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When things don’t go our way, it is easy to blame ourselves for not anticipating and dodging the misfortunes we’ve fallen into. But even worse than our own internal monolog about these shortcomings—whether they be economic, academic, or professional—is the lack of empathy they can evoke in others.
Believing that the misfortune of others is their own doing, for example that a poor person must be poor because they are lazy, has directly led to policies that make it difficult to rise above such circumstances once you’ve fallen into them.
According to David Kinney, a complex systems theorist, epistemologist, and postdoctoral fellow at the Santa Fe Institute, this attitude couldn’t be more wrong from a mathematical perspective.
In an essay published this week in Psyche based on his research, Kinney argues that a mathematical theory explains that individuals with good intentions don’t meet the minimal requirements for blameworthiness when unforeseen curveballs change their plans. In the essay, he uses the example of a cab driver put out of work by Uber. The driver isn’t somehow morally deficient because of their circumstances, but rather is caught up in complex systems beyond their control or ability to perfectly predict: corporations, capital, laws, and so on.
“In a lot of my work I use mathematical tools… to think about both how human belief in knowledge works and also the limits of human belief and knowledge,” Kinney told Motherboard. “There's what you might call a knowledge constraint or sort of epistemic constraint on when someone is to blame for the downside effects of certain actions that they take.”
In other words, having a limited knowledge of how your decision might interact with other variables in the world (say, buying a house before a financial crisis) may relieve you of blame associated with its failure. To make this assertion, Kinney turns to a theory called computational complexity.
Computational complexity is a branch of mathematics that considers how resources, such as computer memory or computational power, affect an algorithm. Understanding the distribution of these resources and other interacting variables can help computer scientists make probabilistic predictions about the efficiency of different algorithms. But when you apply this same thinking to the real world, things get messy pretty quickly, explains Kinney.
As real life’s unforeseen variables begin to mount, Kinney explains that solving the problem of how to make the algorithm (in this case, your life’s trajectory) run smoothly becomes increasingly hard to do. In fact, it becomes so difficult that the time it would take to crunch those numbers becomes intractable, meaning there exists no plausible solution.
Computer scientists called these kinds of problems “NP-hard,” referring to the fact that they cannot be solved in polynomial time, A.K.A. roughly the lifetime of the universe.
To be considered truly blameworthy based on their good-intentioned, but ill-fated, decisions, Kinney says that people would need a full working knowledge of ever-changing variables like politics, innovation, and economics, which computational complexity shows to be an impossibility.
Does this mean that we’re always doomed to make blind and potentially detrimental decisions? Not necessarily, says Kinney.
One way to make more informed bets on the future is to have more resources, such as time or money, that help you better weather changes to your plan. This is the classic example of not placing all your eggs in one basket, says Kinney, and is readily utilized by large corporations and banks. But of course, wielding such resources is a kind of privilege that not many people have.
Instead, Kinney suggests that group action instead of individual action can be a solution both to protect individuals from misfortune and to protect them from the blame others may place on them because of it.
“At the individual level [it’s important] to take advantage of more things that we can do when we act as collectives and act in collectives,” says Kinney. “We get abilities to hedge risk in a way that we don't when we act as individuals.”