Social Influence and the Logic of Collective Action, by Sergey Gavrilets

Collective action has been a fundamental aspect of human societies throughout history, from building irrigation systems and defenses in Neolithic times to coordinated disaster relief and scientific collaborations today. In this book, Sergey Gavrilets explains when and why groups of people cooperate, presenting a quantitative framework that unifies game theory with models of social influence, cognition, and individual and cultural variation. He shows how humans’ deep susceptibility to social influence—grounded in evolutionary need to cooperate and learn from peers, reinforced by deference to parents and elders, and extended to cultural, religious, and political leaders—shapes norms, beliefs, and collective outcomes.

Integrating previously separate literatures, Gavrilets introduces explicit dynamics for norms and beliefs, quantifies the effects of individual and cultural differences, and tests predictions across societies. Drawing on formal, data-based mathematical modeling supported by behavioral experiments and studies of online behavior, he concludes that successful collective action depends on six interacting forces: material payoffs, personal norms and attitudes, social influence, cognition, evolving social norms and beliefs about others, and individual and cultural differences. Lasting cultural change, he argues, depends on norms and institutions that shape behavior through persuasion, nudging, and enforcement. Gavrilets translates this theory into practical, testable strategies for policy and design, including targeted messaging, dynamic norms, and culturally sensitive approaches, and connects it to broader theories of behavior change.

More at: press.princeton.edu

From description to design: Automated engineering of complex systems with desirable emergent properties

Thomas F. Varley, Josh Bongard
The study of complex systems has produced a huge library of different descriptive statistics that scientists can use to describe the various emergent patterns that characterize complex systems. The problem of engineering systems to display those patterns from first principles is a much harder one, however, as a hallmark of complexity is that macro-scale emergent properties are often difficult to predict from micro-scale features. Here, we propose a general optimization-based pipeline to automate the difficult problem of engineering emergent features by re-purposing descriptive statistics as loss functions, and letting a gradient descent optimizer do the hard work of designing the relevant micro-scale features and interactions. Using Kuramoto systems of coupled oscillators as a test bed, we show that our approach can reliably produce systems with non-trivial global properties, including higher-order synergistic information, multi-attractor metastability, and meso-scale structures such as modules and integrated information. We further show that this pipeline can also account for and accommodate constraints on the system properties, such as the costs of connections, or topological restrictions. This work is a step forward on the path moving complex systems science from a field predicated largely on description and post-hoc storytelling towards one capable of engineering real-world systems with desirable emergent meso-scale and macro-scale properties.

Read the full article at: arxiv.org

Antifragility: A Cross-Cutting Concept for Understanding Ecological Responses to Variability

Jonas Wickman, Christopher A. Klausmeier, and Elena Litchman

The American Naturalist

Environmental variability, in the form of either temporal fluctuations or intermittent perturbations, affects virtually all ecological systems. However, while temporal variability is widely recognized to play an important role across many ecological and evolutionary subdisciplines, there is no high-level cross-cutting concept that describes how species, communities, and ecosystems respond to variability. In this article we propose that “antifragility” could serve well as such a concept. Initially used in economics, antifragility denotes that a property or metric of performance increases with variability. To showcase the breadth of applicability and utility of the concept, we examine two mathematical models for antifragility in ecosystem services and competition. We also demonstrate some of the nuances and possible misapplications of the concept. Under global change, the variability of environmental conditions is expected to change. We believe that antifragility could serve as a useful concept in coordinating research efforts toward understanding the effects of these changes.

Read the full article at: www.journals.uchicago.edu