COMBINING SYSTEM DYNAMICS AND DISCRETE EVENT SIMULATIONS - OVERVIEW OF HYBRID SIMULATION MODELS <s> COMPARISON <s> Simulation can be an effective way to evaluate alternative decisions in sense-and-respond systems prior to taking actions to resolve existing or anticipated business situations. In sense-and-respond systems, business situations arise within predefined contexts that specify what aspects of the business need to be monitored and what information is needed to make decisions. We have designed a decision support system that dynamically configures simulation models based on business context and interactively presents simulation results to business analysts. In this paper, our decision support system is applied to the IBM demand conditioning process, in which mismatches between supply and demand are identified and corrective actions are initiated. <s> BIB001 </s> COMBINING SYSTEM DYNAMICS AND DISCRETE EVENT SIMULATIONS - OVERVIEW OF HYBRID SIMULATION MODELS <s> COMPARISON <s> This paper contrasts the tradeoffs of modeling the same dynamic problem at a micro scale and at a macro scale of analysis: discrete system simulation (DS) versus continuous system simulation or system dynamics (SD). Both are employed to model the influence of entertainment education on terrorist system decay, with implications for field application. Each method optimizes different design, scope/scale, data availability/accuracy, parameter settings, and system sensitivities. Whether the research served by the computer model is applied or theoretical, DS tends to be useful for understand low-level individual unit/step influences on system change over time, whereas SD tends to shine when a wide-angle focus upon sociological/aggregate change is required. <s> BIB002
The SD and DES are very different approaches when trying to model a situation and there are distinctive communities that follow each, respectively. Little bit inspired by the title of  , the following comparison will be made in order to clarify some things. If a task of analysing a forest is given to these two types of modellers, the SD modellers will try to look at the forest from above, or from far away. They will look at the landscape, see how the trees are spread and grouped, analyse the types of trees etc. Meanwhile, the DES modellers will try to go in the forest and search in it, look at every tree as an entity, the leaves of the trees, the structure of the trees etc. Having this in mind, it was not very difficult to accept SD a technique for the attempt to model strategic decisions and use DES for the operational processes and decisions. Based on the work of  and  a meta-comparison of both approaches is shown in Table 1 . There are numerous articles that describe and compare these techniques, particularly. Maybe one of the first attempts was done by  and before that     BIB001 BIB002 share the idea of the authors, presented earlier that SD is more suitable when modelling a system and analysing it as whole and DES when more details are needed for the better representation. The researches have been mainly focused on developing two same models in the different approaches and analysing and sharing the results   BIB002  .  have gone a step further and have analysed a model building process by five SD and five DES modellers on a same situation-a prison population problem. One of the detailed and structured comparison has been done by Chahal & Eldabi (2008) , dividing the analysis in more than thirty categories and explaining every one of them. There are even researches that deal with the third possible option when simulating (e.g. a supply chain) -simulation with agents and compare that along the previous two  ).