Carlos Gershenson

The scientific fields of complexity, artificial life (ALife), and artificial intelligence (A.I.) share several commonalities: historic, conceptual, methodological, and philosophical. It was possible to develop them only because of information technology, while their origins can be traced back to cybernetics. In this perspective, I’ll revise the expectations and limitations of these fields, some of which have their roots in the limits of formal systems. I will use interactions, self-organization, emergence, and balance to compare different aspects of complexity, ALife, and A.I. The paper poses more questions than answers, but hopefully it will be useful to align efforts in these fields towards overcoming — or accepting — their limits.

Read the full article at: www.preprints.org

Laurent Hébert-Dufresne, Antoine Allard, Joshua Garland, Elizabeth A. Hobson & Luis Zaman 
npj Complexity volume 1, Article number: 4 (2024)

Complexity science studies systems where large numbers of components or subsystems, at times of a different nature, combine to produce surprising emergent phenomena apparent at multiple scales. It is these phenomena, hidden behind the often deceptively simple rules that govern individual components, that best define complex systems. Since these behaviors of interest arise from interactions between parts, complex systems are not counterparts to simple systems but rather to separable ones. Their study therefore often requires a collaborative approach to science, studying a problem across scales and disciplinary domains. However, this approach introduces challenges into the ways collaborations function across traditionally-siloed disciplines, and in the publication of complexity science, which often does not fall cleanly into disciplinary journals. In this editorial, we provide our view of the current state of complex systems research and explain how this new journal will fill an important niche for researchers working on these ideas.

Read the full article at: www.nature.com

Guim Aguadé-Gorgorió, Jean-François Arnoldi, Matthieu Barbier, Sonia Kéfi

Ecology Letters

Natural systems are built from multiple interconnected units, making their dynamics, functioning and fragility notoriously hard to predict. A fragility scenario of particular relevance concerns so-called regime shifts: abrupt transitions from healthy to degraded ecosystem states. An explanation for these shifts is that they arise as transitions between alternative stable states, a process that is well-understood in few-species models. However, how multistability upscales with system complexity remains a debated question. Here, we identify that four different multistability regimes generically emerge in models of species-rich communities and other archetypical complex biological systems assuming random interactions. Across the studied models, each regime consistently emerges under a specific interaction scheme and leaves a distinct set of fingerprints in terms of the number of observed states, their species richness and their response to perturbations. Our results help clarify the conditions and types of multistability that can be expected to occur in complex ecological communities.

Read the full article at: onlinelibrary.wiley.com

Andrés Abeliuk, Khaled Elbassioni, Talal Rahwan, Manuel Cebrian, Iyad Rahwan

Algorithmic matching is a pervasive mechanism in our social lives and is becoming a major medium through which people find romantic partners and potential spouses. However, romantic matching markets pose a principal-agent problem with the potential for moral hazard. The agent’s (or system’s) interest is to maximize the use of the matching website, while the principal’s (or user’s) interest is to find the best possible match. This creates a conflict of interest: the optimal matching of users may not be aligned with the platform’s goal of maximizing engagement, as it could lead to long-term relationships and fewer users using the site over time. Here, we borrow the notion of price of anarchy from game theory to quantify the decrease in social efficiency of online algorithmic matching sites where engagement is in tension with user utility. We derive theoretical bounds on the price of anarchy and show that it can be bounded by a constant that does not depend on the number of users in the system. This suggests that as online matching sites grow, their potential benefits scale up without sacrificing social efficiency. Further, we conducted experiments with human subjects in a matching market and compared the social welfare achieved by an optimal matching service against a self-interested matching algorithm. We show that introducing competition among matching sites aligns the self-interested behavior of platform designers with their users and increases social efficiency.

Read the full article at: dl.acm.org

Lakshwin Shreesha and Michael Levin

Individual cells have numerous competencies in physiological and metabolic spaces. However, multicellular collectives can reliably navigate anatomical morphospace towards much larger, reliable endpoints. Understanding the robustness and control properties of this process is critical for evolutionary developmental biology, bioengineering, and regenerative medicine. One mechanism that has been proposed for enabling individual cells to coordinate toward specific morphological outcomes is the sharing of stress (where stress is a physiological parameter that reflects the current amount of error in the context of a homeostatic loop). Here, we construct and analyze a multiscale agent-based model of morphogenesis in which we quantitatively examine the impact of stress sharing on the ability to reach target morphology. We found that stress sharing improves the morphogenetic efficiency of multicellular collectives; populations with stress sharing reached anatomical targets faster. Moreover, stress sharing influenced the future fate of distant cells in the multi-cellular collective, enhancing cells’ movement and their radius of influence, consistent with the hypothesis that stress sharing works to increase cohesiveness of collectives. During development, anatomical goal states could not be inferred from observation of stress states, revealing the limitations of knowledge of goals by an extern observer outside the system itself. Taken together, our analyses support an important role for stress sharing in natural and engineered systems that seek robust large-scale behaviors to emerge from the activity of their competent components.

Read the full article at: osf.io