For fun, I decided to take PolicyMaker for a test drive. I asked the program to tell me how to persuade Harvard University to hand over a small chunk of its mammoth $ 14 billion endowment to the poor communities in nearby Roxbury and South Boston. Miraculously, after I plugged in all the “players” I could think of and their corresponding “strategies,” “opportunities,” “interests,” “coalitions,” and “networks,” I discovered that the “current feasibility” of my proposed policy was virtually guaranteed. The plan scored an impressive 130 points out of a possible 150, with the remaining 20 points scattered among “non-mobilized” and “opposed” alliances. A majority of the “sub-units” and “social groups” at Harvard, including the alumni association and the myriad faculty associations and student groups, would be positively disposed to the scheme, the program told me. Sure, there were a few holdouts at the business school, but their “power scores” were marginal. And once I plugged in the different groups’ “humanitarian” and “ideological” interests, the plan’s prospects jumped considerably. When it came to stretching out a helping hand to the poor, the “influence relationships” among the different players were decidedly “cooperative” as opposed to “conflictual,” and I soon found myself moving swimmingly toward my goal.
I did, however, encounter a minor detour. After I printed out a series of colorful graphs and pie charts documenting the wisdom of my policy, an annoying sign popped up on my video screen. It read, “Warning: Do not confuse your analysis with reality.” Uncertainties are inevitable in policymaking and politics, it advised. PolicyMaker, I learned, was designed only to help “improve the art of the feasible amidst the inevitable uncertainties. Caveat Computator.” Duly noted.
Neil Seeman
