Models and Managers: The Concept of a Decision Calculus

By John D. C. Little. 2016

633 citations recorded by the source corpus.

(This article originally appeared in Management Science, April 1970, Volume 16, Number 8, pp. B-466-B-485, published by The Institute of Management Sciences.) A manager tries to put together the various resources under his control into an activity that achieves his objectives. A model of his operation can assist him but probably will not unless it meets certain requirements. A model that is to be used by a manager should be simple, robust, easy to control, adaptive, as complete as possible, and easy to communicate with. By simple is meant easy to understand; by robust, hard to get absurd answers from; by easy to control, that the user knows what input data would be required to produce desired output answers; adaptive means that the model can be adjusted as new information is acquired; completeness implies that important phenomena will be included even if they require judgmental estimates of their effect; and, finally, easy to communicate with means that the manager can quickly and easily change inputs and obtain and understand the outputs. Such a model consists of a set of numerical procedures for processing data and judgments to assist managerial decision making and so will be called a decision calculus. An example from marketing is described. It is an on-line model for use by product managers on advertising budgeting questions. The model is currently in trial use by several product managers.

Subfields: Management Science and Operations Research

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Citations in the corpus

Manipulating and Measuring Model Interpretability (2018, background): “Because of the impact that model-aided decision-making has on business and policy outcomes, the importance of these interactions has been emphasized in the management science literature for decades [9, 47].”
An Approach to the Measurement, Analysis, and Prediction of Brand Equity and Its Sources (2005, background): “By comparing the attribute perceptions with the “objectively” measured attribute values (as obtained from the firm or independent product testing agencies), we can determine the attribute perception biases.”
Customer Equity: Measurement, Management and Research Opportunities (2007, background): “One problem of marketing science models, and in this CE models are no exception, is that managers rarely use them (Little, 1970).”
Marketing Analytics for Data-Rich Environments (2016, positive): “The application of decision calculus (Little and Lodish 1969; Lodish 1971) to optimize spending on advertising and the sales force became popular after its introduction to marketing by Little (1970). Market share and demand models for store-level scanner data (Nakanishi and Cooper 1974) were derived”
Integrated marketing communications: From media channels to digital connectivity (2009, neutral): “Little 1970, 1979; Lodish 1975).”
Design of virtual plant environment for future generation green production systems (2007, positive): “For this reason, tools developed for product architects must both balance simplicity and rigor, and capture the relevant information so good decisions can be made [Little, 1970].”
Weathering Tight Economic Times: The Sales Evolution of Consumer Durables Over the Business Cycle (2003, methodology): “In this study, we consider sales patterns over multiple decades, which makes a filtering procedure that can properly handle unit root series more appealing; (iv) finally, the method is operational and easy to implement, thereby satisfying an important decision-calculus criterion (Little, 1970).”
Return on Marketing: Using Customer Equity to Focus Marketing Strategy (2004, methodology): “Another way, analogous to the decision calculus approach (Blattberg and Deighton 1996; Little 1970), is to have the manager supply a judgment-based estimate.”
Modeling the Psychology of Consumer and Firm Behavior with Behavioral Economics (2006, methodology): “For example, Little (1970) advised that “A model that is to be used by a manager should be simple, robust, easy to control, adaptive, as complete as possible, and easy to communicate with.”
Model-Based Systems Engineering: An Emerging Approach for Modern Systems (2012, background): “Particular care must be taken in order to ensure that completeness, integration, and synchronization is aligned with focus and simplicity (“managers prefer simple models that they understand and trust, to more realistic ones” [42]).”
Changing systems for supporting farmers' decisions: problems, paradigms, and prospects (2002, methodology): “too high in the manager’s action tree by a scientist-intermediary with a model (Little, 1970).”
Locating agricultural decision support systems in the troubled past and socio-technical complexity of 'models for management' (2002, neutral): “Stabell notes that the reference discipline of the decision calculus approach to DSS development is OR/MS; the title of the paper by the OR researcher cited earlier (Little, 1970) that marked the origin of the DSS era was ‘‘Models and managers: the concept of a decision calculus’’.”