How to Save the World from Future Disasters Being Ignored Today | Richard Clarke | Big Think
The so we talked about a failed warning as a Cassandra event, and we tried to ask ourselves in the book: why did this Cassandra event happen? We find that there are four overall factors. There is the quality of the Cassandra herself or himself; then there's the issue itself and the qualities about the issue that make a warning relevant to it hard for people to accept. The last is the critics—the critics of the person giving the warning, the critics of the Cassandra. What do they say and what do they not say?
In those four column headings—the Cassandra, the decision-maker, the issue itself, and the critics—under each of them, there are several different criteria. Applying that template to a potential Cassandra event, we think we can begin to tell who's right and who's Chicken Little. When we look at the Cassandra herself or himself, that's probably the most important determinant of whether or not we have a Cassandra event coming.
It's the most important determinant in what we call the Cassandra coefficient: it is that human being. Because we think what we're trying to do here is predict the future, not by some algorithm or artificial intelligence program. We think the solution here to particularly the future is finding people—people who can do it. So, what are the qualities of a Cassandra? They're an expert. They're an expert in their field, and they are internationally recognized as such. This is not someone waking up in the middle of the night with a premonition.
This is not someone who every year predicts some other disaster. The Cassandras typically have never predicted a disaster before. They're experts that have never predicted a disaster before, and they are data-driven. Usually, they run the program that collects the data that convinces them. When they look at the data, something pops right out at them. They see, through pattern recognition, in a flash, the problem that other experts in the field just don't see or don't understand.
Sometimes they see it first, and later people come along and agree with them, but they see it so clearly. Then this other quality of the Cassandra kicks in, and that is necessary to be a Cassandra. It is the belief in their own analysis—in their own data—is so strong that they feel they must do something about it. They all feel a personal sense of agency, of responsibility. They have to give the warning, and when they are not heeded, when they are ignored, when they are ridiculed, when, in some cases, they are muzzled or fired, that creates a negative feedback loop.
Because they become more strident or insistent on getting their message out, sometimes that's off-putting. Sometimes they are accused of being obstreperous or obsessive. Words like doom and gloom, and all sorts of criticisms, are leveled on them. What we found in the Cassandras that we know about, and in some of the ones we think might be Cassandras in the future, is that they all had the same type of criticism.
These are people who fit what we learned an Israeli psychiatrist calls "Sentinel intelligence." Sentinel intelligence, he described it as something that is highly functioning, high-anxiety people. It's not something that you can learn; he thinks it's something that is innate. They are the person who's sitting in the restaurant or the theater and smells smoke first before anybody else. They don't just say, "Oh, isn't that awful?" But they get up and, with confidence, pull the fire alarm.
The second factor in our Cassandra coefficient is the decision-maker or the audience. One of the problems with these issues is that frequently there is not a decision-maker, or it's a diffuse decision-making process. It's not clear who can pull the trigger, who can respond to the warning. Another characteristic of the decision-maker is frequently that they have an agenda of their own. They have a position of responsibility—president of the United States, president of the university, CEO of the corporation.
They assumed that job with something in mind; they were going to do X, Y, and Z. Suddenly, somebody comes into the room and says, "You can't do that. You can't do your agenda; you have to pay attention to my agenda." Oh, by the way, paying attention to my agenda is going to cause you to have to spend money in ways that you didn't want to spend it. It's going to make life inconvenient for people.
Decision-makers hearing that are very reluctant for the most part because they are so fixed on their agenda. They don't want to hear about something that would pull them off from that agenda. Decision-makers, also, in many of these cases, frankly, are not experts, and not trained in any way to understand what the decision-maker is saying. In the case of the Ponzi scheme by Bernie Madoff, Harry Markopolos came into the Securities and Exchange Commission six times with mathematical formulas and projections and charts to show the people at the SEC that Madoff was a Ponzi scheme.
He was a quant; he had lots of data. He was talking to lawyers; they didn't get it. Very frequently, the problem is decision-makers don't fundamentally understand the science or the math that the Cassandra is giving them. The issue about which the Cassandra warns is also very important in our Cassandra coefficient. If the issue is of such a magnitude that it would require enormous change, decision-makers don't usually want to do that.
If it's a complex issue, people don't understand it. If it's an issue that has never happened before, people find an excuse for not acting. Sometimes, it's the very fact of the issue itself that suggests nothing's going to happen. For example, one of the ones we're looking at today is the possibility of asteroid impact on the Earth. David Morrison is our possible Cassandra. When we ask people, "What do you think about the risk of a giant asteroid hitting the Earth and destroying a city?" people giggle, people laugh.
The issue itself causes people not to take it seriously, in part because they've seen science fiction movies with Bruce Willis flying up into space and blowing up asteroids. But the issue itself just seems so ridiculous to them. They've never seen it happen before. But Morrison points out that it has happened before; it's happened a lot in the history of this planet. It just hasn't happened a lot in the history of this planet with human beings on it.
The last thing we look at to decide if we're dealing with a possible Cassandra event—the last element of our Cassandra coefficient—is the critics of the Cassandra. Do the critics take on the Cassandra and say, "No, your data is wrong; you collected the wrong data; you collected the data in the wrong way; you did the wrong kind of analysis on the data?" Do they have a dialogue among experts, or do they just reject it out of hand? Sometimes the critics are paid critics, as in the case of the tobacco industry in the United States, which bought experts to say that smoking was not a problem.
Perhaps some oil companies in the past have bought experts to diminish the threat of global warming. So, the question we ask is, what are the critics saying? For example, in the case of Dr. James Hansen, the noted NASA scientist now at Columbia University, who says that sea-level rise will be much faster and higher than the UN prediction. But we asked other experts about that; they come up with all sorts of criticism, but they never say his data is wrong.
So, when we ask them what's wrong with Jim Hansen's data, they say, "Well, he's not really using the scientific method; he's making leaps of faith." Back to Hansen: how do you respond to that? And he says, "If I use the scientific method, I would have to melt Greenland. You want me to melt Greenland repeatedly to prove that it would change the world?" What Hansen says to us is, "When the data is so clear that it meets the scientific method, it will be too late."
We call that scientific reticence—scientific reticence when scientists are reluctant to make a conclusion because the data is not there in the traditional way of experimentation. But what it is: they’ll be too late.