Forecasting Analytics Quotes

As Paul Saffo, a forecaster of large-scale change at Discern Analytics, observes wisely, 'Change is never linear. Our expectations are linear, but new technologies come in S curves, so we routinely overestimate short-term change and underestimate long-term change.' Never mistake a clear view for a short distance, he adds.

guesstimate = better than a guess but not as guaranteed as an estimate... i.e. It's simply a calculated forecast based on probability, historical trends, observations, analytical research, politics, studies of human nature and good ol' common sense (the latter 2 of which usually cause a toxic sediment when mixed, LOL)...

Complex operations, in which agencies assume complementary roles and operate in close proximity-often with similar missions but conflicting mandates-accentuate these tensions. The tensions are evident in the processes of analyzing complex environments, planning for complex interventions, and implementing complex operations. Many reports and analyses forecast that these complex operations are precisely those that will demand our attention most in the indefinite future. As essayist Barton and O'Connell note, our intelligence and understanding of the root cause of conflict, multiplicity of motivations and grievances, and disposition of actors is often inadequate. Moreover, the problems that complex operations are intended and implemented to address are convoluted, and often inscrutable. They exhibit many if not all the characteristics of "wicked problems," as enumerated by Rittel and Webber in 1973: they defy definitive formulations; any proposed solution or intervention causes the problem to mutate, so there is no second chance at a solution; every situation is unique; each wicked problem can be considered a symptom of another problem. As a result, policy objectives are often compound and ambiguous. The requirements of stability, for example, in Afghanistan today, may conflict with the requirements for democratic governance. Efforts to establish an equitable social contract may well exacerbate inter-communal tensions that can lead to violence. The rule of law, as we understand it, may displace indigenous conflict management and stabilization systems. The law of unintended consequences may indeed be the only law of the land. The complexity of the challenges we face in the current global environment would suggest the obvious benefit of joint analysis - bringing to bear on any given problem the analytic tools of military, diplomatic and development analysts. Instead, efforts to analyze jointly are most often an afterthought, initiated long after a problem has escalated to a level of urgency that negates much of the utility of deliberate planning.

In the EPJ results, there were two statistically distinguishable groups of experts. The first failed to do better than random guessing, and in their longer-range forecasts even managed to lose to the chimp. The second group beat the chimp, though not by a wide margin, and they still had plenty of reason to be humble. Indeed, they only barely beat simple algorithms like “always predict no change” or “predict the recent rate of change.” Still, however modest their foresight was, they had some. So why did one group do better than the other? It wasn’t whether they had PhDs or access to classified information. Nor was it what they thought—whether they were liberals or conservatives, optimists or pessimists. The critical factor was how they thought. One group tended to organize their thinking around Big Ideas, although they didn’t agree on which Big Ideas were true or false. Some were environmental doomsters (“We’re running out of everything”); others were cornucopian boomsters (“We can find cost-effective substitutes for everything”). Some were socialists (who favored state control of the commanding heights of the economy); others were free-market fundamentalists (who wanted to minimize regulation). As ideologically diverse as they were, they were united by the fact that their thinking was so ideological. They sought to squeeze complex problems into the preferred cause-effect templates and treated what did not fit as irrelevant distractions. Allergic to wishy-washy answers, they kept pushing their analyses to the limit (and then some), using terms like “furthermore” and “moreover” while piling up reasons why they were right and others wrong. As a result, they were unusually confident and likelier to declare things “impossible” or “certain.” Committed to their conclusions, they were reluctant to change their minds even when their predictions clearly failed. They would tell us, “Just wait.” The other group consisted of more pragmatic experts who drew on many analytical tools, with the choice of tool hinging on the particular problem they faced. These experts gathered as much information from as many sources as they could. When thinking, they often shifted mental gears, sprinkling their speech with transition markers such as “however,” “but,” “although,” and “on the other hand.” They talked about possibilities and probabilities, not certainties. And while no one likes to say “I was wrong,” these experts more readily admitted it and changed their minds. Decades ago, the philosopher Isaiah Berlin wrote a much-acclaimed but rarely read essay that compared the styles of thinking of great authors through the ages. To organize his observations, he drew on a scrap of 2,500-year-old Greek poetry attributed to the warrior-poet Archilochus: “The fox knows many things but the hedgehog knows one big thing.” No one will ever know whether Archilochus was on the side of the fox or the hedgehog but Berlin favored foxes. I felt no need to take sides. I just liked the metaphor because it captured something deep in my data. I dubbed the Big Idea experts “hedgehogs” and the more eclectic experts “foxes.” Foxes beat hedgehogs. And the foxes didn’t just win by acting like chickens, playing it safe with 60% and 70% forecasts where hedgehogs boldly went with 90% and 100%. Foxes beat hedgehogs on both calibration and resolution. Foxes had real foresight. Hedgehogs didn’t.

the use of statistical process control tools to evaluate variation, correlate root cause, forecast capacity, and anticipate throughput barriers. By measuring incidence of preventable venous

Regression: This is a well-understood technique from the field of statistics. The goal is to find a best fitting curve through the many data points. The best fitting curve is that which minimizes the (error) distance between the actual data points and the values predicted by the curve.  Regression models can be projected into the future for prediction and forecasting purposes.

If you were to zoom way out and look at the six steps of my forecasting method, you would see this duality in play. It’s not a happy accident. Scientific and technological advances depend on both ingenuity and rigorous evaluation. The future of our culture—how we communicate, work, shop, play games, and take care of ourselves—necessarily intersects with the future of science and technology. Daydreaming alone won’t bring new ideas to market; ideas require process engineering and budgeting before they can become tangible. However, too much emphasis on logic and linear thinking will kill moonshots while they’re still on the whiteboard. That is why it’s important to afford equal treatment to each hemisphere, alternating between broad creative thinking and more pragmatic, analytical assessment. When executed completely, the forces are balanced, allowing for innovation while ensuring a check-and-balance system for the future.

If your neocortex is the home of your conscious awareness and it’s where you construct thoughts, use analytical reasoning, exercise intellect, and demonstrate rational processes, then you’ll have to move your consciousness beyond (or out of) your neocortex in order to meditate. Your consciousness would have to essentially move from your thinking brain into your limbic brain and the subconscious regions. In other words, in order for you to dial down your neocortex and all the neural activity that it performs on a daily basis, you’d have to stop thinking analytically and vacate the faculties of reason, logic, intellectualizing, forecasting, predicting, and rationalizing—at least temporarily.

SUMMARY A vast array of additional statistical methods exists. In this concluding chapter, we summarized some of these methods (path analysis, survival analysis, and factor analysis) and briefly mentioned other related techniques. This chapter can help managers and analysts become familiar with these additional techniques and increase their access to research literature in which these techniques are used. Managers and analysts who would like more information about these techniques will likely consult other texts or on-line sources. In many instances, managers will need only simple approaches to calculate the means of their variables, produce a few good graphs that tell the story, make simple forecasts, and test for significant differences among a few groups. Why, then, bother with these more advanced techniques? They are part of the analytical world in which managers operate. Through research and consulting, managers cannot help but come in contact with them. It is hoped that this chapter whets the appetite and provides a useful reference for managers and students alike. KEY TERMS   Endogenous variables Exogenous variables Factor analysis Indirect effects Loading Path analysis Recursive models Survival analysis Notes 1. Two types of feedback loops are illustrated as follows: 2. When feedback loops are present, error terms for the different models will be correlated with exogenous variables, violating an error term assumption for such models. Then, alternative estimation methodologies are necessary, such as two-stage least squares and others discussed later in this chapter. 3. Some models may show double-headed arrows among error terms. These show the correlation between error terms, which is of no importance in estimating the beta coefficients. 4. In SPSS, survival analysis is available through the add-on module in SPSS Advanced Models. 5. The functions used to estimate probabilities are rather complex. They are so-called Weibull distributions, which are defined as h(t) = αλ(λt)a–1, where a and 1 are chosen to best fit the data. 6. Hence, the SSL is greater than the squared loadings reported. For example, because the loadings of variables in groups B and C are not shown for factor 1, the SSL of shown loadings is 3.27 rather than the reported 4.084. If one assumes the other loadings are each .25, then the SSL of the not reported loadings is [12*.252 =] .75, bringing the SSL of factor 1 to [3.27 + .75 =] 4.02, which is very close to the 4.084 value reported in the table. 7. Readers who are interested in multinomial logistic regression can consult on-line sources or the SPSS manual, Regression Models 10.0 or higher. The statistics of discriminant analysis are very dissimilar from those of logistic regression, and readers are advised to consult a separate text on that topic. Discriminant analysis is not often used in public

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