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,weS. 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