t.
CA are frequently used as handy abstract systems in the study of complexity, as phenomenologically complex spatial and spatio-temporal patterns – and some that are apparently emergent in Auyang’s terms – can arise from simple rules, lattice topologies, and initial configurations of states. Conway’s “Game of Life” (Berlekamp, Conway and Guy 1982), henceforth simply GoL, is one of the most renowned CA in this regard: GoL enthusiasts have, over the 50-odd years since its invention, discovered or constructed a huge range of interesting patterns, and investigated the CA’s properties in more depth and detail than has been attempted for any other.
I have been interested in GoL since it was first brought to widespread attention by Martin Gardner (Gardner 1970), initially working with pencil and paper in that pre-personal-computer era. I began serious work on the emergence of complexity in GoL in the mid-1990s, and have focused chiefly on the question of what happens in “sparse Life”: infinite fields of GoL in which the initial density of state 1 cells is very low, each cell independently having an arbitrarily low, but non-zero, probability of being in state 1 at time t=0 (Gotts and Callahan 1998, Gotts 2000, 2003, 2011). Over the past two years I have returned to this question, while also considering the same or similar questions for other CA. I have also investigated starting positions with small numbers of state 1 cells that nonetheless grow into highly complex patterns over large numbers of steps (Gotts 2009a, 2011). Both the finite and infinite cases show apparent diachronic as well as synchronic emergence.
CA are frequently used as handy abstract systems in the study of complexity, as phenomenologically complex spatial and spatio-temporal patterns – and some that are apparently emergent in Auyang’s terms – can arise from simple rules, lattice topologies, and initial configurations of states. Conway’s “Game of Life” (Berlekamp, Conway and Guy 1982), henceforth simply GoL, is one of the most renowned CA in this regard: GoL enthusiasts have, over the 50-odd years since its invention, discovered or constructed a huge range of interesting patterns, and investigated the CA’s properties in more depth and detail than has been attempted for any other.
I have been interested in GoL since it was first brought to widespread attention by Martin Gardner (Gardner 1970), initially working with pencil and paper in that pre-personal-computer era. I began serious work on the emergence of complexity in GoL in the mid-1990s, and have focused chiefly on the question of what happens in “sparse Life”: infinite fields of GoL in which the initial density of state 1 cells is very low, each cell independently having an arbitrarily low, but non-zero, probability of being in state 1 at time t=0 (Gotts and Callahan 1998, Gotts 2000, 2003, 2011). Over the past two years I have returned to this question, while also considering the same or similar questions for other CA. I have also investigated starting positions with small numbers of state 1 cells that nonetheless grow into highly complex patterns over large numbers of steps (Gotts 2009a, 2011). Both the finite and infinite cases show apparent diachronic as well as synchronic emergence.
Complex Adaptive Systems, Human-Environmental Systems and Agent-Based Modelling
Many systems meeting the criteria given above for a complex system contain nothing that can reasonably be called a decision-maker, or agent: stars, for example, have multiple interacting levels of structure, small additions of material can change their eventual fate, a star’s history typically consists of a series of stages separated by relatively rapid inter-stage transitions, in each of which it behaves in a qualitatively different way, etc.. Neither do all multi-level complex systems include agents: the case of planetisimals within a developing solar system already mentioned provides one such example. We can distinguish a subset of complex adaptive systems (CASs), which include components that in some clear sense make decisions, that are influenced by the state of other agents and/or the environment, and some at least of which can affect the agent’s survival, or some other measure of success such as inclusive fitness, wealth, or happiness. CASs in this sense can alternatively be called agent-environment systems. Real-world agent-environment systems may be modelled or simulated in a variety of ways – for example, using sets of inter-related differential equations to represent their systems dynamics – but the approach I have worked on is that of agent-based modelling (Gilbert and Troitzsch 2005, Bandini et al. 2009), in which agents or decision-makers and the decisions they take are explicitly represented within the model.
Decision-making agents are not necessarily complex systems – so the sets of complex adaptive systems and multi-level complex systems overlap, but neither contains the other, although it may be that all currently known CASs outside simulations do fall into this category. For any complex adaptive system, I consider that any model that fails to represent the agents’ decision-making processes explicitly in some form (thus making the model a CAS) is unlikely to capture the key features of the system’s dynamics. However, the model need not itself be a multi-level complex system, as the real-world agents may be represented by decision algorithms that cannot be seen as themselves complex systems.
I suggest the following five aspects to an agent-environment system typology (we might call these aspects “dimensions”, but mostly they can’t be used to arrange agents along a scale, even an ordinal one; and they are not wholly independent).
I have mostly applied agent-based modelling in the area of agricultural land use (e.g. Polhill, Gotts and Law 2001, Gotts, Polhill and Law 2003, Gotts and Polhill 2011, Polhill, Gimona and Gotts 2013) and models of domestic energy demand (Gotts 2009b, Gotts and Polhill 2012, 2014, Gotts et al. 2014, Gotts and Polhill 2017); but have also published on social dilemmas – situations such as the misnamed “Tragedy of the Commons” (Hardin 1968, see Monbiot 1994 for an explanation of why it is misnamed) where if each of a group of agents acts in their own immediate interest, the result will be worse for all than if they had agreed or been constrained to cooperate (Gotts, Polhill and Law 2003/2010, Izquierdo, Izquierdo and Gotts 2008), comparative and and methodological issues in agent-based simulation (Cioffi-Revilla and Gotts 2003, Polhill, Izquierdo and Gotts 2006, Robinson et al. 2007, Pignotti et al. 2011, Polhill and Gotts 2017, Gotts et al. 2019, Lippe et al. 2019), pro-environmental behaviours at work (Gotts et al. 2011, Polhill, Gotts and Sanchez-Morano 2012) and the history of information systems (Gotts 2015, 2017). In future, I intend to apply the approach to issues discussed in the next section, on “Macro-History” – which will involve a return to work on social dilemmas and on land use, as well as on information systems.
Decision-making agents are not necessarily complex systems – so the sets of complex adaptive systems and multi-level complex systems overlap, but neither contains the other, although it may be that all currently known CASs outside simulations do fall into this category. For any complex adaptive system, I consider that any model that fails to represent the agents’ decision-making processes explicitly in some form (thus making the model a CAS) is unlikely to capture the key features of the system’s dynamics. However, the model need not itself be a multi-level complex system, as the real-world agents may be represented by decision algorithms that cannot be seen as themselves complex systems.
I suggest the following five aspects to an agent-environment system typology (we might call these aspects “dimensions”, but mostly they can’t be used to arrange agents along a scale, even an ordinal one; and they are not wholly independent).
- What kinds of decision can the agents make?
- What is the structure of the decision-making process?
- How do agents relate to each other?
- How do agents relate to the non-agentive environment?
- How do agents relate to the agent-environment system?
I have mostly applied agent-based modelling in the area of agricultural land use (e.g. Polhill, Gotts and Law 2001, Gotts, Polhill and Law 2003, Gotts and Polhill 2011, Polhill, Gimona and Gotts 2013) and models of domestic energy demand (Gotts 2009b, Gotts and Polhill 2012, 2014, Gotts et al. 2014, Gotts and Polhill 2017); but have also published on social dilemmas – situations such as the misnamed “Tragedy of the Commons” (Hardin 1968, see Monbiot 1994 for an explanation of why it is misnamed) where if each of a group of agents acts in their own immediate interest, the result will be worse for all than if they had agreed or been constrained to cooperate (Gotts, Polhill and Law 2003/2010, Izquierdo, Izquierdo and Gotts 2008), comparative and and methodological issues in agent-based simulation (Cioffi-Revilla and Gotts 2003, Polhill, Izquierdo and Gotts 2006, Robinson et al. 2007, Pignotti et al. 2011, Polhill and Gotts 2017, Gotts et al. 2019, Lippe et al. 2019), pro-environmental behaviours at work (Gotts et al. 2011, Polhill, Gotts and Sanchez-Morano 2012) and the history of information systems (Gotts 2015, 2017). In future, I intend to apply the approach to issues discussed in the next section, on “Macro-History” – which will involve a return to work on social dilemmas and on land use, as well as on information systems.
Macro-History
I am not a historian: but I have spent a lot of time over the past 35 years or so reading and thinking about large-scale historical processes: “macro-history”, as it is sometimes called. This is not an area in which I have published anything (with two minor exceptions - Gotts 2007, 2014, see below), but as noted above, is an area where I hope to apply agent-based simulation techniques in the near future.
The great majority of professional historians focus on one or more quite limited historical periods, geographical areas, and topical foci (political, economic, military, social or cultural history, for example). This is entirely necessary: experts in such specific topics are essential, and no-one can be an expert in more than two or three in the course of a career. However, as McEvedy (1986, p.4) says:
The refusal to look outside the particular society he [sic] is studying is one of the historian’s great strengths. Nothing is easier or more dangerous than a hindsight emphasis on those elements in a society which only acquire importance later. But an avoidance of teleological thinking and a refusal to consider long-term trends at all are two different things. Inside the short period of time that the historian studies there may be little place for the long perspectives: but the long perspective is itself a valid object of study.
A similar point may be made with respect to geographical areas: probably the great majority of historical books and articles published still focus on a single existing (or at least, modern) state, or on a single war or trading, colonial or cultural relationship involving a small number of such states – and those that do not include in their focus either a European state or the USA are probably in a small minority. This is understandable: those states, their wars and other activities, have dominated global history over the past two centuries at least, and how this predominance came about is a major theme in the history of the 16th-18th centuries. But without the broader temporal and spatial context, this rise looks like magic, or the result of some innate characteristic of ethnic Europeans – something much harder to maintain when the long periods during which Europe was on the periphery of Afroeurasian[1] history and technological development are considered. A rather different point can be made with regard to topical foci: while remote time periods do not interact directly, and for much of history, geographically remote areas interacted only rarely, in any specific time and place, economic, political, social, cultural, military and other processes interact constantly – and indeed, this particular division of human activities only makes sense in a limited range of times and places. The proponents of “World-Systems Theory” (see below) advocate a “unified historical social science” (Wallerstein 2000); I would go beyond this to advocate a unified historical human-environmental science, to emphasise both the roots of human potentialities in the evolutionary past[2], and the intricate connections between human societies and their non-human environment, living and non-living.
Ecohistory
By “ecohistory”, I mean the study of how interactions with other organisms have affected large-scale human history. Of course, human activities have also affected other species, their natural physical and biological environment, and their geographical distribution, in many cases radically.
The organisms that have had the most determinative effects on human history fall into two main classes: those that people have domesticated, and the organisms that cause disease in either human beings, or their domesticates. It is widely recognised that the most fundamental change in the lives and societies of modern human beings (Homo sapiens sapiens) came about with the domestication of plants and animals for food, fibre, other materials and in the case of animals, labour. This occurred several times independently (with the possible exception of the earliest domesticate, the dog), but Diamond (1997) argues that it was primarily the presence of more readily-domesticated food crops and animals in Eurasia than elsewhere, and the ease with which they could spread along the main east-west axis of the continent, that has led to it being the main centre of human population throughout historical time; and secondarily, the continent where most of the key inventions originated that made possible the growth of complex, mass societies.
A number of the epidemic diseases of human beings appear to have derived from diseases of domesticated animals (McNeill 1976); these diseases acted as (largely although not entirely) unintentional weapons during the “Great European Land Grab” (Gotts 2007) from the 16th century onwards, during which European settlers colonised the Americas, Australia and New Zealand, largely dispossessing and displacing the previous inhabitants, with the partial exception of the tropical parts of the Americas. Crosby (2004) describes how, along with European (or in a longer-term perspective, Eurasian) diseases, European (or Eurasian) domesticates, weeds, pests, and other species accompanied European colonists to the “neo-Europes” (Australia, New Zealand, and the temperate Americas), and enabled their conquest and settlement. Many recently acquired human diseases, however, appear to have derivded from non-domesticated animals (e.g. AIDS, Ebola, Zika, SARS, MERS and most recently, Covid-19), probably because the expansion of human settlement and activities into hitherto sparsely populated and utilised areas has provided opportunities for pathogens to cross species barriers (Spinney 2017, 2020).
These topics in ecohistory clearly just scratch the surface, but they suffice to refute methodological individualism: the claim that social science can be reduced to the actions and interactions of individual human beings.
World-Systems Analysis
I have a particular interest in world-systems analysis (or world-systems theory), which aims at the construction of a unified historical social science, and regards inter-societal systems rather than individual states, societies or civilizations as the most important level of analysis for doing so. Its origins can be traced to the work of the French historian Fernand Braudel (which I have read only in translation: Braudel 1981, 1982, 1984) on the development of capitalism from the commercial institutions of the high middle ages in western Europe. Notable researchers within the field include Immanuel Wallerstein (2000 – I admit I find Wallerstein hard to read), Janet Abu-Lughod (1989), Giovanni Arrighi (2010) and Christopher Chase-Dunn (Chase-Dunn and Hall 1997a,b; Chase-Dunn and Babones (eds) 2006). The approach focuses on the interaction between intra-societal and inter-societal processes, and particularly the ways in which societal elites maintain their power through relationships (of trade, tribute, warfare, diplomacy, the import and export of technical and social innovations) with other societies. It was originally applied by Wallerstein specifically to the development of the modern (post-1500) “capitalist world-system”, but has since been extended by others to earlier times. In recent years it has increasingly been applied to environmental issues (Gellert et al. 2017), and linked to work in complexity theory (Grimes 2017). Research which does not place itself within world-systems analysis as such but also takes the interaction between societies to be fundamental to historical analysis includes that of Turner (2017), who prefers the broader term “inter-societal dynamics”, and of historian Peter Heather on the fall of the Western Roman Empire and the subsequent development of western Europe in the early medieval period (Heather 2005, 2009). I have only published a single article in the area (Gotts 2007), comparing world-systems analysis, largely favourably, with an approach to the analysis of “social-ecological systems” (what I have called here “human-environmental systems”) referred to as “panarchy” (Gunderson and Holling 2002) – although since the publication of this article, I have become more sceptical of some aspects of the approach, notably its propensity to look for periodic cycles in political, economic and social events. It is always easier to find such periodicity in past events than to extend it successfully into the future: in Gotts (2007) I noted that several world-system researchers expected rising growth and inflation during the following decade – which clearly did not happen.
History of Information Systems
I am particularly interested in the emergence and construction of social formations which exceed the cognitive capabilities of their components, and so can be regarded as “super-agents”; of cooperation and conflicts within and between such formations; and of how the information systems available to the agents influence the dynamics of such systems. (I am using the term “information systems” in a broad sense, to include everything from using ornaments or body modification to indicate social status, to the Internet.). My own work in this area is very limited (Gotts 2015, 2017) – I have so far done a great deal more reading than writing. Here, I will do little more than list a few of the books – mostly popular treatments – that have got me thinking about these issues: more detail will follow.
Gleick (2011) covers some of the topics I am most interested in, but focuses mainly on the last two centuries. A number of the books that have most influenced my thinking deal with the development and repercussions of specific technologies: Monro (2014) on paper, Macfarlane and Martin (2002) on glass, Standage (1998) on the telegraph, Brotton (2012) on cartography, Houston (2004) on writing. (Glass may be less obviously related to information handling than the other examples – but consider spectacles, telescopes and microscopes.) Others relate particularly to the technologies and proto-sciences of medieval Europe (Gimpel 1988, Pacey 1992, Crosby 1997), and their roots in both the classical world of ancient Greece and Rome (Netz and Noel 2007), and Hindu, Islamic, Chinese and other civilizations (Pacey 1990, Ifrah 1998). At least part of the reason why capitalism, industrialism and modern science arose in western Europe rather than elsewhere may be that this part of the world, in the early modern period, was the first place where a sufficient combination of information technologies were simultaneously available. Pomeranz (2000) argues that western Europe was no more economically advanced than China until the 19th century, and that it was “coal and colonies” that enabled Britain in particular to forge ahead; these factors may well have been necessary but Jacob (2014) shows that the industrial revolution, and specifically the development of the steam engine, was crucially dependent on scientific and craft knowledge devloped in Europe over the preceding centuries (which was absent in China). Relevant works dealing with information systems in the modern period include Pettegree (2014) on newspapers and their predecessors, Alder (2002) on the metric system and the measurement of the earth, Moore (2015) on weather forecasting, and the wide-ranging Black (2014). Technical knowledge (despite the undoubted existence of exceptions) has a long-term tendency to accumulate – and information technologies reinforce and accelerate this tendency.
[1] I use “Afroeurasia” to refer to the linked continents of Africa and Eurasia (Europe is geographically just a peninsula of Eurasia).
[2] I am sceptical of most work in “Evolutionary Psychology” in the tradition of Barkow, Cosmides and Tooby (1992), for reasons I will explain at some point, but it is none the less true that human beings are the product of evolutionary processes.
The great majority of professional historians focus on one or more quite limited historical periods, geographical areas, and topical foci (political, economic, military, social or cultural history, for example). This is entirely necessary: experts in such specific topics are essential, and no-one can be an expert in more than two or three in the course of a career. However, as McEvedy (1986, p.4) says:
The refusal to look outside the particular society he [sic] is studying is one of the historian’s great strengths. Nothing is easier or more dangerous than a hindsight emphasis on those elements in a society which only acquire importance later. But an avoidance of teleological thinking and a refusal to consider long-term trends at all are two different things. Inside the short period of time that the historian studies there may be little place for the long perspectives: but the long perspective is itself a valid object of study.
A similar point may be made with respect to geographical areas: probably the great majority of historical books and articles published still focus on a single existing (or at least, modern) state, or on a single war or trading, colonial or cultural relationship involving a small number of such states – and those that do not include in their focus either a European state or the USA are probably in a small minority. This is understandable: those states, their wars and other activities, have dominated global history over the past two centuries at least, and how this predominance came about is a major theme in the history of the 16th-18th centuries. But without the broader temporal and spatial context, this rise looks like magic, or the result of some innate characteristic of ethnic Europeans – something much harder to maintain when the long periods during which Europe was on the periphery of Afroeurasian[1] history and technological development are considered. A rather different point can be made with regard to topical foci: while remote time periods do not interact directly, and for much of history, geographically remote areas interacted only rarely, in any specific time and place, economic, political, social, cultural, military and other processes interact constantly – and indeed, this particular division of human activities only makes sense in a limited range of times and places. The proponents of “World-Systems Theory” (see below) advocate a “unified historical social science” (Wallerstein 2000); I would go beyond this to advocate a unified historical human-environmental science, to emphasise both the roots of human potentialities in the evolutionary past[2], and the intricate connections between human societies and their non-human environment, living and non-living.
Ecohistory
By “ecohistory”, I mean the study of how interactions with other organisms have affected large-scale human history. Of course, human activities have also affected other species, their natural physical and biological environment, and their geographical distribution, in many cases radically.
The organisms that have had the most determinative effects on human history fall into two main classes: those that people have domesticated, and the organisms that cause disease in either human beings, or their domesticates. It is widely recognised that the most fundamental change in the lives and societies of modern human beings (Homo sapiens sapiens) came about with the domestication of plants and animals for food, fibre, other materials and in the case of animals, labour. This occurred several times independently (with the possible exception of the earliest domesticate, the dog), but Diamond (1997) argues that it was primarily the presence of more readily-domesticated food crops and animals in Eurasia than elsewhere, and the ease with which they could spread along the main east-west axis of the continent, that has led to it being the main centre of human population throughout historical time; and secondarily, the continent where most of the key inventions originated that made possible the growth of complex, mass societies.
A number of the epidemic diseases of human beings appear to have derived from diseases of domesticated animals (McNeill 1976); these diseases acted as (largely although not entirely) unintentional weapons during the “Great European Land Grab” (Gotts 2007) from the 16th century onwards, during which European settlers colonised the Americas, Australia and New Zealand, largely dispossessing and displacing the previous inhabitants, with the partial exception of the tropical parts of the Americas. Crosby (2004) describes how, along with European (or in a longer-term perspective, Eurasian) diseases, European (or Eurasian) domesticates, weeds, pests, and other species accompanied European colonists to the “neo-Europes” (Australia, New Zealand, and the temperate Americas), and enabled their conquest and settlement. Many recently acquired human diseases, however, appear to have derivded from non-domesticated animals (e.g. AIDS, Ebola, Zika, SARS, MERS and most recently, Covid-19), probably because the expansion of human settlement and activities into hitherto sparsely populated and utilised areas has provided opportunities for pathogens to cross species barriers (Spinney 2017, 2020).
These topics in ecohistory clearly just scratch the surface, but they suffice to refute methodological individualism: the claim that social science can be reduced to the actions and interactions of individual human beings.
World-Systems Analysis
I have a particular interest in world-systems analysis (or world-systems theory), which aims at the construction of a unified historical social science, and regards inter-societal systems rather than individual states, societies or civilizations as the most important level of analysis for doing so. Its origins can be traced to the work of the French historian Fernand Braudel (which I have read only in translation: Braudel 1981, 1982, 1984) on the development of capitalism from the commercial institutions of the high middle ages in western Europe. Notable researchers within the field include Immanuel Wallerstein (2000 – I admit I find Wallerstein hard to read), Janet Abu-Lughod (1989), Giovanni Arrighi (2010) and Christopher Chase-Dunn (Chase-Dunn and Hall 1997a,b; Chase-Dunn and Babones (eds) 2006). The approach focuses on the interaction between intra-societal and inter-societal processes, and particularly the ways in which societal elites maintain their power through relationships (of trade, tribute, warfare, diplomacy, the import and export of technical and social innovations) with other societies. It was originally applied by Wallerstein specifically to the development of the modern (post-1500) “capitalist world-system”, but has since been extended by others to earlier times. In recent years it has increasingly been applied to environmental issues (Gellert et al. 2017), and linked to work in complexity theory (Grimes 2017). Research which does not place itself within world-systems analysis as such but also takes the interaction between societies to be fundamental to historical analysis includes that of Turner (2017), who prefers the broader term “inter-societal dynamics”, and of historian Peter Heather on the fall of the Western Roman Empire and the subsequent development of western Europe in the early medieval period (Heather 2005, 2009). I have only published a single article in the area (Gotts 2007), comparing world-systems analysis, largely favourably, with an approach to the analysis of “social-ecological systems” (what I have called here “human-environmental systems”) referred to as “panarchy” (Gunderson and Holling 2002) – although since the publication of this article, I have become more sceptical of some aspects of the approach, notably its propensity to look for periodic cycles in political, economic and social events. It is always easier to find such periodicity in past events than to extend it successfully into the future: in Gotts (2007) I noted that several world-system researchers expected rising growth and inflation during the following decade – which clearly did not happen.
History of Information Systems
I am particularly interested in the emergence and construction of social formations which exceed the cognitive capabilities of their components, and so can be regarded as “super-agents”; of cooperation and conflicts within and between such formations; and of how the information systems available to the agents influence the dynamics of such systems. (I am using the term “information systems” in a broad sense, to include everything from using ornaments or body modification to indicate social status, to the Internet.). My own work in this area is very limited (Gotts 2015, 2017) – I have so far done a great deal more reading than writing. Here, I will do little more than list a few of the books – mostly popular treatments – that have got me thinking about these issues: more detail will follow.
Gleick (2011) covers some of the topics I am most interested in, but focuses mainly on the last two centuries. A number of the books that have most influenced my thinking deal with the development and repercussions of specific technologies: Monro (2014) on paper, Macfarlane and Martin (2002) on glass, Standage (1998) on the telegraph, Brotton (2012) on cartography, Houston (2004) on writing. (Glass may be less obviously related to information handling than the other examples – but consider spectacles, telescopes and microscopes.) Others relate particularly to the technologies and proto-sciences of medieval Europe (Gimpel 1988, Pacey 1992, Crosby 1997), and their roots in both the classical world of ancient Greece and Rome (Netz and Noel 2007), and Hindu, Islamic, Chinese and other civilizations (Pacey 1990, Ifrah 1998). At least part of the reason why capitalism, industrialism and modern science arose in western Europe rather than elsewhere may be that this part of the world, in the early modern period, was the first place where a sufficient combination of information technologies were simultaneously available. Pomeranz (2000) argues that western Europe was no more economically advanced than China until the 19th century, and that it was “coal and colonies” that enabled Britain in particular to forge ahead; these factors may well have been necessary but Jacob (2014) shows that the industrial revolution, and specifically the development of the steam engine, was crucially dependent on scientific and craft knowledge devloped in Europe over the preceding centuries (which was absent in China). Relevant works dealing with information systems in the modern period include Pettegree (2014) on newspapers and their predecessors, Alder (2002) on the metric system and the measurement of the earth, Moore (2015) on weather forecasting, and the wide-ranging Black (2014). Technical knowledge (despite the undoubted existence of exceptions) has a long-term tendency to accumulate – and information technologies reinforce and accelerate this tendency.
[1] I use “Afroeurasia” to refer to the linked continents of Africa and Eurasia (Europe is geographically just a peninsula of Eurasia).
[2] I am sceptical of most work in “Evolutionary Psychology” in the tradition of Barkow, Cosmides and Tooby (1992), for reasons I will explain at some point, but it is none the less true that human beings are the product of evolutionary processes.
References
Abu-Lughod, J.L. (1989) Before European Hegemony: The World-System A.D. 1250-1350. Oxford University Press.
Alder, Ken (2002) The Measure of All things: The Seven-Year Odyssey that Transformed the World. Little, Brown.
Arrighi, G. (2010) The Long Twentieth Century: Money, Power and the Origins of our Times. (2nd edition). Verso.
Arthur, W. B., Durlauf, S. N. and Lane, D. (1997) Introduction. In Arthur, W. B., Durlauf, S. N. and Lane, D. (eds.) The Economy as an Evolving Complex System II. Reading, MA: Addison-Wesley.
Auyang, S.Y. (1998) Foundations of Complex Systems Theories in Economics, Evolutionary Biology and Statistical Physics.Cambridge University Press.
Bandini, S., Manzoni, S. and Vizzari, G. (2009). 'Agent Based Modeling and Simulation: An Informatics Perspective'. Journal of Artificial Societies and Social Simulation 12(4)4 <http://jasss.soc.surrey.ac.uk/12/4/4.html>.
Barkow, J., Cosmides, L. and Tooby, J. (1992). The Adapted Mind: Evolutionary psychology and the generation of culture. NY: Oxford University Press.
Berlekamp, E., Conway, J.H. and Guy, R. (1982). Winning Ways (Vol. 2). New York: Academic Press.
Bitterman, P. and Bennett, D. (2016). Constructing stability landscapes to identify alternative states in coupled social-ecological agent-based models. Ecology and Society, 21(3):21.
Black, Jeremy (2014) The Power of Knowledge: How Information and Technology Made the Modern World. Yale University Press.
Braudel, F. (1981) Civilization and Capitalism 15th-18th Century Volume I: The Structures of Everyday Life: The Limits of the Possible. Translation from the French by S. Reynolds. William Collins, and Harper & Row. First published in French (1979) under the title Les Structures du Quotidian: le Possible et l’Impossible. Libraire Armand Colin.
Braudel, F. (1982) Civilization and Capitalism 15th-18th Century VolumeII: The Wheels of Commerce. Translation from the French by S. Reynolds. HarperCollins. First published in French (1975) under the title Les Jeux de l’Echange. Libraire Armand Colin.
Braudel, F. (1984) Civilization and Capitalism 15th-18th Century Volume III: The Persepctive of the World. Translation from the French by S. Reynolds. William Collins, and Harper & Row. First published in French (1979) under the title LeTemps du Monde. Libraire Armand Colin.
Brotton, J. (2012) A History of the World in Twelve Maps. Allen Lane.
Chase-Dunn, C., and Babones, S.J. editors (2006). Global Social Change: Historical and Comparative Perspectives. Johns Hopkins University Press, Baltimore, Maryland, USA.
Cioffi-Revilla, C. and Gotts, N.M. (2003). Comparative analysis of agent-based social simulations: GeoSim and FEARLUS models. Journal of Artificial Societies and Social Simulation 6(4)10. URL: http://jasss.soc.surrey.ac.uk/6/4/10.html.
Crosby, A.W. (1997) The Measure of Reality: Quantification and Western Society, 1250-1600. Cambridge University Press.
Crosby, A.W. (2004). Ecological Imperialism: The Biological Expansion of Europe 900-1900 (2nd edition). Cambridge University Press.
Diamond, J. (1997). Guns, Germs and Steel. Jonathan Cape.
Forrest, S. and Miller, J. (1990) Emergent behaviour in classifier systems. Physica D 42, 213-227.
Gardner, M. (1970). Mathematical Games - The fantastic combinations of John Conway's new solitaire game "life". Scientific American 223. pp. 120–123.
Gilbert, N. (1996) Holism, individualism and emergent properties. Chpater 1, pp.1-12 in Hegselmann, R., Mueller, U. and Troitasch, K.G. (eds) Modelling and Simulation in the Social Sciences from the Philosophy of Science Point of View. Kluwer
Gilbert, N. and Troitzsch, K. G. (1999) Simulation for the Social Scientist (1st edition). Open University.
Gilbert, N. and Troitzsch, K. G. (2005) Simulation for the Social Scientist (2nd edition). Open University. http://cress.soc.surrey.ac.uk/s4ss/.
Gimpel, J. (1988) The Medieval Machine: The Industrial Revolution of the Middle Ages (2nd edition). Wildwood House.
Gleick, J. (2011) The Information: A History, A Theory, A Flood. Fourth Estate.
Gotts, N.M. (2000) Emergent phenomena in large sparse random arrays of Conway’s “Game of Life”. International Journal of Systems Science 31(7), 873-894. DOI: 10.1080/002077200406598.
Gotts, N.M. (2003). Self-organized construction in sparse random arrays of Conway’s Game of Life, pp.1-53 in Griffeath, D. and Moore, C. (eds) New Constructions in Cellular Automata. Santa Fe Institute Studies in the Sciences of Complexity. Oxford University Press.
Gotts, N.M. (2007). Resilience, panarchy and world-systems analysis. Ecology and Society 12, 24. URL: http://www.ecologyandsociety.org/vol12/iss1/art24/.
Gotts, N.M. (2009a). Ramifying feedback networks, cross-scale interactions and emergent quasi-individuals in Conway's Game of Life. Artificial Life 15, 351-375. DOI: 10.1162/artl.2009.Gotts.009.
Gotts, N.M. (2009b) ABMED: A prototype agent-based model of energy demand. European Social Simulation Association Conference (ESSA 2009), University of Surrey, Guildford, 14th-18th September 2009.
Gotts, N.M. (2011) Emergent Complexity in Conway’s Game of Life. In Adamatzky, A. (ed) Game of Life Cellular Automata, Springer, pp.389-436.
Gotts, N.M. (2014) Information Technology and the Topologies of Transmission: a Research Area for Historical Simulation.
Extended abstract presented at Social Simulation 2014, Barcelona 1-5 September 2014.
Gotts, N.M. (2017) Agent-Based Modelling of Military Communications on the Roman Frontier. In Jager, W., Verbrugge, R., Flache, A., de Roo, G., Hoogduin, L. and Hemeltijk, C. (eds) Advances in Social Simulation 2015, Springer, pp.143-148.
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Gotts, N.M. and Polhill, J.G. (2017) Experiments with a model of domestic energy demand. Journal of Artificial Societies and Social Simulation, 20, http://jasss.soc.surrey.ac.uk/20/3/11.html>. DOI: 10.18564/jasss.3467.
Gotts, N.M., Polhill, J.G., Craig, T., Galan-Diaz, C. (2014) Combining Diverse Data Sources for CEDSS, an Agent-Based Model of Domestic Energy Demand. Structure and Dynamics: eJournal of Anthropological and Related Science 7 (1), 1-32. URL http://escholarship.org/uc/item/62x9p0w4.
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Gotts, N.M., Sanchez-Marono, N., Craig, T., Polhill, J.G. (2011) Agent-based modelling in the LOCAW project. European Social Simulation Association Conference (ESSA 2011), Montpellier, France, 19-23 September 2011.
Gotts, N.M.; van Voorn, G.A.K.; Polhill, J.G.; de Jong, E.; Edmonds, B.; Hofstede, G.J.; Meyer, R. (2019) Agent-based modelling of socio-ecological systems: Models, projects and ontologies., Ecological Complexity, Volume 40, Part B, December 2019, 100728. DOI 10.1016/j.ecocom.2018.07.007.
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Heather, Peter (2009) Empires and Barbarians: Migration, Development and the Birth of Europe.. Macmillan.
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Houston,S.D. (ed) (2004) The First Writing: Script Invention as History and Process. Cambridge University Press.
Ifrah, G. (1998) The Universal History of Numbers, from Prehistory to the Invention of the Computer. Translated from the French by Bellos, D., Harding, E.F., Wood, S. and Monk, I. Harvill Press. Originally published as Histoire Universelle des Chiffres (1994) Editions Robert Laffont.
Izquierdo, S.S., Izquierdo, L.R. and Gotts, N.M. (2008). Reinforcement learning dynamics in social dilemmas. Journal of Artificial Societies and Social Simulation 11.2.1. URL: http://jasss.soc.surrey.ac.uk/11/2/1.html.
Jacob, Margaret C. (2014) The First Knowledge Economy: Human Capital and the European Economy, 1750-1850. Cambridge University Press.
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Lane, Nick (2010). Life Ascending. Profile Books.
Lippe, M., Bithell, M., Gotts, N.M., Natalini, D., Barbrook-Johnson, P., Giupponi. C., Hallier, M., Hofstede, G-J., Le Page, C., Matthews, R.B., Schlüter, M., Smith, P., Teglio, A., Thellmann, K. (2019). Using Agent-based modelling to simulate Social-Ecological Systems across scales. GeoInformatica 23:269–298. DOI: 10.1016/j.ecocom.2018.07.007.
McEvedy, C. (1986) The Penguin Atlas of Modern History: to 1815. Penguin.
Macfarlane, A. and Martin, G. (2002) The Glass Bathyscaphe: How Glass Changed the World. Profile Books.
McNeill, W.H. (1976). Plagues and Peoples. Anchor.
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Mitchell, M. (2009) Complexity: A Guided Tour: Oxford University Press.
Monbiot, G. (1994) The Tragedy of Enclosure. Scientific American 270(1), 140.
Monro, Alexander (2014) The Paper Trail: An Unexpected History of the World’s Greatest Invention. Allen Lane.
Moore, Peter (2015) The Weather Experiment: The Pioneers who Sought to See the Future. Vintage.
Netz, R. and Noel, W. (2007) The Archimedes Codex: Revealing the Blueprint of Modern Science. Weidenfeld and Nicholson.
Pacey, A. (1990) Technology in World Civilization: A Thousand-Year History. Basil Blackwell.
Pacey, A. (1992) The Maze of Ingenuity: Ideas and Idealism in the Development of Technology (2nd edition). MIT Press.
Pettegree, Andrew (2014) The Invention of News: How the World Came to Know About Itself. Yale University Press.
Pignotti, E., Polhill, J.G., Edwards, P., Gotts, N.M. (2011) Applying provenance to social simulation. Paper presented at European Social Simulation Association Conference (ESSA 2011), Montpelier, France, 19-23 September 2011.
Polhill, J.G., Gair, Jonathan, Brewer, M., Gotts, N.M., Ge, J., and Butler, A. (2018) Comparing agent-based model versions using Approximate Bayesian Computation. Paper presented at Social Simulation Conference 2018, Stockholm, Sweden, 20-23 August 2018.
Polhill, J.G. and Gotts, N.M. (2017) How precise are the specifications of a psychological theory? Comparing implementations of Lindenberg and Steg’s Goal-Framing Theory of everyday pro-environmental behaviour. In Jager, W., Verbrugge, R., Flache, A., de Roo, G., Hoogduin, L. and Hemeltijk, C. (eds) Advances in Social Simulation 2015, Springer, pp.341-354.
Polhill, J.G., Gotts, N.M. and Law, A.N.R. (2001). Imitative versus nonimitative strategies in a land-use simulation. Cybernetics and Systems 32, 285-307. DOI: 10.1080/019697201300001885.
Polhill, J.G., Gimona, A. and Gotts, N.M. (2013) Nonlinearities in biodiversity incentive schemes: A study using an integrated agent-based and metacommunity model. Environmental Modelling and Software 45: 74-9. DOI: 10.1016/j.envsoft.2012.11.011.
Polhill, J.G. and Gotts, N.M. (2017) How precise are the specifications of a psychological theory? Comparing implementations of Lindenberg and Steg’s Goal-Framing Theory of everyday pro-environmental behaviour. In Jager, W., Verbrugge, R., Flache, A., de Roo, G., Hoogduin, L. and Hemeltijk, C. (eds) Advances in Social Simulation 2015, Springer, pp.341-354.
Polhill, J.G.; Gotts, N.M.; Sanchez-Marono, N. (2012) An ontology-based design for modelling case studies of everyday pro-environmental behaviour in the workplace. Paper presented at Sixth International Congress on Environmental Modelling and Software - iEMSs 2012, Leipzig, Germany, 1-5 July 2012.
Polhill, J.G., Izquierdo, L.R. and Gotts, N.M. (2006). What every agent-based modeller should know about floating point arithmetic. Environmental Modelling and Software 21, 283-309.
Pomeranz, Kenneth (2000) The Great Divergence: China, Europe, and the Making of the Modern World Economy. Princeton University Press.
Robinson, D.T., Brown, D.G., Parker, D.C., Schreinemachers, P., Janssen, M.A., Huigen, M., Wittmer, H., Gotts, N.M., Promburom, P., Irwin, E., Berger, T., Gatzweiler, F. and Barnaud, C. (2007). Comparison of empirical methods for building agent-based models in land use science. Journal of Land Use Science 2, 31-55.
Sayama, Hiroki (2015) Introduction to the Modelling and Analysis of Complex Systems. Open SUNY Textbooks. ISBN 978-1-942341-09-3 (open access ebook), https://textbooks.opensuny.org/introduction-to-the-modeling-and-analysis-of-complex-systems/.
Spinney, Laura (2017) Pale Rider: The Spanish Flu of 2018 and How it Changed the World. Vintage. ISBN 978178470240.
Spinney, Laura (2020) It takes a whole world to create a new virus, not just China. The Guardian, 2020/03/25.
Standage, T. (1998) The Victorian Internet: The Remarkable Story of the Telegraph and the Nineteenth Century’s Online Pioneers. Weidenfeld and Nicholson.
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Turner, J. H. (2017). Principles of Inter-Societal Dynamics. Journal of World-Systems Research, 23(2), 649-677. https://doi.org/10.5195/jwsr.2017.720.
Wallerstein, I. (2000) From sociology to historical social science: prospects and obstacles. The British Journal of Sociology 51(1), 25-36.
Zurlini, G., Riitters, K., Zaccarelli, N., Petrosillo, I., Jones, K. B., and Rossi, L. (2006). Disturbance patterns in a socio-ecological system at multiple scales. Ecological Complexity, 3:119-128.
Alder, Ken (2002) The Measure of All things: The Seven-Year Odyssey that Transformed the World. Little, Brown.
Arrighi, G. (2010) The Long Twentieth Century: Money, Power and the Origins of our Times. (2nd edition). Verso.
Arthur, W. B., Durlauf, S. N. and Lane, D. (1997) Introduction. In Arthur, W. B., Durlauf, S. N. and Lane, D. (eds.) The Economy as an Evolving Complex System II. Reading, MA: Addison-Wesley.
Auyang, S.Y. (1998) Foundations of Complex Systems Theories in Economics, Evolutionary Biology and Statistical Physics.Cambridge University Press.
Bandini, S., Manzoni, S. and Vizzari, G. (2009). 'Agent Based Modeling and Simulation: An Informatics Perspective'. Journal of Artificial Societies and Social Simulation 12(4)4 <http://jasss.soc.surrey.ac.uk/12/4/4.html>.
Barkow, J., Cosmides, L. and Tooby, J. (1992). The Adapted Mind: Evolutionary psychology and the generation of culture. NY: Oxford University Press.
Berlekamp, E., Conway, J.H. and Guy, R. (1982). Winning Ways (Vol. 2). New York: Academic Press.
Bitterman, P. and Bennett, D. (2016). Constructing stability landscapes to identify alternative states in coupled social-ecological agent-based models. Ecology and Society, 21(3):21.
Black, Jeremy (2014) The Power of Knowledge: How Information and Technology Made the Modern World. Yale University Press.
Braudel, F. (1981) Civilization and Capitalism 15th-18th Century Volume I: The Structures of Everyday Life: The Limits of the Possible. Translation from the French by S. Reynolds. William Collins, and Harper & Row. First published in French (1979) under the title Les Structures du Quotidian: le Possible et l’Impossible. Libraire Armand Colin.
Braudel, F. (1982) Civilization and Capitalism 15th-18th Century VolumeII: The Wheels of Commerce. Translation from the French by S. Reynolds. HarperCollins. First published in French (1975) under the title Les Jeux de l’Echange. Libraire Armand Colin.
Braudel, F. (1984) Civilization and Capitalism 15th-18th Century Volume III: The Persepctive of the World. Translation from the French by S. Reynolds. William Collins, and Harper & Row. First published in French (1979) under the title LeTemps du Monde. Libraire Armand Colin.
Brotton, J. (2012) A History of the World in Twelve Maps. Allen Lane.
Chase-Dunn, C., and Babones, S.J. editors (2006). Global Social Change: Historical and Comparative Perspectives. Johns Hopkins University Press, Baltimore, Maryland, USA.
Cioffi-Revilla, C. and Gotts, N.M. (2003). Comparative analysis of agent-based social simulations: GeoSim and FEARLUS models. Journal of Artificial Societies and Social Simulation 6(4)10. URL: http://jasss.soc.surrey.ac.uk/6/4/10.html.
Crosby, A.W. (1997) The Measure of Reality: Quantification and Western Society, 1250-1600. Cambridge University Press.
Crosby, A.W. (2004). Ecological Imperialism: The Biological Expansion of Europe 900-1900 (2nd edition). Cambridge University Press.
Diamond, J. (1997). Guns, Germs and Steel. Jonathan Cape.
Forrest, S. and Miller, J. (1990) Emergent behaviour in classifier systems. Physica D 42, 213-227.
Gardner, M. (1970). Mathematical Games - The fantastic combinations of John Conway's new solitaire game "life". Scientific American 223. pp. 120–123.
Gilbert, N. (1996) Holism, individualism and emergent properties. Chpater 1, pp.1-12 in Hegselmann, R., Mueller, U. and Troitasch, K.G. (eds) Modelling and Simulation in the Social Sciences from the Philosophy of Science Point of View. Kluwer
Gilbert, N. and Troitzsch, K. G. (1999) Simulation for the Social Scientist (1st edition). Open University.
Gilbert, N. and Troitzsch, K. G. (2005) Simulation for the Social Scientist (2nd edition). Open University. http://cress.soc.surrey.ac.uk/s4ss/.
Gimpel, J. (1988) The Medieval Machine: The Industrial Revolution of the Middle Ages (2nd edition). Wildwood House.
Gleick, J. (2011) The Information: A History, A Theory, A Flood. Fourth Estate.
Gotts, N.M. (2000) Emergent phenomena in large sparse random arrays of Conway’s “Game of Life”. International Journal of Systems Science 31(7), 873-894. DOI: 10.1080/002077200406598.
Gotts, N.M. (2003). Self-organized construction in sparse random arrays of Conway’s Game of Life, pp.1-53 in Griffeath, D. and Moore, C. (eds) New Constructions in Cellular Automata. Santa Fe Institute Studies in the Sciences of Complexity. Oxford University Press.
Gotts, N.M. (2007). Resilience, panarchy and world-systems analysis. Ecology and Society 12, 24. URL: http://www.ecologyandsociety.org/vol12/iss1/art24/.
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