Cue-Based Decision-Making in Construction: An Agent-Based Modeling Approach

Ali Lahouti1 & Tariq Sami Abdelhamid2

1Graduate Student Research Assistant, Construction Industry Research and Education Center, School of Planning, Design and Construction, Michigan State University, East Lansing, MI 48824-1323. Email: [email protected]
2Associate Professor, 552 West Circle Rm 214, School of Planning, Design and Construction, Michigan State University, East Lansing, MI 48824-1323. Email: [email protected]


Workers on a construction site face many ambiguities when executing operations at the workface. While they will have received instructions through communications, whether of written (e.g., an engineering drawing; specifications; etc.) and/or oral, about what they are supposed to do once at the workface, they still are required to make a judgment on what will be done and how. This research posits that the more explicit the work instructions the less likely workers will mistakenly execute assignments. We distinguish work instructions based on whether the worker is given clear visual ‘signals’ (e.g., a solid red traffic light) as opposed to having to rely on visual ‘cues’ (e.g., a blinking red traffic light). To test this hypothesis, we investigate how the two different instruction types influence performance of a construction worker during assignment execution. An Agent-Based Modeling (ABM) approach was employed. This enabled us to experiment with different types of instructions to a worker, which in turn allowed observing patterns of behavior and responses to a signal versus a cue. An agent (worker) is introduced to two different environments: One environment directs an agent towards a predetermined destination by utilizing explicit instructions (signals); the other environment uses an agent with same knowledge level as in the first environment but only has implicit instructions to follow (cues). Preliminary modeling focused on one key measure: Performance effectiveness. Compared to the explicit instructions case, outcomes using the implicit instructions environment, i.e. following cues, resulted in a probability of only 38 percent in satisfying a required deliverable (performance effectiveness).


Cue-based Decision-Making, Performance Effectiveness



Lahouti, A. & Abdelhamid, T. S. 2012, 'Cue-Based Decision-Making in Construction: An Agent-Based Modeling Approach' In:, Tommelein, I. D. & Pasquire, C. L., 20th Annual Conference of the International Group for Lean Construction. San Diego, California, USA, 18-20 Jul 2012.

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