This thesis seeks to explore the two working hypotheses: Firstly, that Napoleon’s alleged military superiority in terms of skill and battlefield competence over his peers can be empirically quantified and proven. Secondly, that the results of Napoleonic warfare can be predicted by applying the theory of Contest Success Functions to these battles.
To address these claims this paper is organized into four sections:
Theory
The first of the conceptual sections summarizes the theoretical underpinning behind the economical understanding of conflict. This so called ‘second approach’ and its merits are outlined and the history of these theoretical concepts is explained. Chapter three introduces the Ratio Contest Success Function (RCSF) put forth by Tullock and the Difference Contest Success Function (DCSF) employed by Hirshleifer, the concepts for predicting probabilities of success in conflict theory.
History
The fourth chapter gives a brief report on warfare during the Napoleonic ages. A special emphasis lies on an analysis that evaluates if the key parameters have been homogenous over the time and what kind of technology was employed during these battles. The results are then compared with the demands of conflict theory. The fifth chapter then explicates the data set. The different variables that could be obtained are introduced and at last the scope of the further analysis is specified. This is done by picking the variables that actually can be used for an in-depth quantitative.
Modelling
In the four chapters that deal with the actual modelling, the theory is applied on the historical data to yield the results we need to verify the working hypotheses. After the two different estimators used have been introduced in chapter six, the chapters seven and eight deal with utilising each of the estimators to answer these questions. The results from the estimates are interpreted and are compared in chapter nine. In addition, chapter nine attempts to weigh the explanatory value of the two approaches and places them in the historical perspective.
Résumé
Chapter ten answers comments on Napoleon’s personal worth on the battlefield and applies the findings of the empirical work on three short case studies. The subsequent summary then merges the results of the whole study and concludes with follow-up questions for future research.
Table of Contents
1. Introduction
2. The Economy of Conflict - The Second Approach
3. Modelling Contest via the Contest Success Functions
a. Contest Success Functions in General
b. The Ratio Contest Success Function
c. The Difference Contest Success Function
d. Choosing the Right Contest Success Function
4. The Essentials about Napoleonic Warfare
5. The Data Set
6. The Two Ways of Estimating a CSF
a. The OLS Estimator and its Shortcomings
b. The Logit-Model
c. Synopsis of the Estimators
7. Fitting the Ratio CSF via the Linear Probabilistic Model
a. Approach and Parameters
b. Interpretation
8. Fitting the Difference CSF via the Logit Function
a. Approach and Parameters
b. Interpretation
9. Ratio or Difference CSF? – Comparing the Results
a. Decision Time: The Best Model
b. Interpretation of the Best Model
10. How much was Napoleon actually worth?
a. Case Study: 40,000 men
b. Case Study: Jena and Auerstedt
c. Case Study: Austerlitz
11. Final Consideration and Summary
Objectives and Thematic Focus
This thesis investigates the military effectiveness of Napoleon Bonaparte through an econometric lens by applying Contest Success Functions (CSF) to historical data from the Napoleonic Wars. The primary research question is whether Napoleon's presence on the battlefield provided a quantifiable and statistically significant advantage, and if the outcomes of these historical engagements can be successfully predicted using economic conflict theory models.
- Application of Ratio and Difference Contest Success Functions to military combat.
- Econometric modeling of Napoleon’s influence on French military performance.
- Quantitative assessment of the "40,000 men" historical claim regarding Napoleon's presence.
- Comparative analysis of different estimation techniques (OLS vs. Logit models) for conflict outcomes.
Excerpt from the Book
3. Modelling Contest via the Contest Success Functions
It is important to understand some basic concepts about Contest Success Functions in general before moving on to the special Contest Success Functions by Tullock and Hirshleifer. Hence this chapter will first introduce basic aspects of Contest Success Functions, before the two special ones are introduced and analysed. At last it will be discussed when which function might be applicable.
a. Contest Success Functions in General
The concept of economic contest evolved, when Tullock introduced a new approach to measuring the welfare costs of monopolies. In his paper he argued that the traditional approach to calculate the costs of a monopoly for society were flawed, because they omitted the costs that are connected with the struggle about the monopoly. In principle the patrons are overreached in a monopoly case, as the producer can charge them prices that are higher then they would be in the case of competition. Therefore the monopolist can make a higher profit then he could have by selling the same amount on a competitive market – which is highly desirable for him.
Summary of Chapters
1. Introduction: Outlines the dual approaches to historical military research and introduces the thesis's goal of applying economic conflict theory to Napoleonic battles.
2. The Economy of Conflict - The Second Approach: Discusses the theoretical foundations of economic conflict, focusing on rational choice, rent-seeking, and the role of conflict theory.
3. Modelling Contest via the Contest Success Functions: Introduces the axiomatic requirements for Contest Success Functions and differentiates between the Ratio and Difference models.
4. The Essentials about Napoleonic Warfare: Provides a historical context on armaments, army organization, and tactics, establishing the homogeneity of effort variables for the quantitative model.
5. The Data Set: Describes the methodology for gathering battle data from Digby Smith’s "Napoleonic Wars Data Book" and explains the variable definitions.
6. The Two Ways of Estimating a CSF: Explains the statistical methodologies of Ordinary Least Squares (OLS) and Logit modeling as tools for estimation.
7. Fitting the Ratio CSF via the Linear Probabilistic Model: Applies the Ratio CSF to the battle data using OLS and discusses the resulting predictive power.
8. Fitting the Difference CSF via the Logit Function: Applies the Difference CSF using Logit models and evaluates the fit against the Ratio approach.
9. Ratio or Difference CSF? – Comparing the Results: Contrasts the two modeling approaches and concludes that the Difference CSF is more robust for analyzing the specific dynamics of Napoleonic battles.
10. How much was Napoleon actually worth?: Utilizes case studies (40,000 men, Jena, Auerstedt, Austerlitz) to empirically test Napoleon's influence on combat outcomes.
11. Final Consideration and Summary: Concludes the thesis by confirming that the working hypotheses were proven and that Napoleon’s presence significantly increased French fighting efficiency.
Keywords
Napoleon, Econometrics, Contest Success Function, RCSF, DCSF, Military History, Economic Conflict Theory, Rent-seeking, Battle prediction, Logit-Model, OLS, Warfare, Tactical advantage, Empirical Analysis, Rational Choice.
Frequently Asked Questions
What is the fundamental objective of this work?
The research aims to quantify Napoleon Bonaparte's military superiority and determine if battle outcomes can be predicted using economic models known as Contest Success Functions.
Which specific modeling theories are evaluated?
The thesis evaluates two primary types of models: the Ratio Contest Success Function (RCSF) and the Difference Contest Success Function (DCSF).
What is the primary methodology used?
The author employs econometric regression analysis, using both the Linear Probabilistic Model (via OLS) and the Logit model, to analyze historical data from approximately 2,000 clashes.
What are the core findings regarding the models?
The research concludes that the Difference CSF is superior to the Ratio CSF for modeling Napoleonic battles, consistently achieving higher predictive accuracy (approaching 80%).
Does the data support the "40,000 men" claim?
The empirical results suggest that the Duke of Wellington’s anecdotal claim was a reasonable approximation; the model demonstrates that Napoleon’s presence significantly multiplied the odds of a French victory.
How is Napoleon’s "influence" quantified?
Napoleon's influence is measured by integrating a binary variable for his command, which results in a significant, quantifiable increase in the fighting efficiency of individual soldiers under his control.
Why are standard R-squared measures considered less useful here?
Because the dependent variable (victory/loss) is binary, the author notes that standard R-squared metrics are insufficient, opting instead to use the ratio of correct predictions.
How do the models account for different nations in the coalition?
The Difference CSF allowed for the integration of individual national variables, which demonstrated that while British forces had high estimated efficiency, Napoleon's tactical presence remained the dominant factor.
- Arbeit zitieren
- Felix Christoph Lotzin (Autor:in), 2010, The Emperor on the Battlefield: Napoleon's Worth as a Military Commander, München, GRIN Verlag, https://www.hausarbeiten.de/document/193815
