Wenkriteria vir konvensionele landgevegte

Abstract

Hierdie studie is onderneem met die doel om 'n model te ontwikkel waarmee die wenner in 'n konvensionele landgeveg voorspel kan word. Gegewe die omvang van die vakgebied oorlog, is die studie beperk tot die taktiese vlak en fokus op landgevegte tydens konvensionele oorlogvoering. As eerste stap in die ontwikkelingpsproses, is die faktore wat wen kan bepaal krygskundig nagevors. Die sogenaamde honderdgevegte-datastel is saamgestel uit data van 100 gevegte uit die twintigste eeu en net vroeer, met die klem op gevegte waarin Suid-Afrikaanse magte betrokke was. Verskeie statistiese tegnieke is ondersoek om 'n geskikte tegniek vir die ontleding van die data te vind. Die ondersoek het aangetoon dat logistiese regressie die beste tegniek is vir die data. 'n Ontwikkelingsproses met drie voorspellers is ook saamgestel. Verskeie modelle is ondersoek, naamlik 1 'n Voorspellingsmodel met eensydige sub-modelle sonder gevegshouding, met en sonder opponentdata. I 2 'n Voorspellingsmodel met eensydige sub-modelle met gevegshouding, met en sonder opponentdata. 3 'n Voorspellingsmodel met tweesydige sub-modelle met opponentdata.. Die ontwikkelingsproses lewer verskeie modelle wat baie goed presteer sensitiwiteit > 80%). 'n Finale keuse lewer die volgende resultaat: 1 Vir die geval waar opponentdata nie beskikbaar is nie, is 'n eensydige submode! sonder gevegshouding ontwikkel waarvan die resultaat teen 'n skeidingsgrens gemeet word om die uitslag te bepaal. Die model het 'n sensitiwiteit van 85%, maar kan net 'n wen of gelykop, of, verloor of gelykop voorspel. 2 Vir die geval waar opponentdata beskikbaar is, is 'n eensydige sub-model ivsonder gevegshouding ontwikkel wat in staat is om, deur die opponente se uitslag met mekaar te vergelyk, die wenner aan te wys. Hierdie model het 'n sensitiwiteit van 83,8% Verskeie statistiese en krygskundige gevolgtrekkings word gemaak, die belangrikste waarvan dat die gekose modelle wel daartoe in staat is om gevegsvoorspellings akkuraat te kan uitvoer. Die modelle kan ook aangewend word om gevegte te ontleed en tendense te verklaar. Krygskundig bevestig die resultaat die noodsaaklikheid van die maneuvreringsbenadering en goeie leierskap. Die resultaat van die studie het wye aanwendingspotensiaal op die gebied van die krygskunde, krygsfilosofie, krygspele en militere operasionele navorsing en laat ruimte vir interessante en noodsaaklike verdere navorsing in operasionele navorsing sowel as in die krygskunde.

The aim of this study is to develop models for the efficient prediction of the outcome of a land battle. The study is confined to conventional warfare at the tactical level. The first step was to identify the variables that may determine victory. Thirty such variables enjoying the support of various military historians and philosophers were selected. The hundred-battle data set, consisting of coded data for a hundred twentieth-century battles, was compiled. The thirty variables were encoded for each combatant. Since the outcome and most of the prediction variables are binary but a few are continuous, ordinary linear regression could not be used and several statistical and other techniques were evaluated. Logistic regression was found to be the best. A formalized development and selection process was applied to a number of broad model classes. These were 1 prediction models with one-sided sub-models without combat posture and with (without) opponent data 2 prediction models with one-sided sub-models with combat posture and with (without) opponent data 3 prediction models with two-sided sub-models without combat posture and with opponent data. The process provided several very good models and the following were selected. Without opponent data. A one-sided sub-model without combat posture, utilizing a discriminator was selected. It determines the outcome with a sensitivity of 85%. However, it only predicts victory or a draw, defeat or a draw. With opponent data. A one-sided sub-model without combat posture was selected. It predicts the outcome of battle by comparing the results of the two opponents. This model vishowed a sensitivity of 83,8%. Several statistical and military scientific conclusions followed, the most important being that the chosen models can accurately predict battle outcome or post facto determine the outcome. The models can also be used to analyze battles. In this role they confirm the importance of maneuver warfare and good leadership. The results of this study can be applied in military science, military philosophy and war gaming. The work fuses military philosophy with statistical analysis, is a first in the field and offers the possibility of breaking out of the mind-set of personal views and biases prevalent in military science. The method as such can be applied to different data bases representing war at other levels or with other technologies.