📄 Extracted Text (695 words)
From: "Jeffrey E." <[email protected]>
To: Joscha Bach
Subject: Re: Decision making
Date: Wed, 26 Aug 2015 20:39:10 +0000
probablities vs similiarities. I suggest you take care with the concept of probabilites which really requires
repetition, symmetry, and independence.
On Wed, Aug 26, 2015 at 4:26 PM, Joscha Bach < > wrote:
I. Does luck mean gambling, or getting the probabilities right by intuition?
2. What else tells you anything about the future? (The past also tells us how much is does not tell us about the
future)
Am 26.08.2015 um 16:23 schrieb jeffrey E. <[email protected]>:
luck plays a role. 2 , looking back ( bayesian ) might not tell you anthing about the future.
On Wed, Aug 26, 2015 at 3:44 PM, Joscha Bach < > wrote:
Re motivation: Have you seen the recent movie "Ex Machina"? I liked it; one of the few AI movies that
have not been dumbed down. The main character is a beautiful female looking AI, clearly intelligent, and
able to manipulate humans to an arbitrary degree. What makes her inhuman is that she is probably
motivated by a single principle, like option maximization. That would make her an inscrutable psychopath.
Option maximization would entail energy, physical integrity, perhaps reproduction, certainly learning, but
you will have an agent that you won't like to share a prisoner's dilemma with.
How do you approach decision making? I have recently learned that people that subscribe for Cryonics
(freezing one's head or body in the hope to be revived when future technologies make it possible) assign a
lower probability to that it works than the general population. But whereas "normal" people tend to make
binary models about the world: something is "probably not going to work, so let us not bother", many of
the Cryonics folks will argue that paying $500 a month for a 1% chance of immortality seems like a
bargain.
This seems to generalize: in principle, we should perform a Bayesian approximation for all our major
decisions, attach probability distributions to everything in the space of possible beliefs, and be able to
outperform the vast majority of folks that relies on narratives (i.e. binary yes/no decisions about the facts in
the world). Gigerenzer, Kahnemann and many others have shown that human brains are terrible of getting
this intuitively right, to the point where an absence of fine-grained domain knowledge often leads to better
management decisions etc.
The divide between probabilistic models vs. narrative models is reflected to some degree in the conflict
between probabilistic and logic based Al. In practice, we will probably need to combine both, but I wonder
if there is an intrinsic limit to probabilistic descriptions in a highly complex world, where we cannot
observe baseline probabilities anyway.
How do you decide? Do you Solomonoff-induce and Bayes the hell out of the stock market, do you reason,
do you soak up data and let your intuitions guide you, or is most of the important stuff depending on
communication and negotiation? Is there are general approach, or how much should theories of decision
making be dependent on the domain?
Cheers,
EFTA00847826
Joscha
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the use of the addressee. It is the property of
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