DECISIONS project

SYNERGY: Developing new behavioural models at the intersection of psychology, econometrics and machine learning

Principal investigator
European Research Council Advanced Grant (101020940)
02/2022 – 01/2028

The SYNERGY project is an advanced grant that received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 101020940).

Led by Professor Stephane Hess at the University of Leeds, it will bring together theories from choice modelling, mathematical psychology and machine learning to develop next generation data driven behavioural models.

The team for the SYNERGY project included Dr Thomas Hancock, Dr Jamal Amani Rad, Dr Georges Sfeir, Dr Panagiotis Tsoleridis as PDRAs, and Olivia Barnes, Di Wu and Lorenzo Munoz Torrejon as PhD students.


Major talks

Click to expand
  • ENVECHO 2023, Durham, “Choice Modelling VS AND Machine Learning”, September 2023
  • FAERE 2022, Rouen, France, “One size does not fit all: contrasting the data and model requirements for valuation and prediction”, September 2022

Journal papers

Click to expand

Links to published versions of each paper are provided below. If you do not have access to specific journals/articles, please e-mail me for a pdf copy.

  1. Zannat, K.E., Choudhury, C.F. & Hess, S. (2024), Modelling time-of-travel preferences capturing correlations between departure times and activity durations, Transportation Research Part A 184, 104081.
  2. Zannat, K.E., Choudhury, C.F., Hess, S. & Watling, D. (2024), Investigating the relative accuracy of GPS, GSM and CDR data for inferring spatiotemporal travel trajectories, IET Intelligent Transport Systems, forthcoming.
  3. Wang S., Mo, B. Zheng, Y., Hess, S. & Zhao, J. (2024), Comparing hundreds of machine learning and discrete choice models for travel demand modeling: an empirical benchmark, Transportation Research Part B, forthcoming.
  4. Tsoleridis, P., Hess, S. & Choudhury, C.F. (2024), Accounting for continuous correlations among alternatives in the context of spatial choice modelling using high resolution mobility data, Transportmetrica Part A, forthcoming.
  5. Feng, S., Yao, R., Hess, S., Daziano, R.A., Brathwaite, T., Walker, T. & Wang, S. (2024), Deep neural networks for choice analysis: Enhancing behavioral regularity with gradient regularization, Transportation Research Part C: Emerging Technologies, 166, 104767.
  6. Hancock, T.O., Hess, S., Choudhury, C.F., Tsoleridis, P. (2024), Decision field theory: An extension for real-world settings, Journal of Choice Modelling, 52, 100495.
  7. Czine, P., Balogh, P., Blága, Z., Szabó, Z., Szekeres, R., Hess, S., Juhász, B. (2024), Is It Sufficient to Select the Optimal Class Number Based Only on Information Criteria in Fixed- and Random-Parameter Latent Class Discrete Choice Modeling Approaches? Econometrics 2024, 12, 22.
  8. Amaris, G., Vesely, V., Hess, S. & Klöckner, C.A. (2024), Can competing demands affect pro-environmental behaviour: a study of the impact of exposure to partly related sequential experiments, Ecological Economics, 216, 108023.
  9. Daly, A., Hess, S. & Ortúzar, J. de D. (2023), Estimating willingness-to-pay from discrete choice models: Setting the record straight, Transportation Research Part A, 176, 103828.
  10. Hoen, F.S., Díez-Gutiérrez, M., Babri, S., Hess, S. & Tørset, T. (2023), Charging electric vehicles on long trips and the willingness to pay to reduce waiting for charging. Stated preference survey in Norway, Transportation Research Part A: Policy and Practice, 175, 103774.
  11. Meester, D.A.J., Hess, S., Buckell, J. & Hancock, T.O. (2023), Can decision field theory enhance our understanding of health-based choices? Evidence from risky health behaviors. Health Econonomics, 32(8), pp. 1710-1732.

Conference presentations

Click to expand

This is as close as possible a list of presentations at conferences.

  1. Magor, T., Coote, L. & Hess, S. (2024), New Perspectives on Behavioural Decision Theory: A Choice Modelling, Foundations of Utility and Risk, Brisbane
  2. Amaris, G., Hess, S. & Vesely, S. (2024), Data and modelling considerations for studying preference spillover: applications to travel behaviour, Foundations of Utility and Risk, Brisbane
  3. Hess, S., Hancock, T., Choudhury, C., Mushtaq, F., Mon-Williams, M., Bliemer, M., Beck, M., Fayyaz, M., Amani Rad, J. & Markkula, G. (2024), Paving the way for using neuroscience models in behavioural modelling: application of the active inference/free energy paradigm on immersive data, Foundations of Utility and Risk, Brisbane
  4. Magor, T., Coote, L. & Hess, S. (2024), Combining Sources of Preference Data: A Latent Variable Formulation Using the Factor-Analytic Form of Mixed Logit, 8th International Choice modelling conference, Puerto Varas, Chile.
  5. Ali, A., Flaata, E.H,, Tørset, T., Hess, S. & Choudhury, C.F. (2024), Investigating the effect of imputing trip purposes in a trip generation model, 8th International Choice modelling conference, Puerto Varas, Chile.
  6. von Butler, L. White, B. & Hess, S. (2024), Avoiding fake news: building a toolkit to identify bots and coached respondents in stated preference surveys, 8th International Choice modelling conference, Puerto Varas, Chile.
  7. Tsoleridis, P., Choudhury, C.F. & Hess, S. (2024), Causal determinants of car ownership decisions, 8th International Choice modelling conference, Puerto Varas, Chile.
  8. Hancock, T., Song, F., Choudhury, C.F. & Hess, S. (2024), An integrated choice and latent variable decision field theory model linking preferential choice responses and thinking patterns, 8th International Choice modelling conference, Puerto Varas, Chile.
  9. Bunch, D., Hess, S. & Palma, D. (2024), BGW: A (Apollo-compatible) numerically efficient (aka “fast”) and reliable maximum likelihood estimation package for choice models, with well- characterized stopping diagnostics, 8th International Choice modelling conference, Puerto Varas, Chile.
  10. Amaris, G., Hess, S. & Vesely, S. (2024), Detecting preference spillovers: application of choice models to the analysis of CO2 reducing behaviour and environmental donations, 8th International Choice modelling conference, Puerto Varas, Chile.
  11. Hess, S., Bunch, D. & Daly, A. (2024), Get me out of this hole: a profile likelihood approach to avoiding poor local optima, 8th International Choice modelling conference, Puerto Varas, Chile.
  12. Deckman, T. & Hess, S. (2024), A Novel Approach for Optimizing Healthcare Message Effectiveness, 8th International Choice modelling conference, Puerto Varas, Chile.
  13. Nova, G., van Cranenburgh, S. & Hess, S. (2024), Understanding the decision-making process of discrete choice modellers: one database but many workflows, 8th International Choice modelling conference, Puerto Varas, Chile.
  14. Beck, M.J., Bliemer, M.C.J. & Hess, S. (2024), That Seems Relatively Important! The not so simple question of what maters most to respondents when making a choice, 8th International Choice modelling conference, Puerto Varas, Chile.
  15. Ali, A., Dekker, T., Hess, S. & Choudhury, C. (2024), Using posterior analysis to predict missing information in passively collected data, hEART 2024 – 12th Symposium of the European Association for Research in Transportation, Aalto
  16. Hancock, T. O., Song, F., Choudhury, C. F., Hess, S., & Mushtaq, F. (2023, July). An integrated choice and latent variable decision field theory model linking preferential choice responses and thinking patterns. Abstract published at MathPsych, Amsterdam.
  17. Hancock, T. O., Hess, S., Choudhury, C. F., & Marley, A. (2023). An integrated choice and response time decision field theory model: new insights on choice response times in multi-attribute, multi-alternative choice, MathPsych, Amsterdam.
  18. Tsoleridis, P., Hess, S. & Choudhury, C.F. (2023), Accounting for Distance-Based Correlations Among Alternatives in the Context of Spatial Choice Modelling Using High-Resolution Mobility Data, 102nd Annual Meeting of the Transportation Research Board, Washington, D.C.
  19. Tsoleridis, P., Choudhury, C.F. & Hess, S. (2023), Augmenting Choice Models with Machine Learning Techniques to Capture the Heterogeneity in Travel Behaviour, 102nd Annual Meeting of the Transportation Research Board, Washington, D.C.
  20. Nova, G., Guevara, C.A., Hess, S. & Hancock, T.O. (2023), Random Utility Maximization-Decision Field Theory: Random Utility Maximization Model Considering the Information Search Process, 102nd Annual Meeting of the Transportation Research Board, Washington, D.C.
  21. Hess, S., Hancock, T., Choudhury, C., Mushtaq, F., Mon-Williams, M., Bliemer, M., Beck, M. & Fayyaz, M. (2022), Using a mathematical representation of brain processes to explain decision making: Adapting the free energy/active inference principle for travel behaviour modelling, 16th International Conference on Travel Behaviour Research, Santiago de Chile
  22. Hancock, T., Choudhury, C.F., Hess, S. & Tsoleridis, P. (2022), Implementing psychological choice models on real-world data: Theoretical extensions for Decision Field Theory, 16th International Conference on Travel Behaviour Research, Santiago de Chile
  23. Bliemer, M., Beck, M., Fayyaz, F. & Hess, S. (2022), Route choice decisions under travel time uncertainty with experiential learning in a simulated environment, 16th International Conference on Travel Behaviour Research, Santiago de Chile
  24. Palma, D. & Hess, S. (2022), Extending Multiple Discrete Continuous (MDC) modelling to consider complementarity, substitution, and unobserved budgets, 16th International Conference on Travel Behaviour Research, Santiago de Chile
  25. Hess, S. & van Cranenburgh, S. (2022), Wisdom of the crowd: compare and combine models for out-of-distribution forecasting, 16th International Conference on Travel Behaviour Research, Santiago de Chile
  26. Nova, G., Guevara, C.A. Hess, S. & Hancock, T.O. (2023), Random Utility Maximization model considering the information search process, hEART 2023 – 11th Symposium of the European Association for Research in Transportation, Zurich
  27. Hancock, T., Choudhury, C., Walker, J. & Hess, S. (2023), Quantum choice models leap out of the laboratory: capturing real-world behavioural change, hEART 2023 – 11th Symposium of the European Association for Research in Transportation, Zurich
  28. Hess, S. & van Cranenburgh, S. (2023), Combine and conquer: model averaging for out-of-distribution forecasting, hEART 2023 – 11th Symposium of the European Association for Research in Transportation, Zurich
  29. Hess, S. (2022), Understanding preferences for COVID-19 vaccination: results from a unique longitudinal stated choice study covering 18 countries across 6 continents, 7th International Choice modelling conference, Reykjavik
  30. Sandorf, E.,D., Börger, T., Campbell, D., Crastes Dit Sourd, R., Czajkowski, M., Hess, S., Jacobsen, J.B., Olsen, S.B., Lindhjem, H., Mariel, M. & Meyerhoff, J. (2022), Exploring status quo effects in stated preference experiments: A meta style analysis, 7th International Choice modelling conference, Reykjavik
  31. Calastri, C., Hess, S., Popli, G. & Roberts, J. (2022), Women’s labour market participation and its link with attitudes towards gender roles in the family, 7th International Choice modelling conference, Reykjavik
  32. Amaris, G., Vesely, S. & Hess, S. (2022), A choice modelling analysis of pro-environmental behaviour spillover, 7th International Choice modelling conference, Reykjavik
  33. Hancock, T.O., Choudhury, C.F., Walker, W. & Hess, S. (2022), Quantum choice models leap out of the laboratory: capturing real-world behavioural change, 7th International Choice modelling conference, Reykjavik
  34. van Den Broek-Altenburg, E., Benson, J., Atherly, A. & Hess, S. (2022), Drivers of Health Disparities and Consequences for COVID-19 Vaccine Choices: Modelling Health Preference Heterogeneity among Underserved Populations, 7th International Choice modelling conference, Reykjavik
  35. Palma, D., Hess, S., Molloy, J. & Axhausen, K.W. (2022), Using the extended Multiple Discrete Continuous model to predict kilometres travelled by mode, 7th International Choice modelling conference, Reykjavik
  36. Lin, S., Calastri, C. & Hess, S. (2022), Modelling joint activity engagement: exploring the influence of the characteristics of social network members, 7th International Choice modelling conference, Reykjavik
  37. Amaris, G., Song, F., Calastri, C., Hess, S., Beck, M., Zuidgeest, M., Behrens, R., Moyo, H.T.T & Arellana, J. (2022), A multi-country panel study of behaviour, attitudes and expectations during the COVID-19 pandemic, 7th International Choice modelling conference, Reykjavik
  38. Hess, S., Hancock, T.O., Choudhury, C., Mushtaq, F. & Mon-Williams, M. (2022), Using a mathematical representation of brain processes to explain choices: introducing the free energy principle to mainstream choice modelling, 7th International Choice modelling conference, Reykjavik
  39. Zannat, K., Choudhury, C.F., Hess, S. & Carrasco, J.-A. (2022), Heterogeneity in activity participation: A comparative analysis of Multinomial logit model (MNL) and multiple discrete-continuous choice model (MDCEV), 7th International Choice modelling conference, Reykjavik
  40. Nova, G., Guevara, C.A. & Hess, S. (2022), In-depth, Breadth-first or Both? Toward the Development of a RUM-DFT Discrete Choice Model, 7th International Choice modelling conference, Reykjavik
  41. Tsoleridis, P., Hess, S. & Choudhury, C. (2022), Accounting for distance-based correlations among alternatives in the context of spatial choice modelling using high resolution mobility data, 7th International Choice modelling conference, Reykjavik
  42. Ryseck, B., Zuidgeest, M., Behrens, R., & Hess, S. (2022), Investigating Passenger Information Needs for Hybrid Public Transport Network Journey Planning, 7th International Choice modelling conference, Reykjavik
  43. Hancock, T.O., Hess, S. & Choudhury, C.F. (2022), Is your model the best? Mitigating risk through averaging across different analysts’ competing models, 7th International Choice modelling conference, Reykjavik