DEEP is an adaptive method that dynamically designs elicitation questions for estimating Risk and Time preference parameters. Typically these parameters are elicited by presenting decision makers with a series of static choices between alternatives, gambles or delayed payments. DEEP dynamically (i.e., adaptively) designs such choices to optimize the information provided by each choice, while leveraging the distribution of the parameters across decision makers (heterogeneity) and capturing response error. It also recovers true parameter values well under various circumstances.

Are you interested in using DEEP for your research? Please see here.


DEEP is an inter-university collaboration between researchers from Columbia Business School, INSEAD, and George Washington University. Below you’ll find a list of all the authors by alphabetical order.

Philippe Delquie

Philippe Delquié

Professor Delquié is an Associate Professor of Decision Sciences at INSEAD in Fontainebleau, France. Professor Delquié has taught at École Normale Supérieure de Cachan in Paris, the Fuqua School of Business at Duke University and the Graduate School of Business, the University of Texas at Austin. He taught at the Swiss Banking School, ENPC International School of Management and developed and taught specialized executive courses for the KPMG Executive MBA program and the Scandinavian International Management Institute.

Theodoros Evgeniou

Theodoros Evgeniou

Theodoros (Theos) Evgeniou is an Associate Professor of Decision Sciences and Technology Management at INSEAD. Having won medals in international mathematical olympiads (in places like China, Sweden, Romania, etc), he received two BSc degrees, one in Computer Science and one in Mathematics, simultaneously from MIT, from where he also graduated first in the MIT class of 1995 dual degrees in Mathematics. He then received a Master and a PhD degree in Computer Science, also from MIT. 

Eric J. Johnson

Eric J. Johnson

Professor Johnson, who has a strong research and teaching interest in electronic commerce, teaches the elective Marketing and Electronic Commerce. His other interests include consumer and managerial decision making and brand equity. His articles have appeared in consumer, marketing, management science and organizational behavior journals. 

Oliver Toubia

Olivier Toubia

Professor Toubia’s research focuses on various aspects of innovation and new product development, including idea generation, idea screening, preference measurement, and the diffusion of innovation. He is the winner of two John Little best paper awards, the Frank Bass outstanding dissertation award, and the John Howard dissertation award. 


Toubia, O., Johnson, E., Evgeniou T., & Delquié, P. (2012). Dynamic Experiments for Estimating Preferences: An Adaptive Method of Eliciting Time and Risk Parameters. Management Science. doi:10.1287/mnsc.1120.1570