Motivation

Complex systems are characterized by presenting collective behavior that cannot be understood in a simple way based on the individual behavior of its components. There are examples of these systems in biological, physical, economics, ecology and social sciences too. They usually present coordination, adaptation, synchronization, consensus or evolution, which make them extremely interesting and intriguing. A fundamental tool widely used to analyze those systems are complex networks, which in recent years have proven to be extremely useful for describing interactions observed in complex systems from various areas of science (biochemical, protein, neuronal, social, etc.) including social sciences, where interdependent processes occur at different levels (e.g. relationships between individuals). 

The network approach to complex systems allows to study the structure and quantify emergent properties which can shed light on a system behavior. Networks could be approached differently: in some systems, connections between nodes represent an explicit interaction (as in many examples of natural sciences like prey-predator relationships, or in an electrical transmission network, where nodes can represent electrical stations, and connections, transmission lines); in other systems, the goal is to formalize a connection between nodes that is implicit or unknown and the challenge is to trace it from the node data (as in many examples of the social world, e.g networks of similarity between people regarding musical or movies tastes). The specific approach is related to goals, theoretical frameworks, expert knowledge or even the disciplinary landscape in which certain research is framed. Hence, although network analysis could be a powerful tool to analyze social systems, its transfer to different fields did not happen without troubles. Also, more interdisciplinary work is needed in order to establish its limits and potentials in each discipline. This school is set as an opportunity to promote the met with social scientist, to learn and discuss complex systems and networks in order to incorporate them as tools for understanding problems from social and human fields.

Topics

Complex Networks:

History

Examples of networks from diverse areas: networks of cooperation, social networks,

Statistical properties of networks

Small-world networks

Random networks

Networks visualization: igraph R package, an interaction between R and Cytoscape, networks on maps, Leaflet.

Social Models:

History of Agent-based models

Schelling Model: original segregation model, segregation patterns, variants of a model, coexistence of populations

Axelrod Model: cooperation

Voter model: opinion dynamics, consensus formation, variants of a model, some applications

Spreading models of innovations/opinions about complex networks: information cascades, early adopters role, underlying network topology influence

Minority game: crowd effects, self-organization, coordination, variants of a model

Networks applied to real problems:

Explicit links

Formalizing non-explicit underlying network of a social system

Inference in networks

Networks with incomplete information

Networks with expert information

Some tools of data analysis using R

Final Project

Implement of a computational social model or data analysis on real problems

Responsable

Dr. Inés Caridi

Instituto de Cálculo, UBA y Conicet

Mini-courses included in the School taught by visiting Professors:

1) “How can we model the society?”

2) “Reality and Model: An interdisciplinary discussion about modeling social systems”

3) Examples of other agent-based models

4) Game Theory

Game theory was one of the first attempts to understand the aspects of social systems with tools that were not specific to this area [4].

5) Complexity and Networks in Archaeology.

When?

3/5, 6/5, 8/5 and 10/5
2019

*PARTIAL AND FULL SCHOLARSHIPS WILL BE AVAILABLE

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