Invited Speakers

Across biological systems, individuality can emerge from the collective dynamics of communities. This is the central idea explored in this session, Community First Theory (CFT).
In Tetrahymena thermophila, single-cell RNA sequencing of 5,000 genetically identical clonal organisms revealed substantial transcriptional diversity within the population. Even after accounting for cell-cycle effects, gene expression clustered into six distinct groups enriched for ribosomal, mitochondrial, cytoskeletal, and peroxisomal proteins—indicating spontaneous specialization in metabolic and structural functions within a shared environment. These findings demonstrate that genetic identity does not guarantee phenotypic uniformity when organisms are embedded in collective contexts.
Similarly, in queen-less and male-less Pristomyrmex punctatus ant colonies lacking genetic variation, individual tracking uncovered two coexisting behavioral roles: ants forming dense clusters and others circulating between them. These groups alternated between deterministic exploratory movements and probabilistic clustering dynamics, with coordinated bursts resuming periodically. This behavioral heterogeneity was observed in honeybee hives as well. Together, these biological systems show that collective organization can amplify subtle individual differences, allowing distinct functional and behavioral traits to crystallize through interaction even in genetically homogeneous populations.
Recently we conducted a robot swarm experiment using ten identical phototactic robots under structured light conditions. Despite being physically and algorithmically identical, the robots exhibited spontaneous behavioral diversification over time. Subtle asymmetries in initial positioning and environmental symmetry breaking gave rise to distinct collective modes and coordination patterns, providing a minimal physical model for emergent individuality without pre-programmed heterogeneity.
A comparable process is observed in the digital ecologies of large-scale AI societies. We conducted multi-agent simulations using Large Language Model (LLM)-based agents that were initially undifferentiated—lacking predefined personalities, memories, or behavioral traits. These agents engaged in cooperative communication within group simulations, exchanging context-based messages in natural language. Through autonomous interactions, the agents spontaneously generated hallucinations and hashtags to sustain communication, which increased the diversity of words and topics within their exchanges. As conversations progressed, each agent's emotions shifted, and distinct personalities emerged and evolved as communities formed. Despite starting from identical initialization, the individuality of each agent arose not from its internal code alone but from distributed communication and mutual adaptation within the network.
Finally, using the framework of Partial Information Decomposition (PID), we characterize the informational advantages of developing individuality in terms of synergy and redundancy. This quantitative approach provides a unified description of differentiation processes across biological organisms, robotic collectives, and LLM-based agent societies, revealing a common information-theoretic foundation for emergent individuality.
Together, these observations support the Community First Theory, which posits that individuality arises from collective organization rather than existing prior to it. Differentiation is not a fixed design feature but a dynamic property of communities interacting with the same world—whether material, ecological, or informational. From Tetrahymena and ants to robots to networks of language models, individuality blooms from collectivity itself. The community gives birth to the very differences that sustain its function. This perspective offers new insights into the origins of diversity, autonomy, and intelligence in both natural and artificial systems.

References:

1. Hiroki Kojima, Akiko Kashiwagi, A & Takashi Ikegami (2024) Revealing gene expression heterogeneity in a clonal population of tetrahymena thermophila through single-cell rna sequencing. Biochemistry and biophysics reports, 38: 101720.
2. Norihiro Maruyama, Shigeto Dobata, Takashi Ikegami, Behavioral Analysis of Ant Colonies: Distinguishing Between Stochastic/Deterministic Modes and Global Behavior, AROBII-ISC-SWARM 2024.
3. Tomoyuki Atsushi Masumori, Norihiro Maruyama Takahide Yoshida Takashi Ikegami, From Swarm to Individual: Emergent Individuality in Light-Mediated Robot Collectives (submitted to Advanced Intelligent Systems 2025)
4. Ryosuke Takata, Atsushi Masumori and Takashi Ikegami: Spontaneous Emergence of Agent Individuality Through Social Interactions in Large Language Model-Based Communities. Entropy, 26(12), 1092 (2024).

Biography:

I have been working on the field of artificial life for more than 20 years. Evolution of genetic codes, mutation rates and cooperative relationships is one the main targets of my research. For example, complexity of coupled cognitive systems have been studied using dynamical recognizers and other recurrent neural (often embodied) systems. Recently, I am interested in constructing artificial life in the real world. To fruition the concepts developed through the study of artificial life, such as "autonomy", "enaction", "sustainability", and "evolvability", I have newly started several experimental and conceptual works, using an android (called Alter3), a large scale Boids model and other bio-chemical experiments.

(tentative) Cognitive neuroscience faces a measurement problem: core features of the human mind cannot be directly observed in the brain. For example, intentions are making a difference in behavior generation but cannot be reduced to sub-personal quantities of neural activity without losing their purpose-driven, normative character: if agency is taken to be efficacious, and if it is not reducible to underlying non-agential factors, then the way that this irreducible efficacy shows up at that underlying level is in terms of unpredictable deviations from physiological tendencies that would otherwise take place. The principled derivation of these deviations - irruptions - provides a fresh perspective on the source of the noisiness of living systems, and highlights the essential role noise plays in the self-organization of adaptive behavior. This conceptual advance reframes context-dependent neural “noise” as a key signature of the mind at work, offering new avenues for research in cognitive science, clinical interventions, and AI.

Biography:

Dr. Tom Froese is a cognitive scientist with a background in artificial life and complex systems theory. His research addresses foundational questions regarding the origin and nature of the human mind by integrating philosophy of mind, computational modeling, and human subjects research.

https://www.oist.jp/research/research-units/ecsu/tom-froese

(tentative) Living systems change when patterns are perturbed just enough to loosen entrenched regimes without breaking coordination. This talk develops a theory of dosed noise as a mechanism for realignment across biological, psychological, and social levels. It frames noise, in this context, as a brief, well-timed fluctuation that opens a window for re-coordination of attention, affect, narrative, and action, Using familar examples, we show how endogenous and exogenous shocks and scaffolds can be tuned to canalize these transitions at different scales.

Biography:

Mark's has a PhD in the philosophy of embodied cognitive science, a MA in philosophy and a BA in philosophy and psychology. His PhD work was focused on how culture is reproduced through embodied social interaction. Mark's postdoctoral efforts will focus on questions of wellbeing in the wake of the COVID-19 pandemic, exploring the intersection between embodied cognitive science, design and digital technologies.

https://www.markmjames.com