This post looks at how to define ‘complexity’ and particular its close relationship to the way we think. The 2008 translation of some of Edgar Morin’s essays in ‘On Complexity’ shall serve as the focal point for my discussion.
What is complexity and what is complex thinking? The best way is to understand it is to compare it to its cousin; systems thinking. Anderson and Johnson (1997) identify five features of systems thinking.
Think of the big picture.
Balance short-term and long-term perspectives.
Recognise the dynamic, complex and and interdependent nature of systems.
Take into account measurable and non-measurable factors.
Remember we are part of the systems.
Morin’s oeuvre says ‘complexity is a fabric (complexus: that is woven together) of heterogenous constituents that are inseparably associated: complexity poses the paradox of the one and the many. Next complexity is in fact the the fabric of events, actions, interactions, retroactions, determinations and chance that constitute our phenomenal world. But complexity presents itself with the disturbing traits of a mess, of the inextricable, of disorder, of ambiguity, of uncertainty’ There is a lot to decipher and discuss here. Firstly, it seems, like systems thinking, to highlight dynamism. ‘Events, actions, interactions, retroactions, determinations and chance’ all suggest a movement away from a static worldview. Yet, there are a number of differences in Morin’s complex thinking. Firstly, look at the mention of ‘uncertainty’. Some systems thinkers and indeed complexity theorists search for laws. For example, there is the Sante Fe Institute’s approach to history and its search for the laws that determine the history of the universe (Krakauer, Gaddis, Pomeranz, 2017) or consider Barabasi’s (2002) look at laws in complex networks. One does not sense this approach in Morin. He states ‘complexity coincides with a part of uncertainty that arises from the limits of our ability to comprehend, or with a part of uncertainty inscribed in phenomena. But complexity cannot be reduced to uncertainty; it is uncertainty at the at the heart of richly organised systems.’ There are two things to notice, the focus on uncertain dynamics and the paradoxical nature of what Morin is saying; there is organisation and uncertainty in systems.
Paradoxes are at the heart of ‘complex thinking’. The mention of ambiguity in the first quote supports this, but this is confirmed by Morin’s tackling of organisation and disorder. He states ‘that disorder and order, although enemies, cooperate in a certain way to organise the universe.’ One example he cites in none other than the formation of the Universe, the Big Bang was a ‘giant deflagration’ and ‘the universe began as disintegration and in disintegrating, it organised itself.’ Organisation/disorganisation are therefore not two opposing forces. Kelso (2009) has provided a framework for understanding the interactions between organisation and disorganisation through his coordination dynamics. In short, coordination dynamics looks at how coordination works in living things, and because of this how their coordinated patterns adapt, persist and change. One concept within coordination dynamics is metastability, this helps us understand how a system may neither be organised or disorganised. Kelso (2009) suggests metastability is a state where stable coordination states no longer exist, but attraction remains to where the fixed points used to be. This leads to a flow of phase-scattering and phase-trapping. In other words, there is a mixture of repulsion and gathering within the system.
Morin is, of course, not the first thinker to emphasise ambiguity. Basarab Nicolescu (2016), for example, speaks of ‘The Hidden Third’ and also ‘the infintie ambiguity of the binary thought.’ Nicolescu, like Morin, is a transdisciplinarian and so it is notable to see this convergence on opinion. Derrida (1977) has the idea of Différance, this also implies ambiguity, except in language, by suggesting a word never fully summons its meaning due to its interplay with other words. My point here being that Morin is not alone in theorising states of contradiction, of paradox and ultimately of ambiguity. This nevertheless does not lessen the impact of his work, especially given his application of it to systems.
What does ‘complex thinking’ mean practically? Morin (2014), in an article, announces his surprise at how researchers investigate complex systems. Apparently, ‘they study complex systems with uncertainty, randomness, chaos theory, but they don’t change their mind, they don’t change the structure of their worldview, but in fact they need to undergo a paradigmatic change.’ So how would this changed worldview affect us? Morin, in On Complexity, describes action as a wager and in a wager there is always ‘an awareness of risk and of uncertainty.’ He suggests we adopt a strategy, but this ‘does not mean a predetermined program we can apply.’ Rather, it envisages ‘a certain number of scenarios of action, scenarios that can be modified according to information arriving in the action, and according to chance occurrences that will occur and disrupt the action.’ The best comparison to complex strategy and thought can perhaps be found in software development and project management. ‘Agile’, as a development or management strategy, suggests managers should ‘have flexibility in a project system in order to be able to adjust constantly to emerging challenges and opportunities.’ and that such thinking can aid the tackling of ‘complex and uncertain project situations (Fernandez and Fernandez, 2008). This sounds an awful lot like Morin’s complex thought. In fact, one comparison can be found in ‘Agile’ and Morin’s examination of nonlinearity. Morin writes ‘as soon as an individual takes an action, whatever that action may be, it begins to escape from his intentions. The action enters the universe of interactions.’ One technique in Agile is the use of feedback to affect project or product development, with the service also being delivered incrementally (Dybå, Dingsøyr and Moe, 2014). This means development/management is affected by feedback that goes back and affects the system and the way it does this is inherently unpredictable. ‘Agile’ and ‘Complex Thought’ therefore have some similarities in the approach they take.
Talking about project management/development brings me to another pertinent point. Teams themselves are dependent on coordination dynamics and the way they coordinate affects how effective they perform. Gorman, Polemnia and Cooke (2010) suggests, while discussing surgical teams, that ‘if the surgical team always coordinates in a static, unchanging fashion, regardless of how appropriate it is for a particular situation, then the result is potentially fatal.’ Again, this seems indicate a need for Morin’s ‘complex thinking’, one that adjusts to a situation, like a surgical team should to save lives. This all suggests coordination dynamics, ‘complex thinking’ and teamwork should all work together to tackle the complex problems found in the modern world.
In this post, I have discussed Edgar Morin’s ‘On Complexity’. The point to emphasise in his work is that it is not enough to recognise complex problems, but we must find a way to deal with them. Morin believes this can be done through by being adaptable and changing the way you think; whether this is through identifying how organisation/disorganisation are not always opposites or through adapting a strategy consisting of multiple scenarios. Morin therefore calls for a major shift in how we not only think, but also act.
Bibliography:
Anderson, Virginia and Lauren Johnson. Systems thinking basics. Cambridge, MA: Pegasus Communications, 1997.
Barabasi. Albert L. Linked: How Everything is Connected to Everything Else and What It Means for Business, Science and Everyday Life. New York: Perseus Publishing, 2002
Derrida, Jacques. Of Grammatology translated by Gayatri Chakravorty Spivak. Baltimore: The John Hopkins University Press 1977.
Dybå, Tore, Torgeir Dingsøyr, and Nils Brede Moe. “Agile project management.” In Software project management in a changing world, pp. 277-300. Springer, Berlin, Heidelberg, 2014
Fernandez, Daniel J and John D. Fernandez. “Agile project management—agilism versus traditional approaches.” Journal of Computer Information Systems 49, no. 2 (2008): 10-17.
Gorman, Jamie C, Polemnia G. Amazeen, and Nancy J. Cooke. “Team coordination dynamics.” Nonlinear dynamics, psychology, and life sciences 14, no. 3 (2010): 265
This post contains an MA essay that touches on transdisciplinarity, through arguing Indian Ocean trade networks can be seen as complex.
Introduction
It is becoming increasingly common to think of the Middle Ages as global. The fact that Past and Present dedicated an entire issue to the topic in 2018 (Holmes & Standen, 2018) shows how popular an approach it is becoming. Two concepts and structures often invoked when analysing the Global Middle Ages are the idea of a ‘world system’ and also networks (Abu-Lughod, 1991) (Shepard, 2018). This paper shall analyse the extent to which these two concepts can engage in meaningful dialogue with each other. In particular, it will look at if complex network theory can alter our understanding of the medieval world system. It will do this by looking at if a network of the Indian Ocean world compiled by the author can be described as complex. A network is a series of nodes connected by edges, the features that make a network complex will be explained in the results section. This is not the first paper to apply complex network theory to the Middle Ages, Sindbaek (2007) has applied it successfully to the Viking world. However, this shall be the first attempt to apply it to the early medieval Indian Ocean world. The central thrust behind this paper is that if we can talk of the early medieval Viking and Indian Ocean worlds as both complex, then can we talk about the world system in its entirety as a complex system? The idea of a ‘world system’ was first introduced to the Middle Ages by Abu-Lughod (1991). It describes how the world is connected through different interacting circuits and parts forming a system of exchange. For Abu-Lughod, this system emerged in the thirteenth century, but as will become clear it was present much earlier. Some authors have hinted at the possibility of the medieval world system being viewed as a complex, Dudbridge (2018, pp. 314-315) raises the possibility of using systems biology to understand the world system. However, no systematic attempt has been made to fully analyse the possibility of making a connection between complex network theory and the medieval world system. This paper shall attempt to do this.
It will do this in three sections. The first section will outline the methodology employed for this essay, it will look at how the network analysis was carried out and will examine its limitations. The second section will include the results of the analysis and will examine if the compiled network can be described as a complex network. The final section shall discuss the implications of the results for understanding the world system. The essay shall conclude with a radical call for transdisciplinarity to increase our understanding of the world system.
Methodology
It is now necessary to outline how the network analysis was conducted. The data was collected from five articles. These were chosen through a search of Web of Science using the terms ‘medieval Indian Ocean trade’ and ‘medieval Indian Ocean world.’ Some articles were filtered out and not chosen for data entry. This was usually because they did not fit the temporal limits of the essay, 500-1000, or because they were irrelevant to the topic. It should highlighted that this study is only an preliminary endeavour and so should not be considered comprehensive. Further research will need to be conducted to verify the findings of this essay. The data was entered into Microsoft Excel (Microsoft, 2021). It was put into a nodes table, with each node representing a location from the article, and a source-target table which had the connections between the different places. Appendix A lists the locations in the network and the articles they were taken from. A connection was established if a location shared archaeological material with another. If an article cited literary evidence this was ignored. This is primarily an archaeological paper. Once the tables were complete the data was imported into Gephi (The Gephi Consortium, 2017). Gephi is a network visualisation and analysis computer programme. The statistics used in the results section of this essay derived from this programme. There were a total of 54 nodes in the network and 112 edges. Figure 1 shows the network compiled by the above methodology. Having outlined the methodology of this paper, it is now necessary to proceed and list more limitations of this essay.
Figure 1: Screenshot of Network (Data from author’s own work)
There are two types of limiting factors with regards to what this study can tell us. The first concerns the limitations of the network analysis itself. One limitation is the fact that this essay only contains data from 500-1000. This temporal framework was chosen for two reasons. Firstly, to make sure sites which are from different parts of medieval era are not erroneously seen as connected when in fact they are from a different time. Secondly, to allow a more direct comparison with Sindbaek’s (2007) study of how complex networks apply to the early medieval Northern Europe. A second factor to consider is that some of the nodes are more vague than others. Some articles offered specific locations such as ‘Unguja Ukuu’, while others were more broad and named modern countries such as ‘China’ and ‘India’. The latter were still included in the essay, as to avoid missing too many connections. A further factor to consider is that a connection between two places does not mean they had constant or instantaneous contact. Travel between different parts of the Indian Ocean, even in the later twelfth and thirteenth centuries, could take up to six months (Abu Lughod, 1991, p. 16). Finally, the network created for this essay is static, the data was not precise enough to create a dynamic graph. This makes it harder to analyse change over time. With the limitations of the network analysis considered, it is now worth considering some of the limitations of the material evidence.
The first factor to consider, with regards to the evidence, is that the material presented here only represents the movement of physical material objects. It does not consider how ideas spread across the Indian Ocean or how ideas are connected to objects. Secondly, we must also accept the fact that the presence of evidence in two sites does not necessarily mean that two places are connected. It does, however, indicate an increased likelihood of them being connected or at least that they were part of the same system of exchange. Finally, the evidence used in this essay derives from a variety of material forms, including, but not limited to, ceramics, glass and ship materials. There is not space here to go into depth about each evidence type here, but it is hoped the last two paragraphs have raised some of the associated methodological and evidence-based issues that limited this study. This finishes the methodology section of the essay.
Results
Having outlined the methodology of the essay and the pertinent limitations, the paper will now proceed to present the results of the network analysis. It will do this in four subsections which will cover different features of complex networks. Each subsection will examine if the compiled network of the Indian Ocean has these features. This will help to reveal if the early medieval Indian Ocean can be seen as complex and subsequently, in the discussion section, help us to analyse whether the world system can be described as complex.
The first feature complex networks are generally considered to have is a ‘small world’ effect. A ‘small world’ effect refers to the fact that there is generally a small path between any two nodes (Albert & Barabasi, 2002, p. 2) (Newman, 2003, p. 181) (Strogatz, 2001, p. 278). The most famous example derives from the concept of ‘six degrees of separation’. This idea suggests that there is on average people are separated from each other by six people. Meanwhile, Barabasi (2002, pp. 34-39) has suggested there are on average nineteenth degrees of separation between web pages. There are several statistics which help us to identify if the Indian Ocean world can be seen as a ‘small world’ like the World Wide Web or social contacts. The first is the average path length. This was 2.7330538085255065. This is far lower than 6 or 19 and so it is fair to say that the network in this study can be described as having a ‘small world’ effect. Further evidence confirms this. The closeness centrality of each node, which calculates the distance from a giving starting node to all other nodes in the network, is shown in figure 2. The x-axis displays the distance to all other nodes in the network for each node, whereas the y-axis shows the number of nodes with a particular distance. The closeness centrality for each node is between 0 and 1 indicating that there is on average less than one connection between one node to all other nodes in the network. This shows that the data has a ‘small world’ effect. To summarise, it is clear from this that the Indian Ocean was a ‘small world’ and that it has at least one feature of a complex network. However, it remains to be seen if it has the other features complex networks have.
Figure 2: Closeness Centrality Distribution (Data from author’s own work)
The second feature that complex networks tend to have is clustering. This is the tendency for nodes to form into split groups or as Newman (2003, p 183.) puts it the friend of your friend is likely to be your friend. The first statistic worth analysing is the average clustering coefficient. This metric is on a scale of 0 to 1, with a higher score indicating that individuals tend to be found in groups. The average clustering coefficient for the network was 0.425. This seems to indicate that clustering is not that apparent in the network and raises questions about the Indian Ocean world having this characteristic of a complex network. However, two pieces of evidence suggest that the average clustering coefficient does not tell the whole story. The first is the number of triangles in the network which was 52. This statistic indicates the extent nodes form into groups of 3, in which each node is connected to every other node. The score presented here suggests there are a high amount of communities in the network and is therefore evidence of clustering. A modularity algorithm (Blondel, Guillaume, Lambiotte & Lefebvre, 2008) was also ran on the network. This detected a different number of communities to the triangle measurement. Overall, it identified a total of six communities. Figure 3 displays each community in a different colour. While 6 communities does not seem much, we must remember that there are 54 nodes in the network and so there is around 9 people per group. Small group sizes, such as these, indicate the presence of clustering in the network, even if the average clustering coefficient seems at odd with this. Furthermore, it is possible that the average clustering coefficient is only so low because of the high degree of connectivity in the network, which itself is a sign of a complex network. Regardless, it can be said tentatively that there is evidence of clustering in the network and that it has this feature of complexity.
Figure 3: Modularity Class of Nodes (Data from author’s own work)
The third feature that complex networks have is that the degree distribution (how many edges a node has) tends to follow a power-law (Barabasi & Albert 1999). Networks with such a feature are called scale-free (Strogatz, 2001, p. 214). In such networks, there tend to be some nodes which are more highly connected than others. Figure 4 shows the degree distribution of the Indian Ocean network. The x-axis represents the degree of nodes, whereas the y-axis indicates the count of nodes with a particular degree. As the data shows, there are 16 nodes with a degree of 1, whereas there are only 3 nodes with a degree of 7 and 1 node with a degree of 22. Overall, there does seem to be some nodes which are more highly connected than others. The graph also shows a decay, which is central to it being scale free, there seem to be a few nodes with a high degree and many with a low degree. Nevertheless, a comparison of figure 4 and figure 5 complicates the issue. Figure 5 portrays four real world networks. In figure 5 we see the decay for each graph is more steady than in figure 4. In figure 4, we also see a sudden drop from 15 nodes having a degree of 2 to 2 nodes having a degree of 3. This decline is much more rapid than in other networks which are termed complex (see figure 5). Furthermore, the count of nodes also increases again for having a degree of 4 and 5. There were five nodes with a degree of 4 and 5. This increase goes against the continuous decline we see in the real world networks of figure 5. This would seem to indicate that the degree distribution of the network is only partly scale-free and that there are some anomalous results in the network. Nevertheless, there does seem to be a general decline in figure 4, even if it is not straight forward. It should also be noted that the scale-free property is common but not universal to complex networks (Strogatz, 2001, p. 219). With this and the general decay in the results took into consideration, it appears that would be wrong to dismiss the possibility of the network being complex based on the data.
Figure 4: Degree Distribution of Network (Data from author’s own work)
Figure 5: Degree Distributions of Real World Networks (Strogatz, 2001, p. 273)
The final feature complex networks tend to have relates to their robustness against attacks. Many complex systems display a degree of tolerance against errors, if the attacks are random. Meanwhile, if the attack is targeted at the hubs (the most connected nodes) the network tends to be vulnerable (Albert & Barabasi, 2002, p. 44). To analyse whether the Indian Ocean network has these features, this paper will use the technique of removing nodes randomly and then compare this to taking them away based on their centrality measures (Iyer, Killingback, Sundaram & Wang, 2013). Figure 6 shows what happened when five nodes were removed randomly. A glance at the figure reveals that for the most part the graph remained highly connected. There was only one community that was completely isolated from the rest of the network (see bottom of figure). This indicates, for the most part, that the network displays resilience against random attacks, The next step in examining the network’s robustness was to remove five of the most prominent nodes. The five nodes with the highest closeness centrality were chosen to be removed. This simulates the removal of the hubs of the network. Figure 7 shows the network with the key nodes removed. In comparison to the results from figure 6, it is clear that many more nodes are isolated and cut off from the main network. This indicates that the network has the robustness characteristic of complex networks- a strong defence against random attacks, but a weakness against attacks on the hubs. Therefore, the network has another feature that complex networks have.
Figure 6: Graph with Random Node Removal (Data from author’s own work)
Figure 7: Graph with Selective Node Removal (Data from author’s own work) Discussion
The network compiled for this essay displays the four main features of complex networks suggesting that the Indian Ocean can be seen as a complex network. This section will examine how these four features of a complex network alter our understanding of the world system. However, it is first necessary to establish a connection between the Indian Ocean network and the world system. The way to do this is to remphasise Sindbaek (2007) has already proven the Northern Europe can be seen as a complex network. This, alongside with the Indian Ocean being seen as a complex network, suggests a good part of the early medieval world can be seen as complex. Critics could raise that there might have existed two isolated complex networks rather than a single unified world system. However, when we take into consideration that world systems theory allows for different circuits or spheres that overlap with each other the possibility of a single world system with complex features seems more plausbile. Abu-Lughod (1991, p. 33) with reference to a later timeframe, divides the world system into three main parts: Western Europe, the Middle East and the Far East We can do the same for the early medieval world and suggest Northern Europe and the Indian Ocean formed different circuits of the same world system. To phrase it differently, there existed a network of networks. This does not mean the Indian Ocean and Northern Europe were directly connected, but it does suggest that what happened in one of these spheres might have had direct cosequences in another. As Abu-Lughod (1991, p. 32), puts it ‘no world system is global, in the sense that all parts articulate evenly with one another.’ Nevertheless, there were also a number of nodes in the netwok, such as Fusat and Antioch, which suggest the connections the Indian Ocean had went beyond its own sphere, even if the majority of connections took place within its own limits. Further research could confirm these findings, the study of different parts of the Africa and Eurasia and their connections, could make the presence of a world system more clear. This would especially be true if there was an emphasis on transconintental connections. Regardless, it now seems plausbile to put forward a pleminary thesis that there was an early medieval world system and that it was complex.
With a connection between the complex networks and the world system established, it remains to see how the features of a complex network might alter our understanding of the world system. The first feature to discuss is the ‘small world’ effect. Different spheres of the world system likely had, as Sindbaek (2007, p. 61) and this paper have proven, a ‘small world’ effect. The Indian Ocean and Northern Europe were both small worlds. This would seem to indicate that different spheres of the world system had high levels of connectivity within them, even if there were not many connections between different spheres. This emphasises the importance of viewing the world system as composed of different parts as Abu-Lughod (1991, p. 33) and Beaujard and Fee (2005, p. 43) have suggested.
The presence of clustering in the Indian Ocean and Viking world networks further emphasise the need to view the world system as composed of many small groups forming a wider system. Much of the network, even within spheres, was clustered. Therefore, like above, we need to stop thinking of the world system as distant parts connecting instantaneously, but to view it as an intricate web of smaller pieces joining together to form a wider system. This again highlights the need to view the world system as smaller pieces joining together.
The last two features that needs to be discussed is degree distribution and robustness. The Indian Ocean network had a few nodes with really high connections and many with few connections. This suggests we need to view the world system as composed of hubs facilitating connections between different parts of the world. The implications of this are clear, if the hubs were attacked, then the connections between different parts of the world system would mostly fail. The early medieval world system was therefore vulnerable if a certain city or location that was key was attacked or cut off by other means. Nevertheless, random attacks would not have harmed it much. Regardless, the levels of robustness the world system had is now clear.
To summarise, the Indian Ocean network and Sindbaek’s (2007) Viking network clarify our understanding of the world system in several ways. Firstly, they remphasise the need to view it as composed of many small interwoven pieces. Secondly, they highlight the general robustness of the world system, even if it was vulnerable to attacks on its hubs. These are the two main ways in which complex network theory alters our understanding of the world system.
Conclusion
This paper has proven that the Indian Ocean from 500-1000 was a complex network and consequentially shown that the world system from this period also displayed features of complexity. It did this in three sections. The first introduced the methodology of the essay, in particular it discussed how the network analysis was carried out and highligted some of the limitations of the essay. The second section looked at the results of the network analysis, it concluded that the Indian Ocean network mostly had features of a complex network. The final section took forward these findings and suggested how they might alter our understanding of the world system.
However, this project is just the beginning. Further research is needed. More parts of the world need analysing and transphere analysis needs to be conducted. Furthermore, there are many complex networks in the world, not just systems of exchange. Cells are complex networks of chemicals, the Internet is a complex network of computers and routers. Likewise, the movie industry is a complex network of actors, while language is a network of words (Albert & Barabasi, 2002, pp. 49-54). A radical transdisciplinary approach is therefore required to increase our understanding of the world system, as it shares many of the same features as networks from other disciplines. This paper therefore ends with a call to arms to break disciplinary boundaries, so we might increase our understanding of the world system. As we now know the early medieval world system was complex, we need to see how other disciplines might inform our discussion of it. Regardless, this paper has proven that the early medieval Indian Ocean was a complex system and, along with the work of Sindbaek (2007), has shown that the medieval world system itself was a complex network, even if further research is needed to verify the findings of this preliminary study.
Appendix A: List of Sites and Articles They Were Taken From
Tumbe
(LaViolette and Fleisher, 2013)
Manda
(LaViolette and Fleisher, 2013)
Unguja Ukuu
(LaViolette and Fleisher, 2013) (Flecker, 2001) (Pollard and Kinyera, 2017)
Kilwa
(LaViolette and Fleisher, 2013) (Pollard and Kinyera,2017)
Shanga
(LaViolette and Fleisher, 2013)
Sasanian- Islamic
(LaViolette and Fleisher, 2013) (Pollard and Kinyera, 2017)
Siraf
(LaViolette and Fleisher, 2013) (Flecker, 2001) (Pollard and Kinyera, 2017)
China
(LaViolette and Fleisher, 2013) (Flecker, 2001)
Comoros
(LaViolette and Fleisher, 2013) (Flecker, 2001)
Belitung
(Flecker, 2001)
Changsha
(Flecker, 2001)
Yue
(Flecker, 2001)
India
(Flecker, 2001)
Yangzhou
(Flecker, 2001)
Southeast Asia
(Flecker, 2001)
Sri Lanka
(Flecker, 2001)
Indus Valley
(Flecker, 2001)
Persian Gulf
(Flecker, 2001)
Red Sea
(Flecker, 2001)
Samarra
(Flecker, 2001)
Nishur
(Flecker, 2001)
Fusat
(Flecker, 2001) (Pollard and Kinyera, 2017)
Antioch
(Flecker, 2001)
Sohar
(Flecker, 2001)
Palembang
(Flecker, 2001)
Prambanan Temple
(Flecker, 2001)
Laem Pho
(Flecker, 2001)
Ko Kho Khao
(Flecker, 2001)
Bagamoyo
(Pollard and Kinyera, 2017)
Aydhab
(Pollard and Kinyera, 2017)
Chibuene
(Pollard and Kinyera, 2017) (Pollard, Duarte and Duarte, 2018)
Lamu
(Pollard and Kinyera, 2017)
Madagascar
(Pollard and Kinyera, 2017)
Kharg Island
(Pollard and Kinyera, 2017)
Fukuchani
(Pollard and Kinyera, 2017)
Ras Hufan
(Pollard and Kinyera, 2017)
Egypt
(Pollard and Kinyera, 2017)
Susa
(Pollard and Kinyera, 2017)
Tell Abu Sarifa
(Pollard and Kinyera, 2017)
Zimbabwe
(Pollard and Kinyera, 2017)
Botswana
(Pollard and Kinyera, 2017)
South Africa
(Pollard and Kinyera, 2017)
Maganbani
(Pollard and Kinyera, 2017)
Kaole Village
(Pollard and Kinyera, 2017)
Mso Bay
(Pollard and Kinyera, 2017)
Mikumbi
(Pollard and Kinyera, 2017)
Jiwe la Jahazi
(Pollard and Kinyera, 2017)
Namakuli
(Pollard, Duarte and Duarte, 2018)
Nhanluqui
(Pollard, Duarte and Duarte, 2018)
Manda
(Zhao, 2015)
Fanchang
(Zhao, 2015)
Dembeni
(Zhao, 2015)
Mogadishu
(Zhao, 2015)
Mteza
(Zhao, 2015)
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This post shall review the 2017 collection of essays titled ‘History, Big History & Metahistory’.
In a previous post, I bemoaned the fact that I had not found any scholarship that used complexity theory and history together. Well now, I have discovered a collection of essays published by the Sante Fe Institute that aims to engage with the historical discipline. The Sante Fe Institute is known as one of the world-leading centres for the study of complex systems. It also takes a transdisciplinary approach to its studies. I must admit before starting this review, that the prospect of finding a way to unite the humanities and sciences is something that I find a very attractive idea, so I approached this book eagerly. Nevertheless, as will become clear, there are several themes throughout the collection that come into conflict with my sense of historicism and the role of the contingent in the past.
The introduction to ‘History, Big History & Metahistory’, which is by David Krakauer, Lewis Gaddis and Kenneth Pomeranz, outlines the aims of the book. Firstly, it argues that history involves everything up to the present, which means going beyond written records and using techniques from other disciplines. For example, someone studying the past environment, should know about climatology, palaeontology and geology. The study of astronomy might also be useful for someone who is looking at the history of the cosmos. We need different disciplines if we are to talk about ‘big history’, history which goes beyond the narrow scope of written documents. The authors of the introduction write ‘how much do we really know, therefore, about where we come from, who we are- and where we may be going- if the disciplines we’ve divided ourselves into have lost the languages that would allow them to speak to anyone apart from themselves?’ The introduction also argues there is a need to be a generalist and a specialist at the same, only then can we gain full appreciation of the past. Finally, it also states that all the authors in the collection of essays share the view that history is too important to be analysed exclusively through the methods of qualitative text analysis.
The first essay in the collection, David Christian’s ‘A Single Historical Continuum’, argues that new dating techniques allow us to think of history in a single timeline extending beyond the earliest written records. One example of such a dating technique includes the half-life of radioactive materials. This and other techniques formed part of the ‘chronometric revolution’ that, according to Christian, has removed some of the barriers between the historical and scientific disciplines. The chapter also deals with a supposed pattern throughout this unified historical continuum- the rise of complexity. across time This started with the formation of the universe, the formation of galaxies and stars and ultimately in human complexity. Human society represents a new level of complexity due to its exploitation of biospheric resources.
The second chapter in the volume looks at the relationship between palaeontology and history. One area of overlap includes the investigation of early human history. It also offers a more nuanced view of the relationship between structure and contingency, by suggesting that the history of life is ‘a more complicated melange of the two’. Events, according to the Douglas Erwin, may disrupt larger structures throughout history. While this chapter does offer a more complex view than some of the others in the book, my initial reaction was to still find the application of laws to the past troubling. Reading this collection of essays, as will become clear, forced me to consider other views of the past.
John Lewis Gaddis’ chapter, titled ‘War, Peace & Everthing: Thoughts on Tolstoy’ suggests that Clausewitz and Tolstoy thought that complexity governed history, as early as the nineteenth century. Leo Tolstoy, for example, had a sense of moving between scales that is inherent in complexity. As Gaddis puts it ‘history itself is happening simultaneously across an infinite number of levels’ in Tolstoy. Murray Gell-Man, in his contribution, argues that there are mathematical regularities in human history. It mentions formulae that have been used to consider a range of issues, such as The Great Plague of London in 1665. Certain equations can be applied to find patterns throughout history, according to Gell-Man.
Geoffrey Galt Harpham offers a counterweight to the attempts throughout the collection to create a common transdisciplinary language in his chapter. He uses the example of philology which historically aimed to unite disciplines. He argues that this unification resulted in racist and anti-semitic tendencies. He concludes with one of his most powerful statements ; ‘the gaps between disciplines are not mere empty spaces to be crossed by exceptionally brainy and imaginative people, but are the very spaces of freedom.’ Nevertheless, I do not necessarily agree with Harpham’s criticisms of transdisciplinarity. For example, I cannot imagine complexity leading to racial prejudice due to its appreciation for the sensitive. Likewise, uniting people and disciplines together is not likely to cause further division.
David C. Krakauer, in his essay, uses concepts from non-linear dynamics, stastistical physics and evolutionary biology. He argues that these are useful for historians. He shows how history often uses analogs of concepts and tools expressed quantitatively in the natural sciences. The chapter ‘Homogeneity, Heterogeneity, Pigs & Pandas in Human History’ looks at two issues. Firstly, it looks at the processes of diversification and homogenisation with regards to human culture. Over time, it argues there have certain ‘swings of the pendulum’ in the direction of one or the other. For example, homogenisation as a process is happening in the modern world due to globalisation in an increasingly interconnected world. Mcneill, the author, also looks at how animals can act as analogies for the adaptability of human societies. For example, ‘pig’ societies are versatile like the species they are named after. Whereas, ‘panda’ societies are adapted to one set of conditions making them vulnerable to a change in conditions. The use of analogies my Mcneill is an interesting way of bridging the gap between disciplines. One wonders that if we look for comparisons like these, it might help us communicate more clearly across barriers. Analogical thinking could be a key part of the transdicisiplinary toolkit, especially with the importance our disciplinary languages play in communication.
Kenneth Pomeranz looks at how we name historical phenomena influences our analysis of them. He argues that many of the classification schemes used by historians are not very useful for engaging with scientists. It suggests we should look at clusters of variables, rather than focusing on dramatic events. Rather than simply analysing long-term trends, we should create taxonomies of these variables. Fred Spier argues that big history should be seen as the rise and demise of complexity throughout the universe. Energy flows and matter, he suggests, are pivotal for understanding how complexity grows or shrinks. Complexity itself can only exist within certain favourable boundaries, which Spiers calls ‘Goldilocks Circumstances.’ While I still feel hesitation regarding historical laws, I think the rise and demise of complexity, as almost mentioned by Christian, is a better approach than most. If we view history as the story of complexity, we can still retain an eye for the particular due to the presence of ‘Goldilocks Circumstances’ for most phenomena.
Peter Turchin again argues for looking at regularities throughout history. He argues that mathematic models are necessary precisely due to the complexity of history. He identifies two trends in the past. Firstly, the rise of ‘megaempires’ and their proximity to the steppe. Secondly, the rate of growth of religions once they gain an amount of momentum. Again, like with Gell-Man, I find the use of mathematical models challenging to my views about the past. My feeling is if a formula is sophisticated enough and allows room for the complexity of the past and the presence of the contingent, then it might be acceptable to use it to a degree.
Vermeij uses the idea of competition for locally scarce resources, arguing for a number of patterns that might be found in any system that faces such a scenario. He leaves more room for the contingent in his analysis by writing ‘contingency- randomness and the enduring effects of particular initial conditions and pathways of change- reigns at the level of the precise times, places, order of events and participants involved in historical sequences.’ This is a welcome addition and a combination of law and contingency is more appealing to me than just the former. Finally, Geoffrey West again looks for quantitative approaches to history through coarse-grained variables. He argues that we need to look at the collective level in order to identify patterns rather than the individual. This is an important point; switching between levels and the different techniques suited to them, may be pivotal for transcending disciplines without losing sight of either the particular or whole.
To summarise, I found ‘History, Big History & Metahistory’ a challenging read. While I still show a degree of hesitance regarding the application of laws and formulae to the past, I believe some are better suited to history than others. The authors who invoked the rise and/or the demise of complexity as a theme throughout history were more persuasive to my mind. Furthermore, those who allowed for the particular to have an influence certainly offered a more nuanced view than the authors who did not. Finally, I want highlight that regardless of any shortcomings, I believe the book has a noble aim- to transcend traditional disciplinary boundaries. The relationship between history and science is an gap that needs closing more and I think complexity studies, like the book suggests, it is the right path to go down.
This post shall review Albert-lazlo Barabasi’s 2002 book with the title ‘Linked:How Everything is Connected to Everything and What It Means for Business, Science and Everyday Life‘.
It has been a while since I have read or wrote anything about networks. So when I found a book that took an interdisciplinary approach and aimed to explain the world through networks, I was immediately drawn in. The main thesis behind Barabasi’s book is that networks play a decisive role throughout nature and society. It argues ‘reductionism was the driving force behind much of the twentieth century’s scientific revolution.’ Previously, science aimed to reduce everything down into its individual components, rather than explaining the whole. However, when one tries to do this, you hit a wall- the existence of complex systems, with parts that can be understood and reassembled in various ways. Furthermore, as everything is related to everything else, it is folly to try and understand constituents in isolation. Therefore, to summarise, ‘Linked’ aims to explain reality through a relational framework, while using networks as a concept to help us grasp an understanding of the world. Whether the book successfully achieves these aims is a question I shall return to later.
The book begins by explaining how two figures mastered networks. Mafiaboy, who successfully used the network of the internet to launch hacking attacks, and St Paul who mastered the network of the early Christian community to spread the faith. This idea of needing to think in networks and mastering them is recurring throughout the book. For example, towards the end of ‘Linked’, it argues that we need to think of human biology as a network in order to understand cells and cure diseases. As Barabasi, puts it ‘if we want to understand life- and ultimately cure disease- we must think networks.’ In cells, the nodes of the network are genes and the proteins encoded by the gigantic DNA molecules. Whereas, the links or edges are the various biochemical interactions between these components. Barabasi argues by understanding cells as a network, we might be able to develop medicines that target only the malfunctioning cells, alongside diagnosing illnesses like depression or cancer before symptoms occur. Therefore, mastering networks and thinking in networks may be beneficial to society at large.
Mastering networks may also be beneficial to corporations. Currently, business experts generally think in terms of a ‘tree’ and abstract concepts such as the ‘market.’ But in reality, corporate activity can be seen as a network. As Barabasi suggests ‘the market is nothing but a directed network. Companies, firms, corporations, financial institutions, governments and all potential economic players are the nodes.’ Thinking of the market as a network may, according to the author, allow us to avoid future economic crises. This would be done through the monitoring of the path of damage and strengthening nodes that can stop the spread of macroeconomic ‘fires’. Therefore, whether it is St Paul, human biology or the economy, thinking in terms of networks can have benefits for those who need their advantages.
If we can identify networks in different settings, are there any general patterns between them? Well, firstly networks, are not random as thought in the past, but have a power law degree distribution, in which most nodes have only a few links, but are held together by a few hubs. This pattern can be found throughout networks in nature and society. The internet, for example, is dominated by a few highly connected nodes or hubs. Barabasi frequently uses the example of the internet throughout ‘Linked’ because of his own research on it. His use of it is therefore highly knowledgeable of it. Nevertheless, there are also other patterns in networks found throughout reality. Another common feature of networks is growth- they tend to acquire more nodes as time goes on. Furthermore, nodes always compete for connections because links represent survival in an interconnected worlds. Another feature of networks is the presence of connectors ‘with an anomalously large number of links’. They ‘are present in very diverse complex system, ranging from the economy to the cell.’ These connectors play a pivotal role and can represent the idea of the spread of things like viruses or ideas.
Networks that follow the patterns mentioned above have a certain degree of robustness against failure, while also containing an ‘Achilles heel’. As Barabasi puts it ‘most system displaying a high degree of tolerance against failures share a common feature: Their functionality is guaranteed by a highly interconnected complex networks.’ For example, a cell’s robustness is hidden it in its intricate regulatory and metabolic network. Whereas, as ecoystem’s survival is encoded by a carefully crafted web of species interactions. Highly connected networks are therefore less vulnerable to attacks or threats than those that are sparsely connected. On the other hand, a reliance on hubs also gives networks a weakness. If the hubs are attacked, large chunks of the network can become disconnected from the main cluster, therefore weakening the network.
While the features above can be found throughout nature and society, Barabasi uses the internet to describe another aspect that can be found in networks. According to him, the internet’s topology coexists with numerous small-scale structures that he calls ‘continents’. ‘These are websites brought together by a join idea, hobby, or habitat, forming communities of shared interests. In my mind, these continents sound a lot like clusters. Therefore, it appears, clustering, especially around hubs, can be found throughout networks. A final point about the internet I want to raise is its self-organisation. The web emerges from the individual actions of millions of users and therefore its architecture is much richer than the sum of its parts. The web, as a consequence, has one of the defining features of complex systems.
I have described the importance Barabasi attaches to mastering networks, as well as the common patterns he has found throughout their occurrence in nature and reality. However, I will now discuss what I think about his main thesis- that nature and society can be understood throughout a collection of networks forming a relational view of reality. Firstly, Barabasi provides a substantial amount of evidence to suggest that networks with common patterns can be found throughout reality. However, it is worth noting, he uses the internet heavily throughout the book due to his own research on it. It would have been interesting to see other examples used throughout the book, such as those on biology and the economy used throughout the latter parts of ‘Linked.’ Nevertheless, based on the examples he uses, it is fair to say there are many common features of networks found througout the world.
However, while Barabasi might identify these common patterns, he does not explain how different networks in reality are connected to each other. Therefore, he does not necessarily fully develop his ideas regarding understanding reality as composed of relations rather than particulars. It would have been beneficial for him to describe how several networks affect each other to try and achieve his relational framework more successfully.
Furthermore, Barabasi could have attempted to explain more how networks affect our view of academic disciplines. His book uses examples from several disciplines, but he does not discuss in detail the implications of network theory for interdisciplinarity in any depth. If networks with similar patterns and features can be found throughout the world, then the gap between academic disciplines might be closed to a certain extent. At the same time, as a historian, I am acutely aware there are some barriers to merging the disciplines. For example, as I have raised elsewhere, history deals with phenomena from the past that cannot be verified through repeated observation unlike the scientific and social scientific disciplines. Nevertheless, I still feel a main issue with ‘Linked’ is that the potentialities or dangers of unification are not sufficiently discussed throughout the book.
To conclude, I enjoyed reading Barabasi’s ‘Linked’. I think it is a good starting point for conducting future research, even if it leaves some questions unanswered. My feeling is that if I conduct network analysis again, I would like to build on some of the ideas discussed in this book. I am interested in how disciplines relate to each other and whether we should understand reality as composed of relations or particulars. Furthermore, I believe the book’s idea that we need to understand networks because reality is composed of them is a persuasive idea as well, even if it could be developed further. Therefore, if I were to summarise my opinion of Barabasi’s work, I would say that is a good starting point for future research, even if it does not comprehensively deal with everything it could.
Since reading and reviewing Paul Cillers’ ‘Complexity and Postmodernism: Understanding Complex Systems’ an idea for a post has entered my mind. This post will fulfil that idea and act as thought experimentregarding complexity and its potential application to a historical era I am interested in.
Can we identify complex systems in the past? If so, what are the implications of this for the subject of history and its relation to other disciplines, especially considering the fact that ideas regarding complexity mainly emerge from the sciences and social sciences? As far as I know complexity theory has not been used much in the discipline of history and so this post will act as a novel attempt to see if it is possible to apply it to the past. This post will have two parts; the first will use Cilliers’ criteria for defining complex systems and see if the Late Antique ‘world’ can be called a complex system. I use ‘world’ here to denote Northern Europe, as well the Mediterranean region. The second part of the post will examine any questions that might arise as a result of this interdisciplinary exploration. I will start by copying down Cillier’s criteria for complex systems below, before seeing if they are applicable to Late Antiquity:
Complex systems consist of a large number of elements. When the number is relatively small, the behaviour of the elements can often be given a formal description in conventional terms. However, when the number becomes sufficiently large, conventional means [e.g a system of differential equations] not only become impractical, they also cease to assist in any understanding of the system.
A large number of elements are necessary, but not sufficient. The grains of a sand on a beach do not interest us as a complex system. In order to constitute a complex system, the elements have to interact, and this interaction must be dynamic. A complex system changes with time. The interactions do not have to be physical; they can also be thought of as the transference of information.
The interaction is fairly rich, i.e any element in the system influences, and is influenced by, quite a few other ones. The behaviour of the system, however, is not determined by the exact amount of interactions associated with specific elements. If there are enough elements in the system [of which some are redundant] a number of sparsely connected elements can perform the same function as that of one richly connected element.
The interactions themselves have a number of important characteristics. Firstly, the interactions are non-linear. A large system of linear elements can usually be collapsed into an equivalent system that is very much smaller. Non-linearity also guarantees that small causes can have large results, and vice versa. It is a preconditon for complexity.
The interactions usually have a fairly short range, i.e information is received primarily from immediate neigbours. Long-range interaction is not impossible, but practical constraints usually force this consideration. This does not preclude wide-ranging influence– since the interaction is rich, the route from one element to any other can usually be covered in a few steps. As a result, the influence gets modulated along the way. It can be enhanced, suppressed or altered in a number of ways.
There are loops in the interactions. The effect of any activity can feed back onto itself, sometimes directly, sometimes after a number of intervening stages. This feedback can be positive [enhancing, stimulating] or negative [detracting, inhibiting]. Both kinds are necessary. The technical term for this aspect of a complex system is recurrency.
Complex systems are usually open systems, i.e they interact with their environment. As a matter of fact, it is often difficult to define the border of a complex system. Instead of being a characteristic of the system itself, the scope of the system is usually determined by the purpose of the description of the system. and is thus often influenced by the position of the observer. This process is called framing. Closed systems are usually merely complicated.
Complex systems operate under conditions far from equilibrum. There has to be a constant flow of energy to maintain the organisation of the system and to ensure its survival. Equilibrum is another word for death.
Complex systems have a history. Not only do they evolve through time, but their past is co-responsible for their present behaviour. Any analysis of a complex system that ignores the dimension of time is incomplete, or at most a synchronic snapshotof a diachronic process.
Each element in the system is ignorant of the behaviour of the system as a whole, it responds only to information that is available to it locally. This point is vitally important. If each element ‘knew’ what was happening to the system as a whole, all of the complexity would have to be present in that element. This would either entail a physical impossibility in the sense that a single element does not have the necessary capacity, or constitute a metaphysical move in the sense that ‘conciousness’ of the whole is contained in one particular unit. Complexity is the result of a rich interaction of simple elements that only respond to the limited information each of them are presented with. When we look at the behaviour of a complex system as a whole, our focus shifts from the individual element in the system to the complex structure of the system. The complexity emerges as a result of the patterns of interaction between the elements.
Having listed Cilliers’ criteria for defining complexity, I will now see if we can apply them to Late Antiquity.
This is perhaps the easiest to answer. Late Antiquity certainly consisted of a large number of elements, in terms of the number of people who were part of its ‘world’. However, source-wise we may only have access to a small proportion of the different ‘elements’, an implication I will return to later.
Individuals in Late Antiquity naturally interacted with each other, sometimes physically, other times through the transference of information [such as by letter]. The ‘world’ of Late Antiquity also evolved over time, events arose out of interactions between different elements. As you can see, many of the criteria for complex systems can be applied to human societies in general and not just Late Antiquity.
The interaction between components in Late Antiquity was rich. Individuals interacted with multiple other individuals. Gregory of Tours came into contact with a number of different elements during his career, such as Kings like Chilperic and a number of ecclesiastical figures he worked with. However, it is unlikely sparsely connected elements [such as those with few contacts or political influences] performed the same functions as those with lots of connections- raising doubts about whether we can precisely call Late Antiquity a complex system.
Interactions in Late Antiquity were certainly not predictable or linear, small causes could have large effects [and vice versa]. For example, it seems unlikely that Theoderic the Great’s diplomatic policy [a large cause] would have a had a smaller impact in the form of Hygelac’s raid on the Franks [as argued by Storms].
Interactions can defitnelty be said to be short range. Most of Cassiodorus’ Variae were directed towards people living in Italy or other Ostrogothic provinces. At the same time, rare long-range communication was possible. The Variae contain a few letters directed to the Eastern Roman Empire, as well as to foreign kings, like those of the Heruli and Thuringi. Influence in networks, could certainly be suppressed or enhanced by a number of factors, such as past friendliness and hostility.
This is one of the harder criteria to argue for. Outputs [effects] may have created inputs [causes], but it is hard to identify feedback loops in the sources, due to the diversity of events in Late Antqiuity [it is difficult to assign causal laws to the past].
The ‘world’ of Late Antiquity was certainly an open system. The Eastern Roman Empire, for example, interacted with Persia. It is hard to define the geographic boundaries of the Late Antique ‘world’ and it often seems to be more a historical tool employed by scholars, rather than a rigidly defined actuality.
Late Antiquity operated far from equilibrium due to the diversity and volume of forces within it. There was no state where opposing forces were balanced.
The Late Antique ‘world’ certainly had a history, from the influence of the Roman Empire to events that are recorded in histories or chronicles. Histories, like Gregory of Tours’, preserved memories [fictive or not], allowing the system’s past to affect its present.
Individual components in Late Antiquity did not realise they were part of a wider complex system, people did not recognise all the forces at work in the system and responded to the information they had at hand. However, at the same time, individuals may have had ideas of the a shared Roman past, this might undermine the idea that components were not aware of the whole complex system.
Overall, it seems that the ‘world’ of Late Antiquity fits many of Cillier’s criteria for being a complex system. There are some instances where we encounter difficulties, but it seems we can apply complex systems theory to the past with some sucess. What are the implications of this? It suggests we have reason to argue that the discipline of history should be more open to interdisciplinarity outside of the arts and humanities. Certain scientific ideas, particularly more philosophical ones, are applicable to the past. Another question is also raised, to what extent can the historical discipline add to our knowledge of complex systems? Its preoccupation with time, might be useful for illuminating how we should look at the history of different systems, especially with regards to the methodologies and source criticism involved.
Nevertheless, there are some issues that need to be raised about this potential new area of interdisciplinarity for history. The sciences deal with phenomena that can be found in the world, which can be tested rigorously in repeatable conditions. Whereas, history is reliant on the sources availble to try and reconstruct the past- which might limit what we can learn about past systems. Furthermore, the past cannot be examined in repeatable conditions. There are therefore some limitations that need to be considered when trying to break the boundaries between history and the sciences.
Finally, what might complexity theory tell us about Late Antiquity? It tells us to view it as a nuanced world. Old [and now not very prevalent] ideas about this era as ‘dark’ do not seem reasonable, in light of a complex systems approach. The Late Antique world can not be considered as ‘simple’, when it is viewed as a complex and dynamic system. It also teaches to not apply simple monocausal explanations to Late Antiquity, by allowing us to view its ‘world’ as full of rich and varied connections that affect the wider system. Again, we can no longer view Late Antiquity as simple, when in fact it was full of diverse interactions with multiple causes and impacts.
To conclude, this post has examined whether complex systems theory is applicable to the past by using the case study of Late Antiquity. The overall answer is that it can be, as long as we still take into consideration a number of limitations. I then examined a number of questions that might arise as a result of this applicability and how this might affect interdisciplinarity. I suggested that using complex systems’ ideas might be beneficial for both the historical and scientific disciplines, even if some questions are raised by doing so. It therefore appears that the prospect of further interdisciplinary dialogue may be achievable.
Bibliography:
Primary Sources
Cassiodorus, Variae in The Letters of Cassiodorus: Being A Condensed Translation Of The Variae Epistolae Of Magnus Aurelius Cassiodorus Senator translated by Thomas Hodgkin. London: Henry Frowde, 1886.
Gregory of Tours, Ten Books of Histories in The History of the Franks translated by Lewis Thorpe. London: Penguin, 1974.
This post will reviewPaul Cilliers 1998 book ‘Complexity and Postmodernism: Understanding Complex Systems’. Integrating theory from the sciences and social sciences with postructural thought it aims to encourage interdisciplinarity discussion by looking at how they might mutually benefit each other.
The first issue raised by Cilliers is how do we define complexity? Complexity is a framework used in the sciences to understand systems with a distinct set of features. Cilliers provides ten criteria for thinking about them. These include containing a large amount of components, having non-linear interactions, being open to the environment and operating far from equilibrium, as well as having components that communicate locally. There is not space to list them all attributes of complex systems here, as the definition of them is somewhat debated, but it suffices to say that Cilliers’ definition is broad and incorporates a number of features systems may have. The examples he uses to exemplify complexity, the human brain and language, are introduced here and used throughout the book. Meanwhile, Cilliers also makes an effective comparison to the complexity of a country’s economy as a useful introductory example. A country’s economy can be composed of millions of people, have non-linear interactions like interest and be influenced by outside factors. It also requires constant flow to exist and economic agents who usually operate with those closest to their proximity.
Cilliers also describes connectionism in the opening chapter before providing a more in-depth explanation in the second chapter. Connectionism is a form of information processing inspired by the our understanding of the human brain. It is made up of neurons or nodes that are connected to each other. These connections have a number of weights that determine the characteristics of the network. In a ‘training period’, in which the network learns its function, the weights adjust based on what it inputs they receive. Connectionism, Cilliers argues, should also be defined as a distributed rather than rule-based form of representation, there is no algorithm with central control, a theme which Cilliers returns to later in the book.
This section also introduces the idea that connectionism may be similar to Ferdinand de Saussure’s model of language, which is addressed more thoroughly in the next chapter. In Saussure’s model, language is a system of relationships between different signs. ‘Brown’ gets its meaning from how it differs to ‘black’, ‘blue’, ‘grey’ and ‘train’. It does not get its meaning because it is tied to a particularly concept of brownness. As Cilliers puts it ‘The sign is a node in a network of relationships. The relationships are not determined by the sign; rather, the sign is the result of interacting relationships’. It therefore shares a similarity with a distributed connectionist approach in that relationships take precedence over the individual nodes or signs in this instance.
In the third chapter, Cilliers also engages with the ideas of the continental philosopher Jacques Derrida. This is because Saussure’s’ ideas can have some limitations when arguing for language as a complex system. For example, his theories present language as a closed, rather than open, system. Cilliers engages with the Derridean ideas of trace and différance. Trace, in this instance, refers to how a sign has no component that fully belongs to itself, ‘it is merely a collection of the traces of every other sign running through it.’ Whereas, différance refers in one case to the system of language consisting of differences and in another sense it refers to how meaning is continuously deferred. Cilliers then states ‘the characteristics of the system emerge as a result of the différance of traces, not as a result of essential characteristics of specific components of the system.’ Again, this draws comparisons with connectionist/neural networks. Because a weight only gains significance through its patterns of interaction, it might be possible to suggest, as Cilliers does, that ‘weight’ in a connectionist network and Derrida’s concept of trace are somewhat comparable. Likewise, différance can be used to explain how complex systems always contain loops and feedbacks. Because complex systems have delayed self-altering they are similar to différance, by the fact that they are also suspended between the active and the passive.
The next chapter deals with John Searle who would explicitly reject a postmodern way of understanding complex systems. Instead, he would take an analytical and rule-based approach to the brain and language. Cilliers engages with Searle’s ‘Chinese Room’ argument which is against strong AI and then criticises it. He also discusses the Searle/Derrida debate regarding speech acts. While short, the chapter effectively refutes some of the criticisms that could be launched be against Cilliers’ connectionist approach.
After this, Cilliers moves on to talking about the problem of representation. ‘Models of complex systems’ he states ‘will have to be as complex as the systems themselves’. Based on this Cilliers suggests classical theories of representation are inadequate to describe complex systems. In fact, connectionism puts the whole idea of representation at risk. This chapter consists of four main sections. The first critiques classical approaches, which establish a rule-based approach to understanding systems. It uses Hilary Putnam’s ideas to do this. The second section looks at connectionism and how it is better suited for modelling complex systems rather than algorithmic approaches. Firstly, connectionism does not require a theory to be developed before a solving a problem. Secondly, they can generalise solutions. Thirdly, connectionist networks are robust- they degrade slower than other forms of networks. Cilliers, in this section, also deals with some of the criticisms of connectionism. In the third part of the chapter, Cilliers looks at the practical benefit of connectionism for modelling, before he looks at some of the philosohical implications of connectionism in the final section.
After discussing representation, Cilliers moves on to look at self-organisation. Self-organisation is a feature of complex systems and can be defined as the ability of a system to develop or change their internal structure spontaneously and adaptively cope with its environment. As Cilliers describes, ‘self organisation in complex systems works in the following way. Clusters of information from the external world flow into the system. This information will influence the interaction of some of the components in the system- it will alter the values of the weights in the system.’ The chapter also applies selection- from theory of evolution- to how self-organising systems form, as well as looking again at some of the philosophical implications of the arguments in the chapter.
The final chapter of Cilliers’ book looks at the intersections between postmodernism and complexity. It does this with a very nuanced approach to postmodernism. Postmodernism is presented not as saying ‘anything goes’, but instead as having a sensitivity to complexity. Cilliers uses Lyotard’s The Postmodern Condition to support his arguments in this section. Cilliers then moves on to argue that postmodern society can be compared to some of the features of complex systems he outlined earlier. For example, it is composed of a high number of components and is characterised by a series of local ‘narratives’ over a metanarrative. The author then examines language as a complex system, before arguing for a postmodern approach to science, which values openness and flexibility regarding different narratives, rather than a more constrained and conservative approach. ‘Descriptions’ of the world ‘cannot be reduced to simple, coherent and universally valid discourses’ due to its complexity. Finally, the chapter also looks at the connections between postmodernism and the philosophy of ethics.
Overall, ‘Postmodernism and Complexity’ successfully integrates a number of interdisciplinary themes in a well-structured fashion. The book introduces the idea that postmodernism and complexity may be compared to each other, opening the door to a range of potential research directions. To summarise, Cillier’s contribution here is important as well as an exciting prospect to read.
Philosophical Ostrogoth 