Tag: Explanations

The Poisson Nature of War

The Poisson Nature of War

Posted on by jabittan
When we look back at history and explain why the world is the way it is, we rarely attribute specific causes and results to chance. We don’t say that a group of terrorists happened to choose to fly planes into the World Trade Center on 9/11. We don’t say that a new technology happened to come along to advance the economy. And we don’t say that a war between two countries happened to break out. But in some ways it would make more sense for us to look back at history and view events as chance contingencies. Steven Pinker argues that we should do this when we look back at history’s wars.
 
 
Specifically, when we take a statistical view of the history of war, we see that wars follow a Poisson distribution. When we record all the wars in human history we see lots of short intervals between wars and fewer long gaps between wars. When we look back at history and try to explain wars from a causal standpoint, we don’t look at the pauses and gaps between wars. We look instead at the triggering factors and buildup to war. But what the statistics argue is that we are often seeing causal patterns and narratives where none truly exist. Pinker writes, “the Poisson nature of war undermines historical narratives that see constellations in illusory clusters.”
 
 
We see one war as leading to another war. We see a large war as making people weary of fighting and death, ultimately leading to a large period of peace. We create narratives which explain the patterns we perceive, even if the patterns are not really there. Pinker continues,
 
 
“Statistical thinking, particularly an awareness of the cluster illusion, suggests that we are apt to exaggerate the narrative coherence of this history – to think that what did happen must have happened because of historical forces like cycles, crescendos, and collision courses.”
 
 
We don’t like to attribute history to chance events. We don’t like to attribute historical decisions to randomness. We like cohesive narratives that weave together multiple threads of history, even when examples of random individual choices or chance events shape the historical threads and narratives. Statistics shows us that the patterns we see are not always real, but that doesn’t stop us from trying to pull patterns out of the randomness or the Poisson distribution of history anyway.
Level Two Chaos & History

Level Two Chaos and History

Posted on by jabittan
“It is an iron law of history that what looks inevitable in hindsight was far from obvious at the time,” writes Yuval Noah Harari in his book Sapiens. History seems pretty clear when we look backwards. It is certainly complex, but whether it is our own lives, a sports season, or the rise and fall of an empire, we generally do a pretty good job of creating a compelling narrative to explain how history unfolded and why certain events took place. But these narratives create what we call the hindsight bias, where past events (in this case the course of human history) appear nearly deterministic. The reality is that small changes could shape the course of history in dramatic ways, and that the future is never clear at any point – as our current uncertainty about social media, climate change, and political polarization demonstrate. Harari continues, “in a few decades, people will look back and think that the answers to all of these questions were obvious,” but for us, the right answers are certainly far from obvious.
 
 
History, for human beings, is shaped by the innumerable decisions that we make every day. The course of history is not deterministic, but is instead chaotic. Harari argues that history is a level two chaotic system and writes, “chaotic systems come in two shapes. Level one chaos is chaos that does not react to predictions about it … level two chaos is chaos that reacts to predictions about it, and therefore can never be predicted accurately.”
 
 
The weather is a level one chaotic system because it doesn’t respond (on a day to day basis) to our predictions. Whether our computer models suggest a 1%, 45%, or 88% chance of rain on a given day doesn’t change what is going to happen in the atmosphere and whether it will or will not rain. Despite our sometimes magical thinking, scheduling a wedding or stating our hopes for or against certain weather patterns does not influence the actual weather.
 
 
History, is not a level one chaotic system. History is shaped by elections, the general beliefs within the public, the actions that people take, and how key actors understand risk and probability. Our predictions can greatly influence all of these areas. Predicting a landslide victory in an election can demotivate the losing side of a political divide, possibly turning what could have been a marginal victory into the predicted landslide as a sort of self-fulfilling prophecy. Reporting that general beliefs are becoming more or less prevalent among a population can influence the rate and direction of changing beliefs as people hear the predictions about belief trends (this seems like it may have happened as marijuana and gay marriage gained acceptance across the US). Our reactions to predictions can influence the final outcomes, contributing more uncertainty and chaos to the system.
 
 
We cannot predict exactly how people will react to predictions about the systems they participate in. It makes the predictions and forecasts more challenging since they have to incorporate different levels of response to various inputs. History cannot be thought of deterministically because so many small factors could have changed the outcomes, and those small changes in turn would have influenced exactly what predictions were made, in turn influencing the reactions of the people involved in the various actions of history. Our confidence in understanding history and why history played out as it did is not warranted, and is simply a fallacy.
The Hindsight Fallacy &The Hindsight Fallacy & the How & Why of History - Jim Collins Good To Great Bias the How & Why of History

The Hindsight Fallacy & the How & Why of History

Posted on by jabittan
Looking forward and making predictions and judgments on what will happen in the future is incredibly difficult, and very few people can reliably make good predictions about what will happen. But when we look to the past, almost all of us can describe what did happen. It is easy to look back and see how a series of events unfolded, to make connections between certain conditions and eventual outcomes, and to be confident that we understood why things unfolded as they did. But this confidence is misleading and is something we can reliably expect from people.
 
 
The hindsight fallacy is the term which describes our overconfidence in describing what happened in the past and determining which causal factors influenced the outcomes we observed. When the college football playoff is over this year, sports commentators will have a compelling narrative as to why the winning team was able to pull through. When the stock market makes a jump or dip in the next year, analysts will be able to look backward to connect the dots that caused the rise or fall of the market. Their explanations will be confident and narratively coherent, making the analysts and commentators sound like well reasoned individuals.
 
 
However, “every point in history is a crossroads,” writes Yuval Noah Harari. Strange and unpredictable things could happen at any time in history, and the causal factors at work are hard to determine. It is worth remembering that the best social science studies return an R value of about .4 at most (the R value is a statistical value reflecting how well the model fits reality). This means that the best social science studies we can conduct barely reflect the reality of the world. It is unlikely that any commentator, even a seasoned football announcer or stock market analyst, really understands causality well enough to be confident in what caused what, even in hindsight. Major shifts could happen because someone was in a bad mood. Unexpected windfalls could create new and somewhat random outcomes. Humans can think causally, and this helps us better understand the world, but we can also be overconfident in our causal reasoning.
 
 
Harari continues, “the better you know a particular historical period, the harder it becomes to explain why things happened one way and not another. Those who have only a superficial knowledge of a certain period tend to focus only on the possibility that was eventually realized.” What Harari is saying in this quote is that we can get very good at describing how things happened in the past, but not exactly very good at describing why. We can look at each step and each development that unfolded ahead of a terrorist attack, a win by an army, or as Harari uses for demonstration in his book, the adoption of Christianity in the Roman Empire. But we can’t always explain the exact causal pathway of each step. If we could, then we could identify the specific historical crossroads where history took one path and not another and make reasonable predictions about how the world would have looked had the alternative option been the one that history followed. But we really can’t do this. We can look back and identify factors that seemed important in the historical development, but we can’t always explain exactly why those factors were important in one situation relative to another. There is too much randomness, too much chance, and too much complexity for us to be confident in the causal pathways we see. We won’t stop thinking in a causal way, of course, but we should at least be more open to a wild range of possibilities, and less confident in our assessments of history.
 
 
One of my favorite examples of the hindsight bias in action is in Good to Great by Jim Collins. In the book published in 2001, Collins identifies 11 companies that had jumped form being good companies to great companies. One of the companies identified, Circuit City, was out of business before Collins published his subsequent book. Another, Wells Fargo, is now one of the most hated companies in the United States.  A third, Fannie Mae, was at the center of the 2008 financial crisis, and a fourth, Gillette, was purchased by P&G and is no longer an independent entity. A quick search suggests that the companies in the Good to Great portfolio have underperformed the market since the books publication. It is likely that the success of the 11 companies included a substantial amount of randomness, which Collins and his team failed to incorporate in their analysis. Hindsight bias was at play in the selection of the 11 companies and the explanation for why they had such substantial growth in the period that Collins explored.