COMMENTARY
those who don’t question why someone from Nigeria needs their help. Tis enables them to concentrate their limited time and energy on the most trusting, highest-payoff population.
Just like Nigerian scammers, those facing a large, complex problem can’t afford to focus their limited time and resources on noise or fruitless pursuits. Tey must learn how to carefully discriminate and sift through the mountains of potential data to find the information that matters most. So, before you hire that whiz kid with the machine-learning algorithms, get your information collection process straight.
KISS ALL YOU CAN Occam’s razor is a philosophical principle from the 14th century that is just as true today as ever. In short, it states that when there are two or more explanations with equal explan- atory power, the simpler one is preferred. Alternately, it can be expressed this way: Te more assumptions an explanation requires, the more likely it is to be false. It’s really just a sophis- ticated version of the popular idiom “keep it simple, stupid,” or KISS.
Tis is not to say everything is simple. Rather, just as it was seven centuries ago, the right amount of simplification is crit- ical to our ability to construct accurate models of reality and solve meaningful problems. Again, this is shown by Leinwe- ber’s eT line in Figure 1.
Tere are many examples of simple models outperforming complex ones in this digital age. Take two provided by Nobel laureate Daniel Kahneman, who points out that predicting marriage stability does not require complicated measures of people’s psychology, finances, religion or myriad other considerations. Rather, a simple formula actually can work much better.
It turns out that if we simply sum the frequency of lovemak- ing and subtract the frequency of quarrels between a husband and wife, we have an excellent predictor of the long-term pros- pects of their relationship. If this number is positive, they are in good shape, while a negative number spells trouble. Kahn- eman also offers the example of a model for predicting the value of highly collectible, expensive Bordeaux wines. Here, a very simple model with just three variables (summer temper- ature, previous winter rainfall, and rainfall during harvest) predicts a wine’s value with 90 percent accuracy across a hori- zon of multiple decades.
Don’t be mistaken; simple models that work are not generally simple to develop. Tey require thorough understanding of the often complex phenomena being represented. In other words, someone has to do the hard work of figuring out what matters most among everything under consideration. Ten, they have to figure out how to measure correctly. Until these occur, no model, whether simple or complex, is likely to help. Here, every leader should take note. In most cases, being unable to assemble a straightforward model of your problem is a strong indicator that you don’t fully understand what exactly you are trying to solve.
CONCLUSION Remember, too much information is as bad as too little. Big data analytics can open the aperture so we see more than ever before. Tey can challenge our paradigms and reveal things previously hidden from us. But this depends on the accuracy and precision of the information we feed our algorithms. If done well, combining great computer power with vast data provides great opportunities. If done poorly, it can lead to enormous confusion and spectacular mistakes.
So, when the data miners come knocking, remember you need to already have intimate understanding of the problem you are trying to solve and you must have already recorded reli- able information. Only then should you release them to begin work. Also remember that the powers of technology are not magical solutions to solve every ill. Tey are just one of many tools available to address complex problems. So stay humble, stay in charge, and don’t be easily dazzled.
DANIEL E. STIMPSON, Ph.D., is an operations research systems analyst in the U.S. Army Director of Acquisition Career Management (DACM) Office and an associate professor of oper- ations research at George Mason University. He holds a master’s degree and a Ph.D. in operations research from the Naval Post- graduate School and George Mason University, respectively. Before joining the Army DACM office, he retired from the Marine Corps after 24 years of enlisted and officer service. He has also been an operations research systems analyst with the Center for Naval Analyses, George Mason University research faculty, the Joint Improvised Explosive Device Defeat Organization and Head- quarters Marine Corps.
https://asc.ar my.mil
127
Page 1 |
Page 2 |
Page 3 |
Page 4 |
Page 5 |
Page 6 |
Page 7 |
Page 8 |
Page 9 |
Page 10 |
Page 11 |
Page 12 |
Page 13 |
Page 14 |
Page 15 |
Page 16 |
Page 17 |
Page 18 |
Page 19 |
Page 20 |
Page 21 |
Page 22 |
Page 23 |
Page 24 |
Page 25 |
Page 26 |
Page 27 |
Page 28 |
Page 29 |
Page 30 |
Page 31 |
Page 32 |
Page 33 |
Page 34 |
Page 35 |
Page 36 |
Page 37 |
Page 38 |
Page 39 |
Page 40 |
Page 41 |
Page 42 |
Page 43 |
Page 44 |
Page 45 |
Page 46 |
Page 47 |
Page 48 |
Page 49 |
Page 50 |
Page 51 |
Page 52 |
Page 53 |
Page 54 |
Page 55 |
Page 56 |
Page 57 |
Page 58 |
Page 59 |
Page 60 |
Page 61 |
Page 62 |
Page 63 |
Page 64 |
Page 65 |
Page 66 |
Page 67 |
Page 68 |
Page 69 |
Page 70 |
Page 71 |
Page 72 |
Page 73 |
Page 74 |
Page 75 |
Page 76 |
Page 77 |
Page 78 |
Page 79 |
Page 80 |
Page 81 |
Page 82 |
Page 83 |
Page 84 |
Page 85 |
Page 86 |
Page 87 |
Page 88 |
Page 89 |
Page 90 |
Page 91 |
Page 92 |
Page 93 |
Page 94 |
Page 95 |
Page 96 |
Page 97 |
Page 98 |
Page 99 |
Page 100 |
Page 101 |
Page 102 |
Page 103 |
Page 104 |
Page 105 |
Page 106 |
Page 107 |
Page 108 |
Page 109 |
Page 110 |
Page 111 |
Page 112 |
Page 113 |
Page 114 |
Page 115 |
Page 116 |
Page 117 |
Page 118 |
Page 119 |
Page 120 |
Page 121 |
Page 122 |
Page 123 |
Page 124 |
Page 125 |
Page 126 |
Page 127 |
Page 128 |
Page 129 |
Page 130 |
Page 131 |
Page 132 |
Page 133 |
Page 134 |
Page 135 |
Page 136 |
Page 137 |
Page 138 |
Page 139 |
Page 140 |
Page 141 |
Page 142 |
Page 143 |
Page 144 |
Page 145 |
Page 146 |
Page 147 |
Page 148 |
Page 149 |
Page 150 |
Page 151 |
Page 152 |
Page 153 |
Page 154 |
Page 155 |
Page 156
