Manuel Marques-Pita is an Assistant Professor of Complex Systems at CICANT, Universidade Lusófona (Portugal) and an Associate Researcher in the Complex Systems group at ISTAR (ISCTE-IUL) (Portugal). His research is on the interplay between individual and collective behaviours in complex networks (or how do individuals and collectives shape each other).
Prof. Marques-Pita was awarded a PhD in Artificial Intelligence and Cognitive Science by the University of Edinburgh (Scotland, UK) in 2007. He then pursued postdoctoral training with Prof. Luís Rocha (Indiana University, USA) and Prof. Melanie Mitchell (Portland State University, USA) from 2008 to 2014. During this period, he studied dynamics and control in complex networks, particularly in automata models of biochemical regulation and signalling in Biology. His current main focus is on studying complex communication processes in various types of social network, with a particular focus on group conversation. He is the principal investigator of a project that uses Artificial Intelligence, Data Science and Network Science to study social conversation in communities of secondary school students online. The main goal of the project is to use Artificial Intelligence to support the Portuguese Ministry of Education in designing targeted educational interventions that are informed by evidence, and not by intuitions, while ensuring the adherence to ethical principles, and full accountability. These interventions can be helpful for facing current challenges posed by the increasing divisive narratives, extremism, conspiracy theories and hate speech that are becoming so dangerously commonplace in our societies. We expect to provide such support by informing decision makers on how behaviours such as reflection, argumentation, critical thinking, and allowing a multiplicity of voices and perspectives can be successfully encouraged in a diversity of educational settings, in a constantly changing social reality.
Prof. Marques-Pita is also involved in similar projects that use large datasets to understand collective information processing dynamics in specific conversation topics in online social-network platforms, particularly on topics that are directly or indirectly related to politics.
PhD in Artificial Intelligence and Cognitive Science, 2007
University of Edinburgh
We present schema redescription as a methodology to characterize canalization in automata networks used to model biochemical regulation and signalling. In our formulation, canalization becomes synonymous with redundancy present in the logic of automata. This results in straightforward measures to quantify canalization in an automaton (micro-level), which is in turn integrated into a highly scalable framework to characterize the collective dynamics of large-scale automata networks (macro-level).