Long holding the conviction that genies were creatures of myth and fantasy, I was caught by surprise the other day while working in my study to notice a bluish haze rising from an unseen source in the corner and a figure in a turban materializing within it. Before I had a chance to gather my wits and make an attempt to recover my composure, the figure stepped forward, gave a sweeping bow, and introduced himself as the Analytics Genie. Taking no note of my incredulous state, he announced that the purpose of his visit was to offer me the chance to make three wishes.
Apparently he had access to sources of Secret Genie Information (e.g., the internet) because he assured me he knew of my special interest in practical applications in analytics, and stipulated my wishes should be about features I wanted to have incorporated in methods and models for solving real world analytics problems.
But there were to be a few additional conditions in the bargain. The Analytics Genie made no effort to hide that he was a creature of fantasy, and indeed seemed rather proud of the fact, but he was also evidently accustomed to encountering humans who would try to gain unwarranted concessions from those of his kind. As a result of this, he told me, he was obliged to additionally stipulate that the wishes must conform to the following provisions endorsed by the Fantasy Association for Thwarting Chicanery and Cunning Evasion (FAT CHANCE):
- No iterative or recursive expressions (no wishes asking for more wishes)
- No embedded lists (no wishes with compound conditions)
- No violations of the laws of nature (no wishes for salt shakers or zippers that work properly)
- No sidestepping of personal responsibility (no wishes my first grade teacher or maternal grandmother wouldn’t approve of)
I was pleased to see that this list wasn’t riddled with auxiliary clauses, as I suspected a similar document prepared by the legal staff of a human organization might be. At first glance, wishes that satisfied these provisions seemed to be easy to come by. Immediate examples that sprung to my mind were to select features that would:
Make lots of money
Bring about greater economic and social welfare
Save the environment
Eradicate armed conflict
Eliminate poverty and disease
Protect the world from the political, economic and social policies advocated by my sister-in-law.
The Analytics Genie pointed out, however, that these were consequences of features I might hope to specify, as opposed to the features that create such consequences. I was urged to reflect on the situation a little more thoroughly.
All right, then, what could I formulate that would make good sense? Within the context of practical problem-solving the Analytics Genie had specified, I most certainly wanted to identify features that would permit such problems to be treated intelligently and competently. To have a chance of pulling this off, I required answers to a couple of questions. First, what essential component of real world problems creates the need for handling them better than current approaches are doing? Second, what are the characteristics of such problems that must be exploited if we are to do a more creditable job of solving them?
I might be able to respond to these questions by basing my wishes on “the standard line” everyone knows, that we should have models and methods that are easy to use and that exhibit supporting features such as visualization aids and interactive decision making. But a little reflection made it apparent that these features, desirable as they might be, were in reality secondary. Features that ranked highest in importance were those that made it possible to handle the “terrible triple” of uncertainty, risk and complexity. Without such an ability, there isn’t much hope that our models and methods will have significant value in the real world.
The challenges that arise in business, government and science notoriously create a need for better approaches for dealing with uncertainty, risk and complexity. Applications ranging from health care to energy to bioinformatics all exhibit one or more of these underlying factors.
So how to translate this recognition into wishes about features to embody in analytics models and methods? To answer this compelled me to take a step back and consider the nature of these models and methods that would accomplish the goals I was seeking.
First, to have a model that embraces the diverse forms of uncertainty encountered in the real world (and not just the forms treated by classical mathematical models, which are notoriously handicapped by an array of limiting assumptions) it is essential to take advantage of the modeling flexibility provided by simulation. Through its capacity to capture complex interactions and to evaluate the effects of multiple scenarios on system performance, simulation offers an essential tool for decision-makers who want to build models for practical problem solving. Second, to be able to produce high quality solutions from the model – going beyond the all-too-common practice of “playing with” the model by trial and error guesses about values for decision variables – it is necessary to bring optimization into the picture. To be able to work with a simulation model, however, requires a highly protean type of approach, which can deal with contexts where the formulation consists of more than a set of equations and inequalities that can be written down on a piece of paper. An approach is needed that is robust over the myriads of changing scenarios generated by a simulation, and beyond this, capable of handling stochastic components such as expected values, variance and percentiles.
Evidently, the answer involves a special type of marriage between simulation and optimization, the sort of marriage practitioners in the simulation industry have called simulation optimization. This term is a bit misleading, because it doesn’t refer to optimizing the structure of a simulation process, but to using simulation as a modeling tool which is integrated with an optimization approach having the ability to find high quality solutions within the complex space generated by the simulation.
Those who have been observers of the area know well that a virtual upheaval has been taking place in recent years, as researchers and practitioners have united in an effort to overcome the limitations of approaches available in the early beginnings of the simulation optimization field. Although remarkable progress has been made, observers are also aware that large differences still remain in the scope and efficacy of the approaches developed. The existence of such differences between different simulation optimization systems led me to conclude there was no sense in squandering a wish on something that fell short of being the best of its kind. With this in mind, I faced the genie and expressed my first wish.
“I wish the first feature to have in my analytics system to be a simulation optimization capability with the best possible design.”
The Analytics Genie nodded, thoughtfully rubbing the well-trimmed tuft of a beard on his chin. “Acknowledged. But I am going to do you the favor of letting you formulate all of your wishes before I grant them, should you decide to change any of them once you’ve chosen them all.” I was surprised by this gesture, which the stories I’d read suggested was very uncharacteristic for a genie, but I accepted his offer. “In that case, he concluded, I have other business to attend to – my services as an Analytics Genie are in high demand these days, as I’m sure you can appreciate – but after settling these matters I will return to learn your other two wishes.”
Waving his arm ceremoniously, the Analytics Genie gave a sweeping bow as he had on first presenting himself, and slipped back into the encircling mist to vanish from sight.
Although a little unsettled by this behavior, which I suspected marked my visitor as rather unconventional even among the unpredictable beings that populated the genie kingdom, I was happy to be given the extra time to contemplate the nature of my other two wishes. What other features did I want in my optimization models and methods, from the perspective of solving practical analytics problems? I resolved to reflect on this question and be prepared for the genie’s return, whenever that might be.
(Readers are invited to offer their suggestions and advice about good choices for these remaining wishes to convey to the Analytics Genie!)
