Collective Nouns and Social Dilemmas
By Donald F. MangoCollective nouns let us refer to aggregate phenomena in a concise, efficient manner. In insurance, we have several well-known collective nouns: the Industry, the Market, and the Cycle. When using collective nouns, however, we must not assign an identity to the collective, keeping in mind it is composed of components. Consider a search for the infamous insurance "market price." Like viewing a pointillist painting, the closer we look, the more difficult it is to see the big picture. The components of the market are many individual transaction prices, each of which occurs between an insurer and an insured. What we call the market price may only be an illusion of distance.
The larger the collective, the less influence any single member has. Members may abdicate responsibility for the behavior of the collective. In the case of a softening insurance market, insurers are quick to blame "the market." While understandable, this export of blame and responsibility is never accompanied by the associated import of responsibility that was exported by all other insurers. Try as we might, we are each a little responsible for the prices in the markets. So how do we allow the situation to deteriorate so badly?
Social Dilemmas
Peter Kollock, author of Social Dilemmas: The Anatomy of Cooperation, defines social dilemmas as "situations in which individual rationality leads to collective irrationality."
That is, individual rational behavior leads to a situation in which everyone is worse off than they might have been otherwise. The "everyone is worse off" part seems to describe our insurance market. Is there individual rationality as well? To attempt an answer, consider the role of expectations and comparables:
Expectations are an adaptive mechanism that allows the mind to pre-screen what is acceptable, rather than having to be ready for anything. Evidence that supports expectations actually reinforces those expectations, which in turn increases the likelihood that future evidence is "force fit" to match expectations.
Comparables are helpful in assessing value under uncertainty; examples include the valuation of real estate, private equity, and hedge funds. When facing uncertainty, the actions of others are often reassuring. If I am not sure, the fact that you paid $X for something like this makes me feel better about paying $X.
Insurers use expectations in their planning, pricing, reserving, and claims handling. Every plan is built upon a market price expectation for the coming period.
Insurance comparables are similarly easy to find. The best comparable for an insured is its own expiring premium. That plus an expected market price change benchmarks their renewal. Another comparable: the price a firm pays for another coverage. Other key comparables are the premiums paid by similar firms for the same or different coverages. There are degrees of comparability that affect how much influence one comparable has on other transactions.
The use of comparables is driven by a deeper need: being part of a herd. There is safety in numbers. Underwriters would rather be wrong with the crowd than right on their own. They are not alone in this sentiment: asset managers are notorious herders1.
The True Problem: Signal Propagation
Putting these two together, we have a market with expectations and herding that is susceptible to signal propagation. Consider the dynamics of this hypothetical situation:
- All market participants (insurers, intermediaries, and insureds) expect a drop in market (herd) price level during the current underwriting period.
- Each transaction is a signal.
- This signal propagates (via intermediaries or the social network), because it is a comparable. It will affect the pricing of future programs, based on the degree of comparability.
- Under the expectation that the herd is lowering prices, it is rational for an insurer to transact at a lower price.
- A few transactions at lower prices could trigger feedback and induce the sort of social dilemma illustrated by the accompanying diagram.
What then must we do?
This is a complex problem, and linear cause and effect may not apply. It is difficult to find a single culprit, because we are all simultaneously slightly responsible, yet in aggregate we are completely responsible. Recognizing the dynamics is the first step; we can seek some comfort in the fact that similar dynamics plague nearly every complex human system.
Solution must start with the logic of networks. We will need to better understand interaction effects, interdependency, and network stability. We cannot abandon competition, but we may need structural or regulatory changes in order to improve the reliability and predictability of the insurance system for all its many stakeholders.
1 See Avinash Persaud, "The puzzling decline in financial market liquidity," BIS Papers No. 2, 2001, p.152-157.
