204,534 research outputs found
The Simonian bounded rationality hypothesis and the expectation formation mechanism
Abstract. In the 1980s and at beginning of the 1990s the debate on expectation formation mechanism was dominated by the rational expectation hypothesis. Later on, more interest was directed towards alternative approaches to expectations analysis, mainly based on the bounded rationality paradigm introduced earlier by Herbert A. Simon. The bounded rationality approach is used here to describe the way expectations might be formed by different agents. Furthermore, three main hypotheses, namely adaptive, rational and bounded ones are being compared and used to indicate why time lags in economic policy prevail and are variable. JEL Codes: D78, D84, H30, E00.Keywords: bounded rationality, substantive and procedural rationality, expectation formation, adaptive and rational expectations, time lags
Adaptive Regret Minimization in Bounded-Memory Games
Online learning algorithms that minimize regret provide strong guarantees in
situations that involve repeatedly making decisions in an uncertain
environment, e.g. a driver deciding what route to drive to work every day.
While regret minimization has been extensively studied in repeated games, we
study regret minimization for a richer class of games called bounded memory
games. In each round of a two-player bounded memory-m game, both players
simultaneously play an action, observe an outcome and receive a reward. The
reward may depend on the last m outcomes as well as the actions of the players
in the current round. The standard notion of regret for repeated games is no
longer suitable because actions and rewards can depend on the history of play.
To account for this generality, we introduce the notion of k-adaptive regret,
which compares the reward obtained by playing actions prescribed by the
algorithm against a hypothetical k-adaptive adversary with the reward obtained
by the best expert in hindsight against the same adversary. Roughly, a
hypothetical k-adaptive adversary adapts her strategy to the defender's actions
exactly as the real adversary would within each window of k rounds. Our
definition is parametrized by a set of experts, which can include both fixed
and adaptive defender strategies.
We investigate the inherent complexity of and design algorithms for adaptive
regret minimization in bounded memory games of perfect and imperfect
information. We prove a hardness result showing that, with imperfect
information, any k-adaptive regret minimizing algorithm (with fixed strategies
as experts) must be inefficient unless NP=RP even when playing against an
oblivious adversary. In contrast, for bounded memory games of perfect and
imperfect information we present approximate 0-adaptive regret minimization
algorithms against an oblivious adversary running in time n^{O(1)}.Comment: Full Version. GameSec 2013 (Invited Paper
Bounded Linear Stability Analysis - A Time Delay Margin Estimation Approach for Adaptive Control
This paper presents a method for estimating time delay margin for model-reference adaptive control of systems with almost linear structured uncertainty. The bounded linear stability analysis method seeks to represent the conventional model-reference adaptive law by a locally bounded linear approximation within a small time window using the comparison lemma. The locally bounded linear approximation of the combined adaptive system is cast in a form of an input-time-delay differential equation over a small time window. The time delay margin of this system represents a local stability measure and is computed analytically by a matrix measure method, which provides a simple analytical technique for estimating an upper bound of time delay margin. Based on simulation results for a scalar model-reference adaptive control system, both the bounded linear stability method and the matrix measure method are seen to provide a reasonably accurate and yet not too conservative time delay margin estimation
On the pathwise approximation of stochastic differential equations
We consider one-step methods for integrating stochastic differential
equations and prove pathwise convergence using ideas from rough path theory. In
contrast to alternative theories of pathwise convergence, no knowledge is
required of convergence in pth mean and the analysis starts from a pathwise
bound on the sum of the truncation errors. We show how the theory is applied to
the Euler-Maruyama method with fixed and adaptive time-stepping strategies. The
assumption on the truncation errors suggests an error-control strategy and we
implement this as an adaptive time-stepping Euler-Maruyama method using bounded
diffusions. We prove the adaptive method converges and show some computational
experiments.Comment: 21 page
A biased approach to nonlinear robust stability and performance with applications to adaptive control
The nonlinear robust stability theory of Georgiou and Smith [IEEE Trans. Automat. Control, 42 (1997), pp. 1200–1229] is generalized to the case of notions of stability with bias terms. An example from adaptive control illustrates nontrivial robust stability certificates for systems which the previous unbiased theory could not establish a nonzero robust stability margin. This treatment also shows that the bounded-input bounded-output robust stability results for adaptive controllers in French [IEEE Trans. Automat. Control, 53 (2008), pp. 461–478] can be refined to show preservation of biased forms of stability under gap perturbations. In the nonlinear setting, it also is shown that in contrast to linear time invariant systems, the problem of optimizing nominal performance is not equivalent to maximizing the robust stability margin
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