OR models for discouraging criminal intent

Dr. Subramaniam Swamy, the brilliant lawyer who exposed the 2G scam in India is trying to raise awareness about this issue amongst the Indian public via a series of talks around the country. In one of his talks, he white-boards a simple but useful high-level model (shown below) to determine the level of financial penalty that should be imposed to deter future scams. This is especially important in the case of crooked politicians who tend to be thick-skinned and are willing to ride out their years in jail in order to enjoy their ill-gotten stash after 'serving out their term'. Conditioning on the chances of getting caught (p),

E[reward] = (1-p)R - p.D

where R = reward from the scam, and D = penalty to be paid if you are caught (primary decision variable). Swamy's point was that, in the Indian context, even though 'p' is relatively small in value for various reasons, all is not lost if 'D' can be made sufficiently large (proportional to 'R' times the odds of evasion) to ensure that the LHS value is unattractive enough.

Let's apply this model to the roads of India, where traffic violations are the norm. A given O-D (origin-destination) path is formed by several links, where link (i) is associated with a probability p(i), reward R(i), and penalty D(i). Crooks try to find the path of least resistance, e.g, smallest cumulative 'p' or max cumulative E[reward]. The police can counter this by a solving p-median type problem (this is a different 'p') where they can optimally position their scarce resource (traffic cops) to maximize an aggregate measure of expected penalty based on link traffic volumes. Given budget restrictions, these scarce resources have become even more scarce. However, it appears that in many other parts of the world, police departments have been smart enough to create a multiplier effect via an 'illusion of ubiquity' achieved via a 'ghost archetype' that periodically and randomly enforces the law in prominent locations in a particularly visible and pitiless manner. Recognizing the 'human element', in this case, the nonlinear 'customer response' to certain deployment scenarios can lead to a solution that increases the perceived probability p(i) for certain key links in the transportation network (beyond its actual statistical rate) without increasing actual capacity. Similarly, the presence of crooked 'bribe friendly' cops at busy intersections can lead to a nonlinear drop in public trust and perceived p(i), regardless of an increase in overall capacity.

To summarize, OR based decision support models can be effective in making a dent in corruption via analytically driven decisions that help maximize the perceived penalty for criminal/corrupt acts that in turn, leads to an actual incremental reduction in the relevant crime/scam rate, despite a scarcity of resources.

O.R. Practice: Doomed by Success?

Did you hear about the dissident O.R practitioner who was sentenced to 'a death by a 1000 cuts' ? Legend has it that his body was found remarkably intact, integral ....

The claim here is that an commercial O.R solution to a real-life problem has a finite shelf-life. The graph of potential improvement for a product is concave, and follows the law of diminishing returns. Most of our recent posts have focused on the need to ensure that the first solution has the 'O.R. inside' stamp, since entrenched heuristics of unknown quality are surprisingly resistant to replacement by more smart-logic based O.R methods.

But what happens after you have something with O. R inside? As a former colleague's professor asked him 'So do you sit around waiting for the model to break?' The answer is sometimes yes. Other times, our twitching O.R genes compel us to keep improving upon the solution and after a while, the effort is not worth the improvement. The better the prior effort was, the less likely that you will be allowed to tinker with it any further. Pretty soon it's set in stone and it just becomes an automaton. After a while, it may even cease to be of much competitive value to a company and the functions are likely to be outsourced to a cookie-cutter vendor.

A car designer can spend an entire career endlessly tweaking cars, but an O.R practitioner has to diversify and cannot expect to retire with the same company by endlessly tweaking a product that she or he created and cherished much. O.R. is such a nebulous and ill-defined field in practice that your next manager or director may not have clue as to what the heck your field is, let alone what it is that you have been doing so far. Without strong backing from the highest levels within the management ("Edelman VPs"), the best O.R. efforts can come to nought or go straight to conference and we, the practitioners, have to move on to a different job.

Anyway, this is just one person's take. It would be instructive to hear the experiences of other practitioners.