Jan H. R. Dressler, Peter Kurz, Winfried J. Steiner
arXiv 28 Dec 2025 · Econometrics
arXiv:2512.22864 · PDF · DOI · OpenAlex · Extracted main text
Despite a substantial body of theoretical and empirical research in the fields of conjoint and discrete choice analysis as well as product line optimization, relatively few papers focused on the simulation of subsequent competitive dynamics employing non-cooperative game theory. Only a fraction of the existing frameworks explored competition on both product price and design, none of which used fully Bayesian choice models for simulation. Most crucially, no one has yet assessed the choice models' ability to uncover the true equilibria, let alone under different types of choice behavior. Our analysis of thousands of Nash equilibria, derived in full and numerically exact on the basis of real prices and costs, provides evidence that the capability of state-of-the-art mixed logit models to reveal the true Nash equilibria seems to be primarily contingent upon the type of choice behavior (probabilistic versus deterministic), regardless of the number of competing firms, offered products and features in the market, as well as the degree of preference heterogeneity and disturbance. Generally, the highest equilibrium recovery is achieved when applying a deterministic choice rule to estimated preferences given deterministic choice behavior in reality. It is especially in the latter setting that incorporating Bayesian (hyper)parameter uncertainty further enhances the detection rate compared to posterior means. Additionally, we investigate the influence of the above factors on other equilibrium characteristics such as product (line) differentiation.
appendix boundary found by none_found · 100% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Allenby, G. M., Brazell, J. D., Howell, J. R., and Rossi, P. E (2014) Economic valuation of product features | 1.000 | 8 | 3 | 100% |
| 2 | Andrews, R. L., Ansari, A., and Currim, I. S (2002) Hierarchical Bayes Versus Finite Mixture Conjoint Analysis Models: A Comparison of Fit, Prediction, and Partworth Recovery | 1.000 | 6 | 3 | 100% |
| 3 | Steiner, W. J (2010) A Stackelberg-Nash model for new product design self | 0.928 | 4 | 3 | 100% |
| 4 | Andrews, R. L., Ainslie, A., and Currim, I. S (2002) An Empirical Comparison of Logit Choice Models with Discrete Versus Continuous Representations of Heterogeneity | 0.874 | 7 | 2 | 100% |
| 5 | Gutsche, J (1995) Produktpräferenzanalyse: Ein modelltheoretisches und methodisches Konzept zur Marktsimulation mittels Präferenzerfassungsmodellen | 0.874 | 6 | 2 | 100% |
| 6 | Hein, M., Kurz, P., and Steiner, W. J (2020) Analyzing the capabilities of the HB logit model for choice-based conjoint analysis: a simulation study self | 0.874 | 6 | 2 | 100% |
| 7 | Hein, M., Kurz, P., and Steiner, W. J (2019) On the effect of HB covariance matrix prior settings: A simulation study self | 0.874 | 5 | 2 | 100% |
| 8 | Choi, S. C., and DeSarbo, W. S (1993) Game Theoretic Derivations of Competitive Strategies in Conjoint Analysis | 0.811 | 4 | 2 | 100% |
| 9 | Green, P. E., and Krieger, A. M (1997) Using Conjoint Analysis to View Competitive Interaction through the Customer's Eyes | 0.811 | 4 | 2 | 100% |
| 10 | Steiner, W. J., and Hruschka, H (2000) Conjoint-based product (line) design considering competitive reactions self | 0.811 | 4 | 2 | 100% |
Showing the top 10 of 92 scored citations.