charleshmorse
The author is almost making it seem like models are reality and that people think that. They're not and I don't think anyone has ever thought they were...
Further and like other comments already mentioned, the brain is thought of and treated as a turing machine, not a digital computer. It's done this way, because the brain can be mapped to the definition of a turing machine.
And I have to defend Von Neumann. In his book, he explored turing equivalencies between the brain and computer concepts at the time used to implement the digital turing machine, he didn't actually think that the brain was a one-to-one mapping to a digital computer... He knew the difference between models and reality.
Even for the history of models the author mentions (hydraulics, automata, etc.), these all contain some turing equivalencies if implemented correctly and they were simply using the language and examples at the time to express this.
The author also continues to mangle any and all ideas of modeling, abstraction, and equivalence throughout the whole article. With regard to his 'uniqueness problem', I mean 'information loss' is modeled digitally for a reason.. just because humans are lossy, doesn't mean we can't model them that way. Think of a compressed image file.
I don't think there's a single researcher worth their salt that thinks the 'IP Metaphor' is gospel. That is just a grossly unscientific idea to assume.
We're all free to choose any model or collection of models we wish to approximate reality, but some of them work better than others and the brain is a complicated thing to model.
The author is trying to dramatize a triviality.
Also, this seems to make no mention of 'rearing complexity'. A more developmentally complex brain should imply prolonged rearing and have a bias of survival in favor of shared parenting.
I guess you can find anything you're looking for in gene expressions, if you really want.
A Twist on the Liar's Dilemma (2016)
Bullshit Makes a Good Building Material [Aug]
Bullshit Makes a Good Building Material
Elon Is Wrong About Simulations
Information Gain Is Knowledge Loss – One Cause of Stress
And then there is also the consideration of the lies lifespan. If the effect of a lie is momentary, its residual effects could only last mere moments, instead of continuously into perpetuity (the ripples of water from a rock thrown into a pond only last for so long). It's chain of causality could die-off - now continuous lying is another matter all together.
My points in this blurb merely show that a rational economic member of society should use lies in some cases; for it is a best response in many common instances - but of course not all and, if avoidable, not for the wrong reasons.
A Twist on the Liar's Dilemma
A Twist On The Liar's DilemmaThe liar's dilemma basically implies that a liar is not incentivized to reveal himself as liar. However, there is a twist to this which takes into the time of evaluation, t, of any proposition, x, that a liar would purport. Basically, as t increases a liar is further incentivised to lie. In fact t approaching infinity could be considered a non-sequitur. So if...
Also, I recommend reading Alan Weiss's The Consulting Bible: http://books.google.com/books/about/The_Consulting_Bible.htm...
http://smg.media.mit.edu/library/Granovetter.WeakTies.pdf (coupled with the probabilities of value for each connection, among many other things) -
with the value of working at that moment (maybe with consideration of diminishing returns at the day goes on, seeing as networking events happen later in the day).
And you'll be at the start of quantifying the trade-off.
Then realize you just spent a whole bunch of time, and wasted productivity calculating that, and the next time just promise yourself to make a spur of the moment decision based on mood, feelings of social isolation, how many 'good' friends you have at the moment, how 'included' you feel, and all the other computation reducing techniques your brain employs via intuition and emotional well-being and instead go off of that.