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Just a thought - Irrational Expectations?

Cloud Nate Silver is a clever man. Using "quants" he correctly predicted the outcome of the US presidential race in every single state bar none. Interest in "business analytics" that uses algorithms that detect trends and key relationships in vast amounts of apparently unstructured data is on the rise. According to a report in Bloomberg, UBS bank which is shedding 10,000 staff, will shortly be replacing the head of its credit-default swaps index trading unit with an algorithm. This could well lead to public demand for political leaders to be similarly replaced. Disgruntled constituents could vote for the algorithm of their choice.

In practice that is what most of us do in fact. Following the victory of President Obama, stock markets plummeted for a day or two as wrong bets on stock options came unstuck. Now financial markets, according to standard economic myth, are near perfect, so why did professional punters get it so wrong? Because they voted for the wrong algorithm. They believed what they wanted to believe and tweaked
the analysis of the data accordingly. Man over-rides machine? Well maybe Man subconsciously (or not) programmes the machine in a way that produces the desired results. Mitt Romney's political supporters read the tea leaves and gave their opinions. They thought Mitt Romney was onto a winner when evidently he was not. Ironic really given that Mr. Romney was a former hedge fund manager.

In 1980 Mrs. Margaret Thatcher, Britain's indefatigable Prime Minister was so convinced that the more pliable Joshua Nkomo, leader of the ZAPU in Zimbabwe, would win the election she was happy to see it through. Practically anyone in Zimbabwe at the time knew that Robert Mugabe of ZANU-PF would win by a landslide. Simple demographics was all that was needed, not an algorithm in sight, but wishful thinking in Britain trumped what would have been an algorithmic truth.

President Obama's team was more astute. Behind locked doors they were focused on a Big Data analytics operation that was running 66,000 simulations a night using four data streams from each state, often with sample sizes of 1% of the population, which is a huge number.1 Every step of the campaign from raising funds through personalized emails and recommendations by social media to getting out the voters in the swing states was carefully calibrated and acted upon. By the time of the election, the Democrats knew they had the election in the bag, as did Nate Silver, even as the fatal binge of betting on stock options by, among others, hedge fund managers, was in full flood.

Of course, in theory Big Data analytics has a very high probability of being correct, but it is monitoring and analyzing events that are beyond its own control. If President Obama had decided to send roses to Mr. Mahmoud Ahmadinejad, President of Iran he might have seen the election slip from his grasp. It seemed almost like that after the first TV debate. So in theory Big Data can do a lot, in practice it can contribute to an outcome but not determine it absolutely. In Asia where the practice of Big Data analytics is just beginning to emerge, appreciating this distinction will be key to its successful application. As Albert Einstein is said to have said: "In theory, theory and practice are the same. In practice, they are not."

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Author:

Peter Lovelock

 

Peter Lovelock: ACCA Board member and Director and Telecoms & IT Advisor and Consultant; TRPC

John Ure: TRPC

 

 


About the Asia Cloud Computing Association.

The Association, launched in November 2010, is a forum for hardware and software developers, carriers, enterprise users, policy makers, and researchers. We drive the adoption of cloud computing in Asia by addressing regional issues of regulation and policy, security infrastructure and awareness As the only forum focused on cloud computing issues in Asia, The Association is a place for collaboration and innovation for all stakeholders with an interest in Asia's cloud market.