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When people make purchasing decisions, pricing models that are too complex make them less likely to purchase. If it's too confusing to figure out whether something is a good deal or not, we generally tend to just assume it's a bad deal. See http://ideas.repec.org/p/ags/ualbsp/24093.html (Choice Environment, Market Complexity and Consumer Behavior: A Theoretical and Empirical Approach for Incorporating Decision Complexity into Models of Consumer Choice), for example.
I occasionally read the blog of Scott Adams, the author of Dilbert. He claims to believe that the world is a simulation, but who can blame him? His own situation is so improbable he must cast about for some explanation. I predict that among celebrities (and the unusually successful in other fields), there is an unusually high amount of belief that just by wanting things hard enough they will come to you-- because, like everyone else, they wished for something in life, but unlike most people, they actually got it.
Perhaps Columbus's "genius" was simply to take action. I've noticed this in executives and higher-ranking military officers I've met-- they get a quick view of the possibilities, then they make a decision and execute it. Sometimes it works and sometimes it doesn't, but the success rate is a lot better than for people who never take action at all.
This sort of argument was surprisingly common in the 18th and 19th century compared to today. The Federalist Papers, for example, lay out the problem as a set of premises leading inexorably to a conclusion. I find it hard to imagine a politician successfully using such a form of argument today.
At least that's my impression; perhaps appeals to authority and emotion were just as common in the past as today but selection effects prevent me from seeing them.
I really enjoyed the first part of the post-- just thinking about the fact that my future goals will be different from my present ones is a useful idea. I found the bit of hagiography about E.Y. at the end weird and not really on topic. You might just use a one or two sentence example: He wanted to build an A.I., and then later he didn't want to.
Regarding Cyberpunk, Gibson wasn't actually making a prediction, not in the way you're thinking. He was always making a commentary on his own time by exaggerating certain aspects of it. See here, for instance:
Great! This means that in order to develop an AI with a proper moral foundation, we just need to reduce the following statements of ethical guidance to predicate logic, and we'll be all set:
1. Be excellent to each other.
2. Party on, dudes!
I think trying to understand organizational intelligence would be pretty useful as a way of getting a feel for the variety of possible intelligences. Organizations also have a legal standing as artificial persons, so I imagine that any AI that wanted to protect its interests through legal means would want to be incorporated. I'd like to see this explored further. Any suggestions on good books on the subject of corporations considered as AIs?
Perhaps you would suggest showing the histograms of completion times on each site, along with the 95% confidence error bars?
Can you give me a concrete course of action to take when I am writing a paper reporting my results? Suppose I have created two versions of a website, and timed 30 people completing a task on each web site. The people on the second website were faster. I want my readers to believe that this wasn't merely a statistical coincidence. Normally, I would do a t-test to show this. What are you proposing I do instead? I don't want a generalization like "use Bayesian statistics, " but a concrete example of how one would test the data and report it in a paper.
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