Search

We consider integer‐valued processes with a linear or nonlinear generalized autoregressive conditional heteroscedastic models structure, where the count variables given the past follow a Poisson distribution. We show that a contraction condition imposed on the intensity function yields a contraction property of the Markov kernel of the process. This allows almost effortless proofs of the existence and uniqueness of a stationary distribution as well as of absolute regularity of the count process. As our main result, we construct a coupling of the original process and a model‐based bootstrap counterpart. Using a contraction property of the Markov kernel of the coupled process we obtain bootstrap consistency for different types of statistics.

We consider a stochastic individual-based model in continuous time to describe a size-structured population for cell divisions. This model is motivated by the detection of cellular aging in biology. We address here the problem of nonparametric estimation of the kernel ruling the divisions based on the eigenvalue problem related to the asymptotic behavior in large population. This inverse problem involves a multiplicative deconvolution operator. Using Fourier technics we derive a nonparametric estimator whose consistency is studied. The main difficulty comes from the non-standard equations connecting the Fourier transforms of the kernel and the parameters of the model. A numerical study is carried out and we pay special attention to the derivation of bandwidths by using resampling.

In this article, the dynamics and stability of a linear system with stochastic delay and additive noise are investigated. It is assumed that the delay value is sampled periodically from a stationary distribution. A semi-discretization technique is used to time-discretize the system and derive the mean and second-moment dynamics. These dynamics are used to obtain the stationary moments and the corresponding necessary and sufficient stability conditions. The application of the proposed method is illustrated through the analysis of the Hayes equation with stochastic delay and additive noise. The method is also applied to the control design of a connected automated vehicle. These examples illuminate the effects of stochastic delays on the robustness of dynamical systems.

This note reconsiders the marginal density of a threshold moving average process and proposes a simple yet effective numerical algorithm to implement that by solving an associated integral equation. This algorithm can also be applied to calculate stationary probability density or distribution functions of a few other types of nonlinear stationary stochastic processes numerically.

We consider a remote state estimation problem in the presence of an eavesdropper over packet dropping links. A smart sensor transmits its local estimates to a legitimate remote estimator, in the course of which an eavesdropper can randomly overhear the transmission. This problem has been well studied for unstable dynamical systems, but seldom for stable systems. In this article, we target at stable and marginally stable systems and aim to design an event-triggered scheduling strategy by minimizing the expected error covariance at the remote estimator and keeping that at the eavesdropper above a user-specified lower bound. To this end, we model the evolution of the error covariance as an infinite recurrent Markov chain and develop a recurrence relation to describe the stationary distribution of the state at the eavesdropper. Monotonicity and convergence properties of the expected error covariance are further investigated and employed to solve the optimization problem. Numerical examples are provided to validate the theoretical results.

We consider a class of dynamic random graphs known as preferential attachment models, where the probability that a new vertex connects to an older vertex is proportional to a sublinear function of the indegree of the older vertex at that time. It is well known that the distribution of a uniformly chosen vertex converges to a limiting distribution. Depending on the parameters, the tail of the limiting distribution may behave like a power law or a stretched exponential. Using Stein's method we provide rates of convergence to zero of the total variation distance between the finite distribution and its limit. Our proof uses the fact that the limiting distribution is the stationary distribution of a Markov chain together with the generator method of Barbour.

I introduce a dynamic framework to analyze platforms. The (single) platform owner sets prices at the beginning of each period. Agents (buyers, sellers, readers, consumers, merchants, etc.) make platform membership decisions occasionally. I show that an optimal platform pricing addresses two externalities: across sides and across time periods. This results in optimal prices which depend on platform size in a nontrivial way. By means of numerical simulations, I examine the determinants of equilibrium platform size, showing that the stationary distribution of platform size may be bimodal, that is, with some probability the platform remains very low or takes very long to increase in size. I also contrast the dynamics of proprietary versus nonproprietary (i.e., zero‐priced) platforms, and consider the specific case of asymmetric platforms (one side cares about the other but not vice versa).

This paper introduces a class of Monte Carlo algorithms which are based upon the simulation of a Markov process whose quasi-stationary distribution coincides with a distribution of interest. This differs fundamentally from, say, current Markov chain Monte Carlo methods which simulate a Markov chain whose stationary distribution is the target. We show how to approximate distributions of interest by carefully combining sequential Monte Carlo methods with methodology for the exact simulation of diffusions. The methodology introduced here is particularly promising in that it is applicable to the same class of problems as gradient based Markov chain Monte Carlo algorithms but entirely circumvents the need to conduct Metropolis-Hastings type accept/reject steps whilst retaining exactness: the paper gives theoretical guarantees ensuring the algorithm has the correct limiting target distribution. Furthermore, this methodology is highly amenable to big data problems. By employing a modification to existing na{\"\i}ve sub-sampling and control variate techniques it is possible to obtain an algorithm which is still exact but has sub-linear iterative cost as a function of data size., Comment: Substantially revised with clearer presentation and more extensive simulation study. 59 pages, 6 figures

In this article, we analyze a discrete‐time queue that is motivated from studying hospital inpatient flow management, where the customer count process captures the midnight inpatient census. The stationary distribution of the customer count has no explicit form and is difficult to compute in certain parameter regimes. Using the Stein's method framework, we identify a continuous random variable to approximate the steady‐state customer count. The continuous random variable corresponds to the stationary distribution of a diffusion process with state‐dependent diffusion coefficients. We characterize the error bounds of this approximation under a variety of system load conditions—from lightly loaded to heavily loaded. We also identify the critical role that the service rate plays in the convergence rate of the error bounds. We perform extensive numerical experiments to support the theoretical findings and to demonstrate the approximation quality. In particular, we show that our approximation performs better than those based on constant diffusion coefficients when the number of servers is small, which is relevant to decision making in a single hospital ward.

Summary This paper introduces nonparametric econometric methods that characterize general power law distributions under basic stability conditions. These methods extend the literature on power laws in the social sciences in several directions. First, we show that any stationary distribution in a random growth setting is shaped entirely by two factors: the idiosyncratic volatilities and reversion rates (a measure of cross-sectional mean reversion) for different ranks in the distribution. This result is valid regardless of how growth rates and volatilities vary across different economic agents, and hence applies to Gibrat's law and its extensions. Second, we present techniques to estimate these two factors using panel data. Third, we describe how our results imply predictability as higher-ranked processes must on average grow more slowly than lower-ranked processes. We employ our empirical methods using data on commodity prices and show that our techniques accurately describe the empirical distribution of relative commodity prices. We also show that rank-based out-of-sample forecasts of future commodity prices outperform random-walk forecasts at a 1-month horizon.

Summary In this paper, first we introduce a new tensor product for a transition probability tensor originating from a higher-order Markov chain. Subsequently, some properties of the new tensor product are explained, and its relationship with the stationary probability vector is studied. Also, similarity between results obtained by this new product and the first-order case is shown. Furthermore, we prove the convergence of a transition probability tensor to the stationary probability vector. Finally, we show how to achieve a stationary probability vector with some numerical examples and make some comparison between the proposed method and another existing method for obtaining stationary probability vectors. Copyright © 2016 John Wiley & Sons, Ltd.

This paper is part II of a two-part series devoted to the study of systematic measures in a complex bionetwork modeled by a system of ordinary differential equations. In this part, we quantify several systematic measures of a biological network including degeneracy, complexity, and robustness. We will apply the theory of stochastic differential equations to define degeneracy and complexity for a bionetwork. Robustness of the network will be defined according to the strength of attractions to the global attractor. Based on the study of stationary probability measures and entropy made in part I of this series, we will investigate some fundamental properties of these systematic measures, in particular the connections between degeneracy, complexity, and robustness.© 2016 Wiley Periodicals, Inc.

We study a joint capacity leasing and demand acceptance problem in intermodal transportation. The model features multiple sources of evolving supply and demand, and endogenizes the interplay of three levers--forecasting, leasing, and demand acceptance. We characterize the optimal policy, and show how dynamic forecasting coordinates leasing and acceptance. We find (i) the value of dynamic forecasting depends critically on scarcity, stochasticity, and volatility; (ii) traditional mean-value equivalence approach performs poorly in volatile intermodal context; (iii) mean-value-based forecast may outperform stationary distribution-based forecast. Our work enriches revenue management models and applications. It advances our understanding on when and how to use dynamic forecasting in intermodal revenue management.

The feedback control scheme for a Bose-Einstein condensate (BEC) in a double-well trapping potential located in one arm of Mach-Zehnder interferometer (MZI) is investigated. The off-resonant light beam performs the phase probing in one of the wells, thus creating information about the number of atoms in this well. The parameters of the trapping potential are controlled via a feedback loop based on the measured output of the MZI. The problem is analyzed in the framework of master equations for hybrid quantum-classical systems. Significant modifications of the stationary distribution of atoms over the wells are predicted. These distributions can effectively be controlled by the tunable phase shift in the other arm of the MZI.

In this article, we study a class of Quasi-Skipfree (QSF) processes where the transition rate submatrices in the skipfree direction have a column times row structure. Under homogeneity and irreducibility assumptions we show that the stationary distributions of these processes have a product form as a function of the level. For an application, we will discuss the -queue that can be modeled as a QSF process on a two-dimensional state space. In addition, we study the properties of the stationary distribution and derive monotonicity of the mean number of the customers in the queue, their mean sojourn time and the variance as a function of for fixed mean arrival rate. © 2016 Wiley Periodicals, Inc. Naval Research Logistics, 2016

Summary A stochastic Lotka–Volterra predator–prey system driven by both Brownian motion and Poisson counting measure is modeled and studied in this paper. A new ergodic method is proposed to solve the classical optimal harvesting problem. Equivalency between time averaged yield function and sustained yield function is proved by this new approach. The optimal harvesting strategy and the corresponding maximum yield with respect to stationary probability density are obtained. Several examples are taken to show that results in this paper are new even in the deterministic case. The method proposed in this paper can avoid trouble of solving the corresponding partial differential equations, and it can be extended to a more general high-dimensional case or some other stochastic system. Copyright © 2015 John Wiley & Sons, Ltd.

Even though many computational methods (recursive formulae) for blocking probabilities in finite-capacity M/D/1 queues have already been produced, these are forms of transforms or are limited to single-node queues. Using a distinctly different approach from the usual queueing theory, this study introduces explicit (transform-free) formulae for a blocking probability, a stationary probability, and mean sojourn time under either production or communication blocking policy. Additionally, the smallest buffer capacity subject to a given blocking probability can be determined numerically from these formulae. With proper selection of the overall offered load , the approach described herein can be applicable to more general queues from a computational point of view if the explicit expressions of random vector are available.

The �Principle of Least Action" is the foundational principle of fundamental physics. Application of this principle to the supply chain naturally results in an �uncertainty principle� linking variation in production to variation in net-inventory. The model also provides an easy means of determining the stationary distribution of net-inventory for a variety of control strategies. The formalism results in a control strategy that outperforms commonly used control methods.

SUMMARY Probabilistic theories aim at describing the properties of systems subjected to random excitations by means of statistical characteristics such as the probability density function ψ (pdf). The time evolution of the pdf of the response of a randomly excited deterministic system is commonly described with the transient Fokker–Planck–Kolmogorov (FPK) equation. The FPK equation is a conservation equation of a hypothetical or abstract fluid, which models the transport of probability. This paper presents a generalized formalism for the resolution of the transient FPK equation by using the well-known mesh-free Lagrangian method, smoothed particle hydrodynamics). Numerical implementation shows notable advantages of this method in an unbounded state space: (1) the conservation of total probability in the state space is explicitly written; (2) no artifact is required to manage far-field boundary conditions; (3) the positivity of the pdf is ensured; and (4) the extension to higher dimensions is straightforward. Furthermore, thanks to the moving particles, this method is adapted for a large kind of initial conditions, even slightly dispersed distributions. The FPK equation is solved without any a priori knowledge of the stationary distribution, just a precise representation of the initial distribution is required.Copyright © 2013 John Wiley & Sons, Ltd.

In this paper, we consider information transmission over a block Rayleigh fading channel, where a finite size buffer is employed to match the source traffic with the channel service capability. Given the buffer size, the transmission capability of a block fading Rayleigh channel is characterized from two aspects: (i) the buffer behavior when the input traffic rate is constant; and (ii) the traffic rate that can be supported by the channel for a given overflow probability constraint. For the first problem, the stationary distribution of the queue length in the buffer is derived by discretizing the queue length using a uniform quantization strategy. It is also shown that the overflow probability of the finite size buffer decreases exponentially with buffer size. An explicit upper bound on the overflow probability is also given. For the second one, a new concept of e-overflow rate is proposed to measure the transmission capability of a block fading channel under overflow probability constraints. It will be shown that the e-overflow rate is larger than the e-outage capacity under the same outage constraint and will meet the great gap between outage capacity and ergodic capacity as the overflow probability constraint varies. Copyright © 2012 John Wiley & Sons, Ltd.

Let n be the compact convex set of tridiagonal doubly stochastic matrices. These arise naturally in probability problems as birth and death chains with a uniform stationary distribution. We study ‘typical’ matrices T∈ n chosen uniformly at random in the set n. A simple algorithm is presented to allow direct sampling from the uniform distribution on n. Using this algorithm, the elements above the diagonal in T are shown to form a Markov chain. For large n, the limiting Markov chain is reversible and explicitly diagonalizable with transformed Jacobi polynomials as eigenfunctions. These results are used to study the limiting behavior of such typical birth and death chains, including their eigenvalues and mixing times. The results on a uniform random tridiagonal doubly stochastic matrices are related to the distribution of alternating permutations chosen uniformly at random.© 2012 Wiley Periodicals, Inc. Random Struct. Alg., 42, 403–437, 2013

A biomathematical model is described by stochastic differential equations with Markovian switching. The long-time dynamical properties are studied both theoretically and numerically. Results show that both the persistence and extinction have close relationship with environmental noises (white and color noises). These results are of important biological significance for biological conservation. Copyright © 2012 John Wiley & Sons, Ltd.

In this article, we deal with a 1-out-of-2 system with a cold-standby component. The failure process of these two components is statistically identical and characterized by the delay-time concept, which divides the component's life into a two-stage process with a normal stage from new to a defect initiation point and a following failure delay stage from this point to failure. A fixed interval periodic inspection is carried out to check whether the working component is defective or not. If a defect is identified, the working component is shut down and sent to be repaired. The repair shop capacity is limited for repairing one component only. If the repair shop is occupied by the standby component (under repair) when the working one is failed, the system is shut down. There are five scenarios of the state of the system at an inspection epoch, which are derived analytically. The expected total cost per unit time of the system consists of the expected cost caused by preventive repair, unplanned failure repair, and system downtime. In this article, these three costs are determined and the optimal inspection interval with respect to the minimum of the expected total cost per unit time is found. To incorporate a nonexponential distribution for the normal time, delay stages, and repair time of the components, the elapsed time in the normal time, delay stages, and repair time at inspection is taken into consideration. A stationary distribution of the elapsed time exists, and it is calculated by an iteration method. Copyright © 2012 John Wiley & Sons, Ltd.

SUMMARY This work focuses on tracking and system identification of systems with regime-switching parameters, which are modeled by a Markov process. It introduces a framework for persistent identification problems that encompass many typical system uncertainties, including parameter switching, stochastic observation disturbances, deterministic unmodeled dynamics, sensor observation bias, and nonlinear model mismatch. In accordance with the ‘frequency’ of the parameter switching process, we divide the problems into two classes. For fast-switching systems, the switching parameters are stochastic processes modeled by irreducible and aperiodic Markov chains. Because accurately tracking real-time parameters in such systems is not possible because of the uncertainty principles, the effect of parameter switching is evaluated on their average by the stationary distribution of the Markovian chain and estimated by the least squares algorithms. We derive upper and lower bounds on identification errors, which characterize how identification accuracy depends on the earlier uncertainty terms. When the system parameters switch their values infrequently in a probabilistic sense, their values can be tracked based on input/output observations. Stochastic approximation algorithms with adaptive step sizes are used for such systems. Simulation studies are carried out to demonstrate that slowly varying parameters could be tracked with reasonable accuracy.Copyright © 2012 John Wiley & Sons, Ltd.

SUMMARY In this work the controlled continuous-time finite-state Markov chain with safety constraints is studied. The constraints are expressed as a finite number of inequalities, whose intersection forms a polyhedron. A probability distribution vector is called safe if it is in the polyhedron. Under the assumptions that the controlled Markov chain is completely observable and the controller induces a unique stationary distribution in the interior of the polyhedron, the author identifies the supreme invariant safety set (SISS) where a set is called an invariant safety set if any probability distribution in the set is initially safe and remains safe as time evolves. In particular, the necessary and sufficient condition for the SISS to be the polyhedron itself is given via linear programming formulations. A closed-form expression for the condition is also derived as the constraints impose only upper and/or lower bounds on the components of the distribution vectors. If the condition is not satisfied, a finite time bound is identified and used to characterize the SISS. Numerical examples are provided to illustrate the results. Copyright © 2011 John Wiley & Sons, Ltd.

Many traits and/or strategies expressed by organisms are quantitative phenotypes. Because populations are of finite size and genomes are subject to mutations, these continuously varying phenotypes are under the joint pressure of mutation, natural selection and random genetic drift. This article derives the stationary distribution for such a phenotype under a mutation–selection–drift balance in a class-structured population allowing for demographically varying class sizes and/or changing environmental conditions. The salient feature of the stationary distribution is that it can be entirely characterized in terms of the average size of the gene pool and Hamilton's inclusive fitness effect. The exploration of the phenotypic space varies exponentially with the cumulative inclusive fitness effect over state space, which determines an adaptive landscape. The peaks of the landscapes are those phenotypes that are candidate evolutionary stable strategies and can be determined by standard phenotypic selection gradient methods (e.g. evolutionary game theory, kin selection theory, adaptive dynamics). The curvature of the stationary distribution provides a measure of the stability by convergence of candidate evolutionary stable strategies, and it is evaluated explicitly for two biological scenarios: first, a coordination game, which illustrates that, for a multipeaked adaptive landscape, stochastically stable strategies can be singled out by letting the size of the gene pool grow large; second, a sex-allocation game for diploids and haplo-diploids, which suggests that the equilibrium sex ratio follows a Beta distribution with parameters depending on the features of the genetic system.

A Markov chain approach to detecting a threat in a given surveillance zone by a network of steerable sensors is presented. The network has a finite number of predetermined states, and transition from one state to another follows a Markov chain. Under the assumption that the threat avoids detection, two game theoretic problems for finding an optimal Markov chain (two surveillance strategies) are formulated: the first maximizes the probability of threat detection for two consecutive detection periods, whereas the second minimizes the average time of detection for the worst-case threat's trajectory. Both problems are reduced to linear programming, and special techniques are suggested to solve them. For a dynamic environment with moving noise sources, the optimal Markov chain changes at each detection period, and the rate of convergence of the Markov chain to its stationary distribution is analyzed. Both surveillance strategies are tested in numerical experiments and compared one with another. © 2011 Wiley Periodicals, Inc. Naval Research Logistics, 2011

A device that can fail by shocks or ageing under policy N of maintenance is presented. The interarrival times between shocks follow phase-type distributions depending on the number of cumulated shocks. The successive shocks deteriorate the system, and some of them can be fatal. After a prefixed number k of nonfatal shocks, the device is preventively repaired. After a fatal shock the device is correctively repaired. Repairs are as good as new, and follow phase-type distributions. The system is governed by a Markov process whose infinitesimal generator, stationary probability vector, and availability are calculated, obtaining well-structured expressions due to the use of phase-type distributions. The availability is optimized in terms of the number k of preventive repairs. Copyright © 2009 John Wiley & Sons, Ltd.

In this paper, our interest is in the perturbation analysis of level‐dependent quasi‐birth‐and‐death (LD‐QBD) processes, which constitute a wide class of structured Markov chains. An LD‐QBD process has the special feature that its space of states can be structured by levels (groups of states), so that a tridiagonal‐by‐blocks structure is obtained for its infinitesimal generator. For these processes, a number of algorithmic procedures exist in the literature in order to compute several performance measures while exploiting the underlying matrix structure; among others, these measures are related to first‐passage times to a certain level L(0) and hitting probabilities at this level, the maximum level visited by the process before reaching states of level L(0), and the stationary distribution. For the case of a finite number of states, our aim here is to develop analogous algorithms to the ones analyzing these measures, for their perturbation analysis. This approach uses matrix calculus and exploits the specific structure of the infinitesimal generator, which allows us to obtain additional information during the perturbation analysis of the LD‐QBD process by dealing with specific matrices carrying probabilistic insights of the dynamics of the process. We illustrate the approach by means of applying multitype versions of the susceptible‐infective (SI) and susceptible‐infective‐susceptible (SIS) epidemic models to the spread of antibiotic‐sensitive and antibiotic‐resistant bacterial strains in a hospital ward.

Many of the most widely used models in finance fall within the affine family of diffusion processes. The affine family combines modeling flexibility with substantial tractability, particularly through transform analysis; these models are used both for econometric modeling and for pricing and hedging of derivative securities. We analyze the tail behavior, the range of finite exponential moments, and the convergence to stationarity in affine models, focusing on the class of canonical models defined by Dai and Singleton (2000). We show that these models have limiting stationary distributions and characterize these limits. We show that the tails of both the transient and stationary distributions of these models are necessarily exponential or Gaussian; in the nonGaussian case, we characterize the tail decay rate for any linear combination of factors. We also give necessary and sufficient conditions for a linear combination of factors to be Gaussian. Our results follow from an investigation into the stability properties of the systems of ordinary differential equations associated with affine diffusions.

On a symmetric homogeneous oligopoly market with stochastic demand, firms can either hire employees or buy their labor input on a competitive labor market. Whereas the wage of hired labor does not depend on the realization of stochastic demand, the price of ‘bought’ labor reacts positively to product demand. We derive the equilibrium price vector to define an evolutionary process, assuming that the number of hiring firms increases when they earn more than buying firms. We then derive and discuss the stationary distribution of this stochastic adaptation process.

We provide a simple proof of geometric ergodicity for Samuelson's (1971) commodity pricing model. The proof yields a rate of convergence to the stationary distribution stated in terms of model primitives. We also provide a rate of convergence for prices to the stationary price process, and for the joint distribution of the state process to the stationary state process.

Article first published online: 27 November 2008 This paper studies the population dynamics of multiple preference traits in a model of intergenerational cultural transmission. Parents socialize and transmit their preferences to their children with endogenous intensities. Populations concentrated on a single cultural group are in general not stable. There is a unique stable stationary distribution, and it supports two or more cultural groups, in particular those with greater intolerance with respect to others' traits. The larger the heterogeneity of intolerance levels across cultural groups, the smaller the number of traits that are supported in the stable stationary distribution.

The Random Direction (RD) and the Random Waypoint (RWP) mobility models have been discussed extensively in the literature. The focus of this discussion was the stationary distribution of users and speeds of users. In this paper, we extend the investigation to street-based variants of the two mobility models. We compare statistical properties including user distribution, sojourn times in hotspots and link durations in ad hoc networks. In order to motivate the use of street-based mobility models, we also perform a comparison with the basic models. Finally, we present our approach to achieve identical user distributions in both street-based models and investigate the remaining differences, which prove to be negligible for wireless network simulations. Copyright © 2007 John Wiley & Sons, Ltd.

Consider a population subjected to constant effort or constant quota fishing with a generaldensity-dependence population growth function (because that function is poorly known). Consider environmental random fluctuations that either affect an intrinsic growth parameter or birth/death rates, thus resulting in two stochastic differential equations models. From previous results of ours, we obtain conditions for non-extinction and for existence of a population size stationary density. Constant quota (which always leads to extinction in random environments) and constant effort policies are studied; they are hard to implement for extreme population sizes. Introducing cut-offs circumvents these drawbacks. In a deterministic environment, for a wide range of values, cutting-off does not affect the steady-state yield. This is not so in a random environment and we will give expressions showing how steady-state average yield and population size distribution vary as functions of cut-off choices. We illustrate these general results with function plots for the particular case of logistic growth.

1. Communities of competing sessile organisms are often modelled using Markov chains. Sensitivity analysis of the stationary distribution of these models tells us how we expect the abundance of each organism to respond to changes in interactions between species. This is important for conservation and management. 2. Markov models for such communities have usually been formulated in discrete time. Each column of the discrete-time transition matrix must sum to 1 (column stochasticity). Sensitivity analysis therefore involves defining a pattern of compensation that maintains column stochasticity as a single transition probability changes. There is little biological theory about the appropriate compensation pattern, but the usual choices involve changing only the elements of a single column of the transition matrix. 3. I argue that if the underlying dynamics occur in continuous time, each transition probability is the net outcome of direct and many indirect interactions. 4. Determining the consequences of changing a single direct interaction will often be of interest. I show how this can be achieved using a continuous-time model. The resulting discrete-time compensation pattern is quite different from those that have been considered elsewhere, with changes occurring in many columns. 5. I also show how to determine which direct interactions are being changed under any discrete-time compensation pattern.