Information elasticity is a new concept which characterizes the role of information in making effective decisions in sensor processing. Information elasticity is defined as the ratio of the fractional increase in decision effectiveness to the fractional increase in information. Increasing the quantity of information used in radar processing has the ability to decrease the performance of the radar in certain contexts, depending on what constraints and objectives exist. Because of this phenomenon (known as information overload), it is advantageous to find the optimal amount of information tailored to the specific context the radar is used in. This paper analyzes the process of finding the point of information overload using the information elasticity model. This model is used in observation of different contexts in pseudorandom code pulse compression. In this model, the length of the pseudorandom code represents the amount of information. Increasing this quantity affects both the quality of pulse compression and constraints of the system. We observe this relationship between the constraints and information quantity by developing constraint functions. In this paper, two decision metrics are created for pseudorandom pulse compression, the first based on the peak to side-lobe ratio and the second based on the detection region of the radar.