Model coding 40

Model coding involves the conversion of the conceptual model into a computer model. In DES and SD modelling, model coding involves creating the model using the relevant computer software. In this section, statements made regarding aspects of DES and SD model coding on the computer, are discussed. These are: model complexity, modelling structures and other model elements.

2.7.1 Model complexity

Looking at both simulation approaches in terms of model complexity, it is maintained that DES is more concerned with detailed complexity, while SD with dynamic complexity (Taylor and Lane, 1998; Lane, 2000). This is due to their inherent features, where DES can model great complexity and detail, representing specific individuals and the subsequent interactions, while SD represents the aggregate picture of the system.

In DES, complexity is the result of multiple random processes and the endogenous structure of the system (Lane, 2000; Morecroft and Robinson, 2005). DES models represent systems of small operational tasks or individual items, which comprise distinct entities with multiple attributes, individually defined. Complexity results from the interconnections and effects between variables. In SD, a model’s

behaviour is determined by the feedback structure and dynamic complexity arising from the influences among endogenous variables. SD models represent systems consisting of causal relationships of variables (the latter are aggregated here and

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contain relatively few attributes, resulting in low detail complexity). Consequently, dynamic complexity arises due to “non linear, delayed and accumulative/draining causal relationships” (Lane, 2000). Therefore, SD models produce counter-intuitive behaviour.

Let us consider the dynamic complexity resulting from the existence of several nonlinear relationships in a SD model. In this type of system, under a set of conditions, one part of the model becomes more active and under other conditions another part dominates. For illustration purposes, let us consider the well-known fishery example (Morecroft and Robinson, 2005). There are two main non-linear loops in the system, the reinforcing loop of natural fish regeneration and the balancing loop of fish catch depending on the ship fleet size (Figure 2-1).

Depending on the size of the fish population in the sea, the dominance of the two loops in the model changes resulting in an s-shaped graph of fish catch. Fish catch initially increases exponentially with the increase of fishing ships due to the fact that the fish regeneration loop dominates. However, the fish population and the catch rate start dropping exponentially after a point where the harvest rate becomes equal to the fish regeneration rate and thus the balancing loop of fish catch becomes more dominant. This explains the collapsing fish population, referred to as the system’s puzzling dynamics (Morecroft and Robinson, 2005).

Chapter 2 Fish stock Fish density Harvest rate Fish regeneration Catch Ships at sea + + + + + - +

Figure 2-1: Simulation of a harvested fishery with stepwise changes in fleet size (Morecroft and Robinson, 2005)

Taking a philosophical view at model representation, Morecroft and Robinson (2005) in their empirical study of a fishery model, maintain that SD deals with ‘deterministic complexity’, whereas DES with ‘constrained randomness’. While in the SD model the system’s behaviour is predetermined by the feedback structure, the interaction among endogenous, deterministic variables, the system’s future behaviour is unknown to the subjects in the system. In the DES model, system behaviour is affected by “endogenous factors and also by random operational factors”. The future behaviour “is assumed to be partly and significantly a matter of chance” and consequently complexity arises from multiple random processes. This represents different worldviews taken inherently by each approach, which results in the specific modelling practice followed (Morecroft, 2007). While in SD the system behaviour is explained by determining the underlying feedback structure and

performance is improved by re-designing polices, in DES the interacting random processes are primarily investigated in order to find alternative ways of improving the stochastic structure of the system or managing the variability better.

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2.7.2 Modelling structures

Another practice referred to in the SD field is the use of already existing modelling structures in the representation of decision making. Decision making represents the aggregate judgements of actors in the system and decision rules can be specified as part of the modelling process. Decision makers create a view based on purposive or judgmental information. The well-known ‘asset stock adjustment’ process is a central structure in the representation of feedback in business and social systems (Sterman, 2000). In DES there are no model building principles to incorporate the decision making processes, but insights from discussions with and observation of decision makers can be incorporated in the model with the use of additional formulae or decision rules. According to Lane (2000), human agents in SD are modelled as bounded rational policy implementers, whereas in DES as decision makers. However, Morecroft and Robinson (2005), maintain that in both simulation approaches decisions are made subject to bounded rationality, in SD taking into account information based on objective evidence (not implied) readily available to actors in the system. In DES decisions are made based on the uncertainty of future random events. For example, in their SD fishery model the decision to buy new ships is made referring to the catch without taking into consideration the fish population or regeneration rate, whereas in the DES model the same decision is made based on total catch in the fishery, making the assumption that the fishery

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2.7.3 Other model elements

Another aspect of interest in SD modelling is the handling of delays. Both DES and SD represent delays in social systems. However, this may have different meanings to DES and SD modellers. In DES delays are represented in the form of queues or buffers, where elements or parts of the system wait until the next activity (work- centre or machine) becomes available. In SD delays are represented in the form of the time lag between taking a decision and its effects on the state of a system (Sterman, 2000). As a result, referring back to the information/action/consequences loop (Figure 1-2), decision makers continue to intervene to correct the perceived discrepancies by recruiting people, even after sufficient action has been taken to restore equilibrium in the system. Due to the fact that new recruits undertake training which is an example of a delay, the results of the action taken, that of recruitment, is not instantly obvious in the system. Consequently, there will be more employees than desired, which will influence the occurrence of lay-offs in the future. Therefore, delays can cause instability (overshoot or oscillation) in the system, and a further slow down in the rate of learning (Sterman, 2000). The simplest type of delay is the exponential delay, which is represented by the fraction of the stock level and the length of the delay time.

In both simulation approaches material flows can be incorporated in the model. These are measurable and conserved throughout the system. SD models can also

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include information flows, which can be part of the feedback loops, whereas, in DES models information flows can be incorporated with the use of priority rules or attributes, but these are not obvious to the users (Mak, 1993).