2008.12.07 | Karl Klose

Title: Theory of mind and bounded rationality without interpretive overhead

Abstract:
Computers and humans that work well together have beliefs about each
other's intentions, about each other's desires about each other's
beliefs, and so on. To practise such a _theory of mind_, agents need
to slip easily into each other's shoes. Ideally, when Agent A reasons
about Agent B with completecertainty, Agent A should simulate Agent B's
mind as efficiently as if that simulation were reality. Modeling agents
as programs, we want Agent A to interpret Agent B's program _without
interpretive overhead_, that is, as efficiently as if that program ran
directly. A programming language with _delimited control operators_
lets us eliminateinterpretive overhead in a computational model of
bounded-rational agents that reason about each other probabilistically.
The key is to reify stochastic programs as probability distributions
using the increasingly popular _finally tagless_ technique for embedding
programming languages. Wedemonstrate the idea with a simplistic model
of plausiblydeniable bribing.

Joint work with Oleg Kiselyov.