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Iterated Mutual Observation with
"... This paper introduces a simple model of interacting agents that learn to predict each other. For learning to predict the other's intended action we apply genetic programming. The strategy of an agent is rational and fixed. It does not change like in classical iterated prisoners dilemma model ..."
Abstract
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This paper introduces a simple model of interacting agents that learn to predict each other. For learning to predict the other's intended action we apply genetic programming. The strategy of an agent is rational and fixed. It does not change like in classical iterated prisoners dilemma models. Furthermore the number of actions an agent can choose from is infinite. Preliminary simulation results are presented. They show that by varying the population size of genetic programming, different learning characteristics can easily be achieved, which lead to quite different communication patterns.
Iterated Mutual Observation with Genetic Programming
, 2001
"... This paper introduces a simple model of interacting agents that learn to predict each other. For learning to predict the other's intended action we apply genetic programming. The strategy of an agent is rational and fixed. It does not change like in classical iterated prisoners dilemma model ..."
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This paper introduces a simple model of interacting agents that learn to predict each other. For learning to predict the other's intended action we apply genetic programming. The strategy of an agent is rational and fixed. It does not change like in classical iterated prisoners dilemma models. Furthermore the number of actions an agent can choose from is infinite. Preliminary simulation results are presented. They show that by varying the population size of genetic programming, di#erent learning characteristics can easily be achieved, which lead to quite di#erent communication patterns. 1
Blind Signal Separation: Statistical Principles
, 2003
"... Blind signal separation (BSS) and independent component analysis (ICA) are emerging techniques of array processing and data analysis, aiming at recovering unobserved signals or `sources' from observed mixtures (typically, the output of an array of sensors), exploiting only the assumption of mut ..."
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Cited by 529 (4 self)
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Blind signal separation (BSS) and independent component analysis (ICA) are emerging techniques of array processing and data analysis, aiming at recovering unobserved signals or `sources' from observed mixtures (typically, the output of an array of sensors), exploiting only the assumption
Fast and robust fixedpoint algorithms for independent component analysis
 IEEE TRANS. NEURAL NETW
, 1999
"... Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. In this paper, we use a combination of two different approaches for linear ICA: Comon’s informat ..."
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Cited by 884 (34 self)
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Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. In this paper, we use a combination of two different approaches for linear ICA: Comon’s
Segmentation of brain MR images through a hidden Markov random field model and the expectationmaximization algorithm
 IEEE TRANSACTIONS ON MEDICAL. IMAGING
, 2001
"... The finite mixture (FM) model is the most commonly used model for statistical segmentation of brain magnetic resonance (MR) images because of its simple mathematical form and the piecewise constant nature of ideal brain MR images. However, being a histogrambased model, the FM has an intrinsic limi ..."
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Cited by 639 (15 self)
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based methods produce unreliable results. In this paper, we propose a novel hidden Markov random field (HMRF) model, which is a stochastic process generated by a MRF whose state sequence cannot be observed directly but which can be indirectly estimated through observations. Mathematically, it can be shown
Mutual Fund Flows and Performance in Rational Markets
, 2002
"... We develop a simple rational model of active portfolio management that provides a natural benchmark against which to evaluate observed relationship between returns and fund flows. Many effects widely regarded as anomalous are consistent with this simple explanation. In the model, investments with ac ..."
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Cited by 306 (16 self)
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We develop a simple rational model of active portfolio management that provides a natural benchmark against which to evaluate observed relationship between returns and fund flows. Many effects widely regarded as anomalous are consistent with this simple explanation. In the model, investments
Mutual information and minimum meansquare error in Gaussian channels
 IEEE TRANS. INFORM. THEORY
, 2005
"... This paper deals with arbitrarily distributed finitepower input signals observed through an additive Gaussian noise channel. It shows a new formula that connects the inputoutput mutual information and the minimum meansquare error (MMSE) achievable by optimal estimation of the input given the out ..."
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Cited by 288 (34 self)
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This paper deals with arbitrarily distributed finitepower input signals observed through an additive Gaussian noise channel. It shows a new formula that connects the inputoutput mutual information and the minimum meansquare error (MMSE) achievable by optimal estimation of the input given
Venture Capital and the Professionalization of Startup Firms: Empirical Evidence
 Journal of Finance
, 2002
"... This paper examines the impact venture capital can have on the development of new firms. Using a handcollected data set on Silicon Valley startups, we find that venture capital is related to a variety of professionalization measures, such as human resource policies, the adoption of stock option pl ..."
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Cited by 354 (26 self)
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plans, and the hiring of a marketing VP. Venturecapitalbacked companies are also more likely and faster to replace the founder with an outside CEO, both in situations that appear adversarial and those mutually agreed to. The evidence suggests that venture capitalists play roles over and beyond those
Mutual Observability and the Convergence of Actions in a MultiPerson TwoArmed Bandit Model
, 1998
"... This paper studies a model of a twoarmed bandit played in parallel by two or more players. Players observe the actions of all other players, but not the outcome of their experiments. It is shown that if the parameters of the two arms (i.e., their success probabilities) are different by a fixed marg ..."
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Cited by 20 (1 self)
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This paper studies a model of a twoarmed bandit played in parallel by two or more players. Players observe the actions of all other players, but not the outcome of their experiments. It is shown that if the parameters of the two arms (i.e., their success probabilities) are different by a fixed
Corrigendum: Mutual Observability and the Convergence of Actions in a MultiPerson TwoArmed Bandit Model
, 2011
"... claims in equation (a2) that α̂m converges μ∗almost surely to 1 by the consistency of Bayes estimates. However, conditional on Fi, there is no guarantee that player i’s trials on arm X are Bernoulli trials since the random outcomes on the arm may not be independent conditional on Fi. In this sense, ..."
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, (a2) is unwarranted. The proof below fixes this problem. Proof of Lemma 1 Fix player i, and value x = rk, and write αt for αtσ(i, k): i’s period t posterior belief that the value of arm X is rk. Let qtix be the outcome on arm X by player i in period t. Player i will observe qtix in period t if he
Results 1  10
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