A s a special evening presentation, William Rouse introduced John Casti, author of the books X-Events: The Collapse of Everything (Casti, 2012) and Mood Matters: From Rising Skirt Lengths to the Collapse of World Powers (Casti, 2010). His presentation was followed by a discussion among all participants.
John Casti, Senior Research Scholar,
International Institute for Applied Systems Analysis
John Casti noted a number of the previous presentations used a “trend-following” principle—in other words, the presenter identified a trend and explored its ramifications for various periods of time. He said this principle ties in with his topic about how trends end. He said extreme events (referred to as X-events) will encompass these endings.
Casti then posed a simple paradox: Surprises happen, but no one particular surprise ever happens. In other words, most people acknowledge that surprises occur, but, if presented with a specific surprise scenario, they would say that particular surprise is impossible. Casti then pointed out that trends always end with either a “bang” or a “whimper.” In his observation, most go out with a “bang.” He pointed out that the time horizon for trends can be very different, ranging from several days to
many centuries. He listed several historical trends and their time horizons: dinosaurs (137 million years), the Roman Empire (677 years), the global financial boom (32 years), and the current popular culture fad of vampires (5 years or so).
Casti explained the trajectory of a trend by looking at a typical time series (reproduced in Figure 5-1). If one were to pick an ordinary point at random along the time series, one is likely to pick a point at which there is not a lot of unusual action (somewhere along the dashed lines in Figure 5-1). The likelihood of accurately predicting the next point is quite high, which is why trend-following is such a popular and successful analytical method. These points are common and expected, and the next moment is unlikely to contain an extreme event. The main activity in the time series is primarily focused at critical points (labeled a, b, and c on the plot). These points are short time frame, so the probability of being in that position is virtually zero. However, these critical points are the points that need to be understood. They can correspond to X-events, which are high impact and low probability. These critical points are the complement of the ordinary points of the time series and represent moments when the current trend is changing. If the curvature of the critical point is great, the X factor of the event is even greater.
Casti noted, however, that a time series curve to evaluate for a given trend is uncommon. What if a certain event has never happened before? In that case, there are no data, much less any sort of time series. How can the unknowns be measured and characterized? He explained that there are two pieces to assessing unknowns: (1) context, such as the landscape
FIGURE 5-1 Time series of a trend.
NOTE: Critical points are labeled as a, b, and c.
SOURCE: Casti presentation, slide 3.
of possible events for the next moment; and (2) random triggers, the watershed event that selects the event at the next moment from the set of possibilities.
To predict an extreme event, it does not help to analyze random triggers, he said. They are unpredictable, so there is no pattern to discern or additional information to glean. Some examples of recent random triggers he cited included the collapse of Lehman Brothers, which precipitated the financial fallout, and the self-immolation of the Tunisian fruit seller that was the trigger for the Arab Spring. Casti asserted that, in contrast to random triggers, context is something that can be assessed. Two different drivers shape context: complexity mismatch and collective beliefs about the future. Casti cautioned that this is not a fully developed operational theory, and additional research is underway to better understand these drivers. For now, he said he can provide insights, not specific results.
Casti first addressed the idea of complexity mismatch. Tainter (1988) sought to look across history for common causes among civilizations that collapse and documented some common features, labeled “complexity overload.” Casti explained that each civilization has problems that it must address. Typically, the civilization creates a new layer of structure to address a problem. The solution will long outlive the problem. As more problems are faced, an operational government becomes increasingly complicated and structured. At some point, all of the civilization’s resources are consumed maintaining that structure, with nothing left to address the next problem. Casti identified the U.S. response to September 11, 2001, as an example. A new government structure (Department of Homeland Security) was put in place to address the new problem of terrorism. Casti defined complexity as the number of independent actions that a system can take at any time.
He then turned to a discussion of collective beliefs about the future, also known as human group psychology or social mood. How does a group think about its future? This metric is useful whether or not the beliefs correspond to the truth. Beliefs can strongly bias the character of the type of events a society can expect to see. Also important is the time frame under consideration—tomorrow, next week, next year, or future generations. Events have a natural unfolding time, and it is important to match the time frame of the collective beliefs to the time horizon of the event. For instance, the rise and fall of a civilization can take hundreds of years, so measuring day-to-day mood is not relevant. In contrast, popular culture changes rapidly, so it would be important to measure collective beliefs on a time frame of weeks if interested in popular culture. Casti emphasized beliefs, not feelings. Feelings can be ephemeral, but beliefs tend to be consistent. To be operationally useful, there needs to be a metric to assess collective beliefs. The metric should relate to actions that
are the result of collective beliefs. One good way to characterize this is by looking at the financial markets, because they are a representation of decisions taken by people. Casti emphasized that this is a first step, not the last, in measuring collective beliefs. He observed that this metric has been the frequent subject of criticism, and he proactively addressed how the actions of a handful of traders could represent a whole population. He referred to his book (Casti, 2010) for more detail, but he postulated that traders do not act independently. They gather information and receive input from many different sources, as well as respond to the wishes of portfolio owners.
Casti applied the financial market metric to several examples. The first was U.S. presidential elections. Looking at the social mood (via financial indices) one week prior to presidential elections, there are 26 instances of clear trends. Of those 26 instances, 19 were positive and seven negative. In the case of the 19 positive trends, the incumbent or his party’s candidate won the election in a landslide 13 times, and retained the office in all 19 cases. In the case of the seven negative trends, four incumbents or their party’s candidate were ousted in a landslide, and all seven lost the election.
In a second example, Casti examined the relationship between the social mood metric and health epidemics. In this case, the effects are regional, so a global financial market indicator would not be sufficient. Instead, he looked at foreclosure rates 2 months before a large outbreak of the H1N1 virus in 2009. There was a strong correlation between foreclosure rates (Casti referred to this as the “fear index”) and the incidence of H1N1 influenza. He did not attempt to explain this correlation, but he pointed out that the correlation indicates a possibility that people can be more susceptible to a health epidemic if they are in a negative place. Casti referred participants to the work of Alan Hall (Hall, 2009) for further studies of this effect.
According to Casti, X-events pose an opportunity as well as a challenge. For instance, those who survive an X-event are more resilient. X-events “clean out” social structures and processes that may have outlived their utility. While recovery can take a long while, the X-event can bring with it new opportunities. In the aftermath of the Fukushima nuclear incident, the Japanese are seriously questioning old structures, in place since World War II, that overemphasize government-industry relationships and are no longer viable. In an even more extreme X-event example, the asteroid that extinguished the dinosaurs provided opportunities for other organisms to evolve—leading to humans’ presence on Earth today. In general, the system starts afresh after an X-event, new systems are put in place, and the process begins again. Casti stressed that this development cycle is neither linear nor circular, but spiral. A fresh
start does not originate from the same place started from in the previous cycle. Instead, the new beginning is from a different (hopefully higher) level, progressing to new heights. Casti said X-events are necessary for rebirth and human progress.
In the discussion period, Casti was asked about the use of the stock market metric to gauge social mood. The questioner’s concern was not that so few people were moving the market, but rather that the market matters for only a small segment of the population. Seventy percent of U.S. wealth is held by 10 percent of its citizens, while 50 percent of its citizens have no net assets. Perhaps the wealthiest Americans dominate the public discourse and therefore create the social mood for the nation, the participant suggested. However, in other nations it would be impossible to gauge public mood via the stock market. Casti agreed, and pointed out that he gave a talk about social mood in Havana, Cuba, which has no stock market. Also, a financial market index is a highly aggregated measure that does not allow the assessment of subsets of the population. In those cases, it may be important to use alternate measures of social mood. He expressed enthusiasm for social media and data mining. He also described other advantages to financial markets as a gauge of social mood: the data are clean, have a long history, and are publicly available.
A participant brought up the fact that in Casti’s scenario, X-events are considered cleansing, removing institutions and procedures that have become obsolete. However, institutions are fairly adaptive, and a resilient institution may be more adept at adapting to a new world. Why should such institutions be removed? Casti clarified that he provided no arguments for which social structures would survive an X-event and which ones would be eliminated. It depends entirely on the nature of the event. He posited that to adapt in the aftermath of an X-event, an institution needs (1) to survive, (2) to absorb the event and carry on with its function, and (3) to show agility and take on new opportunities.
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