In order to adapt to the issues of climate change and public health, urban policies are trying to encourage soft mobility, but the share of the car remains significant. Beyond known constraints, we study here the impact of perception biases on individual choices. We designed a multi-criteria decision model, integrating the influence of habits and biases. We then conducted an online survey, which received 650 responses. We used these to calculate realistic mobility perception values, in order to initialise the environment and the population of a modal choice simulator, implemented in Netlogo. This allows us to visualize the adaptation of the modal distribution in reaction to the evolution of urban planning, depending on whether or not we activate biases and habits in individual reasoning. This is an extended and translated version of a demo paper published in French at JFSMA-JFMS 2024 "Un simulateur multi-agent de choix modal subjectif"

To adapt cities to the issues of climate change and public health, urban policies are trying to encourage soft mobility [14] in order to reduce traffic and pollution, via financial incentives or new infrastructure. However, mobility evolves very slowly, and the share of the car remains significant (74% in France [9]), despite increased public awareness of global warming, and increased concern for ecology. The pandemic offered an opportunity to explore the impact of reduced car mobility and new urban planning policies, for instance with temporary cycle paths [19]. But these public policies normally take longer to implement and are not always well accepted by the car-loving population; many of these temporary cycle paths were gradually returned to cars after the end of the lockdowns [6]. Many explaining factors of this inertia of mobility and reluctance to shift from the car are already known, both contextual, such as a lack of alternatives (limited public transportation options), individual constraints (transporting children or tools), or higher costs of newer or electric vehicles...); and psychological, such as the difficulty to change habits [8, 17], individualism [12], or influence of cognitive biases [15, 13].

Mobility is a central issue in the transition to a more sustainable lifestyle. The average daily distance traveled by the French population has increased considerably, from 5 km on average in the 1950s to 45 km on average in 2011 [58], as has the number of personal cars (11,860 million cars in 1970 [7] compared to 38,3 million in 2021 [15, 28]). For example in Toulouse, cars concentrate 74% of the distances traveled by the inhabitants and contribute up to 88% to GHG emissions [25]. The evolution of mobility is therefore an essential question, both for the global climate crisis and for public health: negative impact of a sedentary lifestyle [9], road accidents, air and sound pollution [44]. Indeed, 40000 deaths per year are attributable to exposure to fine particles (PM2.5) and 7000 deaths per year attributable to exposure to nitrogen dioxide (NO2), i.e. 7% and 1% of the total annual mortality [38]; the 2-month lockdown of spring 2020 in France saved 2300 deaths by reducing exposure to particles, and 1200 more deaths by reducing exposure to nitrogen dioxide [38].

This report presents results from an M1 internship dedicated to agent-based modelling and simulation of daily mobility choices. This simulation is intended to be realistic enough to serve as a basis for a serious game about the mobility transition. In order to ensure this level of realism, we conducted a survey to measure if real mobility choices are made rationally, or how biased they are. Results analysed here show that various biases could play a role in decisions. We then propose an implementation in a GAMA agent-based simulation.

Artificial Intelligence is more and more pervasive in our lives. Many important decisions are delegated to AI algorithms: accessing higher education, determining prison sentences, autonomously driving vehicles... Engineers and researchers are educated to this field, while the general population has very little knowledge about AI. As a result, they are very sensitive to the (more or less accurate) ideas disseminated by the media: an AI that is unbiased, infallible, and will either save the world or lead to its demise. We therefore believe, as highlighted by UNESCO, that it is essential to provide the population with a general understanding of AI algorithms, so that they can choose wisely whether to use them (or not). To this end, we propose a serious game in the form of a civic debate aiming at selecting an AI solution to control a pandemic. This game is targeted at high school students, it was first experimented during a science fair, and is now available freely.

In recent decades, the average daily distance traveled by the French population has increased considerably (from 5 km on average in the 1950s to 45 km on average in 2011 [33]), as has the number of personal cars (11,860 million cars in 1970 [5] compared to 38,3 million in 2021 [9, 19]). For example in Toulouse, cars concentrate 74% of the distances traveled by the inhabitants and contribute up to 88% to GHG emissions [30]. The evolution of mobility is therefore an essential question, in the context of the climate crisis but also in terms of public health: the negative impact of a sedentary lifestyle [6], road accidents, air pollution and sound pollution [28]. Indeed, 40000 deaths per year are attributable to exposure to fine particles (PM2.5) and 7000 deaths per year attributable to exposure to nitrogen dioxide (NO2), i.e. 7% and 1% of the total annual mortality [16]; this report also concludes that the 2-month lockdown of spring 2020 in France made it possible to avoid 2300 deaths by reducing exposure to particles, and 1200 more deaths by reducing exposure to nitrogen dioxide. This shows that public policies and individual behaviour changes (modal shift towards cycling, more extensive teleworking) can have an impact on public health.

The diffusion of innovations is an important topic for the consumer markets. Early research focused on how innovations spread on the level of the whole society. To get closer to the real world scenarios agent based models (ABM) started focusing on individual-level agents. In our work we will translate an existing ABM that investigates the role of opinion leaders in the process of diffusion of innovations to a new, more expressive platform designed for agent based modeling, GAMA. We will do it to show that taking advantage of new features of the chosen platform should be encouraged when making models in the field of social sciences in the future, because it can be beneficial for the explanatory power of simulation results.

This paper proposes an agent-based simulation of a presidential election, inspired by the French 2017 presidential election. The simulation is based on data extracted from polls, media coverage, and Twitter. The main contribution is to consider the impact of scandals and media bashing on the result of the election. In particular, it is shown that scandals can lead to higher abstention at the election, as voters have no relevant candidate left to vote for. The simulation is implemented in Unity 3D and is available to play online.

A voting process involves the participation of many people that interact together in order to reach a common decision. In this paper, we focus on voting processes in which a single person is elected. A voting method is defined as the set of rules that determine the winner of the election, given an input from each voter, for example their preferred candidate or an order relation between all candidates. Social Choice Theory is the field that studies the aggregation of individual preferences towards a collective choice, like for example electing a candidate or choosing a movie. Computational social choice is a recent field which aim is to apply computer science to social choice problems [3].

How should computer science and social science collaborate to build a common model? How should they proceed to gather data that is really useful to the modelling? How can they design a survey that is tailored to the target model? This paper aims to answer those crucial questions in the framework of a multidisciplinary research project. This research addresses the issue of co-constructing a model when several disciplines are involved, and is applied to modelling human behaviour immediately after an earthquake. The main contribution of the work is to propose a tool dedicated to multidisciplinary dialogue. It also proposes a reflexive analysis of the enriching intellectual process carried out by the different disciplines involved. Finally, from working with an anthropologist, a complementary view of the multidisciplinary process is given.