How They Fit Together: Bell Curves, Bayes and Black Swans

Probability is defined as the possibility, chance or odds of likelihood that a certain event or occurrence will take place now or in the future.  In a world where business managers like to “know the odds”, how does probabilistic thinking (Frequentism and Bayesian) mesh with extreme events (i.e. Black Swans) that just cannot be predicted?

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Statisticians lament how few business managers think probabilistically. In a world awash with data, statisticians claim there are few reasons to not have a decent amount of objective data for decision making. However, there are some events for which there are no data (they haven’t occurred yet), and there are other events that could happen outside the scope of what we think is possible.

The best quote to sum up this framework for decision making comes from the former US Defense secretary Donald Rumsfeld in February 2002:

“There are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns – there are things we do not know we don’t know.”

Breaking this statement down, it appears Mr. Rumsfeld is speaking about Frequentism, subjective probability (Bayes) and those rare but extreme events coined by Nassim Taleb as “Black Swans”.

Author Sharon Bertsch McGrayne elucidates the first two types of probabilistic reasoning in her book “The Theory That Would Not Die”.  Frequentism (conventional statistics), she says, relies on measuring the relative frequency of an event that can be repeated time and again under the same conditions. This is the world of p-values, bell curves, coin flips, casinos and actuaries where data driven decision making is objective based on sampling or computations of large data sets.

The greater part of McGrayne’s tome concentrates on defining Bayesian Inference, or subjective probability also known as a “measure of belief”. Bayes, she says, allows making of predications with no prior information at all (no frequency of events).With Bayes, one makes an educated guess, and then keeps refining that guess based on new information, thus updating and revising the probabilities, and getting “closer to certitude.”

Getting back to Rumsfeld’s quote, Rumsfeld seems to be saying we can guess the probability of  “known knowns” because they’ve happened before and we have frequent data to support objective reasoning. These “known knowns” are Nassim Taleb’s White Swans. There are also “known unknowns” or things that have never happened before, but have entered our imaginations as possible events (Taleb’s Grey Swans). We still need probability to discern “the odds” of that event (e.g. dirty nuclear bomb in Los Angeles), so Bayes is helpful because we can infer subjective probabilities or “the possible value of unknowns” from similar situations tangential to our own predicament.

Lastly, there are “unknown unknowns”, or things we haven’t even dreamed about (Taleb’s Black Swan).  Dr. Nassim Nicholas Taleb labels this “the fourth quadrant” where probability theory has no answers.  What’s an illustration of an “unknown unknown”? Dr. Taleb gives us an example of the invention of the wheel, because no one had even though or dreamed of a wheel until it was actually invented. The “unknown unknown” is unpredictable, because—like the wheel—had it been conceived by someone, it would have been already invented.

Rumsfeld’s quote gives business managers a framework for thinking probabilistically. There are “known knowns” for which Frequentism works best, “unknown knowns” for which Bayesian Inference is the best fit, and there is a realm of “unknown unknowns” where statistics falls short, where there can be no predictions. This area outside the boundary of statistics is the most dangerous area, says Dr. Taleb, because extreme events in this sector usually carry large impacts.

This column has been an attempt to provide a decision making framework for how Frequentism, Bayes and Black Swans fit together—by using Donald Rumsfeld’s quote.

What say you, can you improve upon this framework?

Don’t Follow Rules Based Decision Making Blindly!

A rules based, structured decision making approach works for many occasions, especially when choices and outcomes are relatively well documented and repetitive. But an exclusive focus on following pre-determined business rules (even when business conditions change) is a recipe for financial disaster.

Author Michael Lewis of Moneyball and The Big Short fame, has long critiqued decisions made by government officials and bankers in just about every country connected to the Great Recession.  In a recent Vanity Fair article titled, “It’s the Economy Dummkopf!” Lewis stays on the attack with his description of how some banks continued to invest in structured products such as collateralized debt obligations (CDOs), long after investors fled the market.

As the US housing market declined in 2006 and CDOs based on souring loans lost significant value, many investment bankers sold their vast CDO portfolios. However, one banker interviewed by Michael Lewis says that even as the market for CDOs took a turn for the worst, his firm loaded up on CDOs. Adding insult to injury, the banker says; “(The bank’s portfolio) would have gotten bigger if they had more time to buy. They were still buying when the market crashed.”

Picture this scenario: Every other company is fleeing the market and only a few are buying. Did these banks know something others did not? Was this calculated “big bet” that the market would turn and they’d make tons of profit?  Michael Lewis explains the opposite was true; “This was a mindless, rule based investment strategy”, he says. “As long as the bonds offered up by Wall Street firms abided by the rules (as designed by the banks, the bonds were purchased.)”

In search of yield, all that mattered for some banks was whether investments met a few significant criteria. Check box one, two and three, then buy.  Never mind that the market for such bonds was tumbling. In fact, Michael Lewis asserts the only thing that stopped these banks from losing more money was that the CDO market ceased to exist. “Nothing that happened—no fact, no piece of data—was going to alter their approach to investing money,” Lewis says.

Rules based decision making codifies decisions into “if –then” trees to arrive at optimum outcomes. And rules are often straightforward and inflexible because in most “normal” environments when business conditions fluctuate within prescribed volatility, the right decision can be counted on most of the time. However, focusing on the wrong metrics (in this instance “yield”) to the detriment of other considerations such as risks, ended up costing some banks billions of dollars.

Take a lesson from Michael Lewis. Follow the rules, but be wise to regularly examine changing business conditions and adjust rules based decision making accordingly.

Rules, it appears, are sometimes made to be broken.

Zero Latency: Faster Isn’t Always Better

Vendors often promise some derivative of the term “faster” in marketing and sales literature (i.e. faster decisions, quicker time to value, rapid implementations etc…). And to be sure, in plenty of cases, speed wins especially in terms of gaining insights into markets and customers before competitors get a clue. However, when it comes to decision making, too much speed without attention to improvements in logic and business processes can be disastrous.

It’s easy to confuse “fastest” with “best”. That’s what Jennifer Hughes writes in a Financial Times article on the arena of high frequency trading (HFT). The term HFT refers to buying and selling financial instruments in microseconds with the help of supercomputers, sophisticated algorithms, and in most instances co-location of equipment near stock exchange servers. In HFT, the goal is to make profitable trades faster than competitors, and this means that massive amounts of data must be examined in real time and buy/sell decisions executed in microseconds.

While an extreme case, high frequency traders are truncating the decision making window between “event” and “action” to near zero. In the previously mentioned Financial Times article, Kevin Rogers of Deutsche Bank says; “With some parts of the market we are getting to the point where the speed of light (is the only constraint).” And certainly, if one company can spot deals and trade faster than another, microseconds can be a significant advantage in profitability.

However, while in many cases speed wins, there are concerns, especially in terms of cost. After all, throwing millions of dollars in compute power to shave off a couple of microseconds might not be worth the investment. “We’re looking at a tipping point,” says Harpal Sandu, founder of electronic trading network Integral Development. “Trading isn’t going to get much faster than a few dozen microseconds—physical machines don’t run much faster than that.”

In addition, making decisions faster than competitors is useless if careful attention is lacking in data input, decision logic (possibly manifesting in algorithm development) and continual process improvement.  Moreover, the best decision today, or even ten minutes ago, might not be the best decision tomorrow, especially because external conditions make for a moving target with governmental policy changes, mergers and acquisitions, new technology development and more.

A final consideration is fragility. In high frequency trading for example, as trading decisions move closer to zero latency, there is less opportunity to remedy a potential mistake whether it consists of a “fat finger order”, or simply a poor trading decision that a company would like to correct. Adding insult to injury, in a complex environment such as stock markets, a poor decision made quickly can cause cascading effects to other players creating a massive market disruption.

In the countdown to zero latency, the focus is currently on speed. However, the returns on faster decision making are diminishing and equal opportunity should also be given to risk management considerations, business process improvement, and monitoring of business conditions to continually upgrade and refine decision making logic.

Questions:

• Can speed drastically increase without introducing fragility?
• Does a focus on speed provide an opportunity for companies to “get better” in how they deliver products and services?

The Next Wave in Recommendation Systems?

While some internet privacy experts fret over use of cookies and web profiles for targeted advertising, the quest for personalization is about to go much deeper as web companies create new profiling techniques based on the science of influence.

Behavioral targeting on the web using cookies, http referrer data, registered user accounts and more is about to be significantly enhanced says columnist Eli Pariser.  In the May 2011 issue of Wired Magazine, in an article titled “Mind Reading”, Pariser discusses how website recommendation and targeting algorithms; “analyze our consumption patterns and use that information to figure out (what to pitch us next).”   However, Parser notes that the next chapter for recommendation systems is to discern the best approach in influencing online shoppers to buy.

In the article, Pariser cites an experiment by a doctoral student at Stanford where online shopping sites attempted to not only track clicks and items of interest, but also determine the best way to pitch a product. For example, pitches would alternate between an “Appeals to Authority”; as in someone you respect says you’ll like this product to “Social Proof”—everyone’s buying this product, so should you!

Taking a cue from the work completed by Dr. Robert Cialdini it appears that the next wave in recommendation algorithms is to learn our “decision triggers”, or the best way to persuade us to act. In his book “Influence: Science and Practice”, Cialdini documented six decision triggers of consistency, reciprocation, social proof, liking, authority and scarcity as mental shortcuts that help humans deal with the “richness and intricacy of the outside environment.”

Getting back to the Wired Magazine article, Eli Pariser says this means that websites will hone in on the best pitch for a particular online consumer and –if effective—continue to use it.  To illustrate this concept, Pariser says; “If you respond a few times to a 50% off in the next ten minutes deal, you could find yourself surfing a web filled with blaring red headlines and countdown clocks.”

Of course, shoppers buy in various ways and not always in the same manner. However, the work of Robert Cialdini shows that in the messy and complicated lives of most consumers that mental shortcuts help with the daily deluge of information. Therefore, this new approach of recommendation systems using principles of psychology in tailoring the right way to “pitch” online shoppers, might just work.

There’s no doubt that recommendation systems already take into account principles of social proof and liking, but there’s a lot more room for improvement, especially other areas that Cialdini has researched. The answer to ‘why we buy’ is about to be taken to a whole new level.

Questions:

• What’s your take on this next development in recommendation systems? Benefit or too much “Big Brother”?
• Are you moved by “act now” exhortations? What persuasion technique/s work best on you?

Implications of Computers Reading the News (News Analytics)

Wall Street analysts and traders have a new weapon at their disposal: news analytics. In an effort to keep up with the deluge of news, events and alerts, some investment firms are turning to machines to read and score news for sentiment and word counts. This information is then inserted into trading models, which may be responsible for a huge buy or sell in your company’s stock. Marketers, with machines reading news and making buy/sell decisions in near real time, what are the implications for your PR, communications, and social media strategies?

Wall Street analysts and traders have long believed that stock prices jump on the release of positive and/or negative news. But the sheer number of news sources and volume makes comprehension a daunting task for individual traders. Adding insult to injury, most of the data in the world is unstructured, meaning that it is not in a database and may consist of text, JPEG images, flash videos, etc. So, interpreting the “meaning” of unstructured data often takes too much time.

Enter analytics. With the assumption that news flow is a good indicator of trading volume and stock price volatility, traders are using real-time data feeds, advanced algorithms, and computer power to digest and execute trades on “news” in sub-seconds.Machines are reading press releases, news stories, analyst reports, stock alerts, and more to gauge the sentiment, relevance, novelty, and volume of news. And trading firms are busy designing models to forecast stock prices based on historical news volumes.

Machines reading the news are scanning for two key criteria: sentiment and counts.

Let’s tackle sentiment first. Reading for sentiment, algorithms are scanning news looking for key phrases such as “better than expected” or other verbiage. They score news on how relevant a news item is to your particular company, whether the news is unique, the source of news (key analyst vs. small time shop), and what the specific headline says.

When examining counts, news algorithms seek how many times a key phrase shows up in the news, how often that key phrase is used over a time period (e.g., last 24 hours, past three days, and even how many articles were placed over a specified time frame to discern news volume.

This trend has significant implications for marketing and PR professionals. While we may not know the “weighting” system of what is most important to these algorithms (e.g., word counts might be more important than uniqueness), we should definitely bear in mind that in addition to human readers, we’ll now have to contend with machines.

At some point, most marketers have solely written, edited or approved a corporate press release. However, with machines starting to “read” the news items, your communication strategies might need more than a simple tweak. Ultimately, this means that press releases may need to be optimized for machine scanning. In addition, as these algorithms monitor news feeds from analysts, commentators, and other news professionals, one strategy might be doubling down on press and analyst relations to help shape content before the computers read it.

Machines are now reading the news and trading on what they discern. And news analytics isn’t just for large cap stocks! In fact, any company that trades on an exchange is fair game. Knowing this, your company’s stock price might go significantly up or down depending on your future marketing, social media and PR strategies. Fortunately news analytics is in the early adopter phase, but if there’s money to be made then surely this will be a growing trend.

Questions:

• Other than those listed above, what are the implications of machines reading the news?
• What might be some PR, marketing, and social media strategies to take advantage of this trend?