Over time there have been many improvements in models that are used to predict the outcome of battles. Currently there is much supposition and speculation surrounding the use of human performance related factors as additional inputs to battle simulation models to improve their accuracy. However there is no conclusive scientific evidence which shows that these factors do make a significant difference. This study investigates the use of factors that may impact on the human performance directly or indirectly in battle prediction models. These factors consist of traditional human factors and external factors that may influence the human performance. The research performs hypothesis testing to determine if these human factors aspects of a battle have a significant effect on the accuracy of battle prediction models. The data for this research consisted of a database from Concepts Analysis Agency with 660 data records, 138 different variables, and spans over 400 years. Neural networks and regression algorithms are used to create models based upon these inputs and test the significance of adding these variables to the traditionally used variables. This research failed to reject the hypothesis, which states there is no significant difference between battle outcome prediction models using the available human factors variables and battle outcome prediction models that do not include these variables. Also reflected in this study is the need for additional research leading to more complete and appropriate human factors data to be used in battle outcome simulation models.
","abstract_html":"<p>Over time there have been many improvements in models that are used to predict the outcome of battles. Currently there is much supposition and speculation surrounding the use of human performance related factors as additional inputs to battle simulation models to improve their accuracy. However there is no conclusive scientific evidence which shows that these factors do make a significant difference. This study investigates the use of factors that may impact on the human performance directly or indirectly in battle prediction models. These factors consist of traditional human factors and external factors that may influence the human performance. The research performs hypothesis testing to determine if these human factors aspects of a battle have a significant effect on the accuracy of battle prediction models. The data for this research consisted of a database from Concepts Analysis Agency with 660 data records, 138 different variables, and spans over 400 years. Neural networks and regression algorithms are used to create models based upon these inputs and test the significance of adding these variables to the traditionally used variables. This research failed to reject the hypothesis, which states there is no significant difference between battle outcome prediction models using the available human factors variables and battle outcome prediction models that do not include these variables. Also reflected in this study is the need for additional research leading to more complete and appropriate human factors data to be used in battle outcome simulation models.</p>","abstract_has_math":false,"creators":["Glovier, David Andrew"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Engineering Management & Systems Engineering","degree_department":null,"school":null,"contributors":["Derya A. Jacobs","Charles B. Keating","Resit Unal","Mark A. Scerbo"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":1998,"date_issued":"1998-04-01T08:00:00Z","date_published":"1998-04-01T08:00:00Z","updated_at":"2026-07-24T03:34:25Z","subjects":["Neural networks","Models","Human performance","Factors","Artificial Intelligence and Robotics","Industrial Engineering","Operational Research"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["9780591815764"],"render_values":[{"text":"9780591815764","href":null,"code":true}]}]},"links":{"outbound_url":"https://digitalcommons.odu.edu/emse_etds/82","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Derya A. Jacobs","Charles B. Keating","Resit Unal","Mark A. 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Currently there is much supposition and speculation surrounding the use of human performance related factors as additional inputs to battle simulation models to improve their accuracy. However there is no conclusive scientific evidence which shows that these factors do make a significant difference. This study investigates the use of factors that may impact on the human performance directly or indirectly in battle prediction models. These factors consist of traditional human factors and external factors that may influence the human performance. The research performs hypothesis testing to determine if these human factors aspects of a battle have a significant effect on the accuracy of battle prediction models. The data for this research consisted of a database from Concepts Analysis Agency with 660 data records, 138 different variables, and spans over 400 years. Neural networks and regression algorithms are used to create models based upon these inputs and test the significance of adding these variables to the traditionally used variables. This research failed to reject the hypothesis, which states there is no significant difference between battle outcome prediction models using the available human factors variables and battle outcome prediction models that do not include these variables. Also reflected in this study is the need for additional research leading to more complete and appropriate human factors data to be used in battle outcome simulation models.
"]},{"key":"dc:title","label":"Title","values":["Study of Human Factors Variables in Battle Outcome Prediction Models"]}]}],"canonical_facts":{"dc:contributor":["Derya A. Jacobs","Charles B. Keating","Resit Unal","Mark A. Scerbo"],"dc:creator":["Glovier, David Andrew"],"dc:date.available":["2019-03-15T07:00:00Z"],"dc:description.abstract":["Over time there have been many improvements in models that are used to predict the outcome of battles. Currently there is much supposition and speculation surrounding the use of human performance related factors as additional inputs to battle simulation models to improve their accuracy. However there is no conclusive scientific evidence which shows that these factors do make a significant difference. This study investigates the use of factors that may impact on the human performance directly or indirectly in battle prediction models. These factors consist of traditional human factors and external factors that may influence the human performance. The research performs hypothesis testing to determine if these human factors aspects of a battle have a significant effect on the accuracy of battle prediction models. The data for this research consisted of a database from Concepts Analysis Agency with 660 data records, 138 different variables, and spans over 400 years. Neural networks and regression algorithms are used to create models based upon these inputs and test the significance of adding these variables to the traditionally used variables. This research failed to reject the hypothesis, which states there is no significant difference between battle outcome prediction models using the available human factors variables and battle outcome prediction models that do not include these variables. Also reflected in this study is the need for additional research leading to more complete and appropriate human factors data to be used in battle outcome simulation models.
"],"dc:identifier":["9780591815764","https://digitalcommons.odu.edu/emse_etds/82"],"dc:subject":["Neural networks","Models","Human performance","Factors","Artificial Intelligence and Robotics","Industrial Engineering","Operational Research"],"dc:title":["Study of Human Factors Variables in Battle Outcome Prediction Models"],"thesis:degree_discipline":["Engineering Management & Systems Engineering"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-24T03:34:25Z"}