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15th World Congress on Computational Mechanics (WCCM-XV)
8th Asian Pacific Congress on Computational Mechanics (APCOM-VIII)
31 July – 5 August 2022, Yokohama, Japan
Modelling Cognitive Bias in Safety using Bayesian Inference
1 The University of Tokyo
7-3-1 Hongo, Bunkyo, Tokyo, JAPAN, email@example.com
Key Words: Safety, Perception, Cognitive Bias, Bayes, Covid-19
In a human centred society, sense of safety perceived by people is important to consider as well
as actual safety. The gap between expectations and reality in safety causes social problems.
Overestimation of safety provokes dangerous behaviours, while underestimation of safety
causes excessive behavioural restraint. For example, in COVID 19 pandemic, the
overestimation may lead to the spread of infection due to dangerous behaviours, and the
underestimation may lead to slow consumption due to excessive self-control.
In this talk, I propose a mathematical framework to model sense of safety using Bayesian
inference. Recent neuroscience studies suggest that human brain activities can be explained as
a Bayes’ model(Knill & Pouget, 2004). Here, based on Helmholtz’s epistemology, I assume
that safety x is inferred as the cause of observation (data: y) and define the sense of safety as
Bayesian posterior p(x|y) that is proportional to a product of a prior p(x) and likelihood p(y|x).
The prior and the likelihood represent the expectation of safety and the safety based solely on
the data (observed safety), respectively. In this model, the prior (safety belief) is updated to the
posterior (perception of safety).
The difference between the posterior mean and the peak of likelihood represents the gap
between perceived and observed safety. This gap is regarded as a cognitive bias termed
expectation effect (Yanagisawa, 2016). In our previous study, we modelled the expectation
effect as a function of three parameters: a prediction error (difference between prior mean and
peak of likelihood), prior precision (inverse variance of the prior), and observation precision
(inverse variance of the likelihood). We found that there are two types: assimilation and contrast.
Assimilation diminishes the prediction error, and contrast exaggerates the prediction error. The
expectation effect explains several psychological biases that lead to abnormal behaviours, such
as normalcy bias, excessive anxiety, and overconfidence. Based on the expectation effect model,
I discuss the condition of each psychological bias in safety. In addition, I discuss how to model
emotions such as anxious and fear in a mathematical manner by applying information theoretic
quantities such as surprisal and free energy (Yanagisawa, Kawamata, & Ueda, 2019).
Knill, D. C., & Pouget, A. (2004). The Bayesian brain: the role of uncertainty in neural coding and
computation. Trends in Neurosciences, 27(12), 712-719. doi:10.1016/j.tins.2004.10.007
Yanagisawa, H. (2016). A computational model of perceptual expectation effect based on neural coding
principles. Journal of Sensory Studies, 31(5), 430-439. doi:10.1111/joss.12233
Yanagisawa, H., Kawamata, O., & Ueda, K. (2019). Modeling Emotions Associated With Novelty at
Variable Uncertainty Levels: A Bayesian Approach. Frontiers in Computational Neuroscience,