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

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
Latest version

Published & Revision Invited

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)
MKTG "The Consumer DNA Revolution: Potential Uses and Misuses of Genetic Data in Marketing Strategy",
Remi Daviet, Gideon Nave, Yoram Wind
Under Review, 2nd round (Journal of Marketing), draft available on demand.
MKTG "Hamiltonian Sequential Monte-Carlo: Application to Consumer Demand",
Martin Burda, Remi Daviet
R&R (Econometric Reviews), Latest version

NEUR "Genetic underpinnings of risky behavior relate to altered neuroanatomy",
Gökhan Aydogan, Remi Daviet, Gideon Nave, Philipp Koellinger et Al.
Under Review, 2nd round (Nature Human Behaviour), Latest version, Pre-registered with the Open Science Framework
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 (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 (Experimental Econ.), Submitted for pre-results review.

Under Review

MKTG "Bayesian Deep Learning for Small Datasets: Leveraging Information from Product Pictures",
Remi Daviet
Under Review at Marketing Science
MKTG "A Double Decoy Experiment to Distinguish Theories of Dominance",
Remi Daviet, Ryan Webb
Under Review at Science Advances

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
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 "Save the Best for Last: Consumer Beliefs about Product Attributes Drive Intertemporal Context Effects",
Remi Daviet, Lin Fei.
MKTG "Causal Influence of Visual Feature Combinations on Digital Advertising Performance",
Yang Gao, Remi Daviet.


"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