CV

An updated version can be found here


Research interests

I focus on understanding and predicting decisions using quantitative modeling and advanced statistics / machine learning. My models of decision-making rely on insights from many fields: Neuroscience, Cognitive and Mathematical Psychology, Genomics, Economics and Computer Science.

  • Quantitative Marketing
    • Quantitative Modeling of Behavior and Decision Processes
    • Demand analysis
    • Consumer Neuroscience and Consumer Genomics
  • Econometrics / Machine Learning
    • Neural Networks
    • Bayesian Methods and Computational Statistics
    • Big Data and High Performance Computing (HPC)
  • Behavioral Neuro-Genomics

Papers and publications

Recent updates:

  • 2020-11-11: "Bayesian Deep Learning for Small Datasets: Leveraging Information from Product Pictures" is now R&R at Marketing Science.
  • 2020-10-08: "Genetic underpinnings of risky behavior relate to altered neuroanatomy" is now conditionally accepted at Nature Human Behavior.
  • 2020-09-28: "Genetic Data: Potential Uses and Misuses in Marketing" is now conditionally accepted at the Journal of Marketing.
MKTG Marketing
MTHD Method / Statistics
NEUR Consumer Neuroscience & Neuro-Genomics

Job Market Paper

MKTG "Bayesian Deep Learning for Small Datasets: Leveraging Information from Product Pictures",
Remi Daviet
R&R at Marketing Science, Latest version

Accepted & Published

MKTG "Genetic Data: Potential Uses and Misuses in Marketing",
Remi Daviet, Gideon Nave, Yoram Wind
Journal of Marketing (Forthcomimg), Latest version.
NEUR "Reflecting on the Evidence: A Reply to Knight, McShane, et al. (2020)",
Gideon Nave*, Remi Daviet*, Amos Nadler, David Zava, Colin Camerer (* for equal contribution)
Psychological Science (article, supplement)
NEUR "Genetic Underpinnings of Risky Behavior Relate to Altered Neuroanatomy",
Gökhan Aydogan, Remi Daviet, Gideon Nave, Philipp Koellinger et Al.
Nature Human Behaviour (Forthcoming), Latest version,
Pre-registered with the Open Science Framework

Revision Invited

MKTG "Bayesian Deep Learning for Small Datasets: Leveraging Information from Product Pictures",
Remi Daviet
R&R at Marketing Science
MKTG "Hamiltonian Sequential Monte-Carlo: Application to Consumer Demand",
Martin Burda, Remi Daviet
R&R at Econometric Reviews, Latest version

NEUR "Multimodal brain imaging study of 19,825 participants reveals adverse effects of moderate drinking",
Remi Daviet, Gideon Nave, Philipp Koellinger, Reagan Wetherill et Al.
R&R at Nature Communications, Latest version,
Pre-registered with the Open Science Framework
MKTG "Social Preference Estimation Using Adaptive Experimental Design",
Taisuke Imai, Devdeepta Bose, Remi Daviet, Gideon Nave, Colin Camerer
R&R at Experimental Econ., Submitted for pre-results review.

Working Papers

MKTG "Neural Attribute Normalization: An Application to Product Portfolio Optimization",
Remi Daviet
Latest version
MTHD "Sequential Optimal Inference for Experiments with Bayesian Particle Filters",
Remi Daviet
Reject & Resubmit (JMR), Latest version
MKTG "A Double Decoy Experiment to Distinguish Theories of Dominance",
Remi Daviet, Ryan Webb
Draft available on demand.
MTHD "Sequential Monte Carlo for Hierarchical Bayes with Large Datasets",
Remi Daviet
Latest version
MTHD "Multialternative Drift Diffusion Model: Estimation with Hit and Run Particles",
Remi Daviet
Draft available on demand.
MTHD "Inference with Hamiltonian Sequential Monte Carlo Simulators",
Remi Daviet
Arxiv, Latest version , C++/CUDA code

Work in Progress

MKTG "The Face of Your Brand: Automated Model Casting and Visual Enhancement for Advertising",
Remi Daviet, Gideon Nave.
MKTG "Foundations of the Decoy Effect: Putting Theory to the Test",
Ulrich Bergmann, Remi Daviet, Ernst Fehr
MKTG "How Beliefs About Attributes Affect Product Value Perception",
Remi Daviet, Lin Fei.
MKTG "Causal Influence of Visual Feature Combinations on Digital Advertising Performance",
Yang Gao, Remi Daviet.

Thesis

"Methods for statistical analysis and prediction of discrete choices"
Published version

  • Chapter 1: Attribute Divisive Normalization : A Neural Decision Model for Discrete Choice
  • Chapter 2: Hamiltonian Sequential Monte Carlo with Application to Consumer Choice Behavior
  • Chapter 3: Sequential Optimal Inference for Experiments with Sequential Monte Carlo Methods