Firm clusters are seen as having a positive effect on innovations, what can be interpreted as economies of scale or knowledge spillovers. The processes underlying the success of these clusters remain difficult to isolate. We propose in this paper a stylised agent-based model to test the role of geographical proximity and informal knowledge exchanges between firms on the emergence of innovations. The model is run on synthetic firm clusters. Sensitivity analysis and systematic model exploration unveil a strong impact of interaction distance on innovations, with a qualitative shift when spatial interactions are more intense. Model bi-objective optimisation shows a compromise between innovation and product diversity, suggesting trade-offs for clusters in practice. This model provides thus a first basis to systematically explore the interplay between firm cluster geography and innovation, from an evolutionary perspective.

Spatial resolution is relevant for many processes in population dynamics because it may give rise to heterogeneity. Simulating the effect of space in two or three dimensions is computationally costly. Furthermore, in Euclidean space, the notion of heterogeneity is complemented by neighbourhood correlations. In this paper, we use an infinite-dimensional simplex as a minimal model of space in which heterogeneity is realized, but neighbourhood is trivial and study the coexistence of viral traits in a SIRS - model. As a function of the migration parameter, multiple regimes are observed. We further discuss the relevance of minimal models for decision support.

To feed the growing human population we require increased food production and security, while using less land and causing less environmental damage. Significant changes in agriculture are needed to meet these demands. One widely touted solution is smart, Al-enhanced Agricultural Technology. In this article we argue that improved technology is insufficient to address the needs of many farmers, but that by taking a whole-of-system approach native to Artificial Life we can shift towards creating sustainable, ethical and effective AgTech. This can innovate industrial agriculture in developed nations and benefit small landholders from vulnerable communities, whilst reducing the environmental impacts of food production globally.

The value of integrating concepts and methods from Complex Adaptive Systems and the interdisciplinary study of soundscape for better understanding, monitoring and managing human-environment interactions is proposed. Through four examples of our recent research the value of soundscape and its interdisciplinary study, the relevance of ecoacoustics to a socially-concerned Alife is illustrated. From this position, the failure of current computational ecoacoustic methods to capture the fundamentally complex, adaptive and dynamic nature of ecosystems is noted and the potential for Acoustic Early Warning Signals is outlined. Development of an acoustic dimension to the study of complex adaptive systems promises to spawn valuable conceptual frameworks and cost-effective methods for investigating, understanding, predicting, managing and living in future techno-eco-systems, and better tuning the anthroposphere, technosphere and biosphere such that people and the planet can thrive.

The global COVID-19 pandemic has given rise to an unprecedented level of complex societal challenges to the humanity. Meanwhile, it also has created a huge demand for scientific expertise in modeling, simulation and analysis of epidemic and other socio-economical dynamics, to which Artificial Life researchers can make great contributions. This extended abstract presents a brief overview of projects the author has been working on in response to COVID-19 since early 2020, summarizing the lessons learned and identifying unique skills and abilities of Artificial Life researchers that have potential to help address various societal challenges.

The use of interaction analysis for creating agent-based models could lead to more empirical simulations. This study focuses on pedestrian-street interaction during negotiations at a signal-controlled crossing. It examines which data from interaction analysis could be used in the development of artificial pedestrian societies. This article sets out a framework for structuring the data deriving from interaction analysis and demonstrates the process of developing an agent-based model by translating the acquired data into the model. The data collected through interaction analysis serves as input for the agent-based model. The structuring performed on the interaction analysis data is used to define the outcome variables for agent-based modelling. The study concludes by proposing an initial framework that describes the use of interaction analysis in simulations.

Mechatronic devices installed as Ecosystem Management and Monitoring Units, EMUs, are an emerging trend with the potential to improve our understanding of natural and agricultural ecosystems. They may improve biodiversity and provide socio-economic benefits, but if poorly implemented such technology can undermine conservation efforts, damage habitat and drive people into poverty. This article proposes draft guidelines that help to ensure EMUs embedded within ecosystems generate more global benefit than harm, preserve the aesthetic and cultural value of their environment, and kill organisms only as a last resort.

At the end of 2020, policy responses to the SARS-CoV-2 outbreak have been shaken by the emergence of virus variants. The emergence of these more contagious, more severe, or even vaccine-resistant strains have challenged worldwide policy interventions. Anticipating the emergence of these mutations to plan ahead adequate policies, and understanding how human behaviors may affect the evolution of viruses by coevolution, are key challenges. This article hopes to provide a simple, starting illustration to these important, complex dynamics. We present a dual genetic algorithm model in which viruses fighting for survival and policy measures aiming at minimising infection rates in the population competitively evolve. Simulation runs reproduce the emergence of more contagious variants, and identifies the evolution of policy responses as a determinant cause of this phenomenon. This evolution opens new possibilities to visualise the impact of governments interventions not only on outbreak dynamics, but also on its evolution, to improve the efficacy of policies.

This extended abstract introduces “ArchaeaBot: A Post Singularity and Post Climate Change Life-form” an underwater biomimetic robotic artwork that explores what ‘life’ might mean in a post singularity, post climate change future, by artists Anna Dumitriu and Alex May. The project is based on new research about archaea (a group of unicellular micro-organisms believed to be the oldest form of life on earth adapted to life in extreme conditions) combined with robotics, 3D modelling and printing, artificial intelligence and machine learning. The artwork embodies a speculative future life-form which suggests what might be the ‘ultimate’ species for the end of the world. The robotic installation has become a means of reflection on both the development of new technologies and the impact of climate change for significant artistic audiences around the world.