In a testing session, students may want to use the information of other students, which is cheating. The authors of this paper develop an artificial society to model and simulate this situation. They consider two control factors to increase the incentive of students to not cheat. The first factor is the penalty for similarity between responses (as much as two answer-sheets of two students are the same, their final grades decrease). The second factor is the observers who look into the students and do not allow the observed students to cheat. In this model, agents participate in a test based on their level of knowledge, location and two above factors, deciding whether or not to cheat. These components are used to formulate the utility function. Taking advantage of the developed artificial society, the authors now study the above factors affecting the amount of cheating in a test session.

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