Bibliographic record
Abstract
Abstract If insurance providers cannot determine the risk levels of their various clients, but individuals do know this information, then adverse selection arises. We present the canonical models of adverse selection in insurance markets that shed some light on the economic consequences of this phenomenon. Under symmetric information, companies observe all relevant risk characteristics. By charging risk‐type specific prices, all risk types purchase full coverage. The result is an efficient allocation of resources with no risk bearing costs incurred by individuals. Under asymmetric information (adverse selection), if contracting is exclusive (i.e. each insured can contract with one and only one insurer), high risks end up with full insurance, but low‐risk types end up with partial coverage. Under nonexclusive contracting (i.e. each insured may purchase contracts from more than one insurance provider), higher risk individuals end up with too much insurance, while low risk individuals end up with too little. In either case, the allocation of resources is inefficient. Key Concepts Under symmetric information, each risk type purchases full insurance at the risk type specific actuarially fair odds rate and this allocation is pareto efficient. However, if these contracts were offered under asymmetric information, one expects high risk types to report being low risk in order to receive a more favorable contract If insurance companies do not have access to information about immutable characteristics of their potential customers where these characteristics affect the risk level of some activity, then this creates a situation of asymmetric information and a problem of adverse selection (also called antiselection). As individuals who are ‘bad risks’ (i.e. low risk) will desire more insurance than ‘good risks’ (i.e. high risk) if the price is the same for all, then insurers would end up selling more insurance to the bad risks and so the overall price of insurance would reflect this. If a pooling contract is offered and purchased, there is always a profitable deviation that is only attractive to low risk types but not high risk types. However, if insurance companies have sufficient foresight, they would not offer such a contract in the first place as such a deviation will make losses once other firms react to it. Adverse selection may also result in screening strategies whereby insurers offer higher coverage at a higher unit price in order to attract high‐risk types with lower risk types ending up with less insurance coverage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".