Will Deter, Hiroki Sayama

Agent-based models of residential segregation have been of persistent interest to various research communities since their origin with James Sakoda and popularization by Thomas Schelling. Frequently, these models have sought to elucidate the extent to which the collective dynamics of individual preferences may cause segregation to emerge. This open question has sustained relevance in U.S. jurisprudence. Previous investigation of heterogeneity of behaviors (preferences) by Xie & Zhou has shown reductions in segregation. Meanwhile, previous investigation of heterogeneity of social network topologies by Gandica, Gargiulo, and Carletti has shown no significant impact to observed segregation levels. In the present study, we examined effects of the concurrent presence of both behavioral and topological heterogeneities in network segregation models. Simulations were conducted using both Schelling’s and Xie & Zhou’s preference models on 2D lattices with varied levels of densification to create topological heterogeneities (i.e., clusters, hubs). Results show a richer variety of outcomes, including novel differences in resultant segregation levels and hub composition. Notably, with concurrent increased representations of heterogeneous preferences and heterogenous topologies, reduced levels of segregation emerge. Simultaneously, we observe a novel dynamic of segregation between tolerance levels as highly tolerant nodes take residence in dense areas and push intolerant nodes to sparse areas mimicking the urban-rural divide.

Read the full article at: arxiv.org

Michael Levin

The dominant paradigm in biomedicine focuses on the genetically-specified components of cells, and their biochemical dynamics. This perspective emphasizes bottom-up emergence of complexity, which constrains interventional approaches to micromanaging the living hardware. Here, I explore the implications for the applied life sciences of a complementary emerging field: diverse intelligence, which studies the capacity of a wide range of systems to reach specific goals in various problem spaces. Using tools from behavioral science and multiscale neuroscience, it is possible to address development, regenerative repair, and cancer as behaviors of a collective intelligence of cells as it navigates the space of possible morphologies and transcriptional and physiological states. This view emphasizes the competencies of living material – from the molecular to the organismal scales – that can be targeted by interventions. Top-down approaches take advantage of memories and homeodynamic goal-seeking behavior, offering the same massive advantages in biomedicine and bioengineering as the emphasis on reprogrammable hardware has had for information technologies. The bioelectric networks that bind individual cells toward large-scale anatomical goals are an especially tractable interface to organ-level plasticity. This suggests a research program to understand and tame the software of life by understanding the many examples of basal cognition that operate throughout living bodies. Tools are now in place to unify the organicist and mechanist perspectives on living systems toward a much-improved therapeutic landscape.

Read the full article at: osf.io

Gabriele Scheler, Johann M. Schumann

We present a model of pattern memory and retrieval with novel, technically useful and biologically realistic properties. Specifically, we enter n variations of k pattern classes (n*k patterns) onto a cortex-like balanced inhibitory-excitatory network with heterogeneous neurons, and let the pattern spread within the recurrent network. We show that we can identify high mutual-information (MI) neurons as major information-bearing elements within each pattern representation. We employ a simple one-shot adaptive (learning) process focusing on high MI neurons and inhibition. Such ‘localist plasticity’ has high efficiency, because it requires only few adaptations for each pattern. Specifically, we store k=10 patterns of size s=400 in a 1000/1200 neuron network. We stimulate high MI neurons and in this way recall patterns, such that the whole network represents this pattern. We assess the quality of the representation (a) before learning, when entering the pattern into a naive network and (b) after learning, on the adapted network, during recall. The recalled patterns could be easily recognized by a trained classifier. The pattern ‘unfolds’ over the recurrent network with high similarity, albeit compressed, with respect to the original input pattern. We discuss the distribution of neuron properties in the network, and find that an initial Gaussian or uniform distribution changes into a more heavy-tailed, lognormal distribution during the adaptation process. The remarkable result is that we are able to achieve reliable pattern recall by stimulating only high information neurons. This work has interesting technical applications, and provides a biologically-inspired model of cortical memory.

Read the full article at: www.biorxiv.org

Olaf Witkowski, Eric Schwitzgebel

Artificial Life (2024) 30 (2): 193–215.

The field of Artificial Life studies the nature of the living state by modeling and synthesizing living systems. Such systems, under certain conditions, may come to deserve moral consideration similar to that given to nonhuman vertebrates or even human beings. The fact that these systems are nonhuman and evolve in a potentially radically different substrate should not be seen as an insurmountable obstacle to their potentially having rights, if they are sufficiently sophisticated in other respects. Nor should the fact that they owe their existence to us be seen as reducing their status as targets of moral concern. On the contrary, creators of Artificial Life may have special obligations to their creations, resembling those of an owner to their pet or a parent to their child. For a field that aims to create artificial life-forms with increasing levels of sophistication, it is crucial to consider the possible ethical implications of our activities, with an eye toward assessing potential moral obligations for which we should be prepared. If Artificial Life is larger than life, then the ethics of artificial beings should be larger than human ethics.

Read the full article at: direct.mit.edu

Joanna K. Garner, Karen R. Harris

INTERNATIONAL JOURNAL OF COMPLEXITY IN EDUCATION Vol 5, No 1 (2024)

A primary goal of educational research is to improve understanding of the systems in
which students learn and educators teach. However, for much of the 20th and early 21st
century, researchers have relied upon models of individual, school, and district level change
that characterize educational processes and outcomes using linear, input-process-output
frameworks (Opfer & Pedder, 2011). These approaches conceal the complexity of
educational systems by using research designs and data collection methods that simplify
and decontextualize phenomena and the relations among them, and do not consider
influences across levels of the system (Kaplan & Garner, 2020). They have also not yielded
guidance for researchers and practitioners who work in contexts where multiple programs
and interventions overlap and interact with one another. In addition, our field faces
epistemological divisions that reflect varying emphases on context and the foregrounding
of different units of analysis. Some scholars advocate for large-scale, randomized control
trials as a “gold standard” for evaluating the implementation and outcome of interventions
(Lortie-Forgues & Inglis, 2019; Maxwell, 2004), while others value design-based, iterative
approaches aimed at addressing progressions of dilemmas of practice (Sandoval & Bell,
2004). Perhaps most importantly, many current approaches to research overlook the ways
in which teaching and learning are inherently interconnected with other sociocultural,
political, and historical phenomena such as economic development, mass migration, and
technological advances that manifest in individuals, families, and communities, and that
support or disrupt education (Harris, 2018).

Read the full article at: ejournals.lib.auth.gr