Uber, Ola and others: The secret (chilli) sauce of surge pricing

By Probir Roy Surge pricing, where fares are raised when demand is higher than the number of available cabs, has been a topic of discussion for sometime now. While surge pricing was banned in Delhi, it is now back, with Uber spokesperson claiming it was just a temporary ban. Meanwhile, Karnataka Transport Department has also been planning to tame cab operators’ surge pricing. An efficient ‘surge pricing’ algorithm has the potential  to serve the public - help overcome the anomaly of asymmetry of information, and, do that dynamically  without even with a moment’s delay. So you are less of a hostage to the whims of a regular cab. And you get a cab when you want, at the best value to you. How does it do so? By  continuously re-estimating the demand and supply  of cabs  in  short intervals, and converging  to a  minimum price at which  demand and  supply  match and Voila! you are on your way. Or  as well known Economist Dr Subir Gokarn former Deputy Governor of the RBI  (now on the Board of the IMF,Washington D.C) likes to call it  -  a " dynamic cobweb model " . In the early stages there is  ‘overshoot’, as  seen in the Melbourne café hostage situation last year (4x hike), or the Arianna Grande  Madison Square Concert in March 2015 (4x hike), as also ‘undershoots’, as exhibited during New Years eve in Times Square on 31 st Dec 2014. Very simply  the black-box ensures that  as many people  get  rides ,within the shortest waiting period  (say ideally   3 - 5 minutes). So are these principles compromised? Whilst the surge is quick to move up, it would seem a tad slow to settle down back  - thus making for unfair rent seeking practice. My sense is that the algo is not that dynamic enough to make these instant adjustments and give the most efficient and equitable price for the  maximum duration of time. For instance, during one long weekend I noticed the surge factor during peak period by default!  It would seem that some form of rent seeking is predetermined, maybe based on post processed data sets on which the algorithm may be feeding off, rather than contemporaneous demand-supply considerations. Or perhaps the set of GPS coordinates marking the grids in a city and vertices within a grid, is not  adequately mapped or defined for the NCR or Greater Metropolitan Area of Mumbai to ensure computational efficiency. Just to illustrate, in New York City there are 167 neighbourhoods, and city is divided into 100 fence polygons (grids). And  in some neighbourhoods 100 vertices in a fence! That seems lead to quick (100 milliseconds response), and  optimal pricing ( surge of upto 5 minutes ). There are some oddities. For example, the ‘wait time’ displayed in the app seems to be way off, sometimes by a factor of two or even three during surge periods.  Or whilst one app indicates surge, the other competing service has no sign of it in the same area! Or the inverse - one indicates  surge, and there are no cabs available on the other! To be fair an off surge period (90% of the time) does also give you a cheaper fare than your kaali peeli too. Or that a surge period (10 % of time)  is self calibrating, as fares top off to match the next best alternative - radio cabs, and substitution effect kicks in. Or demand just drops off a cliff because of no takers. (Tip: if one hangs about a little bit. Or if you walk around to the boundaries of the surge area you  get  normal  base fare or one with lower surge price) Then there are behavioural oddities. As everyone knows most of the driver-partners and customers carry more than one competing app. Drivers also serve an   offline loyal customer base on fixed rates too! And they can always check out the surge area and price multiple by opening a customer app on another phone to see when they should come online,with which cab aggregator and where! On the other side customers too have many choices. They have close substitutes (auto, kaali peeli, radio Cabs, A. C Cabs, share cabs, share autos, share minis, shuttles,etc). So are we being held hostages to an algorithm or just bad behaviour ? If  there a variance between price and value. Then one could say there is market failure and there is a case for intervention. And  argue for some sort of cap on account of inefficiency and collusion by cab cos and cab drivers (Remember the Govt contemplated price caps last year on airlines on some routes during the festive season, and is contemplating regional flights to have a maximum price cap as I write this). But the verdict is still out. It certainly getting a lot of press. A  good algorithm is a learning one - which means a high level of sophistication in design, tuning of the code and servers, CPU speed, good math, use of big data, etc is required. So either there is scope for improvement on how things work. Or the outreach efforts of the key stakeholders  is behind the curve. To conclude one is not for caps, and for a full play for market determined surge. But in India it seems to trigger some unfair practices on account of just about any whack- a – mole rent seeking opportunity. The author is a serial entrepreneur and  commentator on matters of public policy and FINTECH