Advisors who have undertaken any number of designation or certification exams can attest that a mainstay of multiple-choice test instruction is the admonition to select the “single best answer.”
Generally, on those examinations there’s only one “correct” answer, but there are times when the questions posed are sufficiently imprecise, or the available responses so general, that more than one response is viable. Of course, that’s where the test architects generally fall back on their notion of the “best” answer — because even if a credible argument can be made for an alternative response, it’s a lot easier to grade when there’s just one predetermined result.
When it comes to projecting possible outcomes in situations where there might be hundreds or even thousands of different results, it’s not uncommon to pick a single point to focus on. For example, projections in financial analysis might use the most likely rate of claim, the most likely investment return, or the most likely rate of inflation, while projections in engineering analysis might use both the most likely rate and the most critical rate. That choice provides a point estimate — one that ostensibly provides a best single estimate for purposes of analysis.
The downside of this approach, of course, is that it overlooks what advisors all know — that there is a wide range of possible outcomes, some of which are more probable, and some less so. The alternative, stochastic modeling, doesn’t just pick a single likely result, but uses random variations to look at what a broad range of conditions might be like. It does this based on a set of random outcomes, projects results, and then repeats with a new set of random variables in a process that is repeated thousands of times. In fact, this approach of modeling many thousands of possible outcomes is one that is routinely employed by advisors in considering investment returns along the “efficient frontier.”
Applying this stochastic approach to retirement planning allows you to consider a distribution of outcomes — and with that, to not only consider the most likely estimate, but what ranges are reasonable as well. It is, quite simply, a more complete and realistic assessment of potential outcomes, because, unlike so-called “deterministic” models that rely on picking a single point of experience, it includes a wide range of possibilities.
Deterministic models can predict outcomes under a few economic and demographic scenarios, but generally they don’t present a distribution of the wide range of scenarios that could arise from different combinations of economic and demographic variable values. Only stochastic models — like that embodied in the EBRI Retirement Security Projection Model® [1. The EBRI Retirement Security Projection Model® (RSPM) simulates 1,000 alternative retirement paths for each household to explicitly model investment, longevity and stochastic health care risks (i.e., nursing home and home health care costs).] — can measure the “risk” of their performance measure values. This is because stochastic models, using Monte Carlo methods, are based on probability distributions. [2. More information on stochastic versus static/deterministic modeling can be found here.] To put it another way, stochastic modeling takes into account the volatility and variability of experience that are part of living in the real world.
Those deterministic models may offer a “single” answer, but life is rarely that simple — and projections that attempt to help us make better decisions about the future needn’t be.
