New-Hire Prediction Markets

In my last post, I suggested that the most promising place to test and develop prediction markets is this: get ordinary firms to pay for mechanisms that induce their associates to advise their key decisions. I argued that what we need most is a regime of flexible trial and error, searching in the space of topics, participants, incentives, etc. for approaches that can add value here while avoiding the political disruptions that have plagued previous trials.

If you had a firm willing to participate in such a process, you’d want to be opportunistic about the topics of your initial trials. You’d ask them what are their most important decisions, and then seek topics that could inform some of those decisions cheaply, quickly, and repeatedly, to allow rapid learning from experimentation. But what if you don’t have such a firm on the hook, and instead seek a development plan to attract many firms?

In this case, instead of planning to curate a set of topics specific to your available firm, you might want to find and focus on a general class of topics likely to be especially valuable and feasible in roughly the same way at a wide range of firms. When focused on such a class, trials at any one firm should be more informative about the potential for trials at other firms.

One plausible candidate is: deadlines. A great many firms have projects with deadlines, and are uncertain on if they will meet those deadlines. They should want to know not only the chance of making the deadline, but how that chance might change if they changed the project’s resources, requirements, or management. If one drills down to smaller sub-projects, whose deadlines tend to be sooner, this can allow for many trials within short time periods. Alas, this topic is also especially disruptive, as markets here tend to block project managers’ favorite excuses for deadline failure.

Here’s my best-guess topic area: new hires. Most small firms, and small parts of big firms, hire a few new people every year, where they pay special attention to comparing each candidate to small pool of “final round” candidates. And these choices are very important; they add up to a big fraction of total firm decision value. Furthermore, most firms also have a standard practice of periodically issuing employee evaluations that are comparable across employees. Thus one could create prediction markets estimating the N-year-later (N=2?) employee evaluation of each final candidate, conditional on their being hired, as advice about whom to hire.
Yes, having to wait two years to settle bets is a big disadvantage, slowing the rate at which trial and error can improve practice. Yes, at many firms employee evaluations are a joke, unable to bear any substantial load of criticism or attention. Yes, you might worry about work colleauges trying to sabotage the careers of new hires that they bet against. And yes, new hire candidates would have to agree to have their application evaluated by everyone in the potential pool of market participants, at least if they reach the final round.

Even so, the value here seems so large as to make it well worth trying to overcome these obstacles. Few firms can be that happy with their new hire choices, reasonably fearing they are missing out on better options. And once you had a system working for final round hire choices, it could plausibly be extended to earlier hiring decision rounds.

Yes, this is related to my proposal to use prediction markets to fire CEOs. But that’s about firing, and this is about hiring. And while each CEO choice is very valuable, there is far more total value encompassed in all the lower personnel choices.

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