Modeling Reduced Human Performance as a Complex Adaptive System

Current cognitive models not only lack flexibility and realism, they fail to model individual behavior and reduced performance. This research analyzes current cognitive theories (namely, symbolism, connectionism, and dynamicism). It hypothesizes that reduced human performance can be best modeled as a complex adaptive system. The resulting multi-agent model "Reduced Human Performance Model (RHPM)" implements reactive agents competing for cognitive resources. Lack of resources is used to trigger the simulation of imperfect perception and imperfect cognition. The simulation system is calibrated with human experimental data in scenarios involving vigilance decrement, wherein vigilance is decreased during the first 30 minutes of a screening task. RHPM is then validated against previous unknown vigilance task scenarios. RHPM generates realistic reduced human performance with a new cognitive modeling hypothesis. The developed multi-agent system generates adaptive and emergent behavior. Its use for computer generated forces (i.e. radar screen operator) would improve the realism of simulation systems by adding human like reduced performance. This research's main contribution is the development of a well suited tool to mediate between vigilance theories such as signal detection theory and experimental data. It generates insights creating likely hypotheses to improve the theories.http://archive.org/details/modelingreducedh109459885Major, German ArmyApproved for public release; distribution is unlimited

Optimal use of German Army maintenance resources

The German Army's maintenance branch has lost 25 percent of its soldiers since the end of the cold war. The maintenance branch has insufficient military personnel within maintenance units to maintain all combat unit equipment. The Army, therefore, purchases civilian man hours (mhrs) to satisfy some required maintenance. This thesis develops a mixed integer linear program, named ADOPT (administrative order optimizer), to optimally assign combat unit equipment to maintenance units and to distribute a budget to purchase civilian mhrs. ADOPT also determines beneficial cross-training of soldiers from one maintenance type to another. Since it is not always possible to maintain all combat unit equipment, ADOPT minimizes the gap, prioritized by equipment types, between needed maintenance mhrs and available military and civilian maintenance mhrs. ADOPT provides a tool to determine and evaluate options and principles that impact the readiness of a German Army Division's materiel. ADOPT validates its effectiveness with data of Military District VIII' Mechanized Infantry Division. Results indicate a potential budget saving of one-third when cross- training of maintenance soldiers from one maintenance type to another is allowed. ADOPT also shows that the regional principle (as-signing common combat unit equipment to the nearest maintenance units) is inefficient.http://www.archive.org/details/optimaluseofgerm00wellCaptain, German ArmyApproved for public release; distribution is unlimited

Modeling Vigilance Performance as a Complex Adaptive System

Journal of Defense Modeling and Simulation, Volume 1, No.1, 2004, January 2004, pp.29-42.Accepted/Published Paper (Refereed

Vigilance Performance modeled as a Complex Adaptive System

2004 Simulation Interoperability Workshop, Paper Number 7 & PresentationSimulation Interoperability Standards Organization (SISO) SIW Conference PaperThis research has addressed the need for modeling human performance more realistically. It developed a computational model for vigilance performance, embedded in a new cognitive framework that utilizes recent advances in system neuroscience, evolutionary psychology, and complexity theory. A computational model of vigilance is needed —for example to simulate airport security screeners, radar screen operators, sonar operators, and intelligence analysts. The developed model allows the simulation of realistic human errors in monitoring tasks; it can thereby generate surprises in simulation programs that might show weaknesses of security systems. After studying human performance especially vigilance, experiments were conducted to establish correlations between personality and performance and to collect data for calibrating and validating the model. The robust model shows a reasonable range of individual behaviors and represents a tool well suited for gaining insights into vigilance theories. The insights can potentially be used to improve existing theories and monitoring procedures, minimizing errors that might lead to catastrophic outcome

Vigilance Performance Modeled As A Complex Adaptive System With Listener Event Graph Objects (LEGOs)

Proceedings of the 2004 Winter Simulation Conference, R .G. Ingalls, M. D. Rossetti, J. S. Smith, and B. A. Peters, eds.There has been an increasing need to incorporate human performance in simulation models. Situations in which human performance is subject to degradation over time, such as vigilance tasks, are not represented. This article describes a computational model for vigilance performance embedded in a new cognitive framework that utilizes recent advances in system neuroscience, evolutionary psychology and complexity theory. The Reduced Human Performance Model (RHPM) captures human errors in monitoring tasks to a greater degree than previous attempts. RHPM is implemented as a discrete event simulation using Listener Event Graph Objects (LEGOs). The model captures leading vigilance theories and can be used as a tool to improve existing vigilance theories and to improve current monitoring procedures minimizing errors that could lead to catastrophic outcomes

4. TITLE AND SUBTITLE

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