The Future of Decision Making: Less Intuition, More Evidence

A fantastic post by Andrew McAfee

Human intuition can be astonishingly good, especially after it's improved by experience. Savvy poker players are so good at reading their opponents' cards and bluffs that they seem to have x-ray vision. Firefighters can, under extreme duress, anticipate how flames will spread through a building. And nurses in neonatal ICUs can tell if a baby has a dangerous infection even before blood test results come back from the lab.

The lexicon to describe this phenomenon is mostly mystical in nature. Poker players have a sixth sense; firefighters feel the blaze's intentions; Nurses just know what seems like an infection. They can't even tell us what data and cues they use to make their excellent judgments; their intuition springs from a deep place that can't be easily examined. . Examples like these give many people the impression that human intuition is generally reliable, and that we should rely more on the decisions and predictions that come to us in the blink of an eye.

This is deeply misguided advice. We should rely less, not more, on intuition.

A huge body of research has clarified much about how intuition works, and how it doesn't. Here's some of what we've learned:

•It takes a long time to build good intuition. Chess players, for example, need 10 years of dedicated study and competition to assemble a sufficient mental repertoire of board patterns.

•Intuition only works well in specific environments, ones that provide a person with good cues and rapid feedback . Cues are accurate indications about what's going to happen next. They exist in poker and firefighting, but not in, say, stock markets. Despite what chartists think, it's impossible to build good intuition about future market moves because no publicly available information provides good cues about later stock movements. Feedback from the environment is information about what worked and what didn't. It exists in neonatal ICUs because babies stay there for a while. It's hard, though, to build medical intuition about conditions that change after the patient has left the care environment, since there's no feedback loop.

•We apply intuition inconsistently. Even experts are inconsistent. One study determined what criteria clinical psychologists used to diagnose their patients, and then created simple models based on these criteria. Then, the researchers presented the doctors with new patients to diagnose and also diagnosed those new patients with their models. The models did a better job diagnosing the new cases than did the humans whose knowledge was used to build them. The best explanation for this is that people applied what they knew inconsistently — their intuition varied. Models, though, don't have intuition.

•It's easy to make bad judgments quickly. We have a many biases that lead us astray when making assessments. Here's just one example. If I ask a group of people "Is the average price of German cars more or less than $100,000?" and then ask them to estimate the average price of German cars, they'll "anchor" around BMWs and other high-end makes when estimating. If I ask a parallel group the same two questions but say "more or less than $30,000" instead, they'll anchor around VWs and give a much lower estimate. How much lower? About $35,000 on average, or half the difference in the two anchor prices. How information is presented affects what we think.

•We can't know tell where our ideas come from. There's no way for even an experienced person to know if a spontaneous idea is the result of legitimate expert intuition or of a pernicious bias. In other words, we have lousy intuition about our intuition.

My conclusion from all of this research and much more I've looked at is that intuition is similar to what I think of Tom Cruise's acting ability: real, but vastly overrated and deployed far too often.

So can we do better? Do we have an alternative to relying on human intuition, especially in complicated situations where there are a lot of factors at play? Sure. We have a large toolkit of statistical techniques designed to find patterns in masses of data (even big masses of messy data), and to deliver best guesses about cause-and-effect relationships. No responsible statistician would say that these techniques are perfect or guaranteed to work, but they're pretty good.

The arsenal of statistical techniques can be applied to almost any setting, including wine evaluation. Princeton economist Orley Ashenfleter predicts Bordeaux wine quality (and hence eventual price) using a model he developed that takes into account winter and harvest rainfall and growing season temperature. Massively influential wine critic Robert Parker has called Ashenfleter an "absolute total sham" and his approach "so absurd as to be laughable." But as Ian Ayres recounts in his great book Supercrunchers, Ashenfelter was right and Parker wrong about the '86 vintage, and the way-out-on-a-limb predictions Ashenfelter made about the sublime quality of the '89 and '90 wines turned out to be spot on.

Those of us who aren't wine snobs or speculators probably don't care too much about the prices of first-growth Bordeaux, but most of us would benefit from accurate predictions about such things as academic performance in college; diagnoses of throat infections and gastrointestinal disorders; occupational choice; and whether or not someone is going to stay in a job, become a juvenile delinquent, or commit suicide.

I chose those seemingly random topics because they're ones where statistically-based algorithms have demonstrated at least a 17 percent advantage over the judgments of human experts.

But aren't there at least as many areas where the humans beat the algorithms? Apparently not. A 2000 paper surveyed 136 studies in which human judgment was compared to algorithmic prediction. Sixty-five of the studies found no real difference between the two, and 63 found that the equation performed significantly better than the person. Only eight of the studies found that people were significantly better predictors of the task at hand. If you're keeping score, that's just under a 6% win rate for the people and their intuition, and a 46% rate of clear losses.

So why do we continue to place so much stock in intuition and expert judgment? I ask this question in all seriousness. Overall, we get inferior decisions and outcomes in crucial situations when we rely on human judgment and intuition instead of on hard, cold, boring data and math. This may be an uncomfortable conclusion, especially for today's intuitive experts, but so what? I can't think of a good reason for putting their interests over the interests of patients, customers, shareholders, and others affected by their judgments.

So do we just dispense with the human experts altogether, or take away all their discretion and tell them to do whatever the computer says? In a few situations, this is exactly what's been done. For most of us, our credit scores are an excellent predictor of whether we'll pay back a loan, and banks have long relied on them to make automated yes/no decisions about offering credit. (The sub-prime mortgage meltdown stemmed in part from the fact that lenders started ignoring or downplaying credit scores in their desire to keep the money flowing. This wasn't intuition as much as rank greed, but it shows another important aspect of relying on algorithms: They're not greedy, either).

In most cases, though, it's not feasible or smart to take people out of the decision-making loop entirely. When this is the case, a wise move is to follow the trail being blazed by practitioners of evidence-based medicine , and to place human decision makers in the middle of a computer-mediated process that presents an initial answer or decision generated from the best available data and knowledge. In many cases, this answer will be computer generated and statistically based. It gives the expert involved the opportunity to override the default decision. It monitors how often overrides occur, and why. it feeds back data on override frequency to both the experts and their bosses. It monitors outcomes/results of the decision (if possible) so that both algorithms and intuition can be improved.

Over time, we'll get more data, more powerful computers, and better predictive algorithms. We'll also do better at helping group-level (as opposed to individual) decision making, since many organizations require consensus for important decisions. This means that the 'market share' of computer automated or mediated decisions should go up, and intuition's market share should go down. We can feel sorry for the human experts whose roles will be diminished as this happens. I'm more inclined, however, to feel sorry for the people on the receiving end of today's intuitive decisions and judgments.

What do you think? Am I being too hard on intuitive decision making, or not hard enough? Can experts and algorithms learn to get along? Have you seen cases where they're doing so? Leave a comment, please, and let us know.

Time to Invest: Predicting What’s Next for Technology in Hospitality

Time to Invest: Predicting What’s Next for Technology in Hospitality


3/1/2012

Douglas C. Rice


One of the biggest challenges for any technology executive is predicting the landscape of toolsets and IT infrastructure that will be available in the future. If you make the right choice, today’s investments may last for 10 or even 20 years. In contrast, the wrong choice could force you to replace core elements of your systems strategy in half that time, or less.

The hospitality industry is largely a consumer of these building blocks, which include such things as network protocols (think TCP/IP), materials (silicon, copper, fiber), data protocols (SQL, ODBC), operating systems (Linux, Windows, iOS, Android), and presentation and messaging protocols (HTML, SOAP). These are not developed for hospitality; rather they serve a wide variety of consumer and business needs. The building blocks just referenced are familiar names in the industry now, but how can you determine what the building blocks of the future will be?
One of the best lessons I learned from a wise person many years ago was that if you want to predict the future, find out where the big money is being invested. In technology, this means learning where the industry giants are investing their billions in research and development (R&D) – companies like Microsoft, Intel, Apple®, Cisco, Google, Oracle, IBM and AT&T, to name just a few. When many of them invest in the same building blocks, you can count on those building blocks becoming mainstream and supportable for many years to come. If you were watching these barometers, you foresaw the end of the mainframe era. You also saw the Internet coming years before the dot-com boom began, and you anticipated the mobile app revolution.



A Window to the Future

One of the great privileges I enjoy from the vantage of running on of the hotel technology industry’s largest trade associations is frequent opportunities to see the world from the vantage point of many different industry technology leaders, including those that focus on hospitality, as well as those serving the broader technology space. HTNG’s regular face-to-face meetings of industry technology leaders offer great insights into where technology industry leaders expect to go in coming years, and how hospitality technology providers view those trends. These insights provide clues as to which investments will be future proof and which will be risks.

The Cloud

Despite that no one really even agrees on the meaning of the word, there is no question that the cloud is by far the biggest area of investment. Microsoft, Amazon, Force.com, Apple, and now even networking companies like Cisco are placing huge bets on moving complexity and cost up the wire, away from the user and into data centers where they can benefit from scalability, shared support resources and load balancing. You can argue that much of the money being spent is on marketing hype rather than technology, and there is undoubtedly some truth to that point of view. But these companies would not spend money on marketing if they didn’t expect sales, which means they expect to deliver product. We are only in the early days of the cloud revolution currently, but the amount of money these companies are spending ensures that it will catch on.
One of the most important aspects of this, from a hospitality perspective, is the development of cloud service brokerages. In an industry where the dozens of different systems controlling a hotel must be mashed together from different parties – at a minimum this includes the building owner, the management company and the franchisor – if services are going to be cloud based, there must be cloud-level interoperability. Brokerage services can be thought of as cloud-based middleware that ensure robust and reliable communication between cloud-based systems operating in different clouds. They are what will enable a cloud-based CRS running in the Force.com cloud, for example, to easily connect with a cloud-based PMS running on Microsoft Azure. They can also allow a hotel company to engage a single vendor to manage the aggregation, integration, customization and governance of cloud services. Intel is one of many companies making big investments in the cloud services brokerage arena, which by definition are independent of any single cloud services provider.
This is good news for hospitality. It holds the promise of relieving the hotel owner of responsibility for managing the operation and integration of premise-based systems, with associated costs for deployment, equipment and maintenance performed by on-site or locally based staff. There is a healthy debate as to which technology services must remain premise based to avoid major problems in the event of network outages. But the number of hotel technologies that are proving to be robust in cloud deployments – at least in parts of the world with good Internet access – are growing every year as obstacles are overcome. There is a distinct possibility that every aspect of hotel technology except for end-user devices and portions of the network infrastructure may ultimately move to the cloud.



Mobility

Turning from infrastructure to hardware, it’s hard to deny that the big money is moving to mobility. Apple may have been the first of the megacompanies to figure this out –arguably, they became a megacompany by doing so – but other giants like Samsung, Microsoft and Amazon have also been leading the charge. Tablets have not yet fully replaced PCs in business travel, but the gap is narrowing rapidly. Indeed, the form factor of notebooks is getting smaller as that of tablets gets larger. We are fast approaching the day when the difference between the two is the presence or absence of a paper-thin keyboard.

For hospitality, this creates both opportunity and challenge. Mobility gives us the ability to communicate with our guests and staff in real time. This capability can be used to both define new service models and revenue streams, and to improve existing ones. Today’s challenge is that mobility requires massive investment in wireless infrastructure and bandwidth. (More about that challenge in the third trend.)

The key takeaway for hospitality is that when you invest in user interfaces, it will typically be wise to design for mobile devices first, rather than for PCs. Certainly this is true for applications that face guests, but also for staff-facing applications where the staff is or could benefit from being mobile. This includes a large proportion of front-of-house, back-of-house and guest-facing hospitality applications.

Don’t bet on a particular operating system, the leadership in this area will change based on competitive dynamics outside the control of anyone in hospitality. Multiplatform toolsets such as HTML5 are widely supported and are becoming de facto standards for deployment of applications across multiple platforms. While not yet perfected in all environments, it’s the clear winner in overall investment by mobile operating system and device manufacturers.



Cellular Offload

Mobile devices create the need for massive bandwidth. iBAHN collects extensive data on these trends, which it has generously shared with the industry, and the data is downright scary: bandwidth requirements are roughly doubling every year, with mobile devices leading the way.

Many hotels have shortchanged the investment in upgrading bandwidth and supporting Wi-Fi infrastructure, believing that the migration of mobile devices to 4G/LTE cellular technologies will solve the problem by ultimately reducing or eliminating Wi-Fi. But a look at where the megacarriers are investing proves this assumption completely false.

Carriers such as AT&T, Verizon and Sprint realized in 2007 to 2008 that the data tsunami was coming, and there was simply not enough cellular radio spectrum for them to outrun it, even given future advances in cellular technology through LTE and beyond. Carriers such as these and their counterparts in other countries know they cannot satisfy the demand for mobile data with cellular technologies, at least not in densely populated areas. Their strategies for satisfying the need are based on moving cellular traffic to terrestrial networks – meaning Wi-Fi. Virtually all major carriers in developed countries are aggressively investing in what they call offload, meaning they are building out or gaining access to Wi-Fi networks, and enabling their devices to roam onto these networks automatically. If you have an AT&T smartphone and leave wireless enabled, and walk into a Starbucks, McDonalds, American Airlines Club room or Hilton-branded U.S. hotel, you have probably already experienced this. These few examples exemplify the economics: in congested areas, it is far cheaper for a cellular carrier to build or fund a Wi-Fi network, than to install an additional cell tower and/or buy additional spectrum.

This is good news for hotels, because it means that cellular companies have an economic reason to help fund hotel Wi-Fi networks. In New York and San Francisco, where cellular coverage is saturated, some carriers have gone so far as to offer free Wi-Fi networks to certain hotels, because it was the least expensive option for them to satisfy the needs of their customers. Hotels in less congested areas won’t get free Wi-Fi networks anytime soon, but many hotels can now find, at a minimum, willing investment partners to help offset the cost of a Wi-Fi network in return for the ability to route cellular traffic through it. In remote areas, the cellular network may be sufficient to meet consumer needs. Urban and suburban hotels are well positioned to benefit from this trend, but will need to forge appropriate alliances with carriers to do so. Over time, carriers expect roaming models to develop, enabling phones from different carriers to offload to a single Wi-Fi network, with payments to the provider of that network based on traffic volumes or other factors.

There are many risky bets in technology, but a few safe ones. When you are making decisions on investments, strive to determine the major trends, and then invest in solutions that align with those trends. If you aren’t looking at the cloud, expecting the user interface to migrate to mobile devices, or thinking about how your hotel can benefit from carrier investments in Wi-Fi, you’re probably missing the boat.



Douglas C. Rice is the executive vice president and CEO of Hotel Technology Next Generation.







www.htng.org

Time Series and its application in Predictive Analytics

Time Series Foundation (TSF) is an open, .NET platform for exploring and prototyping new algorithms in time series analysis and forecasting. TSF is based on state space model methodology that includes all types of exponential smoothing, some autoregressive algorithms, and innovative algorithms for event detection and calendar event impact prediction. TSF relies on Excel charting and presentation APIs by implementing an Excel interop layer. Numerical and graphical results of time series analysis and forecasting can be put in programmatically generated workbooks with the help of this layer. TSF also offers an Excel add-in that exposes a large subset of the platform's functionality through the Excel ribbon UI.

Time Series Foundation is discussed by a research developer in Microsoft.

BI News from Panorama

Sort through data with business intelligence


With endless information accessible in today's big data sprawl, it is often difficult for businesses to sort through data that is valuable and isn't without outside assistance. Because of this, more and more organizations are turning to business intelligence in order to sort through vast reams of data to develop concrete analytics.
A new report by DSquared Media provides valuable insight into the worth of adopting business intelligence software and infrastructure. According to the study, for every $1.00 spent on business analytics, $10.66 was yielded in returns. Furthermore, 74 percent of organizatons who manually assembled data from various sources negatively affected daily operations.

The report also found that many large corporations used BI in developmental years in order to become the giants they are today. For example, Febreze used a marketing campaign aided by BI when first released, and now sales total over $1 billion a year. In addition, Target used marketing campaigns aided by BI and revenues grew from $44 billion in 2002 to $67 billion in 2010.
The study found that there was an assortment of reasons why people were influenced by BI. Ninety-five percent of respondents found that they were influenced by BI for its ability to increase insight into operations. Furthermore, 85 percent found business intelligence provided faster process and reporting cycle time, while only 48 percent were influenced by regulatory compliance.

Analytics in Sports

I am fan of football and my favorite team is FC Barcelona. Combining sports, specially football with Analytics is just amazing.