Before Bernie Madoff got caught, before Hurricane Katrina and Fukushima devastated cities, and before ISIS formed, there was an expert for each one of those events warning people in power that it would happen. What did those powerful people do? Absolutely nothing. These experts are called ‘Cassandras’ in hindsight, because as global security expert Richard A. Clarke explains in a previous Big Think video: “Cassandra in Greek mythology was a woman cursed by the gods. The curse was that she could accurately see the future. It doesn’t sound so bad until you realize the second part of the curse, which was no one would ever believe her. And because she could see the future and no one was paying attention to her, she went mad.” So how can we graduate from sheepishly identifying Cassandras in hindsight, to recognizing and acting on their real predictions before the impending chaos hits? It’s tough because everyone and their uncle is trying to get in on the prediction game. Who can you trust? Fortunately, Clarke and his research partner R.P. Eddy have used case studies to build a detailed template of the four aspects that determine whether we can avoid a Cassandra event: the quality and personal traits of the Cassandra themselves, the reaction of the audience or decision makers in power, the nature of the predicted event (is it too ridiculous to believe?), and the critics of the Cassandra. Even today, there are potential Cassandras predicting events that could be catastrophic to humanity this century. Can we learn from our mistakes in time? Richard A. Clarke and R.P. Eddy’s new book is Warnings: Finding Cassandras to Stop Catastrophes.
Read more at BigThink.com: http://bigthink.com/videos/richard-a-clarke-how-to-save-the-world-from-future-disasters-being-ignored-today
Transcript: So we talk about a failed warning as a Cassandra event. And we try 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. And then the last is the critics. The critics of the person giving the warning. The critics of the Cassandra. What did they say and what did they not say. And 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 that whether or not we have a Cassandra event coming. It’s the most important determinate in what we call the Cassandra coefficient is that human being. The Cassandras typically have never predicted a disaster before. They’re experts. They’ve never predicted a disaster before and they are data driven. Usually they run the program that collects the data that convinces them. And when they look at the data something pops right out at them. They all feel a personal sense of agency, responsibility. They have to give the warning and when they are not heated. 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.
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 & 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 understand, fundamentally don’t understand the science or the math that the Cassandra is giving them.