phenomenon in virtual environment, through the utilisation of computational resources164
After assessing some alternative options for validation, the decision has ended up in the utilisation of a simulation model. Simulation models have been used with success in the domain of freight transport and intermodal services. Moreover, simulation models offer the possibility of recreating multiple scenarios and variations of the world market and, thus, to enable to test theories in ceteris paribus situation
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Looking into detail to one of the objects of analysis in this research work – intermodal freight transport service – further limitations were uncovered. Firstly, no suitable real world intermodal freight transport service was found to serve as test bed. Secondly, no transport companies were found willing to either dedicate resources (namely: vehicles, personal, time) or use their shippers’ freight (because of high risk of damaging their image on the market) on a series of experiments. Thirdly, no transport company was willing to provide data about their intermodal transport operations, on grounds of confidentiality. Companies were afraid of revealing data that could be used by competitors to gain some sort of competitive advantage.
165
4.6
. Amongst the various techniques for developing simulation models, the choice was, as explained in Chapter
, for the agent based modelling.
Moving now to the remaining questions “what are models?” and “how to build models?”. Bonabeau (2002, pp 7287) addresses these questions in a very consistent way when he writes that “one issue is common to all modelling techniques: a model has to serve a purpose; a general-purpose model cannot work. The model has to be built at the right level of description, with the right amount of detail to serve its purpose; this remains an art more than a science”. This sentence raises three central questions that should be conveniently discussed.
included theory testing), prediction and decision making support. The primary reason in this research work is theory testing.
164 Other possibilities include, for example: small scale models or mathematical formulation.
165 Ceteris paribus situation is fundamental to ensure the causes and outcomes are de facto consequences of a given variable, and not from the mixed influence of multiples variables.
First, every model exists to serve a specific purpose (Sterman, 2000, pp 84, Randers, 1976, pp 417). A model is, as a matter of fact, a metaphor of an object (regardless of being real or imaginary), since it embodies solely those properties that are meaningful for the objective of the research (as we will see later, it makes no sense to incorporate properties that add no value to the proper functioning of the model). If we consider an all-embracing model of real world, it would have to incorporate every property of the real world and it would end up binge so complex as the real world itself and, therefore, useless. So, every modelling development has to be preceded by a careful specification of the requisites of the model.
The second question is related with the previous one: a model should not contain more detail than the necessary to fulfil its purpose. The issue is that a model is the result of a creative process whereby the modeller incorporates the details and properties she thinks are necessary, which means that the amount of detail is entirely discretionary - details can be added indefinitely. Yet, identifying the necessary amount of detail is not straightforward (Carley, 2002, pp 2, Peterson and Eberlein, 1994, pp 170). On the one hand, a model should be as simple as possible. A complex model is prone to errors and more difficult for validation. Moreover, extra details do not bring added-value to the model (as the model is already complying with the initial requisites) and it can be negative as they may create noise or unnecessarily increase the level of complexity.
Complexity also reduces the model’s legibility and repeatability, which may be a relevant issue if it is meant to be used and interpreted by other people than the modeller.
On the other hand, a model should contain enough detail to represent with sufficient rigour the real world to allow a good perception of the values of the key variable and their influences on the outcomes. The real world is inherently complex, therefore, any model should contain some of its complexity, otherwise it has no meaning. A model emptied from every complex matter cannot be used to represent reality.
Balancing these two forces, simplicity versus thoroughness, is not an easy task neither a novel question. Hoetjes (2007, pp 1), within the planning domains, for example, calls it as “rigour-relevance dilemma, i.e. how can research be both relevant and scientifically rigours?”, and Sterman (2000, pp 96), within system dynamics domain, writes that “a broad model boundary that captures important feedback is more important than a lot of detail in the specification of individual components”. The bottom line is that there is no
right formula for determining the sufficient amount of detail of a model. The modeller has to balance a set of factors, having always in mind the ultimate purpose of the model (and of course its validity).
The third question addresses the absence of methods to support the model development (Sterman, 2000, pp 87, Peterson and Eberlein, 1994, pp 161, Randers, 1976, pp 416).
This process largely remains a question of endurance, intuition, and inspiration – art;
although, experience does play an important role. This is particularly critical in social sciences where the modeller deals with variables that are not easily observed or measured. Model creation is thus a creative and iterative process, whereby the modeller successively eliminates errors and adds details (Sterman, 2000, pp 83).
Despite this absence, considerable efforts have been made on this topic and some authors have brought forward guidelines for the development of models. Sterman (2000, pp 86, 87) considers five main stages (Figure 5.2) on model development process, but he points out that “modelling is a feedback process, not a linear sequence of steps”:
• Problem articulation: it consists in the identification of the problem, purpose and key variables and dynamics of the problem (it is considered the single most important step);
• Formulation of dynamic hypotheses: it consists in the development of the theory for explaining the underlying mechanism of the problems. Theory is laid down through a set of hypotheses;
• Formulation of a simulation model: it consists in the development of a formal model. The model embodies the hypotheses;
• Testing: it consists in subjecting the formal model to various tests and criteria of acceptability. Tests include comparing to reference models, assess robustness under extreme conditions, or carry sensitiveness analysis;
• Policy design and evaluation: consists in applying the model on the design of policies for improvement, specification and evaluation of scenarios, evaluation of interaction of policies166
166 The author of this dissertation does not entirely agree with Sterman's formulation on this stage. The point is that not only a model can serve other purposes than policy design and evaluation, as well as policy design and evaluation is more than what it is written. The author believes that Sterman just meant to transmit the idea that in this stage the model is applied to the real world problem.
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