Business Simulation Decision-Model-Result Linkages
Here I explore the types of structural linkage between decisions, the model and results and how these impact cognitive processing, cognitive load and ambiguity.
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In my 2008 ISAGA I explored the meta-structure of business simulations as two domains - the model domain (the scenario) and the interactions (the decisions made by participants and results reviewed by them). The Model Domain and the Interaction Domain are explored separately and this page explores the linkages between the two and the forms of structures that can be used.
Below there is an example of how the model links decisions to results impacts. The form and complexity of this linkage impacts the ability to determine cause of decisions and the effect of results on business success - it adds to ambiguity and cognitive load (duration).
This is the most common and most complex form of linkage where multiple decisions impact multiple results.
If intersection linkages are complex (as illustrated below), it can be difficult for participants to unravel the way decisions impact results. For all simulations this can be handled using Reflection Triggers (comments) and for Tutor Mediated simulations by the Tutor Support System.
From the design viewpoint one must ensure that there are sufficient intersections for deep cognitive processing but that they are not so complex and ambiguous that they inhibit learning. Because of this it is often useful to break the links down into the simpler forms that follow.
This is the simplest form of linkage where a single decision impacts result(s). At the end of the Tendering Stage of my Prospector simulation participants submit a price to the (virtual) customer and based on this single decision the contract is won or lost.
From the design viewpoint, this structure can be used to forces learners to stop and focus on a specific issue or to reduce the ambiguity of less important decisions. But there is a risk that it may be too unambiguous and hence reduce learning.
Here the model plays no part in deriving a result from a decision. My SEED simulation illustrates this where one learning objective was timely market and business research. If the appropriate market research is not performed it is not possible to plan the business. So, for example, if market size and price research is not done in January then the following month (February) it is not possible to plan the market. However, this market research decision is not processed by the model but is immediately reflected back as information (to be used later by the model).
From the design viewpoint, this structure provides a means of defining a sequence of actions and forcing the learners to think about these.
Here either the model drives results or the model determines the need for a decision.
Model/Result Specific this structure uses data from the model to analyse business performance and from this identify strengths and weaknesses caused by the current economic and business situation. Also, this structure provides for Reflection Triggers.
This is illustrated by my Product Launch simulation where at the end of the simulation, decisions and results are analysed and compared with prior runs generating a list of strengths and weaknesses
From the design viewpoint, the simulation model must generate the data necessary to be analysed and, commonly, this involves calculations that analyse business performance. Examples of these are calculations of ROI, liquidity, capacity use etc..
Model/Decision Specific occurs when the simulation model determines a situation that needs management action and so asks for a decision. For example, in the Beat the Boss simulation, if insufficient raw materials are purchased and this leads to an inventory shortage, the model then asks the participants whether they wish to make an emergency purchase (at a premium price) or do nothing.
From the design viewpoint, this structure provides a way of introducing problems caused by the current economic and business situation – in other words, it provides a way of introducing crisis based on learners’ actions.
Here several follow seemingly independent paths as the simulation progresses. Thus, for several periods, the response of the model to one set of decisions and the outcomes produced by them are apparently not linked to the others sets. Eventually, they dovetail thus emphasising the topic and rewarding long-term consistency and thought.
My Product Launch simulation illustrates this as over time price and promotion decisions impact market penetration. Although this has a short-term effect on profits, capacity need and cash flow, there is a long-term effect on competitive strength. An effect that becomes apparent and important when the (simulated) competition enters the market.
From the design viewpoint, this structure provides a way of focusing on the long-term impact of decisions where the outcomes and decisions eventually link. Also, this structure provides a way to generate Reflection Trigger.
This occurs with simulations when the same message is provided in two (or more ways) to emphasize an issue and stimulate discussion.
For example my DISTRAIN simulation provides participants routine information about sales (at cost) and inventories that allows them to consider whether the inventory level is optimum. But, if inventories are well above the optimum to meet sales demand Reflection Trigger “Accountants feel inventories seem high for …….” is generated.
Here graphics, pictures, sound and music are used to link. For example, impending bankruptcy in the Beat the Boss simulation is indicated by the theme music from Jaws (indicating the threat and, perhaps, the persona of banks). From a design viewpoint, this relationship is perhaps more appropriate to managing the feelings of the learners (affections) than their cognitive development. Also, when the simulation involves participant using the simulators on their own computers spread across a large room, music provides a way of informing fellow teams of strengths, weaknesses and threats.
Most recent update: 29/07/15
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