A Computational Method for Time Cognitive Bias Simulation: Undue Attention Attributed to Information Sequences

The cognitive bias from sequential information affects human decision making. Early information tends to catch undue attention in varying degrees, which is one of the key characteristics to be manifested for the uniformity of analysis and synthesis in human-machine systems. In this study, we propose a computational method to simulate and analyze this human feature of time cognition bias. Our method comprises a weight function to quantify the cognition weight of the sequential information, a window function to fulfill the biased weight selection of the information sequence, and a degree function to statistically compute the undue degree of the human cognition. More precisely, the weight function embodies the sequential importance of timing information; the window function reflects human biased selection of cognition weight in a dynamical passband; and the degree function verifies our simulation of time cognitive bias and undue attention thereof. The computational results demonstrate the advances about the workings of cognitive bias and undue attention arising from sequential information. Our computational method is an important part in the modelling and simulation of human-machine systems.