This program is tentative and is subject to change.
CEU, Vienna, Austria
Márton Karsai is an Associate Professor at the Department of Network and Data Science at the Central European University in Vienna and a researcher at the Rényi Institute of Mathematics in Budapest. He is a network scientist with research interest in human dynamics, computational social science, and data science, especially focusing on heterogeneous temporal dynamics, spatial and temporal networks, socioeconomic systems and social contagion phenomena. He is an expert in analysing large human behavioural datasets and in developing data-driven models of social phenomena.
University of Zaragoza, Zaragoza, Spain
Jesús is an Associate Professor (Profesor Titular) in the Department of Condensed Matter Physics of the University of Zaragoza (Spain). He leads the Group of Theoretical & Applied Modeling (GOTHAM lab) at the Institute of Biocomputation and Physics of Complex Systems (BIFI).
Oregon State University, Corvallis, Oregon, USA
James Watson is an Associate Professor in the College of Earth, Ocean and Atmospheric Sciences at Oregon State University. He and his lab develop mathematical theory and computational methods for application in the domains of ecology, geography, oceanography and sustainability science. He takes inspiration from complex adaptive systems more generally, and in addition to his work on socio-environmental systems he conducts research into financial markets, resource economics and sports data analytics. He is interested in learning from all complex systems to help improve our ability to live sustainably. Previously, he received his PhD in Marine Science from the University of California Santa Barbara, and spent time as a post-doctoral researcher at Princeton University and as a research scientist at the Stockholm Resilience Centre. Hen was the recipient of a DARPA Young Faculty Award and the Oceanography Society’s Early Career Award for his interdisciplinary work.
University of Zaragoza, Zaragoza, Spain
The viability of using white worms as an active defense mechanism against botnets
Classical cyber defense mechanisms are not well suited for some of the threats that we can find nowadays. For instance, IoT devices tend to be poorly secured, making them the perfect target for malware and the creation of large-scale botnets. There have been recent proposals to leverage the weaknesses of IoT devices by using white worms to gain control of them and fix their vulnerabilities. However, besides the ethical concerns that this activity may raise, is it a viable strategy? Under which conditions? These questions will be addressed using models of epidemic-spreading processes with competing pathogens both from a theoretical and a numerical point of view.
UCL, London, UK
The role of proximity for knowledge spillovers in patent citations
Innovation is an essential process in cities, which is nevertheless, very difficult to capture or to measure. There is no simple or universal answer to the questions of 1) what is an innovation, and 2) what are the mechanisms that gave rise to it. Insights to these questions are essential to create the right conditions for the drivers to emerge. The most common proxy for innovation, mainly related to the domain of science and technology, is a patent. Although this can be considered as a poor proxy for many different reasons that we will discuss, it captures to some extent a process of intervening fields or domains that gave rise to the patented innovation. The reference system when patenting is responsible for this: patents need to make reference to other patents that are thought to be essential contributors to the conception of this innovation. In this workshop we will analyse patent data from 1977 to 2019, and identify the influence of industries on each other for the patenting process in the UK. We will look at whether proximity and similarity of industries play an important role in the spillover of knowledge, and whether this has changed over time.
IFISC, Palma de Mallorca, Spain
Analysis and modeling of temporal patterns in communication: beyond pairwise interactions
Many human activities follow temporal patterns that have several interesting characteristics. On the one hand there are temporal regularities that arise (yearly, monthly, weekly, daily). On the other hand many activities occur in bursts, having long periods of inactivity followed by clusters of many events. When talking about
communication activities all these characteristics are observable, but also the appearance of communication networks. How do the communication networks form and evolve in time taking into account burstiness, periodic patterns and mesoscopic characteristics of the network such as cascades and formation of
communities will be the central topic for the project. It will consist of two parts, the first centered on the analysis of communication data; and the second one on the modeling of the observed patterns.
Ca’ Foscari University of Venice, Venice, Italy
Analyzing the Relationship between Echo Chambers and Content Engagement on Twitter
The goal of this project is to study the relationship between content engagement and echo chambers on Twitter. Echo chambers have been shown to contribute to the polarization of online environments, and platform algorithms may exacerbate this issue by limiting users' exposure to differing opinions and increasing segregation. While previous research on echo chambers on Twitter has focused on users' interactions, this project aims to go further by also considering the overall attention generated by a piece of
content and how it relates to the echo chamber it belongs to. This project aims to utilize the "view count" feature on Twitter to compare the number of active users within echo chambers to the outreach of the content. This will allow for an investigation into whether there is any algorithmic bias in favor of one side of the debate. By comparing the number of users actively engaging with content within echo chambers and the overall reach of
that content, this project aims to gain a better understanding of the dynamics at play within these online communities and how they are influenced by platform algorithms. Additionally, this study will compare the results obtained through this method to those
obtained by state-of-the-art models simulating online dynamics, in order to estimate the number of users who view content but do not interact with it. To conduct this research, Twitter data on a heated topic (e.g. politics, vaccines) and information on source reliability
(e.g. classifications from allsides.com, MediaBiasFactCheck.com) will be needed.
Teesside University, Middlesbrough, England, UK
Evolutionary dynamics and cooperation in hybrid human-AI populations
Cooperation, in which agents seek ways to jointly improve their welfare, is ubiquitous in human societies. Arguably, the success of the human species is rooted in our ability to cooperate. Evolutionary Game Theory (EGT) has been used to mathematically study the mechanisms which lead to the emergence and evolution of cooperative behaviours in human societies with diverse behavioural strategies in co-presence. Their systematic study also resorts to agent-based modelling and simulation techniques, thus enabling the exploration of aforesaid mechanisms under a variety of conditions and application domains.
However, similar issues in the context of hybrid mixed populations of humans and artificial intelligence (AI) agents have begun to amass attention only very recently. Since machines powered by artificial intelligence or AI agents are playing an increasingly more important role in our lives, it will be important to equip them with the capabilities necessary to cooperate and to foster cooperation. This project will design EGT models to study the emergence and stability of cooperation behaviours in hybrid human-AI populations where human and AI agents exist in co-presence, interact with each other, and evolve over time. This project will explore new factors that might arise in this hybrid setting (e.g. how AI agents learn socially or individually, process information and update behaviours differently from humans) and design EGT models to explore how they might influence the population dynamics and cooperative outcomes.
Georgetown University, Washington, DC, USA
How recurrent human activities are affected by global crisis
In the last decades, behavioral data, mostly from geolocated mobile phone records, has enabled researchers to quantitatively study individual and collective human behaviors to capture and reproduce regularities in spatio-temporal mobility and social structures and any local anomalies. Such high-resolution data have been
used to predict how infectious diseases spread, to characterize social segregation, or to respond to extreme events such as earthquakes, wildfires, and hurricanes. Over the past 20 months, however, our world has become arrested by the surges caused by SARS-CoV-2, the virus causing COVID-19. With each surge, there are disruptions to routine life through school closures, reductions in leisure activities, and adaptive human behavior (e.g., self-isolation, avoidance of indoor activities, and preferences to keep outdoor venues for gatherings like conferences, sports, and religious
events) [3,4]. COVID-19 surges and the interconnected behavioral aspects alter the recurrent habits of humans. Additionally, environmental and climate changes have a key role in changing daily indoor and outdoor human activities. The combination of these factors makes human behavior extremely difficult to predict.
To help fight the pandemic, however, network operators such as Orange, Vodafone, and Telefonica and companies like Google, Apple, Facebook, Cuebiq, SafeGraph, and Unacast made a huge effort to quickly share their aggregated behavioral data in real-time at large scale and fine resolution. Many challenges have arisen as
a result of this unprecedented data sharing phenomenon. Novel methodological frameworks to pool such empirical data for crisis management are needed. This project will use fine-scale behavioral data from SafeGraph to characterize short- and long-term changes in human activity in a pandemic scenario. Using time series and clustering analysis, we will quantify activity in indoor versus outdoor environments in the United States at 5 million locations nationwide from 2019 to 2022. Developing a SEIR compartmental model for respiratory COVID-like diseases, we will also provide a parameterization of real-time behavior that can be included in infectious disease dynamics models to better predict epidemic activity.
IFISC, Palma de Mallorca, Spain
Temporal and intermittent features of ocean fluid transport from a network perspective and potential implications for marine ecology
Transport, dispersion and mixing of water masses play a fundamental role in several physical and biological processes happening within the oceanic environment. Due to the intrinsic temporal variability of ocean currents, fluid transport patterns can present a marked time dependence across a wide range of scales, from days to years. We propose to assess such temporal features of the ocean using a network approach in which different locations of the ocean are associated with the network nodes while links weights are proportional to the amount of fluid exchanged between node pairs. We focus on sets of snapshot networks (already available) representing oceanic transport across different time windows. Network theory methods will be then used to define and characterize the proprieties of the temporal networks derived from such sets of snapshots. Finally, related aspects of marine ecology, such as gene flow and metapopulation dynamics, will be discussed and possibly investigated.
Structure and dynamics
of signed networks
I would like to propose a tutorial covering the main tools developed for the study of signed networks, focusing on the theory of structural balance. Signed networks capture the role of conflict in networked complex systems. Antagonistic interactions are everywhere in real-world systems (social networks, ecosystems, brain dynamics, diplomatic relations, etc.), and thus this topic has increasingly drawn attention during the last few years. The simplicity of many of the tools used to study signed networks has also contributed to the popularity of the topic. Indeed, most key ideas behind structural balance only require basic knowledge of linear algebra and can be understood within a
During the tutorial, I would propose covering the following topics:
- Signed interactions in complex systems;
- Definition of structural balance;
- Quantitative measurements of balance;
- Dynamics on signed networks;
- Applications in social and biological contexts.
Introduction to Otree and graph representation using Cytoscape
Decision-Making under Deep Uncertainty: Steering Through the Complex Storm
Complex systems often exhibit unpredictable and non-linear behavior, challenging traditional modeling techniques. Decision-making under deep uncertainty (DMDU) presents a valuable framework to address these limitations, offering researchers a more robust approach to complexity modeling. By adopting deep uncertainty principles and embedding models in a multi-objective optimization setup, researchers can uncover vulnerable scenarios, identify optimal policy solutions for multiple objectives, and ensure robustness across a wide range of scenarios. This presentation will emphasize the advantages of integrating DMDU into complexity models, including enhanced adaptability, robustness, and improved scenario analysis. We will discuss the key principles of this approach and demonstrate its practical benefits, such as robust policy design and effective management of unforeseen risks. By embracing DMDU, researchers can unlock new levels of insight, driving more informed and resilient decision-making in complex systems.
Build your own reservoir computer from scratch with ReservoirPy
Reservoir Computing (RC) is a powerful machine learning technique that has been used to
process complex, high-dimensional data, such as time-series and spatial data. At its core, RC is a type of recurrent neural network that uses a fixed, randomly initialized dynamical system, called a reservoir, to process input data. RC has been applied to a variety of areas in complex systems, including forecasting, classification, control, and optimization. The flexibility and power
of RC make it a valuable tool for complex systems researchers.
This tutorial will provide an introduction to the technique and its practical applications using ReservoirPy, an open-source Python library. It provides a user-friendly interface for creating and
training RC models, as well as tools for analyzing and visualizing the model's performance. During the hands-on session, participants will learn how to build an RC model using ReservoirPy and apply it to a real-world dataset.
Palma de Mallorca, Spain
Complexity72h will be held in Palma De Mallorca for the 2023 Edition! Welcome to the capital of Maiorca, famous for its cathedral, its Old City and most of all, its amazing beaches.
Please write us for any information on travel logistics to reach the workshop. We will be glad to help you.
Palma de Mallorca is very well connected with the rest of Spain and Europe (specially Germany, UK, Austria, Switzerland and Scandinavia) through the International "Son Sant Joan" airport (airport code PMI). The airport is located 8 km east of Palma.
How to go from Palma Airport to Palma downtown
Bus: At the Airport take the line A1 at bus stop 547 located outside the Arrivals' Hall. There is a bus every 15-20 minutes. Ticket price: 5€. Tickets can be purchased from the bus driver (only bills up to 10 EUR are accepted). Step down at stop 2 "Plaza España" or "Plaça d'Espanya", where you can take a connection to the UIB campus.
Taxi: Alternatively you can take a Taxi from the Airport to Palma or to the UIB campus. Taxis can be found just outside the Airport Arrivals Hall. Typical fares are around 25€-30€ depending on trafic and number of suitcases. The are suplemental fares for night and holiday services.
How to go from Palma downtown to the UIB campus
Metro (subway) : Take line M1 which starts at Plaza España (marked as Estació Intermodal in ticket machines) and ends at the UIB campus. Travel time is 13 min. There is a metro every 20 min. Single ticket price is 1.80€. Tickets can be purchased at the ticket machines located in the station hall (bills larger than 20€ are not accepted). Keep the ticket until the end, since it is needed to leave the station. Line M1 does not operates on Sundays nor on Saturdays after 2:30 pm.
Bus: From Monday to Friday, take line 19 (labelled as "Universitat"). Single ticket price is 2€. Tickets can be purchased from the bus driver (only bills up to 10 EUR are accepted). Bus frequency is about one every 15 min. and trip time is about 30 min. Line 19 has several stops in Palma including Plaça d'Espanya. For the Student Residence use stop 1101, when arriving from Palma and 1102 when going to Palma. Both stops are labelled "C.Esport-Residència". For IFISC or Antoni M. Alcover building, use stop 571, "Edifici Beatriu de Pinós", when arriving from Palma and stop 1014, "C. Mallorca" (located in front of the Antoni M. Alcover building), when going to Palma. On weekends use line 9 . Bus frequency is about one every hour. When arriving from or going to Palma use bus stop 1101 for Student Residence and stop 1014 for IFISC and Antoni M. Alcover buildings.
Alberto is a mathematician working on evolutionary game theory and experimental economics. He is now an assistant professor at Universidad Carlos III de Madrid.
Sofia develops measures and computational models to understand the evolution of complex systems. Currently, she is an Assistant Professor in the Department of Informatics at Faculdade de Ciências, Universidade de Lisboa.
Maddalena is a researcher based at City, University of London with a background in computational social science and networks.
Eugenio is a researcher at the French National Institute of Health and Medical Research (INSERM) in Paris, France. He is an infectious disease epidemiologist with a background in theoretical physics. He works in infectious disease modeling. His research currently focuses on HIV, COVID-19, and the impact of climate change on the spread of infectious disease epidemics.
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