Using Complexity for Manpower Modeling: A Feasibility Study

Abstract

We examined whether the class of complexity-based model(s) known as Agent-Based Models (ABMs) could be a useful decision-support tool for personnel planning and management. In using ABM, a system is modeled as a collection of autonomous, decision making agents. ABMs are built using an object-oriented programming language. Each agent, the agent's environment, and the schedule that controls the model run are independent objects that can be matched in a variety of ways. A major strength of ABM is its ability to simulate real interactions between individuals and groups allowing for a wide variety of feedback, adaptation, and negotiation behaviors. However, ABM's results are sensitive to initial conditions, and the reliability of such results is limited to ranges of outcomes linked to ranges of input parameters. In examining a variety of ABM applications-including biological, behavioral, and organizational-we determined that ABMs have dealt with the kinds of issues important to the Navy and that, while not a perfect analogy an ABM supply chain type of model would meet many of the Navy's personnel modeling requirements. Given the possible benefits of using an ABM, we feel that there would be value in building a prototype "proof-of-concept" ABM to test its utility.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2003
Accession Number
ADA419628

Entities

People

  • Apriel K. Hodari
  • Daniel D. Burke

Organizations

  • Center for Naval Analyses

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies
  • Human Systems

DTIC Thesaurus Topics

  • Agent-Based Simulations
  • Computational Science
  • Computer Programming
  • Computer Simulations
  • Computers
  • Human Behavior
  • Language
  • Mathematical Models
  • Object Oriented Programming
  • Object-Oriented Programming Language
  • Organizational Structure
  • Personnel Management
  • Programming Languages
  • Prototypes
  • Simulations
  • Social Sciences
  • Supply Chain

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Computational Modeling and Simulation
  • Logistics and Supply Chain Management.