On the basis of the analyses of past cases, we demonstrate how cognitive biases are ubiquitous in the process of incidents, crashes, collisions or disasters, as well as how they distort decision making and lead to undesirable outcomes. Five case studies were considered: a fire outbreak during cooking using an induction heating (IH) cooker, the KLM Flight 4805 crash, the Challenger space shuttle disaster, the collision between the Japanese Aegis-equipped destroyer “Atago” and a fishing boat and the Three Mile Island nuclear power plant meltdown. We demonstrate that heuristic-based biases, such as confirmation bias, groupthink and social loafing, overconfidence-based biases, such as the illusion of plan and control, and optimistic bias; framing biases majorly contributed to distorted decision making and eventually became the main cause of the incident, crash, collision or disaster. Therefore, we concluded that, in addition to human factors or ergonomics approaches, recognition and elimination of cognitive biases is indispensable for preventing incidents, crashes, collisions or disasters from occurring.

In December 2019, China announced the breakout of a new virus identified as coronavirus SARS-CoV-2 (COVID-19), which soon grew exponentially and resulted in a global pandemic. Despite strict actions to mitigate the spread of the virus in various countries, COVID-19 resulted in a significant loss of human life in 2020 and early 2021. To better understand the dynamics of the spread of COVID-19, evidence of its chaotic behavior in the US and globally was evaluated. A 0–1 test was used to analyze the time-series data of confirmed daily COVID-19 cases from 1/22/2020 to 12/13/2020. The results show that the behavior of the COVID-19 pandemic was chaotic in 55% of the investigated countries. Although the time-series data for the entire US was not chaotic, 39% of individual states displayed chaotic infection spread behavior based on the reported daily cases. Overall, there is evidence of chaotic behavior of the spread of COVID-19 infection worldwide, which adds to the difficulty in controlling and preventing the current pandemic.

The primary purpose of this study was to apply structural equation modeling (SEM) integrated with an adaptive neuro-fuzzy inference system (ANFIS) approach to model the safety culture of the petrochemical industry of Japan. Workers from five companies located in the Chugoku region of Japan completed a paper-based survey distributed by email. SEM and ANFIS methods were integrated in order to identify and model the important factors of the safety culture. The results of SEM indicate that employee attitudes toward safety, coworker’s support, work pressure, and plant safety management systems were significant factors influencing violation behavior, personnel safety motivation, and personnel error behavior. Furthermore, the application of the ANFIS modeling approach showed that employees’ attitude was the most critical predictor of violation behavior and personnel error behavior, while coworkers support was the most critical predictor in modeling personnel safety motivation.