For a little over two years, I have been professionally dealing with programs whose behavior is intentionally random. Why would one even have intentionally random programs?

The first kind of “random” program I encountered was the **randomized algorithm**, but I do not consider these to be intentially random in this sense. Such a program’s purpose is to compute something deterministic, and randomness is a trick employed to be sure the algorithm avoids all possible bad patterns.

One classic example of this is the Fermat primality test. The goal is to determine whether some large \(n\) is prime, which is not a random thing at all. The method is to try various \(a\) to see whether \(a^{n-1} \not \equiv 1 \mod n\)—if at least one such witness is found, \(n\) is certainly composite, and if not, \(n\) is “probably” prime. The randomization helps in the following way: if \(n\) is composite (and not a Carmichael number, which are much less common than primes), then at least half of the possible \(1 < a < n-1\) will be witnesses, but there is no theory indicating which ones they will be. So we cannot prove that any particular search pattern will quickly find a witness, but we can prove that choosing candidates at random will quickly find a witness with high probability.^{1}

The second kind of “random” program I encountered is the creation of a **cryptographic secret** (such as a large semiprime for use in the RSA algorithm). I guess key generation really is “intentionally” random, but it nonetheless has a different flavor from the kinds of intentionally random programs I have been working with. The output is a single sample from the probability distribution on secrets. The name of the game is to prevent adversaries from guessing the output, to which end the distribution is engineered to be as high entropy as possible, with as little other structure as possible.

What I have been working with, however, are programs that are intended to produce interestingly random results—programs whose **probability distribution on results is itself the design objective**, and subject to arbitrary domain-specific desiderata. One nice source of examples is recent work in “procedural modeling”: teaching computers to come up with (random) suggestions for designs, for example for computer generated graphics in movies or games. This business gets tricky when the artistic objective has requirements that are unlikely to be met purely at random: trees that grow around obstacles, networks of pipes that cast a specific shadow, space ships or cities that match a given overall shape.

In probabilistic procedural modeling, the goals of randomization are very different from randomized algorithms or cryptography: the output is several different instances of random objects of the same specification; each is expected to more or less meet given artistic constraints; and they should otherwise be as varied (in a domain-specific sense) as practicable, to provide appropriate options for selection or an appropriate base for inspiration.

Another, more staid, source of examples is the field of Bayesian statistics, such as the recently adopted United Nations methodology for projecting human populations. In this case, the output of the intentionally random component is thousands (or millions, for all I know) of candidate population trajectories. These trajectories are used to form the published predictions, and the published error bars on those predictions.

The underlying population model uses probability theory to quantify at least two different kinds of uncertainty—uncertainty about general properties of population growth, which is mitigated but not eliminated by calibrating the model on historical population data, and uncertainty about how population will actually grow in whatever (uncertain) specific circumstances arise in the future. The design requirement for the computer program that realizes this model is for the probability distribution of the output trajectories to faithfully represent the model’s residual uncertainty about what will actually happen.

Why is the United Nations’ population program random? The end-to-end process, from gathering population data to publishing graphs and data tables with projections, might be called “unavoidably random”. No doubt the UN Population Division would prefer to be able to deterministically compute what numbers to put into the Population Prospects report to faithfully represent the uncertainty quantified by their model. This is even theoretically possible—the 80th percentile value in their model’s probability distribution over world population in 2057 is some mathematically defined real number. The trouble is that this number is defined in terms of horrible multi-dimensional integrals over all possible unknowns in the model. For any but the simplest models, these integrals cannot be determined analytically, and are not tractable to accurately determine by numerical integration. The best the UN can do is draw many random samples from (an approximation to) the model’s probability distribution on trajectories and describe the aggregate, relying on the law of large numbers to reduce the residual randomness in their final report to an acceptable level.

The last two examples motivate what **probabilistic programming** is for. Both procedural graphics and population projections (and hosts of other applications I haven’t mentioned) have components that are random programs manipulating complex objects, whose space of possible behavior is the design criterion of interest. Even when there is a “final” output whose randomness is either undesirable (such as the population report) or irrelevant (such as the details of a good-looking computer-generated snowstorm), the program that produces it contains internal interfaces where correctness is defined by the probability distribution on returned samples. Probabilistic programming languages are for writing such programs (more concisely and with fewer errors than otherwise), and probabilistic software engineering needs to be about inspecting, testing, debugging, optimizing, and maintaining such programs.

## References

Daniel Ritchie, Ben Mildenhall, Noah D. Goodman, and Pat Hanrahan, “Controlling Procedural Modeling Programs with Stochastically-Ordered Sequential Monte Carlo”, SIGGRAPH 2015. Preprint: https://stanford.edu/~dritchie/procmod-smc.pdf

Daniel Ritchie, Sharon Lin, Noah D. Goodman, and Pat Hanrahan, “Generating Design Suggestions under Tight Constraints with Gradient-based Probabilistic Programming”, Eurographics 2015. Preprint: https://stanford.edu/~dritchie/graphics-hmc.pdf

United Nations, Department of Economic and Social Affairs, Population Division (2014). “World Population Prospects: The 2012 Revision, Methodology of the United Nations Population Estimates and Projections”, Working Paper No. ESA/P/WP.235. https://esa.un.org/unpd/wpp/publications/Files/WPP2012_Methodology.pdf

## Notes

In practice, of course, the “random” choices of \(a\) are generally pseudo-random, in the sense of being generated from a small amount of physical entropy amplified by a pseudo-random number generator (which is a deterministic program designed to be difficult to reverse-engineer) into a large enough stream of bits to form the desired \(a\)s. In this case, any particular initial seed for the PRNG does define a specific deterministic search pattern; the argument that this is nonetheless acceptable turns into one about the PRNG not being accidentally ill-aligned with the primality testing problem.↩