Modelling the US Federal Spending Process: Overview and Implications

The object of study is the US Federal budget process - an institutional process of increasing prominence in US and world affairs - which is unique in generating quantitative data for scholarly research. The authors first outline their rigorous, but simple, econometric models of how budget decisions are made, coordinated, and implemented and then trace the implications of their high-inertia view of the process for the US economic cycle. They propound a presidential and Congressional ambition model of current and postwar cyclical economic difficulties, including stagflation, in terms of a macroeconomic model of the US economy in which federal governmental expenditure is endogenous. The chapter concludes with speculation on the disastrous consequences for society of the growth of a sluggishly adaptable bureaucratic process operating in a rapidly changing economic and social environment

Scenario trees and policy selection for multistage stochastic programming using machine learning

We propose a hybrid algorithmic strategy for complex stochastic optimization problems, which combines the use of scenario trees from multistage stochastic programming with machine learning techniques for learning a policy in the form of a statistical model, in the context of constrained vector-valued decisions. Such a policy allows one to run out-of-sample simulations over a large number of independent scenarios, and obtain a signal on the quality of the approximation scheme used to solve the multistage stochastic program. We propose to apply this fast simulation technique to choose the best tree from a set of scenario trees. A solution scheme is introduced, where several scenario trees with random branching structure are solved in parallel, and where the tree from which the best policy for the true problem could be learned is ultimately retained. Numerical tests show that excellent trade-offs can be achieved between run times and solution quality

Control of interjoint coordination during the swing phase of normal gait at different speeds

BACKGROUND: It has been suggested that the control of unconstrained movements is simplified via the imposition of a kinetic constraint that produces dynamic torques at each moving joint such that they are a linear function of a single motor command. The linear relationship between dynamic torques at each joint has been demonstrated for multijoint upper limb movements. The purpose of the current study was to test the applicability of such a control scheme to the unconstrained portion of the gait cycle – the swing phase. METHODS: Twenty-eight neurologically normal individuals walked along a track at three different speeds. Angular displacements and dynamic torques produced at each of the three lower limb joints (hip, knee and ankle) were calculated from segmental position data recorded during each trial. We employed principal component (PC) analysis to determine (1) the similarity of kinematic and kinetic time series at the ankle, knee and hip during the swing phase of gait, and (2) the effect of walking speed on the range of joint displacement and torque. RESULTS: The angular displacements of the three joints were accounted for by two PCs during the swing phase (Variance accounted for – PC1: 75.1 ± 1.4%, PC2: 23.2 ± 1.3%), whereas the dynamic joint torques were described by a single PC (Variance accounted for – PC1: 93.8 ± 0.9%). Increases in walking speed were associated with increases in the range of motion and magnitude of torque at each joint although the ratio describing the relative magnitude of torque at each joint remained constant. CONCLUSION: Our results support the idea that the control of leg swing during gait is simplified in two ways: (1) the pattern of dynamic torque at each lower limb joint is produced by appropriately scaling a single motor command and (2) the magnitude of dynamic torque at all three joints can be specified with knowledge of the magnitude of torque at a single joint. Walking speed could therefore be altered by modifying a single value related to the magnitude of torque at one joint

Dynamic tracking error with shortfall control using stochastic programming

In this contribution we tackle the issue of portfolio management combining benchmarking and risk control. We propose a dynamic tracking error problem and we consider the problem of monitoring at discrete points the shortfalls of the portfolio below a set of given reference levels of wealth.We formulate and solve the resulting dynamic optimization problem using stochastic programming. The proposed model allows for a great flexibility in the combination of the tracking goal and the downside risk protection. We provide the results of out-of-sample simulation experiments, on real data, for different portfolio configurations and different market conditions

Measuring Multi-Joint Stiffness during Single Movements: Numerical Validation of a Novel Time-Frequency Approach

This study presents and validates a Time-Frequency technique for measuring 2-dimensional multijoint arm stiffness throughout a single planar movement as well as during static posture. It is proposed as an alternative to current regressive methods which require numerous repetitions to obtain average stiffness on a small segment of the hand trajectory. The method is based on the analysis of the reassigned spectrogram of the arm's response to impulsive perturbations and can estimate arm stiffness on a trial-by-trial basis. Analytic and empirical methods are first derived and tested through modal analysis on synthetic data. The technique's accuracy and robustness are assessed by modeling the estimation of stiffness time profiles changing at different rates and affected by different noise levels. Our method obtains results comparable with two well-known regressive techniques. We also test how the technique can identify the viscoelastic component of non-linear and higher than second order systems with a non-parametrical approach. The technique proposed here is very impervious to noise and can be used easily for both postural and movement tasks. Estimations of stiffness profiles are possible with only one perturbation, making our method a useful tool for estimating limb stiffness during motor learning and adaptation tasks, and for understanding the modulation of stiffness in individuals with neurodegenerative diseases

Applications of Genetic Programming to Finance and Economics: Past, Present, Future

While the origins of Genetic Programming (GP) stretch back over fifty years, the field of GP was invigorated by John Koza’s popularisation of the methodology in the 1990s. A particular feature of the GP literature since then has been a strong interest in the application of GP to real-world problem domains. One application domain which has attracted significant attention is that of finance and economics, with several hundred papers from this subfield being listed in the Genetic Programming Bibliography. In this article we outline why finance and economics has been a popular application area for GP and briefly indicate the wide span of this work. However, despite this research effort there is relatively scant evidence of the usage of GP by the mainstream finance community in academia or industry. We speculate why this may be the case, describe what is needed to make this research more relevant from a finance perspective, and suggest some future directions for the application of GP in finance and economics

Stochastic integer programming by dynamic programming

Stochastic integer programming is a suitable tool for modeling hierarchical decision situations with combinatorial features. In continuation of our work on the design and analysis of heuristics for such problems, we now try to find optimal solutions. Dynamic programming techniques can be used to exploit the structure of two-stage scheduling, bin packing and multiknapsack problems. Numerical results for small instances of these problems are presented