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This study introduces the loss aversion distribution, a novel approach to analyzing consumer behavior toward perishable goods, diverging from traditional exponential models by incorporating a non-memoryless characteristic. It captures the dynamic nature of consumer loss aversion from manufacture to expiry, highlighting an initial muted response that intensifies mid-lifecycle and diminishes as expiry nears. Utilizing derivative analysis of the probability density function, the research establishes the distribution's monotonicity, boundedness within [0,1], and non-negativity. An empirical finding of note is the b-parameter's approximation to a Gaussian distribution, offering insights into consumer psychology. This framework not only advances understanding of loss aversion in perishable goods but also opens avenues for further psychometric exploration.

Behavioral Economics; Loss Aversion; Data Science