Confusion About Causation in Insurance: Solutions for Catastrophic Losses
Bibliographic record
Abstract
The current state of the law with respect to concurrent causation in insurance produces unpredictable results. The insurance system may not reliably function in the wake of a large scale multi-causal disaster - a hurricane, earthquake, or terrorist strike - that causes widespread losses to many policyholders at once. Courts, parties, and academics alike are challenged in evaluating a particular cause in a causal chain of events in the context of an insurance contract. To add to the complexity, insurers often attempt to contract around the rules, with inconsistent success. This article attempts to create a solution for solving concurrent causation disputes in insurance. Underpinning the proposed legal rules in this article are the important analytic and conceptual differences between causation in tort and causation in insurance. The article proposes the adoption of two distinct immutable legal rules for resolving concurrent causation insurance disputes. The choice of legal rule necessarily depends first on a sensible, predictable analysis of the various concurrent causes that brought about a potentially insured loss. Courts and parties need to be able to discuss the contractual relevance of various competing concurrent causes with reference to a contractual, not tort, context. The proposed analysis proceeds first by examining concurrent causes in a given loss scenario on two dimensions: the temporal and the sufficiency dimensions. Next, the analysis must also determine the involvement and necessity of each cause in the end result loss claimed by the insured. Finally, the analysis requires an examination of the sufficiency of the end result effect of the concurrent causes. The article proposes two immutable legal rules based on whether or not a concurrently caused loss results from separate, discrete causes or reciprocal, indivisible causes. These two rules are aimed to longitudinally incentivize insurers to draft more efficient contractual language dealing with concurrent causation.
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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 | 0.002 | 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 | 0.001 |
| 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".