Disjunctions of Conjunctions, Cognitive Simplicity, and Consideration Sets

By John R. Hauser, Olivier Toubia, Theodoros Evgeniou, R. Befurt, Daria Dzyabura. 2010

172 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research, Marketing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Finale Doshi-Velez
2. Tong Wang
3. Erica Klampfl
4. Jun Wang
5. Yimin Liu
6. Cynthia Rudin

In Management Science and Operations Research

1. Finale Doshi-Velez
2. Alexey Ignatiev
3. Tong Wang
4. Berk Ustun
5. Jun Wang
6. Georgina Peake
7. Cynthia Rudin

In Marketing

1. Tong Wang
2. Erica Klampfl
3. Jun Wang
4. Georgina Peake
5. Yimin Liu
6. Cynthia Rudin

Citations in the corpus

• Learning Optimal Decision Trees with SAT (2018, background): “Many other representations have been studied, including rule lists [Angelino et al. , 2017], decision sets [Lakkaraju et al. , 2017], and sums of terms (i.e. DNF) [Wang et al. , 2015; Hauser et al. , 2010], among others.”
• Learning Optimized Or's of And's (2015, methodology): “The closest work to ours are that of Hauser et al. (2010) in the marketing literature, and Wang et al. (2015) on Bayesian modeling of DNF formulae.”
• Or's of And's for Interpretable Classification, with Application to Context-Aware Recommender Systems (2015, background): “There are few recent techniques that do aim to fully learn DNF models [18, 19], which present integer programming approaches for solving the full problems, and also present relaxations for computational efficiency.”
• Learning Classification Models of Cognitive Conditions from Subtle Behaviors in the Digital Clock Drawing Test (2015, background): “…scoring (Martens et al. 2007), crime prediction (Steinhart 2006; Andrade 2009; Ridgeway 2013), national defense (ABS Consulting 2002), marketing (Hauser et al. 2010; Verbeke et al. 2011; Wang et al. 2015), medical diagnosis (Tian and Tibshirani 2011; Van Belle et al. 2012; Letham et al. 2015;…”
• Methods and Models for Interpretable Linear Classification (2014, background): “These domains include credit scoring [76], crime prediction [102, 2, 93], national defense [27], marketing [49, 114], medical diagnosis [106, 112], and scientific discovery [104, 36, 48].”
• Advanced conjoint analysis using feature selection via support vector machines (2015, unknown): “For the compensatory models we estimate a state-of-the-art mixed logit model (LCA) and a q-compensatory model that is used to represent strict compensatory preferences [26, 29].”
• Explanation Mining: Post Hoc Interpretability of Latent Factor Models for Recommendation Systems (2018, background): “Association rule models have been described as a natural representation of how humans make decisions [35] given their ‘and’, ‘or’ structure and have been succesfully applied to modelling user behaviour [13, 17].”
• Machine learning and AI in marketing – Connecting computing power to human insights (2020, unknown): “One additional way to use machine learning methods is for estimation, taking advantage of their ability to perform large-scale optimization (Ansari et al., 2018; Chiong & Shum, 2019; Evgeniou et al., 2005; Evgeniou, Pontil, & Toubia, 2007; Hauser, Toubia, Evgeniou, Befurt, & Dzyabura, 2010).”
• A Bayesian Framework for Learning Rule Sets for Interpretable Classification (2017, methodology): “Hauser et al. (2010); Goh and Rudin (2014) applied integer programming approaches to solving the full problems but would face a computational challenge for even moderately sized problems.”