Showing posts with label Operations Research in the News. Show all posts
Showing posts with label Operations Research in the News. Show all posts

Wednesday, August 14, 2013

Everybody likes to predict, but nobody likes being predictable, nor told what to do

The Netflix algorithm is in the news again.
The Science Behind the Netflix Algorithms That Decide What You’ll Watch Next

Netflix finds rating predictions are no longer as important, trumped by current viewing behaviour, i.e. what you are watching now. However, browsing through the comments, and again, you will see a generally negative reaction. Some people really hate being told what to watch, even if it's just a recommendation. Others say Netflix sucks, because it recommends things they've watched elsewhere. That sounds like a lack of understanding: if you don't tell Netflix you've watched something already, then how could it know?

As "big data" gets more media attention, it is reaching a wider audience who don't yet understand how algorithms work, but only know there are algorithms everywhere in their life, and it's scary to them. The lack of understanding seems to create fear and resentment.

LinkedIn and Facebook's recommendation systems for helping people find colleagues or friends they may know are generally well received, yet these film recommendation systems aren't. The difference between them might underline the success criteria of rolling out such recommendation systems.

Tuesday, August 13, 2013

Machine Learning in Movie Script Analysis Rouses Angry Reactions

An application of Machine Learning is covered in the news lately: movie script analysis.
Solving Equation of a Hit Film Script, With Data

They "compare the story structure and genre of a draft script with those of released movies, looking for clues to box-office success". However, the comments reveal that the general population (at least of the commenters) dislikes the concept for fear of anti-creativity.

Comments like these sum up the overall sentiment:
"Using old data to presage a current idea is both terrible and foolish. It is to writing what Denny's is to fine dining - mediocrity run wild."   
"Data crunchers will take the art out of everything. Paint-by-numbers."  

You be the judge whether this is a good application or not.

I tend to bias towards answers like this from the comments (sadly this was only 1 of 2 positive comments at the time of my reading; the other one was from the CEO of the script analysis business):
"I'm sure people have all sots of assumptions about what audiences like already. This data could be a tool to look deeper into these assumptions. Film makers have always wondered about consumer taste. It is a business. When commerce and art mix, there are inevitable compromises. This tool helps people see possible preferences based on past behavior. Information should never frighten us. It is how this information is applied that most deserves our attention." 

I think it also never helps the image of such machine learning practitioners when the journalist tries to paint him with an antagonist brush, such as "chain-smoking" and "taking a chug of Diet Dr Pepper followed by a gulp of Diet Coke and a drag on a Camel". Reminded me somewhat of another writer's writing style when covering analytics.

Monday, August 12, 2013

Value chain trumps good design - ColaLife

Babies in Africa suffer and die from diarrhoea, but it's easily treatable with medicines that costs pennies. The problem is getting the medicine into the mothers hands - a supply chain problem in a rural and sparsely populated area.

Here comes ColaLife: Turning profits into healthy babies.

Inventing medicine packaging to fit into coca cola bottle gaps is ingenious, but understanding the value chain, so that all hands that touch the supply chain of the medicine has an incentive to ensure its stock and flow, is even more important.

If there is only one message to take away, I would choose:
"What's in it for me?" 
Always ask this to make sure there is a hard incentive for all players to participate. Free give-aways are often not valued, resulting in poorly managed resources and relatively low success rate. Ample training and advertising for awareness and effective usage is also key for product / technology adoption.

Saturday, August 3, 2013

The Slightly Rosier Side of Gambling Analytics

Having posted about the ugly side of analytics - casino loyalty programmes, the Guardian's DataBlog caught my eye with their article on a rosier side of gambling analytics, where UK technology firm uses machine learning to combat gambling addiction.

Of course, a business is still a business. It needs to be profitable, so there are reasons more than just "let's be good". I list out below my take on the reasons for "them" the gamblers clients, and the reasons for "us" the casinos. Note, I simply assumed the machine learning study is sponsored by the casinos.

Just for "them":

Casinos too have a corporate social responsibility (CSP). Helping pathological gamblers, or identifying them before they become one is a nice thing to do.

For "them" and for "us":

More for everyone! They get to play more, and we get to profit more. The more people play a bit for longer is better than playing a lot for a short amount of time due to self exclusion lists. (I'm not sure which is the better evil of the two though...)
That's the business case. It's not all soft and cuddly like the CSP. Well, ok, business cases almost never are.
"If you can help that player have long term sustainable activity, then over the long term that customer will be of more value to you than if they make a short term loss, decide they are out of control and withdraw completely"

Just for "us":

Minimising gambling problems helps keep the country's regulators off the companies' backs, so they don't have to relocate when the country's regulations tighten. Relocation = cost. A lot of it.
"And there's also brand reputation for the operator. No company wants to be named in a case study of extreme gambling addiction, to be named in relation to a problem gambler losing their house"

A side note: This reaffirmed why I don't's a lose-win situation.

"A lot of casino games operate around a return-to-player rate (RTP) whereby if the customer pays, say £100, the game would be set up to pay back an average of £90. Different games will have different RTPs, and there are a few schools of thought on whether certain rates have different impacts on somebody's likelihood of becoming addicted.Some believe that if you lose really quickly, you'll be out of funds very quickly and will leave, and that a higher RTP will keep people on site, but others disagree"

I highly recommend reading the full article on the DataBlog.

Thursday, August 1, 2013

The Ugly Side of Analytics - Casino Customer Loyalty

While listening to This American Life's episode "Blackjack", its Act 2 had me in the car saying, "oh no, they did not!"  The "they" is the Caesars Entertainment Corporations (the casino), and yes, they have a customer loyalty programme that they use to "attract more customers", and claim it's no different than other such programmes in industries like supermarkets, hotels, airlines or dry cleaners.

Well...there is a wee bit of difference.

No one is addicted to dry cleaning.

I am saddened that analytics is used to help the casino loyalty programme and hurt the pathological gamblers. The show indicates that the programme identifies "high value customers" using loyalty cards, tracking all spend and results, and then offer them the "right" rewards to keep them coming back. Most addicted gamblers are "high value customers". The bigger the looser, the more the reward. Rewards include drinks and meals, hotel suites, trips to casinos (if you don't live there), to gifts like handbags and diamonds.

Analytics and Operational Research is supposed to be the Science of Better.

I'd like to call on all professionals in the analytics field to reflect on the moral goodness, or lack of, in your work.

There is still hope though. If casinos can use analytics to identify problem gamblers, then others can too. Given pathological gambling is a mental health issue, is it time for NGOs or governments to catch up with technology and get their hands on those loyalty card data?

Monday, February 13, 2012

Numbers in 2011 - from More or Less podcast

One of my favourite podcasts is BBC's More or Less. At the start of 2012, they did a series on Numbers in 2011. I know it's a little late in sharing this, but here we go - enjoy.

I'm sharing with you a selection of the numbers from the 30min podcast. They are somewhat UK centric, but still worthwhile sharing.

Listen to the whole podcast here.

  1. 80%: developed world's debt to GDP ratio
  2. 1.37: cost of petro in GBP on 9 May 2011 (highest in 2011), due to duty, value added tax (20%) & exchange rate (weaker GBP against USD)
  3. 1%: BBALIBOR (interest to be paid in 3 months time) 10 Nov 2011 crossed 1%, doubling of the bank interest rate. BBALIBOR indicates the risk of money not being paid back in 3 months - a show of lower confidence/trust between banks.
  4. 2.64m: unemployment in UK by December 2011 (highest in 17 years). Note UK population is just over 62m.
  5. 900k: people today working beyond 65 years old in the UK
  6. 12,500: people celebrated their 100's birthday in 2011 in the UK; and will rise to 100,000 over the next 25 years
  7. 7bn: world population
  8. 2.5: average fertility of women on earth (babies per lifetime of earth, falling from 6 from 60 years ago), easing on the environment I suppose
  9. 3,000gbp: cost of sequencing the human genome; in 2003, the first sequencing of human genome cost 600m GBP - that's a 200,000 fold reduction in cost in 8 years
  10. 2 weeks: to sequence 5 human genomes in 2010; in 2003, it took 10 years for one

Friday, December 30, 2011

Operational Research Consulting & Data Journalism

As data becomes more and more accessible, together with visualisation tools becoming more available and user friendly, Data Journalism is heating up. I've been following the Guardian's Data Blog enthusiastically, it is full of interesting information relevant to current affairs, explained with much facts and data.

This article talks about the 10 point guide to data journalism. I particularly like point 5:
Data journalism is 80% perspiration, 10% great idea, 10% output
The Prezi under point 5 explains the process of how data is used to support news, the angles to consider when mashing datasets together, the technical challenges of working with data, iterative calculation and QA process, which finally get turned into the beautiful output with the various (mostly free) visualisation tools.

This is practically the same process that an Operational Research consulting project takes - or any application of OR or Science in general:
  • Understand what the problem/question is
  • Create a hypothesis to be proven or disproved
  • Define what data is needed for the quest
  • Get the data
  • Clean it, and manipulate/wrangle with it so it's usable for analysis
  • Analyse/calculate to come to some conclusion - hence proving or disproving the hypothesis
  • Compare it to subject matter experts' view on what the likely answer should be (sanity check)
  • Refine the analysis until satisfied
  • Shape the output message so it can be easily understood by the audience
  • Communicate the findings
  • All throughout the process, keep communicating to the audience to make sure they are engaged and understand (principle-wise) what you're trying to do, so that they are not unpleasantly surprised when the final answer is presented
  • Best yet, to ensure smooth change management if your solution is to be implemented, work closely with the end users from the start of designing the solution, and then implement and test, so that they believe in the solution because they were part of the creation process.
As the Flowing Data blog points out, this is what statisticians do. I will add that this is what Science does in general. I will also say that in practice, the first step, "understanding what the problem/question is", often takes 70-80% of the time. The technical 'doing' to follow, in practice, is relatively easy compared to what our academic institutions thoroughly prepare us for (which is needed).

For those interested in the how of data journalism, read this about the work that went into reporting on the 2011 London Riots. Fascinating social media analytics at work. Not easy. Impressive and very interdisciplinary.

P.S. Most of this post has been sitting as draft since the summer, hence referencing 'old' news. It's still relevant, so why not.

Sunday, August 14, 2011

Operational Research considered 1 of 6 dsciplines in Social Sciences

Okay, so OR is grouped with Statistics as one of the six disciplines of social sciences, but still, I'm pleasantly surprised that OR is mentioned!

According to QS World University Rankings, the six disciplines considered as part of social sciences are:
  • Finance
  • Economics and Econometrics
  • Law
  • Politics and International Relations
  • Sociology
  • Statistics and Operational Research
Here you can download the full table (yeah, Google Doc!), and see the top 10 universities at a glance for each of the above subjects. For Stats and OR, here are your top 10:

Rank Institution Country
1 Stanford University United States
2 Harvard University United States
3 University of California, Berkeley (UCB) United States
4 University of Cambridge United Kingdom
5 Massachusetts Institute of Technology (MIT) United States
6 University of Oxford United Kingdom
7 National University of Singapore (NUS) Singapore
8 University of Toronto Canada
9 Imperial College London United Kingdom
10 Princeton University United States
P.S. If you haven't discovered it already, the Guardian's Data Blog is great!

Sunday, March 28, 2010

The 5 acts of the financial crisis - review of The Power of Yes

Ever wanted answers to some of the many questions in your head on the current financial crisis? Want to know how the story started? David Hare's play, The Power of Yes, at the National Theatre in London kept me on the edge of my seat feverishly taking notes in the dark, and hanging onto every word said in the 1hr45min stage play. If you have the chance, see it. For someone like me, who's not had much to do with finance but would like to understand, this is investment 101, with interesting, non-monotone lecturers. (Actually, I did take Investment 101 in an MBA module during my master of management in operations research program at the Sauder School of Business in Vancouver, Canada, and the prof was quite fun.)

The story informatively reveals to the audience the complexity of the crisis' origin, however, mainly pointing fingers at the bankers, the governments and the mathematical models which claim to predict the future. Altogether they upset the balance of greed and fear, which the financial market and capitalism survive on. The story tells of the current (2007-present) financial crisis in 5 acts: SLUMP.

  1. Sub-prime
  2. Liquidation
  3. Unravelling
  4. Meltdown
  5. Pumping

1. Sub-prime loans (this is the longest part as much history is involved)
Hare starts the storytelling with a mathematical formula (which perked me up right away) - the Black-Scholes formula for option pricing. Wikipedia says, "Trillions of dollars of options trades are executed each year using this model and derivations thereof". That's why Hare went straight to it, and throughout the play referred to the model and its derivations as to "claim to predict the future". Also mentioned was the Monte-Carlo model of the probability of defaulting.

Perhaps I'm biased, as this is operations research in finance, but I would disagree with Hare's statement about the models claiming to predict the future. All models are an approximation to the real world, but aren't the real world, so they always have inherent flaws and limitations. Understanding the limitations is the key to applying the results from the models in the real world, otherwise it is foolish and risky. If you read through the Wikipedia article on the Black-Scholes formula, you will see that it also tries to make this point across to the readers. Assumptions such as a 'rational market and behaviour' and 'normality' goes out of the window when in a financial crisis like the stock market crash, and the model becomes defunct.

Having set the theoretical stage and outlined one 'villain', Hare goes on to illustrating the roles of the second 'villain' - the governments, in particular, the British and the US governments. In 1997, the British government made the Bank of England an independent entity, and gave the regulatory and monetary policy setting responsibility of the financial system to a new body, FSA (Financial Services Authority), so that the banks could concentrate on running the bank business and managing its products. However, Hare argues that this division of responsibility meant no one was responsible for the overall financial system. The FSA was more of a neighbourhood watchdog than a police of the system. Also, the financial sector grew to 9% of UK's economy paying the government 27% of the taxes it collected. It was a big cash cow, and no government wanted to limit its growth. In fact, the Bush administration wanted every American to own his/her home, which only encourages borrowing.

Then the third 'villain' is revealed, the banker. The banker is greedy, and is driven to be so by targets and "regular incremental growth". The banker encouraged the people to buy homes when they couldn't afford it, and pressured the credit rating agencies to give good credits so the people can get loans. The division of responsibility meant no one was ensuring the credit ratings were reliable when the banks pushed to make more money by lending it out to every living and breathing person, but who can't actually afford it. Sub-prime loans.

2. Liquidation
("The conversion of assets into cash. Just as a company may liquidate an entire subsidiary by selling it to another firm, so too may an investor liquidate by selling a particular type of security.")

Why were the bankers pushing for more loans? Because homes = assets, and assets = more leverage to lend out for the banks. In fact, The Royal Bank of Scotland (RBS) was lending out at 30-to-1 leverage ratio (i.e. you lend out £30 based on £1 of asset).

The game of slicing and dicing of assets into packages and then trading it with other financial institutions (i.e. selling / liquidating debts) meant that soon enough no one knew what was in those packages, but some of them were sub-prime loans, which was toxic debt. The concept of toxic debt is well explained here: "The easiest way to describe toxic debt is to see it as two separate issues. One, large amounts of loans were improperly given higher credit ratings (implying lower risk of default). The second is that the value of the homes securing these loans has dropped".

3. Unravelling
Credit = Trust. Toxic loans ==> bad credit ==> no trust.

On August 9, 2008, banks lost trust, and stopped lending money. One quote from the play says, "Banks don't go bankrupt for any other reason... but that they ran out of money". This brought the financial system to a halt. Capitalism was having a cardiac arrest. Let's just say the media didn't help and drove fear steady into the mass.

4. Meltdown
Subsequently, the cardiac arrest brought down Lehman Brothers in the US first, and in the UK, Northern Rock went down as the nation's first casualty. The fall of Lehman Brothers triggered a world-wide panic and collapse of 'trust' in the financial system. People in the UK were queueing for their money from the banks. The Brits love to queue for things: a quote from the play, "When the Brits see a queue, they join it". In the US, the big financial institutions went one after the other into troubles.

5. Pumping
The US government had to bail them out by spending hundreds of billions of dollars. And if they didn't do so, the other sectors would be dragged down by the fall of the financial sector as well. Then other governments followed suit as this is a global financial crisis, and now governments are wasting and pumping money into the economy to try to rescue it.

This wraps up the 1hr45min play with no intermission. I think the title, the power of yes, is referring to the 3 'villains' of the story saying yes to lending recklessly, and therefore creating debt-laden societies. What's your interpretation? I hope I've done the play justice. I thoroughly enjoyed it, and learned lots from it that is helping me shape my understanding of the financial crisis. I wonder why my alma mater didn't include any financial applications of operations research in the programme. Is it because it is so easily misunderstood by newcomers? Then wouldn't that be a reason for teaching it more broadly?

Saturday, February 27, 2010

Surveys, statistics and statistically significant economic tremors

Once in a while, an article would pop up in the news and make me go, "oh great, here comes another guy who is talking about statistics, but knows nothing about it". This article on the BBC made me feel just like that, but luckily only in the first half: How one woman can cause economic boom or bust. However, having finished reading it, I came to appreciate his point. He is portraying how the world, especially when in crisis like these days, is reacting to 0.1% changes in unemployment rate or deviation from economic forecasts, without fully understanding the data source the conclusions are drawn from, or the statistical significance level it can be trusted to.

The author goes quite the distance to move his reader's emotions, and raise my suspicion:

She (the lady in the fictitious story who just lost her job and by chance was surveyed by the Labour Force Survey) is just one of those surveyed. But Eve, unknowingly, is about to move mountains. She will make economies tremble with a 30-minute interview and a cross in a box on a laptop questionnaire.

Vast sums of money will lurch round the world's financial system. Politicians will reel and businesses be broken.

But then he comes back across the line and is in my good books again:
Check the ONS (Office for National Statistics - UK) and it states clearly that the figure is accurate only to 0.2 per cent, most of the time. This means that a rise of 0.1 per cent in the unemployment rate could be consistent with an actual fall in unemployment across the whole economy of 0.1 per cent.

I like his final point the best, suggesting how people should treat survey results - more like clues, not knee jerk reactions to trigger panics:

... feverish times make attention twitchy. Every piece of evidence about the state of the economy is interpreted, explanations offered, forecasts recalculated, and much is made out of little, perhaps too much.

The difference between a rise and a fall is judged with solemn faces when the truth is the change we observe may not even be there. Economic data is never a set of facts; it is a set of clues, some of which are the red herrings of unavoidable measurement error.

Thursday, September 4, 2008

The Numerati: casting OR folks in an evil light?

I think it is great that operations research is getting some publicity with The Numerati. However, there can be such a thing as a bad publicity. Is it just me or does it seem to everybody (OR folks) that this book is casting us in a rather negative light? I think the general notion is already that the numbers guys are not to be trusted (at least in certain health care places). Now this book may be saying how smart we are and all that, but with a bit of an evil undertone. Just the title itself, "how they will get my number and yours", is painting us as some kind of math hackers out to steal people's information, isn't it?

I have mixed feelings about this book, but I am curious to read it. I just hope we won't scare anybody more than now when us OR people walk down a hospital isle.

Feel free to voice you thoughts on The Numerati.

Friday, February 15, 2008

OR in the News: Golfer performance predictor

Professor Martin Puterman's research is featured in Globe and Mail on Feb 7, 2008;

What's the most valid predictor of a golfer's performance?

Professor Martin Puterman and his research assistant, Stefan Wittman, at the University of British Columbia's Sauder School of Business recently completed a study that helps answer these questions. Meanwhile, U.S. Ryder Cup captain Paul Azinger made some decisions that bear on this discussion.