N& Interdisciplinary Description of Complex Systems 10(1),1-15,2012 A REVIEW OF THE MARITIME CONTAINER SHIPPING INDUSTRY AS A COMPLEX ADAPTIVE SYSTEM* Simone Caschili1.2.**and Francesca Romana Medda UCL QASER LAB,University College London London,United Kingdom 2Centre for Advanced Spatial Analysis,University College London London,United Kingdom Review Received:7.September 2011.Accepted:23.January 2012. ABSTRACT If we consider the worldwide maritime shipping industry as a system,we observe that a large number of independent rational agents such as port authorities,shipping service providers,shipping companies,and commodity producers play a role in achieving predominant positions and in increasing market share.The maritime shipping industry can,from this perspective,be defined as a Complex System composed of relatively independent parts that constantly search,learn and adapt to their environment,while their mutual interactions shape obscure but recognizable patterns.In this work we examine the maritime shipping industry through the Complex Adaptive System(CAS).Although CAS has been applied widely to the study of biological and social systems,its application in maritime shipping is scant.Therefore,our objective in the present paper is to provide a literature review that examines the international maritime industry through the lens of CAS.We also present some of the goals that may be achieved by applying the CAS approach to the container shipping industry in particular.The construction of a tenable ontological framework will give scholars a comprehensive view of the maritime industry and allow them to test the stability and efficiency of the framework to endogenous and exogenous shocks. KEY WORDS international trade,maritime container shipping industry,complex adaptive systems CLASSIFICATION JEL:F10,B52,O18,R12 PACS:89.20.Bb,89.40.Cc,89.75.-k *Extended version of the Working paper No.172,Centre for Advanced Spatial Analysis-University College London **Corresponding author,n:s.caschili@ucl.ac.uk;+44 (0)20 3108 3903; Postal address:90 Tottenham Court Road,London W1T 4TJ,UK
Interdisciplinary Description of Complex Systems 10(1), 1-15, 2012 *Extended version of the Working paper No. 172, Centre*for Advanced Spatial Analysis – University *College London **Corresponding author, η: s.caschili@ucl.ac.uk; +44 (0) 20 3108 3903; **Postal address: 90 Tottenham Court Road, London W1T 4TJ, UK A REVIEW OF THE MARITIME CONTAINER SHIPPING INDUSTRY AS A COMPLEX ADAPTIVE SYSTEM* Simone Caschili1, 2,** and Francesca Romana Medda1 1 UCL QASER LAB, University College London 1 London, United Kingdom 2 Centre for Advanced Spatial Analysis, University College London 1 London, United Kingdom Review Received: 7. September 2011. Accepted: 23. January 2012. ABSTRACT If we consider the worldwide maritime shipping industry as a system, we observe that a large number of independent rational agents such as port authorities, shipping service providers, shipping companies, and commodity producers play a role in achieving predominant positions and in increasing market share. The maritime shipping industry can, from this perspective, be defined as a Complex System composed of relatively independent parts that constantly search, learn and adapt to their environment, while their mutual interactions shape obscure but recognizable patterns. In this work we examine the maritime shipping industry through the Complex Adaptive System (CAS). Although CAS has been applied widely to the study of biological and social systems, its application in maritime shipping is scant. Therefore, our objective in the present paper is to provide a literature review that examines the international maritime industry through the lens of CAS. We also present some of the goals that may be achieved by applying the CAS approach to the container shipping industry in particular. The construction of a tenable ontological framework will give scholars a comprehensive view of the maritime industry and allow them to test the stability and efficiency of the framework to endogenous and exogenous shocks. KEY WORDS international trade, maritime container shipping industry, complex adaptive systems CLASSIFICATION JEL: F10, B52, O18, R12 PACS: 89.20.Bb, 89.40.Cc, 89.75.-k
S.Caschili and F.R.Medda INTRODUCTION The significant expansion of global trade,technological advancements and continuous changes in the world's geopolitical scenarios,has typified the development of the contemporary maritime shipping industry.In 1980 the intercontinental shipping freight volume comprised approximately 23 of the total world volume.At present,many authors estimate that this shipping freight volume ranges between 77 and 90 of the transport demand [1-4].The total number of Twenty-foot Equivalent Units(TEUs)carried worldwide has increased from 28,7 million in 1990 to 148,9 million in 2008;and similarly,average vessel capacity has grown from 1900 TEUs in 1996 to 2400 TEUs in 2006.While in 1996 vessels larger than 5000 TEU constituted only 1 of the world's fleet,in 2001 vessel capacity had increased to 12,7 and to 30 by 2006 [5].In this context the containerization revolution and technical improvements relative to the size,speed and design of vessels,as well as automation in port operations,have been pivotal to the success of maritime shipping activity [2,6].For instance, maritime transport has one of the lowest transport costs per TEU-mile over long distances for large quantities of goods [1].But as Kaluza et al.[7]observe,another reason must account for maritime shipping success,which they reckon is the growth of transpacific trade that has been fuelled by the globalization process.The container shipping industry has arisen as the leading transportation means for inter-oceanic shipping of manufactured goods,and for this reason we focus our critical overview on the container industry. In the rapid development of the global maritime system we can observe the presence of various independent rational agents(shipping companies,commodity producers,ports and port authorities,terminal operators,and freight brokers).Mutual interactions among large numbers of independent rational agents determine the growth,and thus the success,of this industrial sector.From this standpoint,our perspective in the present paper is to examine the container shipping industry in particular as a Complex System of relatively independent parts that constantly search,learn and adapt to their environment,while their mutual interactions shape obscure patterns with recognizable regularities that evolve continuously.The science of Complex Adaptive System(CAS)provides a useful framework for the analysis of shipping systems [8-16];as noted in the literature,CAS refers to a field of study in which its strategic analysis is based on reductionism(bottom-up investigation),and complex adaptive systems are generally 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 rational agents in the maritime industry have continuously evolved within their organizations in response to external stimuli such as market competition.In logistics and management structures in particular,new forms of inter-firm organizations have emerged in the shipping industry.Rodrigue et al.[2]explain succinctly 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. The development of the shipping industry has gone hand-in-hand with changes in port organization.According to a recent study for the European Parliament [17],ports have undergone major transformations in their organizational structures,i.e.,they have evolved from the containerization process to what is known as the 'terminalisation era',where ports carry out multi-functional operations through the development of highly specialized terminals
S. Caschili and F.R. Medda 2 INTRODUCTION The significant expansion of global trade, technological advancements and continuous changes in the world’s geopolitical scenarios, has typified the development of the contemporary maritime shipping industry. In 1980 the intercontinental shipping freight volume comprised approximately 23 % of the total world volume. At present, many authors estimate that this shipping freight volume ranges between 77 % and 90 % of the transport demand [1-4]. The total number of Twenty-foot Equivalent Units (TEUs) carried worldwide has increased1 from 28,7 million in 1990 to 148,9 million in 2008; and similarly, average vessel capacity has grown from 1900 TEUs in 1996 to 2400 TEUs in 2006. While in 1996 vessels larger than 5000 TEU constituted only 1 % of the world’s fleet, in 2001 vessel capacity had increased to 12,7 % and to 30 % by 2006 [5]. In this context the containerization revolution and technical improvements relative to the size, speed and design of vessels, as well as automation in port operations, have been pivotal to the success of maritime shipping activity [2, 6]. For instance, maritime transport has one of the lowest transport costs per TEU-mile over long distances for large quantities of goods [1]. But as Kaluza et al. [7] observe, another reason must account for maritime shipping success, which they reckon is the growth of transpacific trade that has been fuelled by the globalization process. The container shipping industry has arisen as the leading transportation means for inter-oceanic shipping of manufactured goods, and for this reason we focus our critical overview on the container industry. In the rapid development of the global maritime system we can observe the presence of various independent rational agents (shipping companies, commodity producers, ports and port authorities, terminal operators, and freight brokers). Mutual interactions among large numbers of independent rational agents determine the growth, and thus the success, of this industrial sector. From this standpoint, our perspective in the present paper is to examine the container shipping industry in particular as a Complex System of relatively independent parts that constantly search, learn and adapt to their environment, while their mutual interactions shape obscure patterns with recognizable regularities that evolve continuously. The science of Complex Adaptive System (CAS) provides a useful framework for the analysis of shipping systems [8-16]; as noted in the literature, CAS refers to a field of study in which its strategic analysis is based on reductionism (bottom-up investigation), and complex adaptive systems are generally 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 rational agents in the maritime industry have continuously evolved within their organizations in response to external stimuli such as market competition. In logistics and management structures in particular, new forms of inter-firm organizations have emerged in the shipping industry. Rodrigue et al. [2] explain succinctly 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. The development of the shipping industry has gone hand-in-hand with changes in port organization. According to a recent study for the European Parliament [17], ports have undergone major transformations in their organizational structures, i.e., they have evolved from the containerization process to what is known as the ‘terminalisation era’, where ports carry out multi-functional operations through the development of highly specialized terminals
A review of the maritime container shipping industry as a complex adaptive system As the maritime shipping system has evolved,so has the role of port authorities also transformed.Their main duties now involve the optimization of process and infrastructures, logistics performance,the promotion of intermodal transport systems,and increased relations with their hinterlands. If we assume that international trade can be explained through bottom-up phenomena arising from the interaction among individual agents,it may be possible to understand how new patterns emerge in the global shipping system.In light of the above observations,our objective in this study is to conduct a review with the aim to present a framework for the application of CAS theory to the maritime container shipping industry. The analysis is organized as follows.In the subsequent sections we review the main features of Complex Adaptive Systems,provide a detailed discussion on CAS methodology,and discuss the opportunity for scholars and practitioners to apply CAS modelling to the maritime shipping industry.We conclude with a research agenda for future studies COMPLEXITY SCIENCE AND COMPLEX ADAPTIVE SYSTEMS: KEY CHARACTERISTICS Various scholars [14,18,19]define a Complex System by observing particular features within a given system.These features are:emergent,self-organizing/adaptive,non-linear interactions in evolution.For instance,emergent phenomena are classifiable through the demonstration of their unpredictable behaviours when we account for each part of the system.This concept is exemplified by the famous statement"the whole is greater than the sum of the parts"[19,20]. Recessions and financial growth are,for example,emergent phenomena of national economies. The class of CAS is one of the conceptualizations belonging to the framework of Complex Systems.According to Anderson [21],scholars have developed different approaches and theories in their need to better understand Complexity:Mathematical (Turing and Von Neuman),Information Theory,Ergodic theory,Artificial Entities(cellular automata),Large Random Physical systems,Self-Organized Critical systems,Artificial Intelligence,and Wetware.Anderson's classification places CAS into the Artificial Intelligence approach. What most characterizes this distinctive class of Complex System are the processes of adaptation and evolution.A system is adaptive when its agents "change their actions as a result of events occurring in the process of interaction"[22].Evolution is created through the local interactions among agents.In this sense,adaptation can be seen as a passive action in which the agents absorb information from the surrounding environment (or from previous experience);whereas evolution is generated by the mutual actions among agents.Fig.1 shows how adaptation and evolution are embedded in different classes of systems. On the basis of the previous definitions,complex systems must be both adaptive and evolving systems.Unintelligent evolving systems develop through interaction processes but they do not adapt.For example,a crystal is generated by mutual interactions among atoms or molecules that have no intelligence of the process in which they are involved.Furthermore, complicated systems are made by numerous interacting elements that do not adapt or evolve in the system.Complicated artefacts such as a car engine belong to this class.The lower right-hand quadrant in Fig.1 is empty,as no adaptive system shows static structures. Adaptation and evolution play off each other and by this we mean that the adaptation process includes the concept of evolution but not the reverse. According to Wallis [23],there is no consensus on CAS unified theory,but Holland [12] nevertheless calls for a unified theory of CAS.Although many authors have developed comprehensive frameworks [8-11,15],we focus in this work on Holland's [13]approach to 3
A review of the maritime container shipping industry as a complex adaptive system 3 As the maritime shipping system has evolved, so has the role of port authorities also transformed. Their main duties now involve the optimization of process and infrastructures, logistics performance, the promotion of intermodal transport systems, and increased relations with their hinterlands. If we assume that international trade can be explained through bottom-up phenomena arising from the interaction among individual agents, it may be possible to understand how new patterns emerge in the global shipping system. In light of the above observations, our objective in this study is to conduct a review with the aim to present a framework for the application of CAS theory to the maritime container shipping industry. The analysis is organized as follows. In the subsequent sections we review the main features of Complex Adaptive Systems, provide a detailed discussion on CAS methodology, and discuss the opportunity for scholars and practitioners to apply CAS modelling to the maritime shipping industry. We conclude with a research agenda for future studies. COMPLEXITY SCIENCE AND COMPLEX ADAPTIVE SYSTEMS: KEY CHARACTERISTICS Various scholars [14, 18, 19] define a Complex System by observing particular features within a given system. These features are: emergent, self-organizing/adaptive, non-linear interactions in evolution. For instance, emergent phenomena are classifiable through the demonstration of their unpredictable behaviours when we account for each part of the system. This concept is exemplified by the famous statement “the whole is greater than the sum of the parts” [19, 20]. Recessions and financial growth are, for example, emergent phenomena of national economies. The class of CAS is one of the conceptualizations belonging to the framework of Complex Systems. According to Anderson [21], scholars have developed different approaches and theories in their need to better understand Complexity: Mathematical (Turing and Von Neuman), Information Theory, Ergodic theory, Artificial Entities (cellular automata), Large Random Physical systems, Self-Organized Critical systems, Artificial Intelligence, and Wetware. Anderson’s classification places CAS into the Artificial Intelligence approach. What most characterizes this distinctive class of Complex System are the processes of adaptation and evolution. A system is adaptive when its agents “change their actions as a result of events occurring in the process of interaction” [22]. Evolution is created through the local interactions among agents. In this sense, adaptation can be seen as a passive action in which the agents absorb information from the surrounding environment (or from previous experience); whereas evolution is generated by the mutual actions among agents. Fig. 1 shows how adaptation and evolution are embedded in different classes of systems. On the basis of the previous definitions, complex systems must be both adaptive and evolving systems. Unintelligent evolving systems develop through interaction processes but they do not adapt. For example, a crystal is generated by mutual interactions among atoms or molecules that have no intelligence of the process in which they are involved. Furthermore, complicated systems are made by numerous interacting elements that do not adapt or evolve in the system. Complicated artefacts such as a car engine belong to this class. The lower right-hand quadrant in Fig. 1 is empty, as no adaptive system shows static structures. Adaptation and evolution play off each other and by this we mean that the adaptation process includes the concept of evolution but not the reverse. According to Wallis [23], there is no consensus on CAS unified theory, but Holland [12] nevertheless calls for a unified theory of CAS. Although many authors have developed comprehensive frameworks [8-11, 15], we focus in this work on Holland’s [13] approach to
S.Caschili and F.R.Medda Evolving Unintelligent Evolving Complex Systems Systems Non- →Adaptive Adaptive Complicated Systems Static Figure 1.Graph of systems that evolve and adapt. modelling CAS,which is used widely in much of CAS literature,especially in economic applications.In one of the most robust works towards a unified theory of CAS,Holland [13] suggests four properties and three mechanisms that a CAS must possess.Although Wallis [23] argues that Holland's seven attributes for CAS are not definitive,he nonetheless remarks that other candidate features can be derived from appropriate combinations of these seven."We present below a summary of the seven basic features and group them into properties and mechanisms. FOUR PROPERTIES Aggregation The concept of aggregation is twofold.The first facet involves how the modeller decides to represent a system.Decisions on which features to leave in and which to ignore are of paramount importance.In this sense elements are aggregated in 'reusable'categories whose combinations help to describe scenes,or to be more precise,"novel scenes can be decomposed into familiar categories."The second facet can be ascribed to CAS aggregation properties which relate to the emergence of global behaviors caused by local interactions;in this case agents perform actions similar to other agents rather than adopt independent configurations.Furthermore,aggregation often yields co-operation,in that the same action of a number of agents produces results that cannot be attained by a single agent.We can explain this concept using the analogy of the ant nest.An ant survives and adapts to different conditions when its actions are coordinated with ant group (the nest),but the ant will die if it works by itself.Likewise in a CAS,a new action will survive and induce global effects if it is adopted by a large number of agents. Non-linearity Agents interact in a non-linear way so that the global behavior of the system is greater than the sum of its parts. Flows Agents interact with one another to create networks that vary over time.The recursive interactions create a multiplier effect(interactions between nodes generate outcomes that flow from node to node,creating a chain of changes)and a recycling effect (in networks cycles
S. Caschili and F.R. Medda 4 Figure 1. Graph of systems that evolve and adapt. modelling CAS, which is used widely in much of CAS literature, especially in economic applications. In one of the most robust works towards a unified theory of CAS, Holland [13] suggests four properties and three mechanisms that a CAS must possess. Although Wallis [23] argues that Holland’s seven attributes for CAS are not definitive, he nonetheless remarks that “other candidate features can be derived from appropriate combinations of these seven.” We present below a summary of the seven basic features and group them into properties and mechanisms. FOUR PROPERTIES Aggregation The concept of aggregation is twofold. The first facet involves how the modeller decides to represent a system. Decisions on which features to leave in and which to ignore are of paramount importance. In this sense elements are aggregated in ‘reusable’ categories whose combinations help to describe scenes, or to be more precise, “novel scenes can be decomposed into familiar categories.” The second facet can be ascribed to CAS aggregation properties which relate to the emergence of global behaviors caused by local interactions; in this case agents perform actions similar to other agents rather than adopt independent configurations. Furthermore, aggregation often yields co-operation, in that the same action of a number of agents produces results that cannot be attained by a single agent. We can explain this concept using the analogy of the ant nest. An ant survives and adapts to different conditions when its actions are coordinated with ant group (the nest), but the ant will die if it works by itself. Likewise in a CAS, a new action will survive and induce global effects if it is adopted by a large number of agents. Non-linearity Agents interact in a non-linear way so that the global behavior of the system is greater than the sum of its parts. Flows Agents interact with one another to create networks that vary over time. The recursive interactions create a multiplier effect (interactions between nodes generate outcomes that flow from node to node, creating a chain of changes) and a recycling effect (in networks cycles
A review of the maritime container shipping industry as a complex adaptive system improve local performance and create striking global outcomes). Diversity Agent persistence is highly connected to the context provided by other agents so as to define "the niche where the agent outlives."The loss of an agent generates an adaptation in the system with the creation of another agent(similar to the previous)that will occupy the same niche and provide most of the missing interactions.This process creates diversity in the sense that the new specie is similar to the previous one but introduces a new combination of features into the system.The intrinsic nature of a CAS allows the system to carry out progressive adaptations and further interactions,and to create new niches(the outcome of diversity). THREE MECHANISMS Tagging Agents use the tagging mechanism in the aggregation process in order to differentiate among other agents with particular properties;this facilitates a selective interaction among the agents. Internal models Internal models are the basic models of a CAS.Each agent has an internal model that filters inputs into patterns and differentiates learning from experience.The internal model changes through agent interactions and the changes bias future actions(agents adapt).Internal models are unique to each CAS and are a basic schema for each system.The internal model takes input and filters it into known patterns.After an occurrence first appears,the agent should be able to anticipate the outcome of the same input if it occurs again.Tacit internal models only tell the system what to do at a current point.Overt internal models are used to explore alternatives or anticipate the future. Building blocks With regard to the human ability to recognize and categorize scenes,CAS uses the building block mechanism to generate internal models.The building block mechanism decomposes a situation by evoking basic rules learnt from all possible situations it has already encountered. An application using all of the seven features allows analysts to define environments where adaptive agents interact and evolve.In the next section we therefore examine two specific studies dedicated to maritime container shipping (The Global Cargo Shipping Network: GCSN)through the lens of Complex Adaptive Systems. THE GLOBAL MARITIME NETWORK Only a few studies in the maritime literature focus on the global maritime network,of which the acronym GCSN stands for Global Cargo Ship Network.Scholars have mainly addressed sub-networks of the GCSN,such as Ducruet et al.[24],who have analysed the Asian trade shipping network,McCalla et al.[25]the Caribbean sub-network,Cisic et al.[26]the Mediterranean liner transport system,and Helmick [27]the North Atlantic liner port network However,two recent articles [5,7]examine the main characteristics of the complete global network,giving us a view of the macroscopic properties of the global maritime network.In line with our objective here,the aim of both studies is to characterize the global movements of cargo in order to define quantitative analyses on existing structural relations in the rapidly expanding global shipping trade network.But the one main drawback of their studies is their inability to forecast future trends or track changes in the networks. 5
A review of the maritime container shipping industry as a complex adaptive system 5 improve local performance and create striking global outcomes). Diversity Agent persistence is highly connected to the context provided by other agents so as to define “the niche where the agent outlives.” The loss of an agent generates an adaptation in the system with the creation of another agent (similar to the previous) that will occupy the same niche and provide most of the missing interactions. This process creates diversity in the sense that the new specie is similar to the previous one but introduces a new combination of features into the system. The intrinsic nature of a CAS allows the system to carry out progressive adaptations and further interactions, and to create new niches (the outcome of diversity). THREE MECHANISMS Tagging Agents use the tagging mechanism in the aggregation process in order to differentiate among other agents with particular properties; this facilitates a selective interaction among the agents. Internal models Internal models are the basic models of a CAS. Each agent has an internal model that filters inputs into patterns and differentiates learning from experience. The internal model changes through agent interactions and the changes bias future actions (agents adapt). Internal models are unique to each CAS and are a basic schema for each system. The internal model takes input and filters it into known patterns. After an occurrence first appears, the agent should be able to anticipate the outcome of the same input if it occurs again. Tacit internal models only tell the system what to do at a current point. Overt internal models are used to explore alternatives or anticipate the future. Building blocks With regard to the human ability to recognize and categorize scenes, CAS uses the building block mechanism to generate internal models. The building block mechanism decomposes a situation by evoking basic rules learnt from all possible situations it has already encountered. An application using all of the seven features allows analysts to define environments where adaptive agents interact and evolve. In the next section we therefore examine two specific studies dedicated to maritime container shipping (The Global Cargo Shipping Network: GCSN) through the lens of Complex Adaptive Systems. THE GLOBAL MARITIME NETWORK Only a few studies in the maritime literature focus on the global maritime network, of which the acronym GCSN stands for Global Cargo Ship Network. Scholars have mainly addressed sub-networks of the GCSN, such as Ducruet et al. [24], who have analysed the Asian trade shipping network, McCalla et al. [25] the Caribbean sub-network, Cisic et al. [26] the Mediterranean liner transport system, and Helmick [27] the North Atlantic liner port network. However, two recent articles [5, 7] examine the main characteristics of the complete global network, giving us a view of the macroscopic properties of the global maritime network. In line with our objective here, the aim of both studies is to characterize the global movements of cargo in order to define quantitative analyses on existing structural relations in the rapidly expanding global shipping trade network. But the one main drawback of their studies is their inability to forecast future trends or track changes in the networks
S.Caschili and F.R.Medda In Table 1 we highlight the similarities and differences between our two selected studies on the GCSN.Kaluza et al.[7]use the Lloyd's Register Fairplay for year 2007,while Ducruet and Notteboom [5]utilize the dataset from Lloyd's Marine Intelligence Unit for years 1996 (post-Panamax vessels period)and 2006(introduction of 10 000+TEU vessels). By applying different approaches to the network analysis,both studies reach different conclusions in some cases.Ducruet and Notteboom build two different network structures: the first(Graph of Direct Links-GDL)only takes 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 at by at least one ship.Kaluza et al.[7] differentiate among movements according to type of ship and subsequently construct four networks:all available links,sub-network of container ship,bulk dry carriers,and oil tankers. Despite clear differences between the approaches adopted in the two studies,in order to compare them,we consider the complete network of ship movements from Kaluza et al.[7], and the GAL network of Ducruet and Notteboom [5]. All the networks are dense(average ratio between number of edges and nodes is 37,2).Some network measures indicate a tendency for the GCSN to belong to the class of small world networks2,given the high values of the Clustering Coefficient3.Small world networks are a special class of networks characterized by high connectivity between nodes(or in other words Table 1.Overview of the main features of the GCSN as proposed Kaluza et al.[7]and Ducruet and Notteboom [5]. Kaluza et al.network GAL (Year 1996) GAL (Year 2006) (Year2007)[Z] [ [ Asymmetric(59% connections in one Weighted indirect Weighted indirect Main features direction);structural network;small network:small robustness(densely network network connected) Total 11 226;Container Vessels ships 3100;Bulk dry carriers 5498;Oil Container ship 1759 Container ship 3973 tankers 2628 Weights Sum of cargo capacity between port i and port Not specified Not specified No.of nodes 951 910 1205 No.of links 36351 28510 51057 Min.shortest path 2,5 2.23 2,21 Clustering coeff. 0,49 0.74 0,73 Average degree; 76,5;- 64,1;437 87,5:610 Max.degree P(k) Right skewed but not -0.62 -0,65 power law P(w) Power law(1,71±0,14) Suez and Panama Strong correlation Betweenness Canals have high between degree and Centrality centrality with some centrality (vulnerability of the exceptions GCSN)
S. Caschili and F.R. Medda 6 In Table 1 we highlight the similarities and differences between our two selected studies on the GCSN. Kaluza et al. [7] use the Lloyd’s Register Fairplay for year 2007, while Ducruet and Notteboom [5] utilize the dataset from Lloyd’s Marine Intelligence Unit for years 1996 (post-Panamax vessels period) and 2006 (introduction of 10 000+ TEU vessels). By applying different approaches to the network analysis, both studies reach different conclusions in some cases. Ducruet and Notteboom build two different network structures: the first (Graph of Direct Links – GDL) only takes 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 at by at least one ship. Kaluza et al. [7] differentiate among movements according to type of ship and subsequently construct four networks: all available links, sub-network of container ship, bulk dry carriers, and oil tankers. Despite clear differences between the approaches adopted in the two studies, in order to compare them, we consider the complete network of ship movements from Kaluza et al. [7], and the GAL network of Ducruet and Notteboom [5]. All the networks are dense (average ratio between number of edges and nodes is 37,2). Some network measures indicate a tendency for the GCSN to belong to the class of small world networks2 , given the high values of the Clustering Coefficient3 . Small world networks are a special class of networks characterized by high connectivity between nodes (or in other words Table 1. Overview of the main features of the GCSN as proposed Kaluza et al. [7] and Ducruet and Notteboom [5]. Kaluza et al. network (Year 2007) [7] GAL (Year 1996) [5] GAL (Year 2006) [5] Main features Asymmetric (59% connections in one direction); structural robustness (densely connected) Weighted indirect network; small network Weighted indirect network; small network # Vessels Total 11 226; Container ships 3100; Bulk dry carriers 5498; Oil tankers 2628 Container ship 1759 Container ship 3973 Weights Sum of cargo capacity between port i and port j Not specified Not specified No. of nodes 951 910 1205 No. of links 36 351 28 510 51 057 Min. shortest path 2,5 2.23 2,21 Clustering coeff. 0,49 0.74 0,73 Average degree; Max. degree 76,5; - 64,1; 437 87,5; 610 P(k) Right skewed but not power law -0,62 -0,65 P(w) Power law (1,71 ± 0,14) - - Betweenness Centrality Strong correlation between degree and centrality with some exceptions Suez and Panama Canals have high centrality (vulnerability of the GCSN)
A review of the maritime container shipping industry as a complex adaptive system words,low remoteness among the nodes).In the maritime setting this property has a significant value;the connections among ports can in fact create clusters of small specialized ports that gravitate around a large port(hub).The large port uses small sub-peripheral ports to sub-contract operations;by so doing,all the ports(hub and peripheral)reach their goals and increase the economic entropy of the system [28]. The expression of the clustering effect,Degree distributionP(k)shows that"most ports have few connections,but there are some ports linked to hundreds of other ports"[7].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.Both studies show low power law exponents or right skewed degree distributions,but if the authors had shown a ranking of the ports over time,the degree distribution analysis would have had a higher significance.This would have informed them if there had ever been a turnover of dominant hubs,which in turn had led to the detection of competitive markets in maritime shipping.Opposite results would have depicted a constrained market. Kaluza et al.[7]also studied the GCSN as a weighted network where the distribution of weights and Strength displays a power law regime with exponents higher than 1.This finding is in line with the existence of a few routes with high intensity traffic and a few ports that can 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 [28,29] The correlation between Strength and Degree of each node also fits a power law,implying that the amount of goods handled by each port grows faster than the number of connections with other ports.Hub ports also do not have a high number of connections with other ports, but the connected routes are used by a proportionally higher number of vessels. Ducruet and Notteboom's work [5]does not provide results of the weighted network analysis over years 1996 and 2006.An analysis of this type would have allowed us to discuss relevant facts about the dynamics of flows in the main interoceanic routes as well as give constructive criticism on the impacts of the introduction of large loading vessels (post-Panamax era)on specific routes. It is possible to inspect the centrality of ports in a network(i.e.the importance of a node)in addition to other topological measures.In the case of GCSN,both studies use measures of the Betweenness Centrality5.Kaluza et al.[7]emphasize a high correlation between Degree k and the Betweenness Centrality,thus validating the observation 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 in the top ranking positions in terms of network centrality despite their traffic volume.The most central ports in the network are the Suez and Panama Canals(as gateway passages),Shanghai(due to the large number of ships"visiting"the port)and ports like Antwerp(due to its high number of connections. Although maritime shipping has been experiencing a tremendous period of expansion in the last decade,the underlying network has a robust topological structure which has not changed in recent years.Kaluza et al.[7]observe the differences "in the movement patterns of different ship types."For example,container ships show regular movements between ports, which can be explained by the type of the service they provide;whereas 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 stronger relation between European and Asian basins while in 2006 these connections appear to have weakened.Ducruet and Notteboom [5]explain this as
A review of the maritime container shipping industry as a complex adaptive system 7 words, low remoteness among the nodes). In the maritime setting this property has a significant value; the connections among ports can in fact create clusters of small specialized ports that gravitate around a large port (hub). The large port uses small sub-peripheral ports to sub-contract operations; by so doing, all the ports (hub and peripheral) reach their goals and increase the economic entropy of the system [28]. The expression of the clustering effect, Degree distribution4 P(k) shows that “most ports have few connections, but there are some ports linked to hundreds of other ports” [7]. 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. Both studies show low power law exponents or right skewed degree distributions, but if the authors had shown a ranking of the ports over time, the degree distribution analysis would have had a higher significance. This would have informed them if there had ever been a turnover of dominant hubs, which in turn had led to the detection of competitive markets in maritime shipping. Opposite results would have depicted a constrained market. Kaluza et al. [7] 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. This finding is in line with the existence of a few routes with high intensity traffic and a few ports that can 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 [28, 29]. The correlation between Strength and Degree of each node also fits a power law, implying that the amount of goods handled by each port grows faster than the number of connections with other ports. Hub ports also do not have a high number of connections with other ports, but the connected routes are used by a proportionally higher number of vessels. Ducruet and Notteboom’s work [5] does not provide results of the weighted network analysis over years 1996 and 2006. An analysis of this type would have allowed us to discuss relevant facts about the dynamics of flows in the main interoceanic routes as well as give constructive criticism on the impacts of the introduction of large loading vessels (post-Panamax era) on specific routes. It is possible to inspect the centrality of ports in a network (i.e. the importance of a node) in addition to other topological measures. In the case of GCSN, both studies use measures of the Betweenness Centrality6 . Kaluza et al. [7] emphasize a high correlation between Degree k and the Betweenness Centrality, thus validating the observation 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 in the top ranking positions in terms of network centrality despite their traffic volume. The most central ports in the network are the Suez and Panama Canals (as gateway passages), Shanghai (due to the large number of ships “visiting” the port) and ports like Antwerp (due to its high number of connections.) Although maritime shipping has been experiencing a tremendous period of expansion in the last decade, the underlying network has a robust topological structure which has not changed in recent years. Kaluza et al. [7] observe the differences “in the movement patterns of different ship types.” For example, container ships show regular movements between ports, which can be explained by the type of the service they provide; whereas 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 stronger relation between European and Asian basins while in 2006 these connections appear to have weakened. Ducruet and Notteboom [5] explain this as
S.Caschili and F.R.Medda a dual 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 of regional basins as well as the establishment of international commercial agreements such as the NAFTA and MERCOSUR between North and South America [5]. DISCUSSION OF MARITIME SHIPPING USING CAS FRAMEWORK In the previous section we have discussed two recent studies that consider a static analysis of the global cargo-shipping network.From the previous studies [5,7]we can 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 organization emerges from the interaction of lower-level entities. Self-organization in shipping 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 not only to notice that in GCSN our aim is to understand why certain ports are able to play a leading role,but also to estimate the shipping trade trends.Using another example from nature,we know that 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.Shipping companies compete in the market in the same way in accordance with their own interests. The introduction of innovation makes a company more competitive,new rules are resultantly set in the market which compel other companies to co-evolve 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 port management processes(automation of loading and unloading services). Based on the work in [5,7],our next step is to identify a set of CAS features related to shipping systems.We select ten characteristics extracted from a number of works that have proposed applications of CAS modelling [23].In Table 2 we relate each characteristic to Holland's classification described in Section 2 and to a possible CAS modelling application for shipping systems.In the remainder of this section we discuss how our ten characteristics are constructive elements for a CAS shipping system. As discussed previously,international shipping involves a large collection of entities(Table 2 -Feature:Many interacting/interrelated agents)whose interactions create non-linear trends (Table 2-Feature:Non-linear/Unpredictable).Given these two analytical perspectives,we can examine the local interactions among ships and show how they are assigned to different ports according to price and demand for the goods they carry (Table 2-Feature:Goal seeking).Conversely,according to the modelling proposed in [5,7],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-Feature: Co-evolutionary).For instance,in cases of sudden undesired events such as terrorist attacks or extreme natural phenomena(earthquakes and hurricanes),the maritime 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. If we return to our analogy of natural systems,we can raise some fundamental questions:how would an ecosystem evolve if a species were to disappear?Would an extinct species be replaced by new species and would other species be able to survive without it?Similarly,we
S. Caschili and F.R. Medda 8 a dual 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 of regional basins as well as the establishment of international commercial agreements such as the NAFTA and MERCOSUR between North and South America [5]. DISCUSSION OF MARITIME SHIPPING USING CAS FRAMEWORK In the previous section we have discussed two recent studies that consider a static analysis of the global cargo-shipping network. From the previous studies [5, 7] we can 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 organization emerges from the interaction of lower-level entities. Self-organization in shipping 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 not only to notice that in GCSN our aim is to understand why certain ports are able to play a leading role, but also to estimate the shipping trade trends. Using another example from nature, we know that 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. Shipping companies compete in the market in the same way in accordance with their own interests. The introduction of innovation makes a company more competitive, new rules are resultantly set in the market which compel other companies to co-evolve 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 port management processes (automation of loading and unloading services). Based on the work in [5, 7], our next step is to identify a set of CAS features related to shipping systems. We select ten characteristics extracted from a number of works that have proposed applications of CAS modelling [23]. In Table 2 we relate each characteristic to Holland’s classification described in Section 2 and to a possible CAS modelling application for shipping systems. In the remainder of this section we discuss how our ten characteristics are constructive elements for a CAS shipping system. As discussed previously, international shipping involves a large collection of entities (Table 2 – Feature: Many interacting/interrelated agents) whose interactions create non-linear trends (Table 2 – Feature: Non-linear/Unpredictable). Given these two analytical perspectives, we can examine the local interactions among ships and show how they are assigned to different ports according to price and demand for the goods they carry (Table 2 – Feature: Goal seeking). Conversely, according to the modelling proposed in [5, 7], 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 – Feature: Co-evolutionary). For instance, in cases of sudden undesired events such as terrorist attacks or extreme natural phenomena (earthquakes and hurricanes), the maritime 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. If we return to our analogy of natural systems, we can raise some fundamental questions: how would an ecosystem evolve if a species were to disappear? Would an extinct species be replaced by new species and would other species be able to survive without it? Similarly, we
A review of the maritime container shipping industry as a complex adaptive system Table 2.Comparison of Complex Adaptive System(CAS)features with shipping Feature Description Refe- Holland rences basics Maritime shipping system Self-orga- Formation of 34-42 Tagging, The GCSN is a small world nization regularities in non- network with some power law patterns of linearity regimes when inspected as a interactions of weighted network.This evidence agents that pursue shows that the underlying their own structure is not dominated by advantage through random rules,and that complex simple rules. organization emerges from the interaction of lower-level entities. Many Large number of 34-38, Flows, This concept is already interacting, locally- 43-52, tagging embodied in the definition of the interrelated -interconnected and maritime shipping system.If we agents interacting rational only consider the fleet system agents that and the connections established continually pursue between ports,we observe their own approx.10 000 vessels,1000 advantage ports and 50 000 connections (see Table 1 for details). Distributed CAS's outcomes 12,14, Flows, Although there are international control emerge from a self- 43,51, internal trade agreements that unavoidably -organization 52 model influence maritime shipping, process rather than these pacts can be seen as being designed and external forces that increase controlled by a system entropy and prompt more centralized body or economic relationships. externally Non-linear Interactions are 14,22, Non- The GCSN shows power law fit unpredic- non-linear and thus 28,36- linearity distributions and not random table intractable from a 38,40, topological structures,thereby mathematical point 43, signalling the emergence of non- of view. 45-49, linear interactions between a 46,53 system's agents. Co-evolu- The environment is 43,45, Diversity i.e.introduction of post Panamax tionary influenced by the 46,36, and and 10 000+TEU ships change activities of each 39,54, tagging carriers routing networks and agent. 41,52 tariffs as well as the volume of transshipped cargo handled at main ports. Emergence Interplay between 52,12, Aggrega- 1.e.emergence of regional agents shapes an 14,53 tion.flows. clusters of ports. obscure but recogni- internal zable regularity (e.g. model the brain has consci- ousness but single neurons have not) Goal Agents try to adapt 43,34, Flows, Dry carriers and oil tankers tend seeking in order to fulfil 44,50, internal to move in an irregular manner 9
A review of the maritime container shipping industry as a complex adaptive system 9 Table 2. Comparison of Complex Adaptive System (CAS) features with shipping. Feature Description References Holland basics Maritime shipping system Self-organization Formation of regularities in patterns of interactions of agents that pursue their own advantage through simple rules. 34-42 Tagging, nonlinearity The GCSN is a small world network with some power law regimes when inspected as a weighted network. This evidence shows that the underlying structure is not dominated by random rules, and that complex organization emerges from the interaction of lower-level entities. Many interacting, interrelated agents Large number of locally- -interconnected and interacting rational agents that continually pursue their own advantage. 34-38, 43-52, Flows, tagging This concept is already embodied in the definition of the maritime shipping system. If we only consider the fleet system and the connections established between ports, we observe approx. 10 000 vessels, 1000 ports and 50 000 connections (see Table 1 for details). Distributed control CAS’s outcomes emerge from a self- -organization process rather than being designed and controlled by a centralized body or externally 12, 14, 43, 51, 52 Flows, internal model Although there are international trade agreements that unavoidably influence maritime shipping, these pacts can be seen as external forces that increase system entropy and prompt more economic relationships. Non-linear unpredictable Interactions are non-linear and thus intractable from a mathematical point of view. 14, 22, 28, 36- 38, 40, 43, 45-49, 46, 53 Nonlinearity The GCSN shows power law fit distributions and not random topological structures, thereby signalling the emergence of nonlinear interactions between a system’s agents. Co-evolutionary The environment is influenced by the activities of each agent. 43, 45, 46, 36, 39, 54, 41, 52 Diversity and tagging i.e. introduction of post Panamax and 10 000+ TEU ships change carriers routing networks and tariffs as well as the volume of transshipped cargo handled at main ports. Emergence Interplay between agents shapes an obscure but recognizable regularity (e.g. the brain has consciousness but single neurons have not) 52, 12, 14, 53 Aggregation, flows, internal model i.e. emergence of regional clusters of ports. Goal seeking Agents try to adapt in order to fulfil 43, 34, 44, 50, Flows, internal Dry carriers and oil tankers tend to move in an irregular manner
S.Caschili and F.R.Medda goals. 54,41 model because they change routes according to demand for the goods they carry. Nested Each agent can be 55,5] Diversity Port alliances at national or systems considered as a and international level are nested system.Each internal clusters of ports.The same port system is part of model may belong to a cluster of ports something bigger, at national level and to a cluster thus each system of ports at inter-national level, can be a sub- but this category may not system of a bigger necessarily include all the ports to system. which it belongs within the national cluster. can apply such questions to the case of maritime shipping in order to forecast future configurations and prevent global breakdowns in national and international markets(Table 2 -Feature:Self-organization). The maritime shipping industry is comprised by several relevant sectors such as international maritime transport,maritime auxiliary services and port services;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'[33].The outcomes of these collaborations influence the setting of freight rates and shipping company tariffs.In light of the previous remarks,co-operation among agents (shipping companies,port authorities,and so on)should be included in the modelling(Table 2 -Features:Distributed control and Nested Systems). In particular,international economic alliances in trade agreements are influential 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-Feature:Co-evolutionary and Self-organization),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,the Trans-Pacific Strategic Economic Partnership(TPP)in the Asian-Pacific region). On the basis of the observations discussed above,when we model shipping relationships trade agreement memberships should be included for two reasons:firstly,to understand the actual effects on agents involved in the agreements;and secondly,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 assess the role of alliances in trade,the effects of the establishment of new alliances,and the admission of new members in existing agreements(Table 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 -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 national level and also belong to a cluster of ports at international level.But not all ports in a national 10
S. Caschili and F.R. Medda 10 goals. 54, 41 model because they change routes according to demand for the goods they carry. Nested systems Each agent can be considered as a system. Each system is part of something bigger, thus each system can be a subsystem of a bigger system. 55, 5] Diversity and internal model Port alliances at national or international level are nested clusters of ports. The same port may belong to a cluster of ports at national level and to a cluster of ports at inter-national level, but this category may not necessarily include all the ports to which it belongs within the national cluster. can apply such questions to the case of maritime shipping in order to forecast future configurations and prevent global breakdowns in national and international markets (Table 2 – Feature: Self-organization). The maritime shipping industry is comprised by several relevant sectors such as international maritime transport, maritime auxiliary services and port services; 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” [33]. The outcomes of these collaborations influence the setting of freight rates and shipping company tariffs. In light of the previous remarks, co-operation among agents (shipping companies, port authorities, and so on) should be included in the modelling (Table 2 – Features: Distributed control and Nested Systems). In particular, international economic alliances in trade agreements are influential 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 – Feature: Co-evolutionary and Self-organization), 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, the Trans-Pacific Strategic Economic Partnership (TPP) in the Asian-Pacific region). On the basis of the observations discussed above, when we model shipping relationships trade agreement memberships should be included for two reasons: firstly, to understand the actual effects on agents involved in the agreements; and secondly, 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 assess the role of alliances in trade, the effects of the establishment of new alliances, and the admission of new members in existing agreements (Table 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 – 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 national level and also belong to a cluster of ports at international level. But not all ports in a national