2.1 The Evolution of the Shipping and Ports' System

Shipping volumes have grown dramatically in recent decades, and this growth has been coupled with changing technologies – particularly through larger ships and improved port logistics – and with changing geographies. At present, many authors estimate that maritime shipping ranges between 77% and 90% of the intercontinental transport demand for freight by volume compared to shipping in the 1980s when it was around 23% (Rodrigue et al., 2006, 2009; Glen and Marlow, 2009; Barthelemi, 2011). The total number of twenty-foot equivalent units (TEU) carried worldwide ranged from 1,856,927 in 1991 to 7,847,593 in 2006 (Notteboom, 2004), and the average vessel capacity has grown from 1900 TEU in 1996 to 2400 TEU in 2006 (Ducruet and Notteboom, 2012). Kaluza et al. 2010 attribute this substantial increase to the growth in trans-Pacific trade. The lower cost per TEU-mile in long distance transport for large quantities of goods has also driven this growth (Rodrigue et al., 2006) coupled with significant technical improvements in size, speed and ship design as well as automation in port operations (Notteboom, 2004; Rodrigue et al., 2009). For instance, in 1991, the use of vessels larger than 5000 TEU was unheard of, but by 1996 large vessels constituted about 1% of the world's fleet, increasing to 12.7% in 2001 and 30% in 2006.

A variety of independent agents (shipping companies, commodity producers, ports and port authorities, terminal operators and freight brokers) play roles in this process, and through their mutual interactions, they determine the patterns of development and growth. It is helpful to view shipping as a complex system of relatively independent parts that constantly search, learn and adapt to their environment while their mutual interactions shape hidden patterns with recognisable regularities that continuously evolve. In this context, the science of Complex Adaptive System (CAS) provides a useful framework to analyse a shipping system (Arthur, 1997; Dooley, 1996; Gell-Mann, 1994; Gell-Mann, 1995; Holland, 1992; Holland, 1995; Holland, 1998; Levin, 1998; Levin, 2003), a field of study in which strategic analysis is based on what is essentially a bottom-up perspective. CASs are generally assumed to be composed of a set of rational, self-learning, independent and interacting agents whose mutual interrelations generate non-linear dynamics and emergent phenomena.

Since the 1980s, maritime agents have continuously evolved in their organisation in response to both external stimulus and market competition. In the logistics and management perspective, a new form of inter-firm organisation has emerged in the shipping industry. Rodrigue et al. 2009 succinctly explain how this change has occurred:

[…] many of the largest shipping lines have come together by forming strategic alliances with erstwhile competitors. They offer joint services by pooling vessels on the main commercial routes. In this way they are each able to commit fewer ships to a particular service route, and deploy the extra ships on other routes that are maintained outside the alliance. […] The 20 largest carriers controlled 26% of the world slot capacity in 1980, 42% in 1992 and about 58% in 2003. Those carriers have the responsibility to establish and maintain profitable routes in a competitive environment Rodrigue et al. 2009.

The evolution of the shipping industry has gone hand in hand with the changes in port organisation. According to a study for the European Parliament 2009, from the growth of containerisation to what is known as the terminalisation era, where ports have become multifunctional operations through the development of highly specialised terminals, ports have undergone a major transformation in their management structures. The role of port authorities has also changed as the shipping system has evolved. Their main duties now involve the optimisation of process and infrastructures, logistics performance, promotion of intermodal transport systems and increased relations with their hinterlands.

In light of these observations, our objective in this chapter is to examine how maritime shipping can be modelled through the use of CAS theory. If we assume that emergent global behaviour such as international trade can be explained through bottom-up phenomena arising from the local interaction among individual agents, our main goal is to understand how patterns emerge in the global shipping system. The argument of our analysis is organised as follows. In Section 2.2, we summarise the main features regarding the worldwide movements of goods. Section 2.3 provides a detailed discussion of the CAS methodology for maritime trade, and in Section 2.4 we discuss the opportunity to apply CAS modelling to the maritime system and conclude with a research agenda for future studies.

2.2 Analyses of the Cargo Ship Network

Two recent articles (Ducruet and Notteboom, 2012; Kaluza et al., 2010) examine the main characteristics of the Global Cargo Ship Network (GCSN). Other studies focus on sub-networks of the GCSN. Ducruet et al. 2010 have analysed the Asian trade shipping network, McCalla et al. 2005 the Caribbean container basin, Cisic et al. 2007 the Mediterranean liner transport system and Helmick 1994 analysed the North Atlantic liner port network. But the two studies of Kaluza et al. 2010 and Ducruet and Notteboom 2012 are particularly interesting because they examine the complete global network and give us a view of its macroscopic properties. However, one drawback is their inability to forecast future trends or changes in the networks. Nevertheless, for our purpose, the aim of both studies is to characterise the global movements of cargo in order to define quantitative analyses on existing structural relations in the rapidly expanding global shipping trade network.

Table 2.1 highlights similarities and differences between the two studies on the GCSN. Kaluza et al. use the Lloyd's Register Fairplay for the year 2007, while Ducruet and Notteboom utilise the data set from Lloyd's Marine Intelligence Unit for the years 1996 (post-Panamax vessels period) and 2006 (introduction of 10,000+ TEU vessels).

Both studies apply different approaches to the network analysis and sometimes reach different conclusions. Ducruet and Notteboom built two different network structures: the first (Graph of Direct Links – GDL) takes only into account the direct links generated by ships mooring at subsequent ports, and the second (Graph of All Linkages – GAL) includes the direct links between ports which are called by at least one ship. Kaluza et al. 2010 differentiate between the movements according to the type of ship. Four networks are subsequently constructed: all available links, sub-network of container ship, bulk dry carriers and oil tankers. Despite the clear differences between the approaches adopted in the two studies, in order to compare them we have considered the complete network of ships' movements by Kaluza et al. and the GAL network of Ducruet and Notteboom.

All the networks are quite dense (on average, the ratio between number of edges and nodes is 37.2). Some network measures indicate a tendency of the GCSN to belong to the class of small world networks¹ given the high values of the Clustering Coefficient.² Small world networks are a special class of networks characterised by high connectivity between nodes (or, in other words, low remoteness among the nodes). In an economic setting, this property has an underestimated value, for example, in the retail market, and connections among firms can create clusters of small specialised firms that gravitate around a big firm (hub). This large firm uses small sub-peripheral firms to sub-contract production. Thus, both firms (the hub and peripheral ones) reach their goals and increase the economic entropy of the system (Foster, 2005).

The degree distribution P(k)³ shows that “most ports have few connections, but there are some ports linked to hundreds of other ports” (Kaluza et al., 2010). However, when the authors examine the degree distribution in detail, they find that the GCSN does not belong to the class of scale-free networks.⁴ Both studies show low power law exponents or right-skewed degree distributions. In this sense, the degree distribution analysis would have had a higher significance if the authors had showed a ranking of the ports over time. This would have allowed them to understand if there was ever a turnover of dominant hubs that in turn had led to the detection of competitive markets in maritime shipping. Opposite results would have depicted a constrained market.

Kaluza et al. 2010 also studied the GCSN as a weighted network where the distribution of weights and Strength⁵ displays a power law regime with exponents higher than 1. These findings agree with the idea that there are a few routes with high-intense traffic and a few ports that handle large cargo traffic. The detection of power law regimes is often associated with inequality (i.e. distribution of income and wealth) or vulnerability in economic systems (Foster, 2005; Pareto, 1897). The correlation between Strength and Degree of each node also fits a power law; this implies that the amount of goods handled by each port grows faster than the number of connections with other ports. Hub ports do not have a high number of connections with other ports, but the connected routes are used proportionally by a higher number of vessels.

Unfortunately, Ducruet and Notteboom's work (Ducruet and Notteboom, 2012) does not provide results of the weighted network analysis over the years 1996 and 2006. Such as analysis would have allowed us to discuss relevant facts about the dynamics of flows in the main transatlantic routes and also address constructive criticism of the influence of the introduction of large loading vessels (post-Panamax era) on specific routes.

The centrality of ports in a network (i.e. the importance of a node) can be inspected with other topological measures rather than the crude number of connections per node (degree k). In the case of GCSN, both studies use measures of the betweenness centrality.⁶ Kaluza et al. 2010 highlight a high correlation between the degree k and betweenness centrality and then validate the theory that hub ports are also central points of the network. Ducruet and Notteboom detect interesting anomalies in the centrality of certain ports. Large North American and Japanese ports are not on the top rank position in terms of network centrality despite their traffic volume. The most central ports in the network are the Suez and Panama canals (as they are gateway passages), Shanghai (due to the large number of ships that “visit” the port) and ports such as Antwerp (due to its high number of connections.)

Although maritime shipping has been going through a tremendous period of expansion in the last decade, the underlying network has a robust topological structure that has not changed in recent years. Kaluza et al. 2010 observe the differences “in the movement patterns of different ship types.” The container ships show regular movements between ports, which may be explained by the nature of the service they provide. Dry carriers and oil tankers tend to move in a less regular manner because they change their routes according to the demand of goods they carry.

Finally, maritime shipping appears to have gained a stronger regional dimension over the years. In 1996, there was a strong relation between European and Asian basins while in 2006 these connections appear to be weaker. Ducruet and Notteboom 2012 explain this as a twofold phenomenon. Each basin has reinforced the internal connectivity while the Asian basin is witnessing a strong increase in the volume of goods shipped. The direct consequence is that Asian countries have been splitting their links with European countries. Physical proximity also helps to explain the increase in regional basins as well as the establishment of international commercial agreements such as the NAFTA and MERCOSUR between North and South America (Ducruet and Notteboom, 2012).

2.3 A Complex Adaptive Systems (CASs) Perspective

In the previous section, we have discussed two recent studies that take into account a static analysis of the global cargo shipping network. From these studies, we conclude that GCSN is a small world network with some power law regimes when it is examined as a weighted network. This evidence indicates that the underlying structure is not dominated by random rules, and that the complex organisation emerges from the interaction of lower-level entities.

In shipping, self-organisation is identified as a bottom-up process arising from the simultaneous local non-linear interactions among agents (i.e. vessels, ports, shipping alliances or nations according to the scale of analysis). This allows us to note that in GCSN, our aim is to understand why certain ports are able to play a leading role and also to estimate the shipping trade trends. Using an example from the nature, flocking birds generate patterns based on local information. Each bird learns from other birds and adapts its speed and direction accordingly in order to reach the next spot. In the same way, shipping companies compete in the market according to their own interests. The introduction of innovation makes a company more competitive and sets new rules in the market that force other companies to co-evolve accordingly in order to be profitable. This adaptive process has been witnessed in maritime shipping at different stages with the introduction of new technologies such as improvements in the fleets (launch of post-Panamax ships) or in the management processes in the seaports (automation of loading and unloading services).

Based on the work in (Ducruet and Notteboom, 2012; Kaluza et al., 2010), our next step is to identify a set of CAS features related to shipping systems. We select 10 characteristics extracted from a number of works that have proposed applications of CAS modelling (Wallis, 2008). In Table 2.2, we relate each characteristic to Holland's classification described in Appendix and to a possible CAS modelling application for shipping systems. In the reminder of this section, we discuss how these 10 characteristics are constructive elements for a CAS shipping system.

Source: Reproduced with permission from Caschili and Medda (2012).

As shown in Section 2.2, international shipping is a large collection of entities (Table 2.2 – Feature: Many interacting/interrelated agents) whose interactions create non-linear trends (Table 2.2 – Feature: Non-linear/Unpredictable). Given these two analytical perspectives, we can examine the local interactions among ships and how they are assigned to different ports according to price and demand of the goods they carry (Table 2.2 – Feature: Goal seeking).

However, according to the modelling proposed in Ducruet and Notteboom 2012 and Kaluza et al. 2010, seaports may be considered as agents of a CAS. In this case, the most interesting questions revolve around understanding how a shipping system evolves in relation to external shocks (Table 2.2 – Feature: Co-evolutionary). For instance, in cases of sudden undesired events such as terrorist attacks or extreme natural phenomena (earthquakes and hurricanes), the cargo shipping network would co-evolve in order to maintain the same level of provided service if a big seaport hub were to disappear or be severely damaged.

In the case of natural systems, the fundamental questions are: how would an ecosystem evolve if a species disappeared? Would it be replaced by new species and would other species be able to survive without it? We can apply those questions to the case of maritime shipping in order to forecast future configurations as well as prevent global breakdowns in national and international markets (Table 2.2 – Feature: Self-organisation).

The cargo shipping industry comprises several sectors such as international maritime transport, maritime auxiliary services and port services, which play a relevant role; they have a fairly long history of co-operation since the 1990s with the formation of consortia and alliances. Each co-operation is regulated by a wide range of “national and international regulations responding to specific issues that have arisen as the international trading system has evolved” (Hubner, 2003). The outcomes of these collaborations influence the setting of freight rates and shipping companies' tariffs. In the light of the previous remarks, co-operation among agents (shipping companies, port authorities and so on) should be included in the modelling (Table 2.2 – Features: Distributed control and Nested Systems).

In particular, the international economic alliances in trade agreements play a significant role in the definition of trade flows and development. For instance, China's admittance into the WTO has affected the bilateral negotiations between WTO countries and China itself as well as among former members (Table 2.2 – Feature: Co-evolutionary and Self-organisation), but other examples of international trade agreements show similar impacts on international trade processes (NAFTA among North American countries, MERCOSUR in South America, ASEAN-AFTA among five Asian countries and the Trans-Pacific Strategic Economic Partnership (TPP) in the Asian-Pacific region).

Based on these observations, when modelling shipping relationships, trade agreement memberships should be included for two reasons: first, in order to understand the actual effects on

agents involved in the agreements, and second, to understand the effects generated on agents who are not members of a specific trade bloc. In this regard, a CAS application on maritime international trade would help us to better understand the role of alliances in trade, the effects of the establishment of new alliances or the admission of new members in existing agreements (Table 2.2 – Feature: Emergence).

The aforementioned are some of the questions a CAS application should potentially be able to answer when policy constraints are reckoned with the agents' behaviour modelling (Table 2.2 – Feature: Distributed control). Referring to Holland's classification, the modeller has to set up the internal model of each agent so that it takes into account the distinguishing factors an agent uses to direct its economic choices. For example, national and international port alliances are nested clusters of ports. A single port may belong to a cluster of ports at the national level and also belongs to a cluster of ports at the international level. Conversely, not all ports in national cluster are necessarily part of an international cluster. These structures emerge at the time of mutual interactions between the agents.

2.4 Conclusions: The Benefits of a Systems Perspective

In an article in Nature, Farmer and Foley 2009 call for more applications of CAS in economics, asserting that “agent-based models potentially present a way to model the financial economy as a complex system, as Keynes⁷ attempted to do, while taking human adaptation and learning into account, as Lucas advocated.”

Unlike classic top-down approaches, whose modelling components are carefully designed and evaluated, the CAS theory proposes bottom-up methods based on the modelling of simple interactions among its components (or agents) that generate complex, robust and flexible phenomena and macro regularities. The only duty of the modeller is to set the initial rules that regulate the agent–agent interactions and report the initial conditions of the environment where the agents act.

CAS application may be useful for multiple aims. Following is the list of some of the goals that may be achieved in applying CAS theory in maritime trade:

• To test standard economic theories;
• To understand the spatial structure and organisation of economies such as the formation of regional clusters of ports, business agglomerations and industrial alliances;
• To understand why certain types of co-operation among shipping firms appear to be more adaptable than others and to know what factors regulate the stable relationship among them;
• To provide policy makers with a set of comprehensive tools able to address issues of growth, distribution and welfare connected to global trade trends;
• To put into practice an integrated multidisciplinary approach among fields belonging to social science.

Many scholars have thrown down the gauntlet to the scientific community for a multidisciplinary approach based on the CAS paradigm (Holland, 1995; Martin and Sunley, 2007). Communication across disciplines may allow discerning clues from different fields and quicken theoretical and practical advances. Complexity in economics is currently lacking provision of a more general theory but is characterised by the application of ad hoc ideas and concepts. In previous paragraphs, we have argued that even in CAS theory there is not a shared theoretical framework whose scholars gather in the application of its notions. Most of these notions derive from physical, chemical and biological studies and their assumptions are “restricted to the content of the specific scientific models used”. Economic systems are unambiguously complex systems, as they are highly interconnected, adaptive, self-organising and emergent systems. The scientific community should take steps towards the construction of a more tenable ontological framework while scholars and modellers should derive a common approach from that framework.

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Appendix