I run the ERC Starting Grant to understand the future of Urban Mobility. In COeXISTENCE, with the team of 5, we try to foresee what happens when our cities are shared with autonomous, intelligent robots - competing with us for limited resources. We create virtual environments where individual agents compete to arrive faster, more reliably and cheaper at their destinations. Human agents are simulated with detailed behavioural models, estimated and calibrated on the field data to reproduce how we behave and adapt in the cities. In the same environment the deep learning agents try the same - they use deep reinforcement learning to maximise their rewards. This creates a harsh competition in which machines have upper-hands strong enough to beat us.
        Â
COeXISTENCE is a broad and deep experiment in virtual environment on future cities, aimed to discover the new phenomena and propose the new solutions. See the brief overview here and more thorough presentation.
It spans between fields as diverse as:
- game theory;
- deep reinforcement learning;
- complex social systems;
- sustainability;
- urban mobility;
- agent based modelling;
- discrete choice theory.
Vacancies
Our team is happily full at the moment, yet we are always happy to collaborate.
Nonetheless, we may have Master Students, Visiting Professors or prospective PhD students in this project’s ecosystem.
Feel free to reach us out at coexistence@uj.edu.pl
There are funding opportunities at ERC under International Arrangement Funding.
Disclaimer: Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Research Council Executive Agency (ERCEA). Neither the European Union nor the granting authority can be held responsible for them.
Funding acknowledgement: This project has received funding from the European Research Council (ERC) under the European Union’s Horizon Europe research and innovation programme (grant agreement No 101075838).
Publications linked to the project
-
MoMaS: Two-sided Mobility Market Simulation Framework for Modeling Platform Growth Trajectories
Kucharski, Rafal,
and Ghasemi, Farnoud
2024
Mobility platforms such as Uber and DiDi have been introduced in cities worldwide, each demonstrating varying degrees of success, employing diverse strategies, and exerting distinct impacts on urban mobility. We have observed various growth trajectories in two-sided mobility markets and understood the underlying mechanisms. However, to date, a realistic microscopic model of these markets including phenomena such as network effects has been missing. State-of-the-art methods well estimate the macroscopic equilibrium conditions in the market, but struggle to reproduce the individual human behaviour behind and complex growth patterns sensitive to platform strategy and policies. To bridge this gap, we introduce the MoMaS (two-sided Mobility Market Simulation) framework to represent growth mechanism in two-sided mobility markets based on the realistic behavior adjustment of drivers and travelers reactive to platform strategy. In the proposed framework, traveler and driver agents learn the platform utility from multiple channels: their own experience, peers’ word-of-mouth, and the platform’s marketing, all-together constituting the agent’s perceived utility of the platform. Each of these channels is modeled and updated by our S-shaped learning model day-to-day which stabilizes, and at the same time, remains sensitive to the system changes.The platform can simulate any strategy on five levers: trip fare, commission rate, discount rate, incentive rate, and marketing. MoMaS allows to reproduce a variety of market phenomena, including reluctancy, neutrality, and loyalty at the individual level, as well as critical mass, bandwagon effect, positive and negative cross-side network effects at the aggregated level, which are crucial to reproduce realistic growth trajectories.We illustrate the capabilities of MoMaS through an extensive set of real-world experiments. Our results demonstrate that once the platform acquires critical mass, it triggers a significant positive cross-side network effect, accelerating growth. However, this can be reversed if a negative cross-side network effect is triggered, leading to the collapse of the platform. MoMaS is applicable for real-sized problems and available on public repository along with reproducible experiments.
-
Modelling the Rise and Fall of Two-sided Markets
Ghasemi, Farnoud,
and Kucharski, Rafal
In Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems
2024
Two-sided markets disrupted our economies, reshaping markets as diverse as tourism (airbnb), mobility (Uber) and food deliveries (UberEats). New market leaders arose leveraging on platform-based business model, questioning well-established paradigms. The underlying processes behind their growth are non-trivial, inherently microscopic, and leverage on complex human interactions. Platforms need to reach critical mass of both supply and demand to trigger the so-called cross-sided network effects. To this end, platforms adopt a variety of strategies to first create the market, then expand it and finally successfully compete with others. Such a complex social system with many non-linear interactions and learning processes calls for a dedicated modelling approach. State-of-the-art methods well estimate the macroscopic equilibrium conditions, but struggle to reproduce the complex growth patterns and individual human behaviour behind. To bridge this gap, we propose the microscopic S-shaped learning model where agents build their perception on the new service with time, affected by both endogenous (service quality) and exogenous (marketing and word-of-mouth) factors cumulated from experiences. We illustrate it with the case of two-sided mobility platform (Uber), where the platform applies a series of marketing actions leading to rise and then fall on the market where 200 drivers serve 2000 travellers on the complex urban network of Amsterdam. Our model is the first to reproduce not only behaviourally sound, but also empirically observed growth trajectories, it remains sensitive to a variety of marketing strategies, allows reproducing the competition between platforms and is designed to be integrated with machine learning algorithms to identify the optimal market entry strategy.