<table><tr><th>From:</th><th>Joscha Bach &lt;</th></tr><tr><td>Sent:</td><td>Wednesday, August 26, 2015 7:45 PM</td></tr><tr><td>To:</td><td>Jeffrey Epstein</td></tr><tr><td>Subject:</td><td>Decision making</td></tr></table>

Re motivation: Have you seen the recent movie "Ex Machina"= I liked it; one of the few Al movies that have not been dumbed down. =he main character is a beautiful female looking Al, clearly =ntelligent, and able to manipulate humans to an arbitrary degree. What =akes her inhuman is that she is probably motivated by a single =rinciple, like option maximization. That would make her an inscrutable =sychopath. Option maximization would entail energy, physical integrity, =erhaps reproduction, certainly learning, but you will have an agent =hat you won't like to share a prisoner's dilemma with. = How do you approach decision making? I have recently learned that people =hat subscribe for Cryonics (freezing one's head or body in the =ope to be revived when future technologies make it possible) assign a =ower probability to that it works than the general population. But =hereas "normal" people tend to make binary models about =he world: something is "probably not going to work, so let us =ot 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 =pproximation for all our major decisions, attach probability =istributions to everything in the space of possible beliefs, and be =ble to outperform the vast majority of folks that relies on narratives =i.e. binary yes/no decisions about the facts in the world). Gigerenzer, =ahnemann and many others have shown that human brains are terrible of =etting this intuitively right, to the point where an absence of =ine-grained domain knowledge often leads to better management decisions =tc.

The divide between probabilistic models vs. narrative models is =eflected to some degree in the conflict between probabilistic and logic =ased Al. In practice, we will probably need to combine both, but I =onder if there is an intrinsic limit to probabilistic descriptions in a =ighly complex world, where we cannot observe baseline probabilities =nyway.

How do you decide? Do you Solomonoff-induce and Bayes the hell out of =he stock market, do you reason, do you soak up data and let your =ntuitions guide you, or is most of the important stuff depending on =ommunication and negotiation? Is there are general approach, or how =uch should theories of decision making be dependent on the domain?

Cheers,

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