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