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 and Dr Fangqing Song as PDRAs, and Olivia Barnes, Di Wu and Lorenzo Munoz Torrejon as PhD students.


Major talks

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  • Envecho 2026, Prague, “How will choice modelling look in the age of AI”, September 2026
  • 193rd EAAE Seminar; 1st joined EAAE – EAERE seminar on Valuation Methods in Agro-food and Environmental Economics, “Stronger together: synergies between machine learning and choice modelling”, December 2025
  • EWGT 2025, Edinburgh, “AI and travel behaviour research: from an existential threat to the opportunity of a lifetime”, September 2025
  • 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

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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. Amaris, G., Hess, S. & Vesely, S. (2026), When simulator experiences shape later choices: Measuring behavioural spillover on travel preferences, Transportation Research Part D, Volume 162, 105655.
  2. Curtis, J., Bahreinian, A., Fowler, M., Hess, S. & Hui, M. (2026), Who Will Buy an Autonomous Vehicle and for How Much? A Latent Class Choice Model of Vehicle Autonomy Levels, Transportation Research Record, forthcoming.
  3. Vij, A. & Hess, S. (2026), Posterior inference of attitude-behaviour relationships using latent class choice models, Transportation Research Part B, Volume 214, December 2026, 103605.
  4. Tsoleridis, P., Hancock, T.O., Hess, S. & Sfeir, G. (2026), Modelling choices: The conflict between realism and prediction performance, Journal of Choice Modelling, Volume 61, 100636.
  5. Merkert, R., Hess, S., Khan, F. & Bliemer, M.C.J. (2027), The relevance of traditional postal services in a diversifying last-mile delivery landscape: The role of human to human (H2H) interactions, Transportation Research Part E, Volume 217, 105221.
  6. Sfeir, G., Nova, G., Hess, S. & van Cranenburgh, S. (2026), Can large language models assist choice modelling? Insights into prompting strategies and current models capabilities, Journal of Choice Modelling, Volume 60, 2026, 100623.
  7. Hess, S. & van Cranenburgh, S. (2026), Combine and conquer: Model averaging for out-of-distribution forecasting, Transportation Research Part C, Volume 192, 2026, 105846.
  8. Broekaert, J.B., Hancock, T.O., Hafiz, F., Hess, S. & La Torre, D. (2026), Deciding by quantum-like belief morphing or hopping? Its application in multi-agent response influence, Journal of Mathematical Psychology, Volume 130, August 2026, 102994.
  9. Hess, S., Daly, A.J., Bliemer, M., Guevara, C.A., Daziano, R.A. & Dekker, T. (2026), Statistical significance in choice modelling: computation, usage and reporting, Journal of Choice Modelling, Volume 60, September 2026, 100611
  10. Cantillo-Garcia, V., Calastri, C., Liu, H. & Hess, S. (2026), Modelling aggregate travel behaviour over time in the absence of panel data: methods and a case study from Colombia, Transportation Research Part A, Volume 210, 105036.
  11. Ali, A., Hess, S., Dekker, T. & Choudhury, C.F. (2026), Imputing missing trip purposes in smartphone-based travel surveys using Bayesian posterior analysis, Transportmetrica A, forthcoming.
  12. Zhang, X., Hess, S., Hancock, T.O., Zhong, S., Jia, N. & Zhang, Q. (2026), Coordination Behavior in Travel Mode Choice: An Experiment on Autonomous Vehicle Services, Transportation Research Part C, Volume 186, May 2026, 105592
  13. Ali, A., Flaata, E., Tørset, T., Hess, S. & Choudhury, C.F. (2026), Investigating the impact of inferring trip purposes in a daily trip generation model, Transportation Research Part C, Volume 186, May 2026, 105619
  14. Zhang, S., Hancock, T.O., Hess, S. & Jia, S. (2026), Exploring Taste Heterogeneity and Substitution Patterns in Dockless Bike-Sharing Parking Preferences, Transportation Research Part A, Volume 204, February 2026, 104807
  15. Nova, G., van Cranenburgh, S. & Hess, S. (2025), Understanding the decision-making process of choice modellers, Journal of Choice Modelling, Volume 56, September 2025, 100562.
  16. Tsoleridis, P., Choudhury, C.F. & Hess, S. (2025), Using probabilistic clustering techniques as a specification tool for capturing heterogeneity in choice models, Transportation Research Part C, Volume 179, October 2025, 105289.
  17. Tran, M., Maris, R., Hess, S., Dorner, Z., Huynh, E., Glass, K. & Lancsar, E. (2025), Temporal stability of preferences: The case of COVID-19 vaccines in Australia and New Zealand, Social Science & Medicine, Volume 383, October 2025, 118417.
  18. Nova, G., Guevara, C.A., Hess, S. & Hancock, T.O. (2025), A Random Utility Maximisation Model Considering the Information Search Process, Transportation Research Part B, Volume 199, September 2025, 103264.
  19. Maris, R., Dorner, Z., Hess, S. & Tucker, S. (2025), Social Media and the Evolution of Vaccine Preferences During the Covid-19 Pandemic: Evidence from a Discrete Choice Experiment, JMIR Infodemiology, Volume 5.
  20. Tsoleridis, P., Hess, S. & Choudhury, C.F. (2025), Accounting for continuous correlations among alternatives in the context of spatial choice modelling using high resolution mobility data, Transportmetrica Part A, forthcoming.
  21. 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.
  22. 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, 18(S1), pp. 3013-3033.
  23. 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, 190, 103061.
  24. Feng, S., Yao, R., Hess, S., Daziano, R.A., Brathwaite, T., Walker, J. & Wang, S. (2024), Deep neural networks for choice analysis: Enhancing behavioral regularity with gradient regularization, Transportation Research Part C: Emerging Technologies, 166, 104767.
  25. 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.
  26. 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.
  27. Amaris, G., Vesely, S., 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.
  28. 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.
  29. 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.
  30. 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 Economics, 32(8), pp. 1710-1732.

Conference presentations

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

  1. Rajput, K., Zuidgeest, M. & Hess, S. (2026), Airline strategic behaviour in African markets: a discrete-continuous choice modelling approach, 9th International Choice modelling conference, Gold Coast, Australia.
  2. Hancock, T.O., Hess, S., Choudhury, C., Wang, Y., Amani Rad, J. & Sfeir, G. (2026), Better models or better data? What matters more in capturing behavioural change?, 9th International Choice modelling conference, Gold Coast, Australia.
  3. Hess, S., Sfeir, G., Palma, D. & Bunch, D. (2026), Gradient assisted specification of choice models, 9th International Choice modelling conference, Gold Coast, Australia.
  4. Cantillo Garcia, V., Calastri, C., Liu, H. & Hess, S. (2026), Lost in aggregation? Exploring estimation bias and precision in grouped choice data, 9th International Choice modelling conference, Gold Coast, Australia.
  5. Wu, D., Hess, S. & Hancock, T.O. (2026), Modelling Wishful and Reluctant Relocation Spells: A Dynamic Approach to Residential Mobility, 9th International Choice modelling conference, Gold Coast, Australia.
  6. Bliemer, M., Hess, S., Collins, A. & Amaris, G. (2026), On the blocking of non-orthogonal experimental designs for choice experiments, 9th International Choice modelling conference, Gold Coast, Australia.
  7. Vij, A. & Hess, S. (2026), Posterior inference of attitude-behaviour relationships using latent class choice models, 9th International Choice modelling conference, Gold Coast, Australia.
  8. Nova, G., Hess, S. & van Cranenburgh, S. (2026), Sharing modelling decisions across assisted choice model specification tasks, 9th International Choice modelling conference, Gold Coast, Australia.
  9. Amani Rad, J., Hess, S., Hancock, T.O. & Markkula, G. (2026), Single-Trial Joint Neurocognitive Choice Modelling: Moving Beyond Two-Stage EEG-Behaviour Pipelines, 9th International Choice modelling conference, Gold Coast, Australia.
  10. Barnes, O., Hess, S., Hancock, T., Zuidgeest, M., Mofokeng, T., Rathogwa, M. & Amaris, G. (2026), Steering the Frame: Using Choice Modelling to Test Framing Effects in South African Drink-Driving Campaign Design, 9th International Choice modelling conference, Gold Coast, Australia.
  11. Sfeir, G., Hess, S. & Camara Pereira, F. (2026), Symbolic regression for choice models: An interpretable data-driven machine learning for discovering utility specifications, 9th International Choice modelling conference, Gold Coast, Australia.
  12. Muñoz, L., Hess, S., Hancock, T. & Sfeir, G. (2026), Variational Autoencoders for Anomaly Detection in Choice Models, 9th International Choice modelling conference, Gold Coast, Australia.
  13. Barnes, O., Hess, S. & Hancock, T. (2025), Revisiting framing effects: integrating multiple valence frames in choice modelling, EWGT 2025, Edinburgh.
  14. Hancock, T.O., Choudhury, C.F., Garcia, J., Solernou, S. & Hess, S. (2025), An adapted decision field theory model for capturing the impact of experiences on preferential change for new travel modes, EWGT 2025, Edinburgh.
  15. Sfeir, G., Hess, S., Hancock, T., Rad, J.A., Rodrigues, F., Bliemer, M., Beck, M. & Khan, F. (2025), Bridging Econometrics with Mathematical Psychology and Bayesian Variational Inference: The Case of Latent Class Reinforcement Learning, EWGT 2025, Edinburgh.
  16. Muñoz, L., Hess, S. & Hancock, T.O. (2025), Comparing Discrete Choice Models with Machine Learning Performance Metrics, EWGT 2025, Edinburgh.
  17. Nova, G., van Cranenburgh, S., Hess, S. (2025), Improving choice model specification using reinforcement learning, hEART 2025 – 13th Symposium of the European Association for Research in Transportation, Munich.
  18. Magor, T., Coote, L. & Hess, S. (2024), New Perspectives on Behavioural Decision Theory: A Choice Modelling, Foundations of Utility and Risk, Brisbane
  19. 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
  20. Hess, S., Hancock, T., Choudhury, C., Mushtaq, F., Mon-Williams, M., Bliemer, M., Beck, M., Khan, F., 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
  21. 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.
  22. 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.
  23. 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.
  24. Tsoleridis, P., Choudhury, C.F. & Hess, S. (2024), Causal determinants of car ownership decisions, 8th International Choice modelling conference, Puerto Varas, Chile.
  25. 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.
  26. 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.
  27. 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.
  28. 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.
  29. Deckman, T. & Hess, S. (2024), A Novel Approach for Optimizing Healthcare Message Effectiveness, 8th International Choice modelling conference, Puerto Varas, Chile.
  30. 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.
  31. 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.
  32. 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
  33. 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.
  34. 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.
  35. 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.
  36. 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.
  37. 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.
  38. Hess, S., Hancock, T., Choudhury, C., Mushtaq, F., Mon-Williams, M., Bliemer, M., Beck, M. & Khan, F. (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
  39. 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
  40. Bliemer, M., Beck, M., Khan, 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
  41. 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
  42. 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
  43. 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
  44. 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
  45. 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
  46. 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
  47. 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
  48. 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
  49. Amaris, G., Vesely, S. & Hess, S. (2022), A choice modelling analysis of pro-environmental behaviour spillover, 7th International Choice modelling conference, Reykjavik
  50. 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
  51. 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
  52. 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
  53. 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
  54. 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
  55. 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
  56. 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
  57. 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
  58. 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
  59. 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
  60. 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