This paper introduces branching processes as a stochastic tool for understanding the characteristics of the abuse of leaked information, and the associated risks. To understand the risk associated with information lea...
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(纸本)0889864284
This paper introduces branching processes as a stochastic tool for understanding the characteristics of the abuse of leaked information, and the associated risks. To understand the risk associated with information leakage, it is important to produce models capturing important aspects of the abuse of leaked information. This understanding is necessary to limit such risks effectively and efficiently. Filling part of an apparent gap in this area, a stochastic model based on a discrete-time branching process is introduced. Some general challenges associated with modelling information leak risks are identified, as well as some challenges associated with using branching processes to analyze operational risk.
We investigate repeated matrix games with stochastic players as a microcosm for studying dynamic, multi-agent interactions using the stochastic Direct Reinforcement (SDR) policy gradient algorithm. SDR is a generaliza...
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We investigate repeated matrix games with stochastic players as a microcosm for studying dynamic, multi-agent interactions using the stochastic Direct Reinforcement (SDR) policy gradient algorithm. SDR is a generalization of Recurrent Reinforcement Learning (RRL) that supports stochastic policies. Unlike other RL algorithms, SDR and RRL use recurrent policy gradients to properly address temporal credit assignment resulting from recurrent structure. Our main goals in this paper are to (1) distinguish recurrent memory from standard, non-recurrent memory for policy gradient RL, (2) compare SDR with Q-type learning methods for simple games, (3) distinguish reactive from endogenous dynamical agent behavior and (4) explore the use of recurrent learning for interacting, dynamic agents. We find that SDR players learn much faster and hence outperform recently-proposed Q-type learners for the simple game Rock, Paper, Scissors (RPS). With more complex, dynamic SDR players and opponents, we demonstrate that recurrent representations and SDR's recurrent policy gradients yield better performance than non-recurrent players. For the Iterated Prisoners Dilemma, we show that non-recurrent SDR agents learn only to defect (Nash equilibrium), while SDR agents with recurrent gradients can learn a variety of interesting behaviors, including cooperation.
In this paper we examine the usefulness of optimisation methods for practical farm program decisions. We inspect three Brandenburg cash crop farms over the last five years and find that their total gross margins could...
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This research effort attempts to predict one year ahead the concentration of fecal coliforms at the mouth of the Afiasco River, located in Puerto Rico. One of the most efficient techniques to represent stochastic proc...
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This research effort attempts to predict one year ahead the concentration of fecal coliforms at the mouth of the Afiasco River, located in Puerto Rico. One of the most efficient techniques to represent stochastic processes is time series modeling. These models decompose the process into three major components: trend, seasonality and stochastic components. Unfortunately, time series models require observations at equal time intervals. Since the water quality data are not given at regular intervals, an adaptive estimation technique is proposed to estimate the missing values and, therefore, to generate an approximated time series at equal time intervals, to be able to study trend, seasonality, and stochastic components of fecal coliforms. Water quality data were collected from three water quality stations, located on the Anasco River. Historical data from 1973 to 2000 were used to model fecal coliforms at each of the water quality stations of the Anasco River. Time series models were identified at each station and were used to predict for one year the concentration of fecal coliforms at each station. A spatial interpolation algorithm was used to estimate the fecal coliforms at the mouth of the river.
Combinatorial optimization is often used to "plan ahead," purchasing and allocating resources for demands that are not precisely known at the time of solution. This advance planning may be done because resou...
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Combinatorial optimization is often used to "plan ahead," purchasing and allocating resources for demands that are not precisely known at the time of solution. This advance planning may be done because resources become very expensive to purchase or difficult to allocate at the last minute when the demands are known. In this work we study the tradeoffs involved in making some purchase/allocation decisions early to reduce cost while deferring others at greater expense to take advantage of additional, late-arriving information. We consider a number of combinatorial optimization problems in which the problem instance is uncertain - modeled by a probability distribution - and in which solution elements can be purchased cheaply now or at greater expense after the distribution is sampled. We show how to approximately optimize the choice of what to purchase in advance and what to defer.
stochastic activity networks (SANs) are a stochastic generalization of Petri nets. SAN models have been used for performance, dependability and performability evaluation and are supported by several powerful modeling ...
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Quadratic stochastic programming (QSP) in which each subproblem is a convex piecewise quadratic program with stochastic data, is a natural extension of stochastic linear programming. This allows the use of quadratic o...
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Quadratic stochastic programming (QSP) in which each subproblem is a convex piecewise quadratic program with stochastic data, is a natural extension of stochastic linear programming. This allows the use of quadratic or piecewise quadratic objective functions which are essential for controlling risk in financial and project planning. Two-stage QSP is a special case of extended linear-quadratic programming (ELQP). The recourse functions in QSP are piecewise quadratic convex and Lipschitz continuous. Moreover, they have Lipschitz gradients if each QP subproblem is strictly convex and differentiable. Using these properties, a generalized Newton algorithm exhibiting global and superlinear convergence has been proposed recently for the two stage case. We extend the generalized Newton algorithm to multistage QSP and show that it is globally and finitely convergent under suitable conditions. We present numerical results on randomly generated data and modified publicly available stochastic linear programming test sets. Efficiency schemes on different scenario tree structures are discussed. The large-scale deterministic equivalent of the multistage QSP is also generated and their accuracy compared. (C) 2001 Elsevier Science B.V. All rights reserved.
In the paper the optimization problems which may be solved by the direct decomposition method are considered. It is possible when the performance index is a monotone function of other performance indices, which depend...
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For stochastic programs with recourse, we derive quantitative continuity properties of the expectation functionals. This leads to qualitative and quantitative stability results for optimal values and optimal solutions...
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For stochastic programs with recourse, we derive quantitative continuity properties of the expectation functionals. This leads to qualitative and quantitative stability results for optimal values and optimal solutions with respect to perturbations of the underlying probability distributions and the weight parameters in objective function.
In this paper, we develop and test scenario generation methods for asset liability management models. We propose a multi-stage stochastic programming model for a Dutch pension fund. Both randomly sampled event trees a...
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In this paper, we develop and test scenario generation methods for asset liability management models. We propose a multi-stage stochastic programming model for a Dutch pension fund. Both randomly sampled event trees and event trees fitting the mean and the covariance of the return distribution are used for generating the coefficients of the stochastic program. In order to investigate the performance of the model and the scenario generation procedures we conduct rolling horizon simulations. The average cost and the risk of the stochastic programming policy are compared to the results of a simple fixed mix model. We compare the average switching behavior of the optimal investment policies, Our results show that the performance of the multi-stage stochastic program could be improved drastically by choosing an appropriate scenario generation method. (C) 2001 Elsevier Science B.V. All rights reserved.
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