Better Together? How Externalities of Size Complicate Notions of Solidarity and Actuarial Fairness

TL;DR

This paper examines how externalities of size in cost-sharing games, such as insurance, complicate traditional notions of fairness like solidarity and actuarial fairness, revealing new insights into pricing and stability.

Contribution

It introduces a model accounting for externalities of size, demonstrating how they affect fairness concepts and proposing pricing schemes that balance subsidies and incentives.

Findings

Both high and low risk groups benefit from pooled costs.

Naive actuarial fairness becomes inefficient when considering externalities.

Stable pricing schemes may incentivize riskier partnerships.

Abstract

Consider a cost-sharing game with players of different contribution to the total cost: an example might be an insurance company calculating premiums for a population of mixed-risk individuals. Two natural and competing notions of fairness might be to a) charge each individual the same price or b) charge each individual according to the cost that they bring to the pool. In the insurance literature, these general approaches are referred to as "solidarity" and "actuarial fairness" and are commonly viewed as opposites. However, in insurance (and many other natural settings), the cost-sharing game also exhibits "externalities of size": all else being equal, larger groups have lower average cost. In the insurance case, we analyze a model with externalities of size due to a reduction in the variability of losses. We explore how this complicates traditional understandings of fairness, drawing on literature in cooperative game theory. First, we explore solidarity: we show that it is possible for both groups (high and low risk) to strictly benefit by joining an insurance pool where costs are evenly split, as opposed to being in separate risk pools. We build on this by producing a pricing scheme that maximally subsidizes the high risk group, while maintaining an incentive for lower risk people to stay in the insurance pool. Next, we demonstrate that with this new model, the price charged to each individual has to depend on the risk of other participants, making naive actuarial fairness inefficient. Furthermore, we prove that stable pricing schemes must be ones where players have the anti-social incentive of desiring riskier partners, contradicting motivations for using actuarial fairness. Finally, we describe how these results relate to debates about fairness in machine learning and potential avenues for future research.