Advocacy Driven Decision Making

Within much of engineering, and particularly, operations research, the goal is often the “best” decision that maximizes or minimizes a well-defined criterion or objective function.  One can then, for example, employ mathematical programming to calculate the lowest cost routes for delivery trucks.  Often one can even mathematically prove that these routes are best.

Over the past couple of decades, I have addressed complex problems in healthcare, education, transportation and urban systems.  A few problems were amenable to the above approach, for instance, scheduling hospital operating rooms.  However, most problems suffered from an inability to establish an agreed-upon well-defined criterion or objective function.

This difficulty is particularly evident in situations where multiple advocacy groups are lobbying for very different decisions.  In recent work on assistive technologies for disabled and older adults, we encountered different groups advocating for people with physical disabilities, cognitive disabilities, visual impairments, and hearing impairments.  Each group argued for substantial investments to support the population for which they advocated. There had to be tradeoffs, but it was not clear how to make them.

I encountered similar phenomena in work in cancer control over a long period.  Advocates argued for substantial investments in prevention, screening, treatment, survivorship, and/or palliative care for particular cancers they or their families had experienced.  If one were to grant the requests of all the advocates, the monies required would far exceed any feasible budgets.  Even more complicated would be trading off investments in wheelchair securement technologies versus colorectal cancer screening.

This problem gets overwhelmingly complex if we want to address tradeoffs across health, education, transportation, and environment, and of course other aspects of life.  Interestingly, if we were able to determine the optimal allocation of resources across these domains, many and perhaps most of the advocacy groups would not understand or support such a policy.  Another approach is needed to gain their support.

The key is creation of an environment where the full range of stakeholders can explore and debate possibilities.  This requires creating a set of scenarios that, collectively, represent the full range of decisions advocated.  These scenarios can be used to drive interactive visualizations.  These portrayals should be understandable by all stakeholders, with views tailored to each advocacy group.   This enables group X to see how group Y sees the position advocated by X.  Seeing how each group views a particular decision can motivate essential compromises.

The visualizations are based on computational models use to predict what might happen and the conditions under which these outcomes might emerge.  For the types of problems outlined here, it is impossible to accurately predict what will happen.  Anyone advocating the possibility of precise predictions should be promptly dismissed.  The key is Computing Possible Futures, the title of my latest book from Oxford University Press.  The abilities of competing advocacy groups to interactively explore possible futures — seeing the views of each group as the exploration evolves — can enable creative outcomes that few if any of the groups will have anticipated or even imagined.

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