Incentives for Overestimating Risk

Incentives for Overestimating Risk

In the United States, and really across the globe, things are becoming more expensive. The price of food, gasoline, cars, and other goods have gone up quite a bit in the last year as the global economy adjusts to the new realities of the post-COVID world, as economies continue to respond to economic stimulus events, and as uncertainty around whether the pandemic truly is in our rear-view mirror continue to hang over our global consciousnesses. During this time of high inflation, many tv pundits, politicians, and experts are forecasting doom and gloom for national and global economies. Forecasting bad news seems to be the norm right now.
 
 
Steven Pinker explored the incentives for overestimating risk and forecasting bad news in his 2011 book The Better Angels of Our Nature. Pinker specifically looked at violence and war in his book and found that there are incentives for people to predict something negative, but not necessarily incentives for people to predict something positive. Pinker writes:
 
 
“Like television weather forecasters, the pundits, politicians, and terrorism specialists have every incentive to emphasize the worst-case scenario. It is undoubtedly wise to scare governments into taking extra measures to lock down weapons and fissile material and to monitor and infiltrate groups that might be tempted to acquire them. Overestimating the risk, then, is safer than underestimating it – though only up to a point. (emphasis mine)
 
 
We might be safer if everyone predicts a worst case scenario. If the people with the largest platforms focus on the dangerous potential for a terrorist attack, the public will demand action to reduce the risk. If there is a great focus on the need for improved safety equipment in hospitals responding to a new strain of COVID, then public officials are more likely to act. If there is overwhelming concern about inflation and economic collapse, the government will hopefully take better actions to balance the economy. Predicting that everything will work out and hum along on its own could be more dangerous than predicting the worst outcomes. Predicting doom and gloom not only gets attention, it can drive early and decisive decision making.
 
 
But as Pinker notes, it is safer to overestimate risk only to a point. Pinker cites the costs of the war in Iraq in search of weapons of mass destruction that did not exist as an example of dangerous worst case forecasting. Overreacting to COVID in China through excessive lockdowns and insufficient vaccination efforts may be contributing to higher global prices for goods at the moment. And predicting an economic collapse could spook markets and scare consumers, leading to worse economic outcomes than might otherwise occur. There are incentives to predicting the worst, but also costs if our predictions go too far.
 
 
I think our jobs as individuals is to be aware of the worst case scenarios, but not to become too trapped by such predictions. We need to remember that making worst case scenario predictions will provide feedback into what is already a noisy system. It is likely that forecasting the worst and spurring action by individuals will avert the worst. This doesn’t mean we can sit back and let others handle everything, but it should encourage us to think deeply about worse cases, our actions, and how panicked we should be.
Inventing Excuses - Joe Abittan

Inventing Excuses

With the start of the new year and the inauguration of a new president of the United States, many individuals and organizations are turning their eyes toward the future. Individuals are working on resolutions to make positive changes in their lives. Companies are making plans and strategy adjustments to fit with economic and regulatory predictions. Political entities are adjusting a new course in anticipation of political goals, agendas, and actions of the new administration and the new distribution of political power in the country. However, almost all of the predictions and forecasts of individuals, companies, and political parties will end up being wrong, or at least not completely correct.

 

Humans are not great forecasters. We rarely do better than just assuming that what happened today will continue to happen tomorrow. We might be able to predict a regression to the mean, but usually we are not great at predicting when a new trend will come along, when a current trend will end, or when some new event will shake everything up. But this doesn’t mean that we don’t try, and it doesn’t mean that we throw in the towel or shrug our shoulders when we get things wrong.

 

In Risk Savvy Gerd Gigerenzer writes, “an analysis of thousands of forecasts by political and economic experts revealed that they rarely did better than dilettantes or dart-throwing chimps. But what the experts were extremely talented at was inventing excuses for their errors.” It is remarkable how poor our forecasting can be, and even more remarkable how much attention we still pay to forecasts. At the start of the year we all want to know whether the economy will improve, what a political organization is going to focus on, and whether a company will finally produce a great new product. We tune in as experts give us their predictions, running through all the forces and pressures that will shape the economy, political future, and performance of companies. And even when the experts are wrong, we listen to them as they explain why their initial forecast made sense, and why they should still be listened to in the future.

 

A human who threw darts, flipped a coin, or picked options out of a hat before making a big decision is likely to be just as wright or just as wrong as the experts who suggest a certain decision over another. However, the coin flipper will have no excuse when they make a poor decision. The expert on the other hand, will have no problem inventing excuses to explain away their culpability in poor decision-making. The smarter we are the better we are at rationalizing our choices and inventing excuses, even those that don’t go over so well.