The Good-Near Bad-Far Bias

“Why am I late home from work? Terrible traffic slowed everyone down.”
“Why am I early home from work? I wanted to spend more time with you.”

We try to make ourselves look good. So we try to associate closely with good events, and distance ourselves more from bad events. Specifically, we prefer to explain bad events near us in terms of distant causes over which we had little influence, but explain good events near us in terms of our good long-lasting features, such as our authenticity, loyalty, creativity, or intelligence.

For example, managers are reluctant to adopt prediction markets for project deadlines, because it takes away their favorite excuse for failure: “The thing that delayed this project was a rare disaster that came out of left field; no one could have seen it coming.” Note that distant causes work best as excuses if they are rare and unpredictable. Otherwise there comes the question of why one didn’t do more to prevent or mitigate the distant influence.

As another example, when a class of people is doing poorly and we are reluctant to blame them, we prefer explanations far from their choices. So instead of blaming their self-control, laziness, or intelligence, we prefer to blame capitalism, general malaise, discrimination, foreigners, or automation. Recent over-emphasis on a sudden burst of automation as an unemployment cause comes in part from a perfect storm of not wanting to blame low-skilled workers, and wanting to brag about the technical prowess of groups we feel associated with.

Why don’t we blame close rivals more often, instead of distant causes? We do blame rivals sometimes, but if they retaliate by blaming us we risk ending up associated with a lot of blame. Better to keep the peace and both blame outsiders.

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  • interstice

    For the automation example, it doesn’t really seem that we can explain a RISE in unemployment from such individual characteristics, unless there is some reason why a class of people would all simultaneously become more lazy and stupid.

    • interstice

      and it seems that automation is usually used to explain a rise in unemployment, not existing unemployment. Of course, the rise in unemployment could be exaggerated.

  • Unnamed

    I would’ve used the exact opposite headline to describe the traffic example (and I think Trope and Liberman would too).

    Bad results of our actions are due to low-level concrete details, which we talk about in near-mode mechanistic terms such as “Traffic was bad.” Good results of our actions are due to high-level abstract ideals, which we talk about in far-mode value-based terms such as “I wanted to spend more time with you.”

    • I think there’s something interesting going on here. (Although I’m not sure what to make of it.) Construal-level theory posits two correlated but separable components of construal level: distance and abstractness. But when we associate ourselves with good outcomes and disassociate ourselves from bad outcomes, the facets get crossed. (We want the cause of the bad outcome to be distant and concrete: the cause of the good outcome to be near and abstract.)

  • Lord

    And when a class of people is doing very well we want to say it is their creativity, diligence, intelligence, and talent, rather than their power and ability to extract and convince themselves that what is good for themselves is best.

  • The entire point of a deadline is to exert psychological pressure on employees. Using prediction markets makes the deadline sound flexible, which makes it less useful as a psychological tool.

  • Christian Kleineidam

    If you have prediction market on deadlines you would reward certain people for making the deadline fail.

    Simply asking for the probability and maybe publishing Briers scores of good predictiors would also do the job.

    • It is easy to give everyone a positive stake so no one wants the project to fail. If you don’t do this, even those evaluated via Brier scores can have incentives to make it fail.

      • Christian Kleineidam

        Prediction markets incentives participants to avoid making 100 different bets but instead focus on a few bets where the participant believes they have a comparative advantage.

        A person who has five bets has a lot more to gain by fixing a single outcome then a person who gave their credence for 100 outcomes.