Members of the Amsterdam Dynamics Group hold a number of prestigious grants, including an ERC starting grant, an NWO Vidi grants, and NWO Veni grants.

Grant Title: The Logical Dynamics of Information Exchange in Social Networks
Principal Investigator: Fenrong Liu (Tsinghua University) and Alexandru Baltag (ILLC, University of Amsterdam)
Grant Type: China Exchange Programme at  the Royal Academy of Art and Sciences (KNAW)
Duration: 2014-2016
Description: The main problem  of this project is to understand information flow and group belief dynamics in social networks. The research  will be carried out at the  Joint Research Center for Logic with 12 researchers from the ILLC, University of Amsterdam and Tsinghua University during 2014-2016.  Besides the aboved mentioned two PIs, the rest are:  Johan van Benthem, Jan van Eijck, Sonja Smets, Kaile Su, Pingzhong Tang, Chanjuan Liu, Shengyang Zhong, Zoe Christoff, Paulo Galeazzi, Nina Gierasimczuk.

Grant Title: The Logical Structure of Correlated Information Change

Principal Investigator: Sonja Smets
Grant Type: ERC starter grant awarded by the European Research Council (ERC) and the European Community under FP7.
Duration: 2012-2017
Description: One of the central questions in this research project concerns the nature of the logic needed to discuss correlated information change. We seek to develop a uniform logical system that centers around correlated information change and can be used to explain and model various interactive scenarios. One of the aims of this research is to examine the correlations that arise in situations in which the very act of learning new information may directly change the reality that is being learnt. An example is the way in which an introspective agent changes her beliefs when learning new higher-order information, i.e. information that may refer to her own beliefs. A similar situation arises when a scientist learns about a phenomenon by performing measurements that perturb the very phenomenon under study (such as in the case of quantum measurements). More complex forms of correlated information change occur in groups of communicating agents when some agents’ beliefs about the others’ belief changes may trigger or influence their own belief change. In this interdisciplinary project, we will combine insights and techniques from a range of research domains, including logic, quantum mechanics, philosophy of science, belief revision theory, truth approximation and learning theory.

Grant Title: Reasoning about quantum interaction: Logical modelling and verification of multi-agent quantum protocols
Principal Investigator: Sonja Smets
Grant Type: VIDI grant awarded by the Netherlands Science Organization (NWO).
Duration: 2011-2015
Description: As for classical computing, logic is expected to play an essential role in the understanding of quantum computation and quantum information, and especially in the formal verification of quantum communication protocols. Such multi-agent applications involve quantum information flow and classical knowledge transfer (by classical communication) between the agents. So one of our aims in the proposed VIDI research project is to develop the logical tools for modelling complex situations where different types of informational dynamics (classical and quantum) are combined. Our goal is to develop and use a combined classical-quantum logic for the full specification and formal verification of agent-based quantum protocols for secure communication. Towards this goal, we propose to use formalisms based on modal logic, especially combinations of dynamic (or temporal) logics and epistemic (or “spatial”) logics. But other logical formalisms, such as probabilistic logic, linear logic and coalgebraic logic (or categorical logic, in general), may also turn out to be useful in this context.

Grant Title: Evidence-Based Belief Revision
Principal Investigator: Bryan Renne
Duration: 2012-2014
Grant Type: VENI grant awarded by the Netherlands Organization for Scientific Research (NWO)
Description: Belief Revision is the study of how new, possibly contradictory information should rationally affect one’s beliefs. This is an active, multi-disciplinary area of study with applications in Logic, Artificial Intelligence, Philosophy, Law, and Economics. Much work in Belief Revision has focused on the so-called “postulate-based” approach, which characterizes the belief change process in terms of a series of statements that say what ought to be the case after a belief change has occurred. While this extremely popular approach has had its share of successes, it has neglected to address the underlying reasoning process an individual might use in actually changing her beliefs. Such a process ought to take into account the uncertain evidence one has at her disposal, allowing her to perform stepwise reasoning about her evidence without demanding infinite cognitive and logical precision as is typically done in Belief Revision theory.

The aim of this project is to develop a new theory of Belief Revision that describes belief change as a step-by-step evidence-weighing process in which errors might occur but can later be corrected. The theory will combine, adapt, enhance, and otherwise custom-fit ingredients from Dynamic Epistemic Logic and Justification Logic, two fast-growing areas of Applied Logic that present great promise toward this end.

Grant Title: Learning from each other. Formal analysis of multi-agent learning
Principal Investigator: Nina Gierasimczuk
Duration: 2014-2016
Grant Type: VENI grant awarded by the Netherlands Organization for Scientific Research (NWO)
Description: There is something odd about being a member of a group. On the one hand there are clear benefits: forces can be joined, experiences can be pooled, knowledge can be shared, one can learn from one another. On the other hand there are downsides, for instance cases where personal opinion is overridden by conformity. This project will investigate the complex balance between the advantages and disadvantages of being in a group with a particular focus on learning. A unified approach to single-agent learning, providing a full logical analysis of inductive inference in a setting that combines formal learning theory, belief revision theory, and dynamic epistemic logic, was first developed in Nina Gierasimczuk’s PhD Thesis. Lifting this work to the multi-agent case will demand the development of a formal framework for the modeling of, and reasoning about group learning. One of the main goals of the project is focusing on ways of imposing computability restrictions on knowledge change and belief revision. In this respect the project will contribute to the computational view on learning, by proposing plausible and implementable epistemic models.