Lorenzo

• Lorenzo 17 Jun 2010 22:28 UTC

  0 points

  on: Less Wrong Book Club and Study Group

  If it’s not too late, I’d like to participate as well.

  Lbraschi is my account for Google Wave. I’m in Madrid, Spain.

• Lorenzo 19 Apr 2010 21:46 UTC

  3 points

  in reply to: NancyLebovitz’s comment on: Ex­pect­ing Short In­fer­en­tial Distances

  You’re right—evolution might be easier than, say, how and iPhone works (not that an iPhone would work very well in Ancient Greece, or for much long, anyway). Having some high tech to show to good old Aristotle maybe would convince him you come from a very strange land, and maybe he would want to hear more of what you have to say instead of just dismissing you as a lunatic.

  But imagine how much you would have to explain to make him even dimly aware of the way an iPhone works! Electronics, electricity, computation, satellites and astronomy (goodbye lunar sphere), calculus, chemistry, physics… I can barely think of all the relevant topics!

  Of course, as you point out, mysoginy would be a great obstacle too. One more of the ‘steps’ that separate ancient peoples from modern societies.

• Lorenzo 19 Apr 2010 20:57 UTC

  8 points

  on: Wel­come to Less Wrong!

  Huh, I guess I should have come here earlier…

  I’m Lorenzo, 31, from Madrid, Spain (but I’m Italian). I’m an evolutionary psychologist, or try to be, working on my PhD. I’m also doing a Master’s Degree in Statistics, in which I discovered (almost by accident) the Bayesian approach. As someone with a longstanding interest in making psychology become a better science, I’ve found this blog a very good place for clarifying ideas.

  I’ve been a follower of Less Wrong after reading Eliezer’s essays on Bayesian reasoning some 3-4 months ago. I’ve known the Bayes theorem for quite a long time, but little or nothing about the bayesian approach to propability theory. The frecuentist paradigm dominates much of psychology, which is a shame, because I think bayesian reasoning is much better suited to the study of mind. There is still a lot of misunderstanding about what a bayesian approach entails, at least in this part of the world. Oh, well. We’ll deal with it.

  Thanks and keep up the good work!

• Lorenzo 19 Apr 2010 20:38 UTC

  5 points

  on: Ex­pect­ing Short In­fer­en­tial Distances

  Now I think of it, this reminds of something Richard Dawkins used to say at some talks: that we (the modern audience) could give Aristotle a tutorial. Being a fantasist myself, I’ve sometimes wondered how that could be possible. Leaving aside the complications of building a time machine (I leave that to other people), I wondered how would it be to actually meet Aristotle and explain to him some of the things we now know about life, the universe & everything.

  First of all, I’d have to learn ancient greek, of course, or no communication would be possible. That would be the easy (and the only easy) part. More complicated would be that, to teach anything modern to Aristotle, one would have to teach an incredible amount of previous stuff. That is, one would have to step quite a large number of inferential steps. If I wanted to explain, for example, the theory of evolution, that would require a lot of anatomy, geography, zoology, botany, and even mathematics and philosophy. One would have to be a true polymath to achieve the feat. It’s not that we don’t know more about the universe than Aristotle, it is that to cross the inferential ‘gap’ between Aristotle and us would require an inordinate amount of knowledge.

  Maybe a good metaphor is based on Dennett’s crane idea: we develop ideas that help us reach higher levels of understanding, but as soon as we reach those upper levels we discard them to build new ones for higher levels. To help someone on the floor, one has to ‘rebuild’ these old cranes no longer in use.

• Lorenzo 19 Apr 2010 20:24 UTC

  7 points

  on: Ex­pect­ing Short In­fer­en­tial Distances

  As someone who has done (some) teaching, I think this is absolutely correct. In fact, the most difficult thing I find about teaching is trying to find the student’s starting knowledge, and then working from there. If the teacher does not goes back enough ‘inferential steps’, the student won’t learn anything—or worse, they might think they know when they don’t.

  Excellent stuff.