### Venue

Co-located with the CCS 2018.

**Vellidio Convention Center**

Leof. Stratou 3, Thessaloniki 546 39, Greece

Co-located with the CCS 2018.

**Vellidio Convention Center**

Leof. Stratou 3, Thessaloniki 546 39, Greece

**7 Jun** Call for abstracts

**26 Jun** Deadline for abstract submission

**28 Jun** Notification of acceptance

**30 Jun** CCS EarlyBird Registration deadline

**29 Jul** Notification of Acceptance (only for submissions received after 30 June)

**15 Aug** CCS Standard Registration deadline

**26 Sep** Satellite event

Only contributions submitted through the EasyChair system will be considered.

Authors of accepted contributions should register to the main conference.

University of Padua

UC Davis

UC Davis

Griffith University

**Samir Suweis**

*Recently, evidence has been mounting that biological systems might operate at the borderline between order and disorder, i.e., near a critical point. In this talk we will present a general mathematical framework for understanding this common pattern, explaining the possible origin and role of criticality in living adaptive and evolutionary systems. We rationalize this apparently ubiquitous criticality in terms of adaptive and evolutionary functional advantages. We provide an theoretical framework based on information theory and genetic algorithms, which shows that the optimal response to broadly different changing environments occurs in systems organizing spontaneously to the vicinity of a critical point. In particular, criticality turns out to be the evolutionary stable outcome of a community of individuals aimed at communicating with each other to create a collective entity. *

**Carl Henning Reschke**

**Farouk Bonilla, Shahram Sarkani and Thomas Mazzuchi**

**Paul Riechers**

*Can model-free signal-analysis methods detect information processing and thus complex structure in the world around us?
As an obvious candidate, power spectra are a common and convenient way to analyze signals in many disciplines of science and engineering. Their structure not only shows the prominence of various signal-frequencies but also hints at mechanisms of correlation, resonance, and broader behavior. However, here we show that power spectra can nevertheless hide all structure about arbitrarily complex processes, conveying only a flat power spectrum---the renowned signature of structureless white noise. Indeed, we argue that the most insightful signals from complex systems will have large beyond-pairwise correlation that evades power spectra. To offer more than a word of warning, we give three more constructive results:
*

- We characterize the minimal generative structure implied by any power spectrum.
- We show how to construct arbitrarily complex processes with flat power spectra.
- We suggest more sophisticated tools (and introduce the excess entropy spectrum) to detect computational structure.

**Kavan Modi**

*TBD*

**Kristian Lindgren**

**Nora Tischler**

*Technologies that exploit quantum mechanical effects promise to enhance applications in a number of different areas. One celebrated example is the speed-up in factoring numbers that can be obtained through Shor’s algorithm. Recently, a new task has emerged for which quantum information science provides an advantage: the simulation of stochastic, i.e. partially random, processes.
Stochastic process models are used to describe a wide range of natural and social phenomena, including for example the weather and the stock market. The simulation of such processes provides valuable information about the dynamics of complex systems. However, for highly complex processes, a large amount of information about the system's past needs to be stored in order to simulate its future—a quantity formally measured by its statistical complexity. This translates to a large memory requirement, which may limit the feasibility of such a simulation. Here, quantum mechanics promises an advantage: Simulators based on quantum information processing can outperform classical simulators by reducing the memory requirements below the ultimate classical limits.
The first physical demonstrations of this recently proposed quantum memory advantage have been achieved in the Quantum Optics and Information Laboratory at Griffith University, Australia. In a series of four experiments, we have, among other things, explored how the relative simplicity of two stochastic processes can depend on the classical or quantum nature of the information processing, and have demonstrated that quantum resources allow storing information about the past of the process in a lower-dimensional memory system. Our experiments employ single particles of light as the information carrier. In this talk, I will introduce quantum information processing with light and provide an overview of these physics experiments.
*

**Alec Boyd**

*Complex computations typically occur via the composition of modular units, such as the universal logic gates found in logical circuits. The benefit of modular information processing, in contrast to globally integrated information processing, is that complex global information processing is more easily and flexibly implemented via a series of simpler, localized information processing operations that only control and change local degrees of freedom. We show that, despite these benefits, there are unavoidable thermodynamic costs to modularity--costs that arise directly from the operation of localized processing and that go beyond Landauer's dissipation bound for erasing information. We quantify the minimum irretrievable dissipation of modular computations in terms of the difference between the change in global nonequilibrium free energy and the local (marginal) change in nonequilibrium free energy, which bounds modular work production. This modularity dissipation is proportional to the amount of additional work required to perform the computational task modularly, measuring a structural energy cost. It determines the thermodynamic efficiency of different modular implementations of the same computation, and so it has immediate consequences for the architecture of physically embedded transducers, which are information processing agents. Constructively, we show how to circumvent modularity dissipation by designing agents that capture the information reservoir's global correlations and patterns. We prove that these agents, when acting as pattern generators or extractors, must match the complexity of their environment to minimize the modularity dissipation. Thus, there are routes to thermodynamic efficiency by optimizing the modular architecture of computations.
*

**Andrew Garner**

*TBD*

The Information Processing in Complex Systems (IPCS) satellite meeting is organized, since 2012, during the Conference on Complex Systems.

Our goal is to provide a forum for researchers who follow an information-theoretic approach for the analysis of complex systems.
Here they can present recent achievements and discuss promising hypotheses and further research directions, combining both classical and quantum information approaches.

**All systems in nature can be considered from the perspective that they process information.**

Information is registered in the state of a system and its elements, implicitly and invisibly.
As elements interact, information is transferred. Indeed, bits of information about the state of one element will travel – imperfectly – to the state of the other element, forming its new state.
This storage and transfer of information, possibly between levels of a multi level system, is imperfect due to randomness or noise.
From this viewpoint, a system can be formalized as a collection of bits that is organized according to its rules of dynamics and its topology of interactions.

Mapping out exactly how these bits of information percolate through the system reveals fundamental insights in how the parts orchestrate to produce the properties of the system.

A theory of information processing would be capable of defining a set of universal properties of dynamical multi level complex systems, which describe and compare the dynamics of diverse
complex systems ranging from social interaction to brain networks, from financial markets to biomedicine. Each possible combination of rules of dynamics and topology of interactions, with
disparate semantics could be translated into the language of information processing which in turn will provide a *lingua franca* for complex systems.

Assistant Professor in the Complexity Institute at the School of Physical and Mathematical Sciences, Nanyang Technological University