Autobiographies by successful entrepreneurs often depict
their business success as being "against all odds," accomplished only through
extraordinary effort and sometimes also luck at critical junctures. They are
right. Successfully starting an entrepreneurial venture is against the odds, as
least as far as we can tell from the statistics that are available. Although the
numbers differ by nation, the 50-5 rule that more
than 50% of all new ventures are gone in 5 years is pretty good (perhaps a bit
optimistic). A venture that closes within its first five years has probably
lost money. On the upside, the ventures that make it past the 5-year mark are
highly likely to survive the next five year.
These statistics have made
people wonder whether entrepreneurs are overly confident, given the poor odds
of success. In a new article in Organization Science, Robin Hogarth and Natalia
Karelaia show that entrepreneurs are indeed overly confident, but this is – or could
be – a result of them being right on average. This statement is not as
paradoxical as it seems. The point is that even when our judgments are right on
average, as they often are, they are not exactly right. There is some error, plus or minus. I may assess my chances
of success at some task as too high at one time, and too low at another, but in
the long run it averages out. You and I make look at the same task at the same
time and make too high and too low judgments of success, but on average we may
be right. (We are not always right on
average, but for the sake of argument, let's assume we are.)
Now suppose the task in question was to start a new venture.
You and I would only do this if we thought our chances of success were good. On
average we are right. But that means that if we are both looking at the same
opportunity at once, the one who is more confident of success will move. If I
am looking at the same opportunity more than once, I will move when I am more
confident of success. Suppose the value of the venture is exactly zero, or even
negative. Because we only enter when we are confident of success, those who
become entrepreneurs will on average be
overconfident of success even if all potential entrepreneurs are – all the
time – on average correct.
It is a neat model that helps us understand overconfidence
in entrepreneurs. It also happens to be a rediscovery. In a 1984 article in Administrative Science Quarterly, J. Richard Harrison and James G. March made a
model of decision makers that are on average correct about the value of
different alternatives before making a choice. They take the highest-value
alternative. as we all do. But if there is some error in their judgments, even if the error is
unbiased, the act of choosing the alternative with the highest value will usually make them disappointed in their choice, because the alternative that seems
to have highest value is also the one most likely to be overestimated. This is
the same insight as Hogarth and Karelaia, except it is more general: instead of
choosing between doing nothing and entering a business, the Harrison and March model
is a choice between a number of different alternatives.
Rediscovery or not, it is important to be aware of this
trap. The way we make choices sets us up for disappointments, even when we are
on average right. So entrepreneurs can be on average right, but also too confident
of entering new businesses. And scientists can be on average right, but also too
confident of having new findings.
Interesting post and interesting blog overall Henrich -- thanks for sharing this. One of the questions I'd have is to what extent do "unobservables" (for the researcher) explain success, as opposed to pure luck? Our data sets typically include a limited set of variables, and entrepreneurs have a larger set of variables at their disposal. It may be possible that we observe such high rates of failure conditional on what social scientists observe -- but does that mean that nobody can predict success or failure?
ReplyDeleteOf course your mechanism, as well as pure luck or failure, are part of the explanation because otherwise we would see larger success rates. If entrepreneurs had access to all of the potential information on their venture's success, failure rates would be attributed to a lack of skill rather than a lack of information.
Amine, that is a nice point. I think that "unobservables" are important for the researcher and for the entrepreneur. We can try to observe more of the unobservables and as a result fail less. Harrison and March would make that recommendation because signal-to-noise ratio is a part of their model. We teach entrepreneurship that way, because exploring better before you commit is a key lesson. There will still be errors in the evaluation because judging the success of an entrepreneurial venture is genuinely hard, and these models tell us not to be too quick to blame failure on inability.
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