Fiscal and monetary policy interactions: a game theory approach

The interaction between fiscal and monetary policy is analyzed by means of a game theory approach. The coordination between these two policies is essential, since decisions taken by one institution may have disastrous effects on the other one, resulting in welfare loss for the society. We derived optimal monetary and fiscal policies in context of three coordination schemes: when each institution independently minimizes its welfare loss as a Nash equilibrium of a normal form game; when an institution moves first and the other follows, in a mechanism known as the Stackelberg solution; and, when institutions behave cooperatively, seeking common goals. In the Brazilian case, a numerical exercise shows that the smallest welfare loss is obtained under a Stackelberg solution which has the monetary policy as leader and the fiscal policy as follower. Under the optimal policy, there is evidence of a strong distaste for inflation by the Brazilian society.     

Control of linear processes with distributed lags using dynamic programming from first principles

A simple dynamic programming argument is presented for the quadratic-cost controller synthesis problem for discrete-time linear processes with delay. Distributed delays are allowed in both state and control. The solution obtained has a discrete-time Riccati difference structure closely analogous to the Riccati differential structure associated with delay problems in continuous time. Extensions are provided for the cases of varying lag-limits, performance criterion dependent on past variables, and the time-invariant regulator problem. A feedback solution is also obtained for a continuous-time problem with distributed delays in the control, by passage to limit from the discrete results.This work was supported by the Operations Research Center, University of California, Berkeley, California, under NSF Grant No. GP-30961X2. The author would like to thank Professor S. E. Dreyfus for guidance and helpful suggestions.     

On the complexity of simultaneous price-quantity adjustment processes

While the Walrasian price tâtonnement represents the traditional dynamic process in the general equilibrium context with and without production, Walras and other classics designed the process exclusively for pure exchange economies. In productive economies, the short-run output adjustment of existing firms and the entry/exit of firms should be modeled as well. So-called cross-dual processes which represent the classical approach to the dynamics of productive economies are discussed and extended. Complex motion can emerge in a discrete-time version of the original two-dimensional system when the aggregate demand function has a non-standard shape. A simultaneous process of price and short-run quantity adjustment with free entry and exit of competitive firms in a single market with a continuum of firms can generate closed orbits via a Hopf bifurcation when the slope of the demand function is positive at equilibrium. When the continuum economy is replaced by an economy with a finite number of firms, noisy limit cycles and complicated behavior can be observed.     

Heterogeneous information lags and evolutionary stability

We investigate the effect of information lags in discrete time evolutionary game dynamics on symmetric games. At the end of each period, some players obtain information about the distribution of strategies among the entire population. They update their strategies according to this information. In contrast to the previous literature (e.g., Tao and Wang (1997)) where large delays lead to instability, we show that the relationship between information lags and the stability of equilibria is not “monotonic.” In anti-coordination games under smoothed best-response dynamics, a small probability of delay can stabilize the equilibrium, while a large probability can destabilize it.     

Optimal reinsurance policy: The adjustment coefficient and the expected utility criteria

This paper is concerned with the optimal form of reinsurance from the ceding company point of view, when the cedent seeks to maximize the adjustment coefficient of the retained risk. We deal with the problem by exploring the relationship between maximizing the adjustment coefficient and maximizing the expected utility of wealth for the exponential utility function, both with respect to the retained risk of the insurer.Assuming that the premium calculation principle is a convex functional and that some other quite general conditions are fulfilled, we prove the existence and uniqueness of solutions and provide a necessary optimal condition. These results are used to find the optimal reinsurance policy when the reinsurance premium calculation principle is the expected value principle or the reinsurance loading is an increasing function of the variance. In the expected value case the optimal form of reinsurance is a stop-loss contract. In the other cases, it is described by a nonlinear function.     

Outputs and time lags for linear bioreactors

Linear systems of convection reaction-diffusion equations for bioreactors are shown to have a structure which allows a geometric factorization of steady state problems giving a significant reduction in their dimensionality. Moreover, convection dominated linear systems with quasisymmetric reaction terms may be further simplified by matrix transformations, which uncouple the differential equations. The boundary conditions are also uncoupled when the diagonal diffusivity matrix D governing diffusion in the bioparticle is a scalar multiple of the corresponding matrix H describing the diffusivity characteristic of the fluid boundary layers around the bioparticles. The dominant transient behaviour of the systems may be handled by establishing an analogous system of time independent equations for mean action time variables and higher moments. These equations have the same amenable structure. Outputs, time lags and various mean residence and first passage times, associated with establishing steady outputs from a concentration free initial state, can be expressed in terms of the steady state solutions and mean action time variables.     

Accelerated modified policy iteration algorithms for Markov decision processes

We propose a new approach to accelerate the convergence of the modified policy iteration method for Markov decision processes with the total expected discounted reward. In the new policy iteration an additional operator is applied to the iterate generated by Markov operator, resulting in a bigger improvement in each iteration.     

The variational calculus and approximation in policy space for Markovian decision processes

The functional equations of Markovian decision processes yield the state values (and gain rate in the undiscounted case). Variational expressions are exhibited here for these state values (and gain rate); these expressions are stationary when evaluated at the correct values. When guesses for the values (and gain rate) are inserted into these variational expressions, a superior guess is usually obtained. Repetition of this procedure is shown to be equivalent to the method of successive approximations in policy space. Two other unusual features of this procedure are these: when the linear equations determining the Lagrange multipliers are non-singular, the variational expressions for the state variables are precisely one Newton-Raphson iteration; when applied to a linear objective function and piecewise-linear constraints, which arises for the functional equations of Markovian decision processes, the variational test quantity is piecewise constant, i.e., its first variation and higher variations all vanish. The latter explains its good performance (one-step convergence) if good estimates are available.     

Tool replacement policy for one-sided processes with low fraction defective

In most manufacturing industries, tool replacement policy is essential for minimizing the fraction defective and the manufacturing cost. Tool wear is caused by the action of sliding chips in the shear zone, and the friction generated between the tool flank and workpiece. This wear, apparently, is a dominant and irremovable component of variability in many machining processes, which is a systematic assignable cause. As the tool wear occurs in the machining processes, the fraction of defectives would gradually become significant. When the fraction defective reaches a certain level, the tool must be replaced. Therefore, detecting suitable time for tool replacement operation becomes essential. In this paper, we present an analytical approach for unilateral processes based on the one-sided process capability index C _PU (or C _PL ) to find the appropriate time for tool replacement. Accurate process capability must be calculated, particularly, when the data contains assignable cause variation. By calculating the index C _PU (or C _PL ) in a dynamical environment, we propose estimators of C _PU (or C _PL ) and obtain exact form of the sampling distribution in the presence of systematic assignable cause. The proposed procedure is then applied to a real manufacturing process involving tool wear problem, to demonstrate the applicability of the proposed approach.     

A policy improvement method for constrained average Markov decision processes

This brief paper presents a policy improvement method for constrained Markov decision processes (MDPs) with average cost criterion under an ergodicity assumption, extending Howard's policy improvement for MDPs. The improvement method induces a policy iteration-type algorithm that converges to a local optimal policy.     

Optimal policy for minimizing risk models in Markov decision processes

We consider the minimizing risk problems in discounted Markov decisions processes with countable state space and bounded general rewards. We characterize optimal values for finite and infinite horizon cases and give two sufficient conditions for the existence of an optimal policy in an infinite horizon case. These conditions are closely connected with Lemma 3 in White (1993), which is not correct as Wu and Lin (1999) point out. We obtain a condition for the lemma to be true, under which we show that there is an optimal policy. Under another condition we show that an optimal value is a unique solution to some optimality equation and there is an optimal policy on a transient set.     

Multiple-input dual-output adjustment scheme for semiconductor manufacturing processes using a dynamic dual-response approach

This paper presents an optimization-based, multiple-input dual-output (MIDO) run-to-run (R2R) controller for general semiconductor manufacturing processes. This controller, termed ‘adaptive dual-response optimizing controller’ (ADROC), can serve as a recipe regulator between consecutive runs of wafer fabrication. In ADROC, an on-line estimation technique is implemented in a self-tuning (ST) control manner for the adaptation purpose. Subsequently, an ad hoc global optimization algorithm based on the dual-response approach is used to seek the optimum recipe within the acceptability region for the execution of next run. In addition, a ‘responsive-type’ adjustment method is devised, serving as a post hoc filter to alleviate the over-control problem and maintain a better trade-off between two potentially conflicting process responses in MIDO R2R situations. Typical applications of R2R control to chemical mechanical planarization (CMP) in semiconductor manufacturing are demonstrated through simulated processes to illustrate ADROC. The procedure is compared to three benchmark methods (OAQC, single- and double-EWMA controllers) in a simulation experiment.     

A stochastic and dynamic routing policy using branching processes with state dependent immigration

A stochastic and dynamic vehicle routing problem called the Dynamic Traveling Repairman Problem (DTRP) was introduced by Bertsimas and van Ryzin. Several routing policies were analyzed in light traffic and in heavy traffic conditions. But, the good light traffic policies become very quickly unstable with increasing traffic intensity, and the good heavy traffic policies are inefficient in light traffic conditions. In this paper, a new routing policy is defined and analyzed, using results from branching processes with state dependent immigration. This policy not only performs optimally in light traffic, but also performs very well in heavy traffic. This is important to the designer of a service system because the traffic conditions may be variable and/or be unpredictable, and having to switch routing policies could prove to be costly and difficult to implement.     

Capability adjustment for gamma processes with mean shift consideration in implementing Six Sigma program

In the 1980s, Motorola, Inc. introduced its Six Sigma quality program to the world. Some quality practitioners questioned why the Six Sigma advocates claim it is necessary to add a 1.5σ shift to the process mean when estimating process capability. Bothe [Bothe, D.R., 2002. Statistical reason for the 1.5σ shift. Quality Engineering 14 (3), 479–487] provided a statistical reason for considering such a shift in the process mean for normal processes. In this paper, we consider gamma processes which cover a wide class of applications. For fixed sample size n, the detection power of the control chart can be computed. For small process mean shifts, it is beyond the control chart detection power, which results in overestimating process capability. To resolve the problem, we first examine Bothe’s approach and find the detection power is less than 0.5 when data comes from gamma distribution, showing that Bothe’s adjustments are inadequate when we have gamma processes. We then calculate adjustments under various sample sizes n and gamma parameter N, with power fixed to 0.5. At the end, we adjust the formula of process capability to accommodate those shifts which can not be detected. Consequently, our adjustments provide much more accurate capability calculation for gamma processes. For illustration purpose, an application example is presented.     

Average optimality for continuous-time Markov decision processes with a policy iteration approach

This paper deals with the average expected reward criterion for continuous-time Markov decision processes in general state and action spaces. The transition rates of underlying continuous-time jump Markov processes are allowed to be unbounded, and the reward rates may have neither upper nor lower bounds. We give conditions on the system's primitive data and under which we prove the existence of the average reward optimality equation and an average optimal stationary policy. Also, under our conditions we ensure the existence of ?-average optimal stationary policies. Moreover, we study some properties of average optimal stationary policies. We not only establish another average optimality equation on an average optimal stationary policy, but also present an interesting “martingale characterization” of such a policy. The approach provided in this paper is based on the policy iteration algorithm. It should be noted that our way is rather different from both the usually “vanishing discounting factor approach” and the “optimality inequality approach” widely used in the previous literature.     

The flow shop problem with time lags and separated setup times

This paper generalizes Khurana-Bagga's flow-shop model which involves separated setup times and time lags where no restrictions are imposed on the processing and setup times as well as start and stop lags. It solves the two machine case and provides and approximate solution for the multimachine case. It also shows that Khurana-Bagga's algorithm is restricted to a special case of the two machine problem.     

Suboptimal policy determination for large-scale Markov decision processes,Part 1: Description and bounds

This paper is the first of two papers that present and evaluate an approach for determining suboptimal policies for large-scale Markov decision processes (MDP). Part 1 is devoted to the determination of bounds that motivate the development and indicate the quality of the suboptimal design approach; Part 2 is concerned with the implementation and evaluation of the suboptimal design approach. The specific MDP considered is the infinite-horizon, expected total discounted cost MDP with finite state and action spaces. The approach can be described as follows. First, the original MDP is approximated by a specially structured MDP. The special structure suggests how to construct associated smaller, more computationally tractable MDP's. The suboptimal policy for the original MDP is then constructed from the solutions of these smaller MDP's. The key feature of this approach is that the state and action space cardinalities of the smaller MDP's are exponential reductions of the state and action space cardinalities of the original MDP.