The Ninth International Conference on Guided Self-Organisation (GSO-2018) : Information Geometry and Statistical Physics

Abstracts of the Posters

Francesco Caravelli

Nonequilibrium properties of memristive circuits: connection to Hopfield models

Memristors are nonlinear passive circuit elements which can be thought as time varying resistances. Circuits with memristors have been shown both experimentally and numerically to be of use for various problems of machine learning. In this talk we provide further theoretical background for this statement. We show that the dynamics for memristive circuits is such that a specific class of quadratic functional is being minimized. This shows that this class of optimization problems, in general hard to solve, can be thus approximately quickly solved using memristive circuits. We analyze this statement in the case of random circuits, showing in a certain approximation what is the behavior of the number of stationary points as a function of the topological parameters of the circuit. This provides a connection between spin glasses and the Hopfield model.

Bernat Corominas-Murtra
Med Univ. of Vienna/Vienna Complexity Science Hub, Austria

Mutual Information and the problem of Referentiality in Autonomous Systems

Kirill Glavatskiy
The University of Sydney, Australia

Agent-based modeling of housing market: can methods of statistical physics predict human behavior?

Kirill Glavatskiy, Mikhail Prokopenko, Michael Harre.

Following the crashes of global market in 2008 questions were asked of the ability of economics to address the vulnerability of such an important system. It became evident that conventional tools are not able to address systemic risks in markets with heterogeneous structure, where humans behave according to multiple behavioral patterns. This is the case, in particular, for housing market. Over the recent years there has been observed a steady increase of real estate prices in major metropolitan cities in Australia, while some other cities do not show these trends. This indicates that there might be a possibility of “housing bubble” which will eventually burst.
In this work we employ methods of statistical physics to investigate the evolution of the housing market in Australia and possibilities for existence of the housing bubble and its burst. The ideological background for this approach is the fact that when it comes to macroscopic phenomena, individual human choices do not play a significant role. Rather, their collective behavior determines the overall evolution, and statistical interactions between individuals become decisive. Employing the analogy between humans in economic description and molecules in physical description, we investigate the conditions for human self-organization from the perspective of statistical physics.
We address the problem using three approaches, which complement each other on different levels of description: agent-based numerical simulations, maximum entropy principle, and bifurcations at the phase transitions.
Within agent based modeling we introduce heterogeneous society with agents who follow one of the several behavioral patterns. This corresponds to a multicomponent fluid, which self-organize in the mean-field fashion. Unlike a fluid, the agents are allowed to change their identity, switching between behavioral patterns. Depending on the particular state the current community (city), this evolution may or may not lead to a bifurcation and hence phase transition. Coming close to the bifurcation point would indicate that the particular community is close to a “bubble” state.
Within maximum entropy principle we investigate the macroscopic structure of interacting communities. Introducing the analogue of the entropy for the system of our agents, we identify the set of economic constraints, within which they realize their individual strategies. Maximizing the entropy for each of the community allows us to identify the analogy of the spatial distribution of the temperature. Temperature distribution determines the fluxes in the system and allows us to understand the sources and sinks for the market.
Having the recent census data from the Australian Bureau of Statistics we perform analysis for each community (city) within Australia. By doing this we build a map of Australia, which shows how “heated” different regions are with respect to each other. This information can be used by policy making agencies to properly react on market challenges.

Michael Harre
The University of Sydney, Australia

Game Theory, Singularity Theory, and the Computational Foundations of Social Interactions.

J.Michael Herrmann
The University of Edinburgh, United Kingdom

Robots can Understand Physics from Fisher Information

Petru Hlihor
Romanian Institute of Science and Technology and Max Planck Institute for Mathematics in the Sciences, Romania

A Defense Against Adversarial Examples based on Image Reconstruction by Variational Autoencoders

Calum Imrie
University of Edinburgh, United Kingdom

Self-Organised Transitions in Swarms with Turing Patterns

Vladimir Jaćimović
University of Montenegro, Montenegro

Mean fields in networks of interacting particles

Dimitri Marinelli
Romanian Institute of Science and Technology, Romania

Quantum Information Geometry and Stochastic Reconfiguration

Michel Nguiffo Boyom
Université des Sciences et Techniques de Languedoc, France

Complex systems and Geometric structures

Loosely speaking a complex system is a measurable set $(\Xi,\Omega)$; $\Gamma(\Xi,\Omega)$ is the group of measurable isomorphisms of $\Xi$ (viz the group of efficient statistics.) An information Geometry of $(\Xi,\Omega)$ is a $\Gamma$-Geometry in a statistical model of $(\Xi,\Omega)$. Relevant informations are invariants of such a $\Gamma$-geometry. The relevancy of informations is linked with the existence of nice geometric structures in both $(\Xi,\Omega)$ and its chosen model. Among rich geometries in statistical models are the symplectic geometry, the geometry of Koszul, the bi-invariant Riemannian geometry in Lie groups, the left invariant symplectic geometry in Lie groups. The aim of the talk is to address those concerns, some related open geometric problems and a few recent contributions.
A few references.
[AN] Amari and Nagaoka: Methods of information geometry, AMS-Oxford monograph 91.
[BF] Barbaresco F. GeometriC Theory of Heat from Souriau Lie group thermodynamics and Koszul geomtry; Entropy 2016.
[NB1] Nguiffo Boyom M. Foliations-Webs-Hessian Geometry-Information Geometry-Entropy and cohomology; Entropy 12 vol 18 2016.
[NB2] Nguiffo Boyom M. Numerical properties of Koszul connections; arxiv.1708.01106. 3 august 2017.

Thomas Oikonomou
Nazarbayev University, Kazakhstan

The Failure of the MaxEnt Principle for the generalized entropies

Alexandra Peste
Romanian Institute of Science and Technology and Max Planck Institute for Mathematics in the Sciences, Romania

On the Geometry of the Latent Space of Variational AutoEncoders: An Explanatory Analysis

Nathaniel Virgo
ELSI, Tokyo, Japan, Japan

Decomposing multivariate information

We propose a decomposition of multivariate information which is based on a generalisation of Amari's hierarchy over a lattice imposed on combinations of primary random variables, so-called "structures". While related, our construction differs from the well-known lattice construction of Williams/Beer's in that the quantities to be interpreted as information terms sit on the edges and that no variable set is distinguished as a predictor of others; all variables are on the same level, similar to the approach by Rosas et al. (2016). We show that this construction can address some of the questions posed by James & Crutchfield (2017)


Date and Location

March 26 - 28, 2018
Max Planck Institute for Mathematics in the Sciences
Inselstraße 22
04103 Leipzig
see travel instructions

Organizing Committee

Nihat Ay
MPI for Mathematics in the Sciences
Leipzig (Germany)

Mikhail Prokopenko
University of Sydney (Australia)

Program Committee

  • Nihat Ay, MPI for Mathematics in the Sciences, Leipzig (Germany)
  • Domenico Felice, MPI for Mathematics in the Sciences, Leipzig (Germany)
  • Carlos Gershenson, Universidad Nacional Autónoma de México, Computer Sciences Department, Mexico City (Mexico)
  • Paolo Gibilisco, Università degli Studi di Roma "Tor Vergata", Facoltà di Economia, Roma (Italy)
  • Daniel Polani, University of Hertfordshire, Department of Computer Science, Hatfield (United Kingdom)
  • Mikhail Prokopenko, University of Sydney, Sydney (Australia)
  • Richard Spinney, University of Sydney, Sydney (Australia)
  • Justin Werfel, Harvard University, Cambridge (USA)
  • Larry Yaeger, Google Inc., San Francisco (USA)
  • G. Çiğdem Yalçın, İstanbul Üniversitesi, İstanbul (Turkey)

Administrative Contact

Antje Vandenberg
MPI for Mathematics in the Sciences
Contact by Email

22.01.2018, 08:06