32,388 research outputs found
When Can Limited Randomness Be Used in Repeated Games?
The central result of classical game theory states that every finite normal
form game has a Nash equilibrium, provided that players are allowed to use
randomized (mixed) strategies. However, in practice, humans are known to be bad
at generating random-like sequences, and true random bits may be unavailable.
Even if the players have access to enough random bits for a single instance of
the game their randomness might be insufficient if the game is played many
times.
In this work, we ask whether randomness is necessary for equilibria to exist
in finitely repeated games. We show that for a large class of games containing
arbitrary two-player zero-sum games, approximate Nash equilibria of the
-stage repeated version of the game exist if and only if both players have
random bits. In contrast, we show that there exists a class of
games for which no equilibrium exists in pure strategies, yet the -stage
repeated version of the game has an exact Nash equilibrium in which each player
uses only a constant number of random bits.
When the players are assumed to be computationally bounded, if cryptographic
pseudorandom generators (or, equivalently, one-way functions) exist, then the
players can base their strategies on "random-like" sequences derived from only
a small number of truly random bits. We show that, in contrast, in repeated
two-player zero-sum games, if pseudorandom generators \emph{do not} exist, then
random bits remain necessary for equilibria to exist
Calibrating the complexity of Delta 2 sets via their changes
The computational complexity of a Delta 2 set will be calibrated by the
amount of changes needed for any of its computable approximations. Firstly, we
study Martin-Loef random sets, where we quantify the changes of initial
segments. Secondly, we look at c.e. sets, where we quantify the overall amount
of changes by obedience to cost functions. Finally, we combine the two
settings. The discussions lead to three basic principles on how complexity and
changes relate
An architecturally constrained model of random number generation and its application to modeling the effect of generation rate
Random number generation (RNG) is a complex cognitive task for human subjects, requiring deliberative control to avoid production of habitual, stereotyped sequences. Under various manipulations (e.g., speeded responding, transcranial magnetic stimulation, or neurological damage) the performance of human subjects deteriorates, as reflected in a number of qualitatively distinct, dissociable biases. For example, the intrusion of stereotyped behavior (e.g., counting) increases at faster rates of generation. Theoretical accounts of the task postulate that it requires the integrated operation of multiple, computationally heterogeneous cognitive control (“executive”) processes. We present a computational model of RNG, within the framework of a novel, neuropsychologically-inspired cognitive architecture, ESPro. Manipulating the rate of sequence generation in the model reproduced a number of key effects observed in empirical studies, including increasing sequence stereotypy at faster rates. Within the model, this was due to time limitations on the interaction of supervisory control processes, namely, task setting, proposal of responses, monitoring, and response inhibition. The model thus supports the fractionation of executive function into multiple, computationally heterogeneous processes
Secret-Sharing for NP
A computational secret-sharing scheme is a method that enables a dealer, that
has a secret, to distribute this secret among a set of parties such that a
"qualified" subset of parties can efficiently reconstruct the secret while any
"unqualified" subset of parties cannot efficiently learn anything about the
secret. The collection of "qualified" subsets is defined by a Boolean function.
It has been a major open problem to understand which (monotone) functions can
be realized by a computational secret-sharing schemes. Yao suggested a method
for secret-sharing for any function that has a polynomial-size monotone circuit
(a class which is strictly smaller than the class of monotone functions in P).
Around 1990 Rudich raised the possibility of obtaining secret-sharing for all
monotone functions in NP: In order to reconstruct the secret a set of parties
must be "qualified" and provide a witness attesting to this fact.
Recently, Garg et al. (STOC 2013) put forward the concept of witness
encryption, where the goal is to encrypt a message relative to a statement "x
in L" for a language L in NP such that anyone holding a witness to the
statement can decrypt the message, however, if x is not in L, then it is
computationally hard to decrypt. Garg et al. showed how to construct several
cryptographic primitives from witness encryption and gave a candidate
construction.
One can show that computational secret-sharing implies witness encryption for
the same language. Our main result is the converse: we give a construction of a
computational secret-sharing scheme for any monotone function in NP assuming
witness encryption for NP and one-way functions. As a consequence we get a
completeness theorem for secret-sharing: computational secret-sharing scheme
for any single monotone NP-complete function implies a computational
secret-sharing scheme for every monotone function in NP
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