Surveillance Search for a Moving Target

Article Abstract:

The surveillance search problem is a generalization of the detection search problem. In the surveillance version of the problem, the search terminates only if the time and location of a detection satisfy certain problem specifications. The objective is to maximize the expected value of a payoff received during and at the end of the search. For instance, we may wish to maximize the probability of finding the target in a certain space or a specified time. Consequently, we may detect the target several times before the search terminates. A search strategy must account for this strategy. In this surveillance problem the target moves according to a Markov process. A set of necessary conditions for the optimality of a search strategy is derived and algorithms are developed. A graph shows the expected payoff for a two-period, two-cell surveillance search. A large table shows the initial search allocations produced by an algorithm and the heuristic procedure for a random walk on a five by five grid.

Mathematical models, Algorithms, Algorithm, Applications, Systems, Markov Process, Theory, Search Strategy, Searching, Surveillance Systems

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A Selection Artifact in Delinquency Data Revisited

Article Abstract:

Delinquents exhibit a sharp rise in crime just before apprehension. After release from a correctional institution they continue criminal activity but at a lower rate. This phenomenon may be the result of the correctional institution or because very active criminals tend to get caught more often than inactive criminals. This paper supports the latter analysis but not as strongly as a paper by Mattz and Pollock. Graphs present data relating to arrest rates. Other models can be constructed that show that intervention has caused the reduced level of crime.

Correctional institutions, Law enforcement

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Optimal Tests for Initialization Bias in Simulation Output

Article Abstract:

The test for initialization bias in simulations as developed in this paper are powerful in widely differing situations. The forms of the initial transient mean function in the models used are very different. Relatively short runs were used to demonstrate the robustness of the asymptotic theory.

Modeling, Data modeling software, Simulation, Optimization, Simulation Theory, Statistical Analysis, Initialization

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