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The question of how to get to Gatwick is what you might call a ‘wide context’ problem. It allows for vagueness and multiple right answers, and it doesn’t demand absolute adherence to any precise rules. There is no formula for the solution, it allows scope for all kinds of possible ‘rightish’ answers and all kinds of information can be taken into account when coming up with an answer. These are the problems we seem instinctively better equipped to solve, but which computers find hard. If I were to delve into my unconscious and uncover some of the variables at play in my brain when I next have to get to the airport, they might include ‘Is it raining?’, ‘How much luggage do I have?’, ‘How long am I going to be away for?’, ‘What is the average time via the M25 versus taking the A25?’, ‘What is the variance of journey time on the M25 versus the A25?’fn2 and ‘Does my flight leave from the North or South Terminal?’ If you think of getting to Gatwick as a narrow problem in the way your GPS does – a simple question of getting to the airport as quickly as possible – some of these factors may seem irrelevant, but they are all important in real life. The weather affects the traffic. If I am going away for two weeks rather than one night, it affects the cost of parking, and therefore the relative cost of going by train, car or taxi – and the amount of luggage I have. The variance of travel time on the M25 matters to whether it’s worth risking. And heavy luggage makes the train less appealing, especially if you are flying from the North Terminal, which is much further away from the rail station. It’s interesting that we find solving complex problems like this so easy – it suggests that our brains have evolved to answer ‘wide context’ problems because most problems we faced as we developed were of this type. Blurry ‘pretty good’ decision-making has simply proven more useful than precise logic.
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