OF PIRAEUS
6. Computational Methods for Simulation and Life-Cycle Management of Maritime ClustersManagement of Maritime Clusters
cluster of Piraeus, however it would be very difficult for these methods in order to apply multi-cluster comparison. Nevertheless, surveys and inter- view are considered an essential tool when studying and modeling a cluster, since it is vital to identify the consciousness of the major stakeholders.
Concluding cluster analysis can be achieved with several tools and enables accurate and effective policy and management intervention. It is essential to have a good understanding of a cluster’s internal workings — components, structures, processes, routines and development pathways.
6. Computational Methods for Simulation and Life-Cycle
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unique rules, parts and components of a system are represented in the form of individual agents. Agents have varying influence and none of them can solely determine the ultimate outcome of the system. On the other hand, every agent contributes to the results in some way.
Implementing computational agents is the next step. Agents are the decision–making components in complex adaptive systems. They are attributed with sets of rules or behavior patterns that allow them to receive information, process them and then reflect them in the environment.
Another characteristic of agent-through information processing is adaption and learning. Before modeling agents, it is important to understand their structure as units. Agents are individuals with a set of attributes and behavioral characteristics. These are explained in textbox 3.
Textbox 3. Carrying characteristics of agents.
Agent Attributes: There are various agent attributes. These are essentially some key characteristics of the agents that are ascribed by the user, in order to measure the outcome of the simulation. In an agent- based simulation, attributes are carried by each agent and can evolve or change over time as a function of each agent’s learning experiences.
Agent Behaviors: Agents have behavior features that can vary from agent to agent in order to reflect pragmatic situations. There are two levels of rules. The first level specifies how the agent will react to routine events and the second level provides rules for the adaption of changing routines. Generally, agent behaviors follow three steps:
1. Agents evaluate their current state and determine their actions, 2. Agents execute the actions that they have chosen 3. Agents evaluate the results of their actions and adjust their rules.
In this case the firms within the cluster are agents. When seeking a detailed simulation the result is a multi-scale model of cluster behavior with the smaller scale firm interactions combined to produce the larger- scale activities of the cluster as a whole.
6.2. Modeling case study: the maritime cluster of Piraeus
The complex nature of our research, has directed us towards computer sim- ulation with the use of an agent-based model. After identifying previously
some characteristic of agent-based model, we need to define our model for the maritime cluster of Piraeus. Firstly, some principal assumptions need to be considered. Agents in this model represent only firms, organizations and institutions. Every agent belongs to a sector in the maritime cluster. For the purposes of the simulation, the population of firms in each sector is in not full scale; a sample of companies is attributed to each sector. The problem that this model addresses is to determine the emergence of competitive advantages for each individual firm within a cluster. In a knowledge based economy the source of competitive advantage for firms is no more limited to cost and differentiation advantages but it is linked to resources-competences that firms possess and their capability to create knowledge (Carbonara N et al., 2006). The model seeks to investigate if the emergence of knowledge externalities drives the development of clusters and determines the factors that control some critical performance indicators for clusters.
All agents-firms have attributes and behaviors. These can change over time and by sector. After a detailed survey on sector experts, here are the selected attributes and behaviors for our model (Table 4).
Explaining each attribute, Size: The size of each firm is measured in accordance with the number of employees that have attained an educational degree,Knowledge:This is the heart of the model. As explained before, the long-term growth of firms and regions depend on their ability to continually develop and produce innovative products and services that are directly linked to knowledge. Services that are provided and acquired in the market are here modeled as demand and supply of knowledge. Knowledge is therefore exchanged within the cluster, with different rate of accumulation for its firm. Measuring the accumulation of that knowledge can present the emergence of competitive advantage in firms, Innovation: Is critical to measure the innovative capacity of each firm and sector. This is a derivative
Table 4 Attribute and behaviors of each agent–firm.
Firm
Attributes Behaviors
Size Knowledge Demand
Knowledge Stock Knowledge Supply
Innovation Learning
Growth Rate Moving in new positions
Risk Tolerance Market Share Targets Position on the grid
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of knowledge as described above, Position:This attribute indicated the position of the firm in a dimensional grid. The grid contains all the firms and the agent by calculating the maximization of his competitive advantage that depends on the knowledge stock and market share he can acquire; takes the decision to move or not on a more competitive position in the grid. The rest of the attributes are described before as performance indicators, that when attached to each agent they can derive valuable information. The first experimental stage of the simulation uses a sample population of firms from all sectors, assuming that they all position in a dimensional grid, having all the same knowledge capacity but different weight; something that depends on the firms’ size. Starting the simulation, knowledge is circulated according to demand and supply. Then, firms try to locate where networking favors their competitive advantage, from this routine geographical concentrations arise and clusters of firms are developed. The results from this simulation are then validated against realistic data from the existing structure of the maritime community, in Piraeus. This confirms that the assumption of the initial model was pragmatic, that indeed, in reality, knowledge externalities drive clustering and that clustering of firms maximizes the performance indicators chosen. A multi-scale cluster model, as perceived, is shown below, with firms as subagents, sectors and relating institutions and bodies that are agents as well.
6.3. Agent-based modeling toolkit
There are a number of toolkits available for implementing agent-based modeling. Thanks to substantial public and private research, many com- putational environments have been developed and are now available for business use without any charge. The software environment for this research project is Repast (the REcursive Porous Agent Simulation Toolkit) and it is a leading open-source large scale ABMS toolkit. Repast was developed in order to support the development of extremely flexible models of agents focusing on social and economic simulation (North et al., 2007). Repast’s goal is to represent agents as discrete entities that act as social actors and are mutually defined with recombinant motives. The broader scope of the toolkit is to replay cases with altered assumptions (ROAD, 2004).