Roberto Szechtman

Why Defeating Insurgencies Is Hard: The Effect of Intelligence in Counterinsurgency Operations---A Best-Case Scenario

In insurgency situations, the government-organized force is confronted by a small guerrilla group that is dispersed in the general population with no or a very small signature. Effective counterinsurgency operations require good intelligence. Absent intelligence, not only might the insurgents escape unharmed and continue their violent actions, but collateral damage caused to the general population from poor targeting may generate adverse response against the government and create popular support for the insurgents, which may result in higher recruitment to the insurgency. We model the dynamic relations among intelligence, collateral casualties in the population, attrition, recruitment to the insurgency, and reinforcement to the government force. Even under best-case assumptions, we show that the government cannot totally eradicate the insurgency by force. The best it can do is contain it at a certain fixed level.

Optimal Discrete Search with Imperfect Specificity

A target is hidden in one of several possible locations, and the objective is to find the target as fast as possible. One common measure of effectiveness for the search process is the expected time of the search. This type of search optimization problem has been addressed and solved in the literature for the case where the searcher has imperfect sensitivity (possible false negative results), but perfect specificity (no false positive detections). In this paper, which is motivated by recent military and homeland security search situations, we extend the results to the case where the search is subject to false positive detections.

Optimal Budget Allocation for Sample Average Approximation

The sample average approximation approach to solving stochastic programs induces a sampling error, caused by replacing an expectation by a sample average, as well as an optimization error due to approximating the solution of the resulting sample average problem. We obtain estimators of an optimal solution and the optimal value of the original stochastic program after executing a finite number of iterations of an optimization algorithm applied to the sample average problem. We examine the convergence rate of the estimators as the computing budget tends to infinity, and we characterize the allocation policies that maximize the convergence rate in the case of sublinear, linear, and superlinear convergence regimes for the optimization algorithm.

A simulation model of a helicopter ambulance service

We study two different operational scenarios for a regional air ambulance service-company which has bases in northern California. Two of these bases serve the land areas encompassed roughly in a circular area of radius 100 miles centered in Gilroy and Salinas, respectively; with a large part of their coverage areas reachable from either base. The base in Salinas currently operates one helicopter only from Thursday to Monday, whereas the base in Gilroy operates one helicopter 24/7. The company is considering extending the operation of one helicopter to 24/7 for its Salinas base. In this paper we analyze the operational impacts of that extension, and develop a framework that can be applied towards the study of the ambulance assignment problem faced by small operators

Control variates techniques for Monte Carlo simulation

In this paper we present an overview of classical results about the variance reduction technique of control variates. We emphasize aspects of the theory that are of importance to the practitioner, as well as presenting relevant applications.

Carrots, Sticks and Fog During Insurgencies

We formulate a rational choice model of popular behavior during an insurgency. An individual in the population either supports the insurgents or the government depending upon his attitude and the actions taken by each side. We focus on the effect of insurgency actions: benefits, impositions, and coercion. While benefits and impositions are applied uniformly throughout, the insurgents intend to only coerce those actively providing information to the government. However, due to the “fog of war”, which may lead to limited situational awareness, the insurgents may mistakenly coerce their own supporters and potentially drive them to aid the government. We examine how popular behavior varies under different situational awareness scenarios. When the insurgents have little situational awareness, they should take few coercive actions. This implies that the government will be able to foster intelligence sources within the population. If the insurgents have perfect situational awareness, tipping points may occur that result in a significant reduction in active support for the government. In this case the government should take actions to decrease the coercing effectiveness of the insurgents and increase incentives to the population so they continue to provide information.

A Bayesian approach to feasibility determination

We propose a computing budget allocation scheme for feasibility determination in a stochastic setting. More formally, we propose a Bayesian approach to determine whether a system belongs to a given set based on performance measures estimated through Monte Carlo simulation. We introduce two adaptive approaches in the sense that the computational budget is allocated dynamically based on the samples obtained thus far. The first approach determines the number of additional samples required so that the posterior probability that a systems mean performance is correctly classified is at least 1−δ in expectation, while the second approach determines the number of additional samples so that the posterior probability that the system mean lies inside or outside of the feasible region is at least 1−δ with a desired probability. Preliminary numerical experiments are reported.

Multiple comparisons with a standard using false discovery rates

We introduce a new framework for performing multiple comparisons with a standard when simulation models are available to estimate the performance of many different systems. In this setting, a large proportion of the systems have mean performance from some known null distribution, and the goal is to select alternative systems whose means are different from that of the null distribution. We employ empirical Bayes ideas to achieve a bound on the false discovery rate (proportion of selected systems from the null distribution) and a desired probability an alternate type system is selected.

Selecting small quantiles

Ranking and selection (R&S) techniques are statistical methods developed to select the best system, or a subset of systems from among a set of alternative system designs. R&S via simulation is particularly appealing as it combines modeling flexibility of simulation with the efficiency of statistical techniques for effective decision making. The overwhelming majority of the R&S research, however, focuses on the expected performance of competing designs. Alternatively, quantiles, which provide additional information about the distribution of the performance measure of interest, may serve as better risk measures than the usual expected value. In stochastic systems, quantiles indicate the level of system performance that can be delivered with a specified probability. In this paper, we address the problem of ranking and selection based on quantiles. In particular, we formulate the problem and characterize the optimal budget allocation scheme using the large deviations theory.

Chapter 10: A Hilbert Space Approach To Variance Reduction

In this chapter we explain variance reduction techniques from the Hilbert space standpoint, in the terminating simulation context. We use projection ideas to explain how variance is reduced, and to link difierent variance reduction techniques. Our focus is on the methods of control variates, conditional Monte Carlo, weighted Monte Carlo, stratiflcation, and Latin hypercube sampling.