精算先到，保险为何迟到半世纪？

Core Insights - The article discusses the historical delay in the acceptance of life insurance despite the early availability of mathematical models to calculate mortality rates, highlighting the gap between scientific readiness and human acceptance [2][6]. Group 1: Historical Context of Life Insurance - The first mortality table was published by Edmond Halley in 1693, which calculated death probabilities for different age groups, laying the groundwork for life insurance [3][4]. - Despite the mathematical capability to price life insurance, it took 50 years for the industry to mature and for life insurance to become a significant business, indicating a disconnect between scientific advancements and market readiness [5][6]. Group 2: Psychological Barriers - Humans inherently resist confronting their mortality, making it difficult for consumers to engage with life insurance products that require them to consider their own death [4][6]. - The emotional weight of death can skew an individual's perception of statistical probabilities, creating a barrier to the acceptance of life insurance [4]. Group 3: Institutional Trust Issues - In the late 17th century, there were no reliable institutions capable of offering long-term life insurance contracts, leading to a lack of public trust in insurance companies [5][6]. - The absence of a robust regulatory framework and credible institutions hindered the growth of the insurance market, despite the scientific basis for risk calculation [5]. Group 4: Individual vs. Collective Risk - Mortality tables represent collective statistical data, but individuals struggle to relate their personal risk to these averages, complicating the acceptance of insurance as a risk management tool [6][10]. - The transition to modern life insurance only occurred when both mathematical understanding and societal acceptance aligned in the mid-18th century [6][10]. Group 5: Broader Implications - The article draws parallels between the historical delay in life insurance acceptance and contemporary issues in fields like mRNA technology and climate risk modeling, where scientific advancements often outpace societal readiness [7][9]. - The need for public understanding and institutional support is emphasized as crucial for the successful application of scientific knowledge in various industries [10].