183 research outputs found
Voluntary Commitments Lead to Efficiency
Consider an agent (manager,artist, etc.) who has imperfect private information about his productivity. At the beginning of his career (period 1, “short run”), the agent chooses among publicly observable actions that generate imperfect signals of his productivity. The actions can be ranked according to the informativeness of the signals they generate. The market observes the agent’s action and the signal generated by it, and pays a wage equal to his expected productivity. In period 2 (the “long run”), the agent chooses between a constant payoff and a wage proportional to his true productivity, and the game ends. We show that in any equilibrium where not all types of the agent choose the same action, the average productivity of an agent choosing a less informative action is greater. However, the types choosing that action are not uniformly higher. In particular, we derive conditions for the existence of a tripartite equilibrium where low and high types pool on a less informative action while medium (on average, lower) types choose to send a more informative signal.signalling, career concerns
Noise-Tolerant Learning, the Parity Problem, and the Statistical Query Model
We describe a slightly sub-exponential time algorithm for learning parity
functions in the presence of random classification noise. This results in a
polynomial-time algorithm for the case of parity functions that depend on only
the first O(log n log log n) bits of input. This is the first known instance of
an efficient noise-tolerant algorithm for a concept class that is provably not
learnable in the Statistical Query model of Kearns. Thus, we demonstrate that
the set of problems learnable in the statistical query model is a strict subset
of those problems learnable in the presence of noise in the PAC model.
In coding-theory terms, what we give is a poly(n)-time algorithm for decoding
linear k by n codes in the presence of random noise for the case of k = c log n
loglog n for some c > 0. (The case of k = O(log n) is trivial since one can
just individually check each of the 2^k possible messages and choose the one
that yields the closest codeword.)
A natural extension of the statistical query model is to allow queries about
statistical properties that involve t-tuples of examples (as opposed to single
examples). The second result of this paper is to show that any class of
functions learnable (strongly or weakly) with t-wise queries for t = O(log n)
is also weakly learnable with standard unary queries. Hence this natural
extension to the statistical query model does not increase the set of weakly
learnable functions
Usability of Humanly Computable Passwords
Reusing passwords across multiple websites is a common practice that
compromises security. Recently, Blum and Vempala have proposed password
strategies to help people calculate, in their heads, passwords for different
sites without dependence on third-party tools or external devices. Thus far,
the security and efficiency of these "mental algorithms" has been analyzed only
theoretically. But are such methods usable? We present the first usability
study of humanly computable password strategies, involving a learning phase (to
learn a password strategy), then a rehearsal phase (to login to a few
websites), and multiple follow-up tests. In our user study, with training,
participants were able to calculate a deterministic eight-character password
for an arbitrary new website in under 20 seconds
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