Marcus Alexander, Laura Forastiere, Swati Gupta, and Nicholas A. Christakis

PNAS

A deep understanding of social networks can be used to create an artificial tipping point, changing population behavior by fostering behavioral cascades. Here, we experimentally test this proposition. We show that network-based targeting substantially increases population-level adoption of new behaviors. In part, this works by driving indirect treatment effects among the nontargeted members of the population (among people who were not initially part of the treatment group but who were affected by treatment of others in their population). The techniques we demonstrate can be easily implemented in global health (and elsewhere), as they do not require knowledge of the whole network. The novel pair-targeting technique explored here is particularly powerful and easy to implement.

Read the full article at: www.pnas.org

Boldizsár Balázs, Tamás Vicsek, Gergő Somorjai, Tamás Nepusz, Gábor Vásárhelyi

Coordination of local and global aerial traffic has become a legal and technological bottleneck as the number of unmanned vehicles in the common airspace continues to grow. To meet this challenge, automation and decentralization of control is an unavoidable requirement. In this paper, we present a solution that enables self-organization of cooperating autonomous agents into an effective traffic flow state in which the common aerial coordination task – filled with conflicts – is resolved. Using realistic simulations, we show that our algorithm is safe, efficient, and scalable regarding the number of drones and their speed range, while it can also handle heterogeneous agents and even pairwise priorities between them. The algorithm works in any sparse or dense traffic scenario in two dimensions and can be made increasingly efficient by a layered flight space structure in three dimensions. To support the feasibility of our solution, we experimentally demonstrate coordinated aerial traffic of 100 autonomous drones within a circular area with a radius of 125 meters.

Read the full article at: arxiv.org

Keith Yuan Patarroyo, Abhishek Sharma, Sara Walker, Lee Cronin

We introduce AssemblyCA, a framework for utilizing cellular automata(CA) designed to benchmark the potential of open-ended processes. The benchmark quantifies the open-endedness of a system composed of resources, agents interacting with CAs, and a set of generated artifacts. We quantify the amount of open-endedness by taking the generated artifacts or objects and analyzing them using the tools of assembly theory(AT). Assembly theory can be used to identify selection in systems that produce objects that can be decomposable into atomic units, where these objects can exist in high copy numbers. By combining an assembly space measure with the copy number of an object we can quantify the complexity of objects that have a historical contingency. Moreover, this framework allows us to accurately quantify the indefinite generation of novel, diverse, and complex objects, the signature of open-endedness. We benchmark different measures from the assembly space with standard diversity and complexity measures that lack historical contingency. Finally, the open-endedness of three different systems is quantified by performing an undirected exploration in two-dimensional life-like CA, a cultural exploration provided by human experimenters, and an algorithmic exploration by a set of programmed agents.

Read the full article at: openreview.net

Richard Watson

Biological Theory

In this essay we aim to present some considerations regarding a minimal but concrete notion of agency and goal-directed behavior that are useful for characterizing biological systems at different scales. These considerations are a particular perspective, bringing together concepts from dynamical systems, combinatorial problem-solving, and connectionist learning with an emphasis on the relationship between parts and wholes. This perspective affords some ways to think about agents that are concrete and quantifiable, and relevant to some important biological issues. Instead of advocating for a strict definition of minimally agential characteristics, we focus on how (even for a modest notion of agency) the agency of a system can be more than the sum of the agency of its parts. We quantify this in terms of the problem-solving competency of a system with respect to resolution of the frustrations between its parts. This requires goal-directed behavior in the sense of delayed gratification, i.e., taking dynamical trajectories that forego short-term gains (or sustain short-term stress or frustration) in favor of long-term gains. In order for this competency to belong to the system (rather than to its parts or given by its construction or design), it can involve distributed systemic knowledge that is acquired through experience, i.e., changes in the organization of the relationships among its parts (without presupposing a system-level reward function for such changes). This conception of agency helps us think about the ways in which cells, organisms, and perhaps other biological scales, can be agential (i.e., more agential than their parts) in a quantifiable sense, without denying that the behavior of the whole depends on the behaviors of the parts in their current organization.

Read the full article at: link.springer.com