Jan H. R. Dressler, Peter Kurz, Winfried J. Steiner
arXiv 14 Jun 2026 · Econometrics
arXiv:2606.15593 · PDF · DOI · OpenAlex · Extracted main text
Although discrete choice (choice-based conjoint) analysis has become a widely used technique for the elicitation of consumer preferences and hence a foundation for product design, to the best of our knowledge, there exists neither free and open-source nor commercial software that covers the game-theoretic simulation of competitive reactions among firms based on discrete choice models to improve decision making beyond traditional product (line) optimization. The R package cash (conjoint + Nash) does not only provide functions to fill this gap but comprises an entire simulation pipeline including the upstream processes of discrete choice analysis itself. cash ranges from preference generation, choice design, error and response simulation, through Bayesian model estimation and evaluation, to Nash equilibrium computation. Doing so, it partly draws from established R packages concerned with discrete choice analysis. While the structure of cash generally aims towards end-to-end simulation as well as simulation of competitive dynamics based on real data, all its key elements mentioned above may be of use independently of each other.
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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 | Dressler JHR, Kurz P, Steiner WJ (2025) Computing Nash Equilibria for Product Design based on Hierarchical Bayesian Mixed Logit Models | 1.000 | 16 | 9 | 100% |
| 2 | Andrews RL, Ainslie A, Currim IS (2002) An Empirical Comparison of Logit Choice Models with Discrete versus Continuous Representations of Heterogeneity | 1.000 | 6 | 3 | 100% |
| 3 | Hein M, Kurz P, Steiner WJ (2019) On the Effect of HB Covariance Matrix Prior Settings: A Simulation Study | 1.000 | 5 | 4 | 100% |
| 4 | Andrews RL, Ansari A, Currim IS (2002) Hierarchical Bayes versus Finite Mixture Conjoint Analysis Models: A Comparison of Fit, Prediction, and Partworth Recovery | 0.928 | 4 | 3 | 100% |
| 5 | Hein M, Kurz P, Steiner WJ (2020) Analyzing the Capabilities of the HB Logit Model for Choice-Based Conjoint Analysis: A Simulation Study | 0.928 | 4 | 3 | 100% |
| 6 | Goeken N, Kurz P, Steiner WJ (2024) Multimodal Preference Heterogeneity in Choice-Based Conjoint Analysis: A Simulation Study | 0.928 | 4 | 3 | 100% |
| 7 | Train KE (2009) Discrete Choice Methods with Simulation | 0.843 | 3 | 3 | 100% |
| 8 | Wirth R (2010) Best-Worst Choice-Based Conjoint-Analyse. Eine Neue Variante der Wahlbasierten Conjoint-Analyse | 0.811 | 4 | 2 | 100% |
| 9 | Wirth R (2010) HB-CBC, HB-Best-Worst-CBC or No HB At All? | 0.811 | 4 | 2 | 100% |
| 10 | Vriens M, Wedel M, Wilms T (1996) Metric Conjoint Segmentation Methods: A Monte Carlo Comparison | 0.737 | 3 | 2 | 100% |
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