Algorithms to Support Social Science
Research areas focused on game theory, social choice, and collective decision-making are traditionally at the intersection of mathematics and economics.
The advent of artificial intelligence bolstered the importance of research on computational social choice, which crucially employs computer science.
This research field is therefore developed at Dauphine-PSL in typical multidisciplinary fashion.
Applications to Address Societal Challenges
The research conducted at Dauphine-PSL geared toward decision support is used to address various societal issues.
- Health and aging
- Development and mobility
- Macroeconomic policy
- Environment and climate
- Finance
Our Research in the University's Labs
The Laboratory for Analysis and Modeling of Decision Support Systems is one of the few research centers in France or Europe developing a specific vision of computer science – as it relates to decision science and technologies.
UMR CNRS 7243
LAMSADE
The Center for Research in Decision Mathematics is world-renowned for its work in non-linear analysis, finance and economics, and probability and statistics.
UMR CNRS 7534
CEREMADE
The Dauphine Economics Laboratory gathers all the Dauphine economics teams around theoretical and applied work, common tools and international networks.
UMR CNRS IRD 8007-260
LEDa
Our Research
Game Theory, Strategic Behaviors, and Auctions
Game theory plays a pivotal role in the analysis of optimal behaviors exhibited by digital actors, in areas as varied as responding to attacks, defining and understanding bidding strategies, etc.
It typically involves repeated games with very short time-steps that can be tackled in relation to infinite-horizon or continuous-time games.
There is a team from CEREMADE and LEDa (JEET) devoted to these issues. They are particularly interested in repeated or zero-sum differential games, the role of information, auctions, evolutionary games, and no-regret dynamics.
CEREMADE is also the birthplace of mean-field games, under the influence of Jean-Michel Lasry and Pierre-Louis Lions. This formalism makes it possible to simplify problems with games featuring a "large number" of players, in which one or more could be seen as a major player. Finally, CEREMADE is where the theory of stochastic targets was developed, including its version in the form of games.
- Repeated games
- Information
- Bayesian games
- Auctions
- No-regret dynamics
- Mean-field games
- Stochastic game
Our Researchers
Pierre-Louis Lions, Jean-Michel Lasry, Pierre Cardaliaguet, Françoise Forge, David Ettinger, Miquel Oliu-Barton, Guillaume Vigeral, Yannick Viossat, Bruno Zilliotto, and Bruno Bouchard
Published Research
- Ettinger D., Michelucci F. Jump Bids and the Winner’s Curse. Annals of Economics and Statistics, vol. 133, p. 87-92. (2019)
- Viossat Y., Zapechelnyuk A. No-regret Dynamics and Fictitious Play. Journal of Economic Theory, vol. 148, n°2, p. 825-842. (2013)
- Forges F., Horst U., Salomon A. Feasibility and individual rationality in two-person Bayesian games. International Journal of Game Theory, vol. 45, n°1, p. 11-36. (2016)
- Sorin, S., Vigeral, G. Zero-sum Stochastic Games : Limit Optimal Trajectories. arXiv preprint arXiv:1812.08414. (2018)
- Oliu-Barton, M. Asymptotically optimal strategies in repeated games with incomplete information and vanishing weights. Journal of Dynamics & Games, 6(4), 259. (2019)
- Cardaliaguet P., Hadikhanloo S. Learning in Mean Field Games : the Fictitious Play. ESAIM : Control, Optimisation and Calculus of Variations, vol. 23, n°2, p. 569-591. (2017)
- Bouchard B., Nutz M. Stochastic Target Games and Dynamic Programming via Regularized Viscosity Solutions. Mathematics of Operations Research, vol. 41, n°1, p. 109-124. (2015)
Algorithmic Games and Computational Social Choice
This involves computationally examining the various mechanisms of collective decision-making as well as situations with strategic interactions.
Studies conducted at LAMSADE include the formulation of normative models (notably through the axiomatic characterization of voting rules, interactive situations, solution concepts, etc.) and the examination of these models in terms of their algorithmic difficulty and their computation.
The research is structured around two areas:
- Computational social choice – the study of collective decision-making (including voting and equitable resource-sharing) with regard to the axiomatic characterization of decision-making mechanisms and the impact, on their feasibility, of their algorithmic complexity, information needs, and vulnerability to strategic behavior.
- Algorithmic games – the efficient computation of solutions for games and the study of the complexity of these problems, combinatorial optimization problems in game structures, compact representation of games, and learning algorithms in dynamic games.
Equity
Collective decision-making
Axiomatic characterization
Voting
Preference aggregation
Strategic behavior
Algorithmic games
Computational social choice
Compact representations
Our Researchers
Jérôme Lang, Rida Laraki, Laurent Gourvès, Julien Lesca, Meltem Ozturk, Denis Bouyssou, Stephano Moretti, M. Remzi Sanver, Yann Chevaleyre
Published Research
- Flesh J., Laraki R., Perchet V. Approachability of convex sets in generalized quitting games. Games and Economic Behavior, vol. 108, p. 411-431. (2018)
- Cazenave T. Residual Networks for Computer Go. IEEE Transactions on Games, vol. 10, n°1, p. 107-110 (2018)
- Aziz H., Biro P., Lang J., Lesca J., Monnot J. Efficient reallocation under additive and responsive preferences. Theoretical Computer Science, vol. 790, p. 1-15. (2019)
- Airiau S., Aziz H., Caragiannis I., Kruger J., Lang J., Peters D. Portioning Using Ordinal Preferences: Fairness and Efficiency. IJCAI 2019 : 11-17
- Beynier A., Chevaleyre Y., Gourvès L., Harutyunyan A., Lesca J., Maudet N., Wilczynski A. Local envy-freeness in house allocation problems. Autonomous Agents and Multi-Agent Systems 33(5) : 591-627. (2019)
- Darmann A., Elkind E., Kurz S., Lang J., Schauer J., Woeginger GJ. Group activity selection problem with approval preferences. Int. J. Game Theory 47(3) : 767-796. (2018)
- Kruger J., Sanver MR. Restricting the domain allows for weaker independence. Social Choice and Welfare 51(3) : 563-575 (2018)
- Bouyssou D., Marchant T. The β-ranking and the β-measure for directed networks : Axiomatic characterizations. Social Networks, 52, 145-153. (2018)
- Moretti S., Öztürk M. Some axiomatic and algorithmic perspectives on the social ranking problem. In International Conference on Algorithmic Decision Theory (pp. 166-181). Springer, Cham. (2017, October).