Acoustic ecologist Bernie Krause hypothesized that rich soundscapes in mature ecosystems are generated by sound communication between different species with differentiating acoustic niches. This hypothesis, called the acoustic niche hypothesis, proposes that in a mature ecosystem, the singing of a species occupies a unique bandwidth in frequency and shifts in time to avoid competition, thus making the communication efficient. We hypothesize that selective pressure on communication complexity is required for differentiating and filling acoustic niches by a limited number of species, in addition to selective pressures on communication efficiency. To test this hypothesis, we built an evolutionary model where agents can emit complex sounds. Our simulations with the model demonstrate that selective pressure on communication efficiency and complexity leads to an evolution in spectral differentiation with a limited number of species filling the acoustic niche. This is the first demonstration of acoustic niche differentiation using an artificial life model with complex-sounding agents. We also propose multi-timescale complexity measurement, extending the Jensen–Shannon complexity using multi-scale permutation entropy. We analyze the evolved soundscape in the simulations using this measure. The result shows that multi-timescale complexity in soundscape evolved, suggesting that evolving niche differentiation leads to ecological complexity. We implement the extended model in real space and demonstrate that the system can adaptively generate sounds, differentiating acoustic niches with environmental sounds.

Social network services (SNSs) are examples of non-living systems that evolve in response to internal and external events and have many similar characteristics assumed in biological evolution. In the present study, we analyzed the evolution of hashtag use on an SNS called RoomClip. Using a biological evolution analogy, we viewed each post (photo submission) as a species and each set of associated hashtags with a photo as genome. Further, we virtually defined parent–offspring relationships among posts based on their hashtag use and observed the resulting family tree of posts. Our analysis revealed that there was weak selection on hashtag usages relative to the Yule–Simon processes with strong feedback, and hashtag use quickly diverged. The evolution of novel hashtag combinations was observed, which is more salient than an evolution of individual novel hashtags.

Honeybees are highly social animals who live in large colonies, called hives. This study explores global patterns and dynamics observed in the beehive by tracking the individual behaviors. Previous research developed a high-throughput automatic monitoring system for honeybees ( Apis mellifera ) that tracked every individual bee in a hive, recording their positions, speed and orientations. This has been used to analyze the bees trophallaxis (two bees touching each other with their antennae to orally transferring liquid food (Free (1956))). network and calculate how often they communicate; it was found that the bee networks communicate in the intermittent manner in time, called bursts, much like human communication networks (Gernat et al. (2018)). Using this same dataset, we developed a new, complementary analysis that examined a different bee behavior that also follows a burst pattern: the bursts of kinetic energy that occur in beehives. Such bursts may be endogenous (i.e., spontaneous activity resulting from the internal interactions of bees) or exogenous (i.e., resulting from external perturbations). We sought to identify relationships between endogenous and exogenous bursts and the contributions of individual bees, as well as the relationship between the bees trophallaxis network and their kinetic bursting behaviors.

Based on the principle of artificial life, we developed an upper-body android called "Alter." Alter is human-like in appearance, receives sensory information from the outside via an autonomous sensor system located around the android, and moves spontaneously using two autonomous systems of internal dynamics. Its body and arms contain a central pattern generator with seven degrees of freedom and hundreds of plastic artificial neurons. We investigated Alter’s environmental adaptability and the spontaneity of is behavioral patterns. In addition, we discuss the conditions under which a robot can become lifelike.