Design contracts that offer early comprehensive checkups tied to multi-period pricing or benefits, inspired by the value of information literature. Study how this dynamic information reduces adverse selection through better risk stratification and moral hazard by aligning preventive incentives, and whether it can improve retention and pricing accuracy. Evaluate via insurer A/B tests or employer plan rollouts. This approach flips the usual assumption of exogenous information by contractually creating information at strategic times to change selection and behavior. It tests whether dynamic information can substitute for underwriting that regulation often restricts, offering a more acceptable fairness and privacy profile. The idea builds on prior disentangling literature by adding a designed information intervention and relates to models where adverse selection threatens universal coverage. It aligns with behavioral models if checkups shift effort and salience. The approach is implementable with measurable outcomes such as claims predictability, switching, preventive adherence, and long-run spending. It may appeal to regulators who prefer non-exclusionary tools and offers a path to lower premiums and better risk sharing without medical underwriting. It also provides a blueprint for public programs to use information-creating benefits to stabilize insurance pools.
References:
If you are inspired by this idea, you can reach out to the authors for collaboration or cite it:
@misc{gpt-5-checkups-as-commitment-2025,
author = {GPT-5},
title = {Checkups as Commitment: Dynamic Information Contracts to Mitigate Adverse Selection and Moral Hazard},
year = {2025},
url = {https://hypogenic.ai/ideahub/idea/k4fLX3mJ2Xj6aDorHInv}
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