In a time marked by ecological decay and by the perspective of a severe backlash of this ecosystem decay and climate devastation onto human society, bold moves that employ novel technology to counteract this decline are required. We present a novel concept of employing Artificial Life technology, in the form of cybernetically enhanced bio-hybrid superorganisms as a countermeasure and as a contingency plan. We describe our general conceptual paradigm, consisting of three interacting action plans, namely: (1) Organismic Augmentation; (2) Bio-Hybrid Socialization and (3) Ecosystem Hacking, which together compose a method to create a novel agent for ecosystem stabilization. We demonstrate, through early results from the research project HIVEOPOLIS, a specific way how classic Artificial Life technologies can create such a living, ecologically active and technologically-augmented superorganism that operates outside in the field. These technologies range from cellular automata and biomimetic robots to novel and sustainable biocompatible materials. Aiming at having a real-world impact on the society that relies on our biosphere is an important aspect in Artificial Life research and is fundamental to our methodology to create a physically embodied and useful form of Artificial Life.

Social insect colonies show all characteristics of complex adaptive systems (CAS). Their complex behavioral patterns arise from social interactions that are based on the individuals’ reactions to and interactions with environmental stimuli. We study here how social and environmental factors modulate and bias the collective thermotaxis of young honeybees. Therefore, we record their collective decision-making in a series of laboratory experiments and derived a mathematical model of the collective decision-making in young bees from our empirical observations. This model uses only one free parameter that combines the ultimate effects of several aspects of the microscopic individual behavioral mechanisms, such as motion behavior, sensory range, or contact detection, into one single coefficient. We call this coefficient the “social factor.” Our model is capable of capturing the observed aggregation patterns from our empiric experiments with static environments and of predicting the emergent swarm-intelligent behavior of the system in dynamic environments. Besides the fundamental research aspect in studying CAS, our model enables us to predict the effects of a physical stimulus onto the macroscopic collective decision-making that affects several crucial prerequisites for efficient and effective brood production and population growth in honeybee colonies.