Logical fallacy and cognitive bias vulnerabilities inherent to process safety management

Process Safety Management (PSM) programs are essential for preventing catastrophic incidents in high‐risk business sectors, such as oil and gas, petrochemicals, liquefied natural gas, ammonia refrigeration, and agricultural chemical industries. However, logical fallacies and cognitive biases undermine these programs, leading to flawed decision‐making and increased safety risks. This paper examines key logical fallacies, such as post hoc ergo propter hoc (inferring causation from mere temporal sequence), false dichotomy (oversimplifying choices), and appeal to authority (relying on unverified expertise), alongside cognitive biases, including confirmation bias (favoring data that supports existing beliefs), overconfidence bias (overestimating judgment accuracy), and anchoring (over‐relying on initial information). These human‐driven errors impact critical PSM elements—hazard identification, risk assessment, training, and incident investigation—often resulting in systemic failures. For example, overconfidence may lead to underestimating hazards in risk assessments, while confirmation bias can skew incident investigations by focusing on familiar causes and ignoring critical evidence. Geographic and cultural factors may amplify these vulnerabilities. In the Global South (e.g., Africa and Latin America), resource constraints may foster optimism bias, where limited safety investments are sometimes deemed sufficient by business decision‐makers. In contrast, industrialized nations like the United States may exhibit overconfidence due to robust regulations, assuming compliance ensures safety. Cultural norms, such as hierarchical structures in Japan or collectivist tendencies in parts of Asia, can reinforce Groupthink or appeal to authority, suppressing dissenting safety concerns. These regional differences highlight the need for tailored PSM strategies. To mitigate these risks, the paper proposes practical solutions: structured decision‐making tools, like risk matrices, reduce biases such as anchoring; bias‐awareness training encourages critical thinking to counter fallacies like post hoc reasoning; data‐driven analytics challenge subjective assumptions; and generative artificial intelligence and large language models enable broader knowledge sharing and cross‐organizational learning. These measures are adaptable across global contexts, from resource scarcity to advanced economies, ensuring robust PSM without ideological influence.