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IIP-Complexity Science Hub Vienna
Vienna, Austria (Outgoing Program)
Program Terms:
Program Terms: Summer
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This program is currently not accepting applications.
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Restrictions: Princeton applicants only
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Fact Sheet:
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Dept Offering Program: International Internship Program (IIP) Program Type: Internship
Degree Level: 3rd year u/g students Time Away: Summer
Housing options: Student Responsibilty with support from IIP and/or Host Organization Program Group: International Internship Program
Duration of Program: 8 or more weeks Program Adviser: Shahreen Rahman
Program Description:
Program Description:
Complexity Science Hub Vienna

Organization Overview

The objective of the Complexity Science Hub Vienna is to host, educate, and inspire complex systems scientists who are dedicated to collect, handle, aggregate, and make sense of big data in ways that are directly valuable for science and society. Focus areas include smart cities, innovation dynamics, medical, social, ecological, and economic systems. CSH is a joint initiative of AIT, IIASA, Medical University of Vienna, TU Graz, TU Wien, and Vienna University of Economics and Business.

Intern Responsibilities:

The IIP intern will contribute to the work of the Hub and may be able to conduct research for an independent project.

1. Statistical physics of systems with structures and emerging states
The nature of statistics, statistical mechanics and consequently the thermodynamics of stochastic systems is largely determined by how the number of states W(N) depends on the size N of the system. It has been recently shown [1], that all stochastic systems can be classified according to the so-called scaling expansion. The corresponding expansion coefficients (exponents) define the universality class the system belongs to. Systems within the same universality class share the same statistics and thermodynamics. For sub-exponentially growing systems, such expansions have been shown to exist [2,3]. By using the scaling expansion, this classification can be extended to all stochastic systems, including correlated, constraint and super-exponential systems. The extensive entropy of these systems can be easily expressed in terms of these scaling exponents. Systems with super-exponential phase-space growth contain important systems, such as magnetic coins that combine combinatorial and structural statistics [4]. To this class of systems belong all chemical and biological systems, where atoms form molecules or systems where elementary entities form higher-order structures. The main aim of this project is to investigate the statistics and physics of these systems. It is possible to focus either on the theoretical description of the topic, for example, the connection of this approach to the grand-canonical ensemble, examples of spin-like systems or application to social dynamics. Alternatively, it is possible to focus on numerical simulation of these systems including modeling of thermodynamic properties The topic of the project is open to the particular interest of the IIP interns. The concrete topic of the internship can be arranged before start of the internship.

Prerequisites:
  • Basic course of calculus
  • Basic course of thermodynamics/statistical physics (optional)
  • Basic course of probability/statistics (optional)

2. Opinion Dynamics on Adaptive Social Networks
The role of Social Balance In Heider’s social balance theory [1], three agents (human beings, countries) form a balanced arrangement in their relationships, if either all the three are mutual friends or two of them are friends who both have the same enemy in the third. They, however, form an imbalanced arrangement, if either all the three are mutually hostile, or one of them has two friends who detest each other. If such a situation occurs, then the theory posits that agents strive to eliminate imbalanced arrangements by flipping one of the three links, making their relationships balanced. Without any random influences from their milieu, a finite system of agents is led by this tendency to a final arrangement in which no imbalanced triads remain – a state called balanced [2]. The structure of the balanced state is rather simple, namely, either all agents are friends, or they are grouped into clusters, such that agents within a given cluster are friends of each other, but they are enemies of members of the other clusters. As shown by empirical studies on social networks, real systems, however, are not balanced [3, 4]. The main reason for this failure of the balance theory is it does not account for any random influence that agents are exposed to, e.g. their family traditions, personal circumstance, etc, as what is often observed in reality. By introducing a temperature-like parameter 𝑇 as an overall approximation for all these random factors, Rabbani et al. [5] have recently formulated a model for the system of agent links. Their model is able not only to show how the balanced state emerges in the low exposure regime 𝑇 < 𝑇𝑐, for 𝑇𝑐 being the model critical temperature but also, to account for the existence of imbalanced arrangements in real systems if 𝑇 > 𝑇𝑐. Although the results are very much interesting, the crucial role of agent opinions in driving the changes in their relationships has been neglected in these approaches. In fact, when confronted by a binary choice as in a referendum like the Brexit, an individual may revise his links to become friends with like-minded citizens, and turn hostile to the supporters of the opposing view. If such a mechanism prevails, then independently from the ”social balance” effect, the society will immediately fall into two antagonistic groups. On the other hands, the agent relationships often significantly influence his opinion on discussed issues, he may adopt the opinions of his friends or switch to an opinion opposite to his enemies. The complex dynamics which results from the interplay between opinions and social interactions has not been captured by the present models yet. The aim of the project is to study this dynamics by Monte Carlo simulations and statistical physics methods. The successful candidate will acquire a good knowledge of opinion dynamics in social networks – a currently active research field [6, 7] and may have chance to come up with a new framework for these phenomena.

Prerequisites:
  • Basic course of calculus
  • Basic course of thermodynamics/statistical physics (optional)
  • Basic course of probability/statistics (optional)

3. Computational social science​
Overview of possible topics:
  • Complex privacy in online media. Analyzing public profiles of social media users and their connections to evaluate the shadow profile hypothesis: whether the information of users in the social network predicts features of non-users.
  • Future orientation in social media. A text analysis project of social media posts (e.g. Twitter datasets available at the CSH) to identify future references and expression of emotions and sentiment. The aim is to test the temporal orientation of emotional states as expressed in big data.
  • Open projects with social media data. We count with datasets from a variety of platforms, including Twitter, Reddit, and Instagram, as well as access to historical and large-scale data through a contract with Brandwatch. We are open to ideas about how to analyze well-being, mental health, inequality, privacy and polarization using these privileged datasets.

4. Summer Internship in Complexity Economics
Traditional economic theory considers a pool of homogeneous agents, interacting in a market equilibrium. However, practical and historical experience has proven many assumptions of standard economics to be wrong. Complexity Economics offers new insights by interpreting the economy as a complex system [1]. Recent research suggests that how well banks and firms perform is to a large extent determined by the structure of the networks in which they are embedded [2, 3, 4]. These networks include supply chains that record different types of bilateral business ties between companies or networks of monetary flows between financial institutions. Economic and financial crises can then be understood by means of dynamically cascading non-equilibrium processes on such networks. The proposed project is planned to investigate the distribution of the production and the flow of goods in the Austrian inter-firm network. Based on publicly available data and a commercial Database of Balance sheets [5] we reconstruct the Supply Chain of Austrian firms [6], analyze the regional complexity [7] and identify systemically important firms. Together with the successful IIP intern, we will define a research question based on his or her skills and prior knowledge that contributes to our ongoing research.


Qualifications:
IIP candidates with interests in network science, big data and complexity science, understanding complex systems and computer science are encouraged to apply. Programming skills would be an asset.

Juniors are encouraged to apply.
 

Dates / Deadlines: - unrelated header
Dates / Deadlines:
This program is not currently accepting applications. Please consult the sponsoring department's website for application open dates.
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This program is currently not accepting applications.