Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Abstract Computational tools and techniques for modeling team performance have advanced significantly in recent years. However, there have been few efforts to combine complementary modeling approaches. In the Manning Affordability Initiative, we have applied three modeling technologies to experimental data from a single domain (air defense warfare), a single scenario, and common watchstation technologies (current AEGIS technology and an advanced prototype). The conclusion of this multi-year project in early 2002 offers an opportunity to review the findings. The proposed panelists will summarize a human-in-the-loop experiment conducted to provide modeling data and present findings from efforts to integrate three modeling approaches for design and design validation. Team Optimal Design (TOD) focuses on team modeling. The Integrated Performance Modeling Environment (IPME) uses a general task modeling technique that applies well to individuals or teams. The GOMS Language Evaluation and Analysis Tool (GLEAN) combines individual models of users interacting as a team. IntroductionThe completion this year of the Manning Affordability Initiative (MAI), sponsored by the Office of Naval Research, offers the military R&D community an opportunity to assess a human-centered approach to designing and manning Navy systems. The project consisted of three initiatives. Prototype watchstations were developed using human-centered design methods to enable watchstanders to access

This paper examines three of the Integrated Performance Modelling Environment's builtin workload methodologies: Prediction of Operator Performance (POP), Information Processing/Perceptual Control Theory (IP/PCT), and POPIP. The newly-implemented POPIP is compared to its forerunner methodologies, POP and IP/PCT. POP, developed by QinetiQ, predicts performance degradation from interference between concurrent tasks using estimates of workload on different channels. IP/PCT, developed by DRDC Toronto, theorizes that all factors that impact human cognitive workload can be reduced to their effects on the amount of information to be processed, and the amount of time available before the task must be completed. POPIP uses components from both POP and IP/PCT for a combined workload algorithm that offers interference based on time pressure, and task scheduling. A sample IPME model is discussed, and used as an example in comparing all three workload methodologies. INTRODUCTIONThe Integrated Performance Modelling Environment (IPME) is a commercially available Linux-based discreteevent simulation software application for building models that simulate real-life processes. With IPME models, users can gain useful information about processes that might be too expensive or time-consuming to test in the real world. These models also allow system designers to evaluate system configurations, designs, or proposed changes before expensive software and infrastructure elements are developed. Some common application areas for simulation modeling using IPME include: evaluating procedures, workload, and crewing issues for aircraft and ships to identify human performance bottlenecks or likely errors; modeling adaptation and alertness to changing time zones; and analyzing information flow, staff sizes, and workload for a staff of 15-20 people in a military control center.This paper first provides a high-level overview of IPME's history and its approach to simulation modeling. Next, the three built-in workload methodologies are described: Prediction of Operator Performance (POP), Information Processing/Perceptual Control Theory (IP/PCT), and POPIP. Then, a sample model of a pedestrian is described, providing initial results for comparing the newly-implemented POPIP workload method to its forerunner methodologies, POP and IP/PCT. Finally, the last section discusses the model results and presents future directions for POPIP. BACKGROUND

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