It is •impossible• that automobile #insurance actuaries are not tracking all of this and increasing premiums to avoid #AdverseSelection.
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#adverseselection
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#ClimateChange represents #AdverseSelection for the #insurance industry.
Property, lives, and capital is at risk.
https://www.superversive.co/blog/actuaries-are-the-worlds-oldest-data-scientists
https://mastodon.social/@CelloMomOnCars/113600454499476709
SuperversiveActuaries: World’s Oldest Data Scientists — SuperversiveIn the US, there are 50 regulatory regimes with oversight, math nerds trying to avoid adverse selection , and bonuses tied to effectively maintaining mandated loss ratios. When math calculations & simulations demonstrate potential losses - due to severity and duration of events - outweighi
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@ZaneSelvans
Climate change will break the insurance industry before it breaks everything else.
Actuaries are the world’s oldest data scientists.
The math doesn’t lie.
They are paid to avoid #AdverseSelection.
When the risks manifest everywhere, on everything, all the time, actuarial models collapse.
#AdverseSelection is short-hand for “not worth it” and #ThingsFallApart.
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A lot of words that aren’t #AdverseSelection: https://www.superversive.co/blog/no-renewal-due-to-adverse-selection
#Actuaries are the world’s oldest analyzers of empirical data: https://www.superversive.co/blog/actuaries-are-the-worlds-oldest-data-scientists
SuperversiveNo Renewal Due to Adverse Selection — SuperversiveInsurance companies like AIG spend a lot of money on catastrophic modeling systems, weather-related databases, and actuaries to have more and better information to place calculated bets on future outcomes. Insurance is regulated at the state level. There are 50 different insurance ecosystems i
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We need a creative meme or TikToker to articulate #AdverseSelection
https://www.superversive.co/blog/actuaries-are-the-worlds-oldest-data-scientists
@TeamMidwest @jalefkowit@octodon.social
SuperversiveActuaries: World’s Oldest Data Scientists — SuperversiveIn the US, there are 50 regulatory regimes with oversight, math nerds trying to avoid adverse selection , and bonuses tied to effectively maintaining mandated loss ratios. When math calculations & simulations demonstrate potential losses - due to severity and duration of events - outweighi
"One million #Florida properties are projected to become chronically flooded: properties that today fund nearly 30% of local revenues for more than half of the state’s municipalities, according to a new study conducted by researchers at Cornell and Florida State Universities."
https://www.wmfe.org/environment/2023-10-16/sea-level-rise-drain-floridas-financial-future
WMFE · New study projects sea level rise to drain Florida’s financial future
When insurance companies pull out of markets due to adverse selection, it reminds me of Ben Shapiro saying people would just move.
Sure, bro.
https://sampathpanini.medium.com/no-renewal-due-to-adverse-selection-123d0c6832bb
Of Models and Tin Men
https://arxiv.org/abs/2307.11137
An ambitious research agenda:
"In a #PrincipalAgentProblem, conflict arises because of information asymmetry together with inherent misalignment between the utility of the agent and the principal… argue the assumptions underlying principal-agent problems are
crucial to capturing the essence of safety problems involving pre-trained AI models in real-world situations."
#llm #AIEthics #MoralHazard #AdverseSelection #economics
arXiv.orgOf Models and Tin Men -- a behavioural economics study of principal-agent problems in AI alignment using large-language modelsAI Alignment is often presented as an interaction between a single designer
and an artificial agent in which the designer attempts to ensure the agent's
behavior is consistent with its purpose, and risks arise solely because of
conflicts caused by inadvertent misalignment between the utility function
intended by the designer and the resulting internal utility function of the
agent. With the advent of agents instantiated with large-language models
(LLMs), which are typically pre-trained, we argue this does not capture the
essential aspects of AI safety because in the real world there is not a
one-to-one correspondence between designer and agent, and the many agents, both
artificial and human, have heterogeneous values. Therefore, there is an
economic aspect to AI safety and the principal-agent problem is likely to
arise. In a principal-agent problem conflict arises because of information
asymmetry together with inherent misalignment between the utility of the agent
and its principal, and this inherent misalignment cannot be overcome by
coercing the agent into adopting a desired utility function through training.
We argue the assumptions underlying principal-agent problems are crucial to
capturing the essence of safety problems involving pre-trained AI models in
real-world situations. Taking an empirical approach to AI safety, we
investigate how GPT models respond in principal-agent conflicts. We find that
agents based on both GPT-3.5 and GPT-4 override their principal's objectives in
a simple online shopping task, showing clear evidence of principal-agent
conflict. Surprisingly, the earlier GPT-3.5 model exhibits more nuanced
behaviour in response to changes in information asymmetry, whereas the later
GPT-4 model is more rigid in adhering to its prior alignment. Our results
highlight the importance of incorporating principles from economics into the
alignment process.