Relationships - Decisions, Models and Results

The relationships between decisions, models and results is vital the the learners indentifying cause and effect and the business simulation design must  allow for this.











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Key to the successful design of business simulations is the way decisions, the model and results link together to ensure the right level of cognitive processing. In otherwords, through careful thought and discussion learners must be able to tease out how their decisions are impacting results. This means that the structure of the relationships and their ambiguity must be appropriate.

Intersecting Relationships

Here several decisions intersect/interact with each other to produce one or more results. For example, sales demand (result) will be impacted by the marketing mix (price, promotion, product offered and market sector) and through this gross margin, cash flow etc. The ambiguity (and hence proclivity for deep cognitive processing) is a combination of the interaction between the decisions and the ambiguity of individual decisions

Interdependent Relationships

Here there is a direct (ambiguous) relationship between a decision and a results. For example where the simulation involves bidding for business, the contract is won (or lost) based on price alone. This relationship is less ambiguous than an intersecting relationship and thus helps reducing the ambiguity of secondary decisions where the learners do not need to think so deeply.

Decision-Specific Relationships

Here a decision leads directly to a result without being processed by a model. For example, a market research decision leads directly to a report with no additional calculations.

Model-Specific Relationships

Here the model drives the results with decisions playing no part or, the model drives the need for a decision with no impact on results. For example, the model may determine that the company is is insufficiently solvent and based on this increase bank financing costs (results). Similarly, a raw materials shortage may lead to the model to ask the learners whether they wish to decide to make emergency purchases.

Duo-Specific Relationships

Here a decision produces two or more results that provide the same information. For example, raw inventory figures may be provided together with a comment about the adequacy of the inventory. When appropriate, this relationship provides a way of emphasing and forcing learners to prioritising.

Parallel Relationships

Here, over several simulated periods, two or more sets of decisions and results move in parallel until, eventually, they combine. For example, price and promotion have a short-term effect on demand and profits and a long-term effect on market penetration and liquidity.

Montage Relationships

Here results or decisions are pictures or sounds rather than the more usual quantitive decisions or results, For example, applause may accompany a company winning a major contract, or staff unrest may be revealed by pictures of unhappy people. However, there may be cultural issues - for example, in the UK pictures of crying rabbits hark to the saying "unhappy bunnies". But in the USA this would be meaningless as there the saying is "happy/unhappy campers" (except here the problem would be to draw obviously "unhappy campers" or, for that matter, draw recognisable campers).

A professionally designed simulation will have built in a suitable mix of these relationships taking into account decision and result importance, the need for cognitive processing (effective learning) and the impact on duration (efficient use of learners time).

This page summarises information from my forth coming book "Corporate Cartooning: the art, science and craft of business simulation design" and my keynote Presentation at the 2008 ISAGA Conference.

2010 Jeremy J. S. B. Hall

Most recent update: 05/11/10
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