Complexity, Development, and Evolution in Morphogenetic Collective Systems

Many living and non-living complex systems can be modeled and understood as collective systems made of heterogeneous components that self-organize and generate nontrivial morphological structures and behaviors. This chapter presents a brief overview of our recent effort that investigated various aspects of such morphogenetic collective systems. We first propose a theoretical classification scheme that distinguishes four complexity levels of morphogenetic collective systems based on the nature of their components and interactions. We conducted a series of computational experiments using a self-propelled particle swarm model to investigate the effects of (1) heterogeneity of components, (2) differentiation/re-differentiation of components, and (3) local information sharing among components, on the self-organization of a collective system. Results showed that (a) heterogeneity of components had a strong impact on the system’s structure and behavior, (b) dynamic differentiation/re-differentiation of components and local information sharing helped the system maintain spatially adjacent, coherent organization, (c) dynamic differentiation/re-differentiation contributed to the development of more diverse structures and behaviors, and (d) stochastic re-differentiation of components naturally realized a self-repair capability of self-organizing morphologies. We also explored evolutionary methods to design novel self-organizing patterns, using interactive evolutionary computation and spontaneous evolution within an artificial ecosystem. These self-organizing patterns were found to be remarkably robust against dimensional changes from 2D to 3D, although evolution worked efficiently only in 2D settings.


Complexity, Development, and Evolution in Morphogenetic Collective Systems
Hiroki Sayama


Mobility and Congestion in Dynamical Multilayer Networks with Finite Storage Capacity

Multilayer networks describe well many real interconnected communication and transportation systems, ranging from computer networks to multimodal mobility infrastructures. Here, we introduce a model in which the nodes have a limited capacity of storing and processing the agents moving over a multilayer network, and their congestions trigger temporary faults which, in turn, dynamically affect the routing of agents seeking for uncongested paths. The study of the network performance under different layer velocities and node maximum capacities, reveals the existence of delicate trade-offs between the number of served agents and their time to travel to destination. We provide analytical estimates of the optimal buffer size at which the travel time is minimum and of its dependence on the velocity and number of links at the different layers. Phenomena reminiscent of the Slower Is Faster (SIF) effect and of the Braess’ paradox are observed in our dynamical multilayer set-up.


Mobility and Congestion in Dynamical Multilayer Networks with Finite Storage Capacity
Sabato Manfredi, Edmondo Di Tucci, Vito Latora


Call for Papers | ALIFE 2018

The 2018 Conference on Artificial Life (ALIFE 2018)

A Hybrid of the European Conference on Artificial Life (ECAL) and the International Conference on the Synthesis and Simulation of Living Systems (ALife)

July 23-27, 2018
Tokyo, Japan

The “ALIFE 2018” conference will be a stimulating home for a rich and diverse research community in Artificial Life and related fields from around the world, with a special emphasis on encouraging communication and building bridges between the different research threads that make Artificial Life such an exciting field. Following in the tradition of recent artificial life conferences, the meeting will also have an overall theme that reflects the global nature of the first joint conference: Beyond AI. We believe that AI is just a side effect of ALIFE and we believe that this conference is going to be a turning point for both ALIFE and AI researchers.

We are inviting especially contributions to solve new challenges in ALife. Since the first ALife conference in 1987, the computational landscape has been completely reshaped in terms of scale, means, capacity, and spheres of application in our society. The use of massive real-world data has now the potential to offer an important new avenue for ALife, to help us understand the nature of living systems by understanding bridges between simple idealized models and complex data-rich phenomena? An epistemology for a modern artificial life that can operate at scale and in partnership with data, but without sacrificing the complexity of the systems that we observe, has yet to be achieved.

Submissions are welcome on all topics.
By widening the focus of artificial life, the field can avoid conventional approaches and be a source of radically new concepts, methods, models, and technologies.

We are honoured to welcome keynote speakers who include:

Rodney Brooks (iRobot, MIT, USA)
Inman Harvey (University of Sussex, UK)
Hiroshi Ishiguro (Osaka University, Japan)
David Oreilly (Artist, USA)
Margaret Boden (University of Sussex, UK)
Kenneth O. Stanley (University of Central Florida, USA).


Serendipity and strategy in rapid innovation

Innovation is to organizations what evolution is to organisms: it is how organizations adapt to environmental change and improve. Yet despite advances in our understanding of evolution, what drives innovation remains elusive. On the one hand, organizations invest heavily in systematic strategies to accelerate innovation. On the other, historical analysis and individual experience suggest that serendipity plays a significant role. To unify these perspectives, we analysed the mathematics of innovation as a search for designs across a universe of component building blocks. We tested our insights using data from language, gastronomy and technology. By measuring the number of makeable designs as we acquire components, we observed that the relative usefulness of different components can cross over time. When these crossovers are unanticipated, they appear to be the result of serendipity. But when we can predict crossovers in advance, they offer opportunities to strategically increase the growth of the product space.


Serendipity and strategy in rapid innovation
T. M. A. Fink, M. Reeves, R. Palma & R. S. Farr
Nature Communications 8, Article number: 2002 (2017)