Mapping Cultural Diversity through Personal Networks (MapCDPerNets) Postdoctoral research fellowship

A two-year postdoctoral position starting in October 2024 is available to work within the MapCDPerNets project (https://www.mapcdpernets.es). The project explores the existence of a sociocultural continuum able to predict the consistency of observed cultural dimensions and patterns of interaction, and develop a set of network measures oriented to capture this “cultural signature”. The project is funded through a generous grant of the Fundamentos Programme of Fundación BBVA and involves researchers from Universidad Carlos III de Madrid (UC3M), Universitat Autónoma de Barcelona and University of Florida.
The postdoctoral research will join the GISC and RySC groups at UC3M. This is a unique opportunity to work at the interface of complex systems with internet measurements and web transparency and privacy. Thus, GISC, led by Anxo Sánchez (project coordinator) focuses on topics such as personal networks: evolution, structure, prediction; social norms and their relationship with behavior, as well as their measurement in experiments; behavioral experiments, and evolutionary game theory, whereas the RySC team participating in the project led by Rubén and Ángel Cuevas has a long background of developing advanced methodologies to collect large scale datasets from major technological players including Google, Facebook, LinkedIn, etc. The postdoctoral researcher will be based at the Leganés campus of UC3M, some 20 km away from Madrid downtown and easily reachable by train.

More at: mapcdpernets.es

Complex thinking and artificial intelligence, October 28, 2024, Online

Workshop | October 28, 2024, 13h00-19h30 (GMT+1), Online

Large Language Models (LLMs) such as ChatGPT have also opened new possibilities for Human-AI interaction towards systems of co and augmented intelligence. On the other hand, developments regarding the practice of complex thinking, as a mode of coupling with the world that is organisationally coherent with the properties that organise complex natural and social systems, has been proposed as potentially leading to more effective ecosystemically positive and sustainable possibilities for action. It is hypothesised that more complex modes of thinking may lead to creative and abductive leaps capable of guiding effective interventions and the process of managing change in “real-world” complex systems, in conditions of uncertainty and risk.  The CT & AI project will explore possibilities and limits of the interaction of a framework for the practice and promotion of Complex Thinking (CT) with AI tools based on LLMs (e.g. Chat GPT, Gemini). It aimed at developing and evaluating preliminary protocols to guide the integration of methods and tools for promoting CT with the use of AI tools towards generating complex understandings for practice and research. Finally, it aimed at exploring stakeholders (policy-makers, practitioners, scientists/academics) stances regarding the use of AI in relation to CT.

More at: ces.uc.pt

ICTP – SAIFR » Minicourse on Bayesian Machine Learning for Scientific Research

October 28 – November 1, 2024

São Paulo, Brazil

ICTP-SAIFR/IFT-UNESP

We will present five 3-hour lectures that will introduce participants to the world of Bayesian Machine Learning for scientific purposes. The minicourse is tailored to suit both senior and junior researchers, catering to their respective levels of experience and interest.

In the first block of each lecture, we aim to transmit the big picture of the lecture’s topic with a focus on the details from a supervising point of view. The fine points and subtleties will be addressed here, but without strict demonstrations or supplied code. This block is intended for both seniors and juniors: for seniors as a summary that shows how to apply these tools to scientific research; and for juniors as an entrance to the second block in which we put our hands in the dough. We conclude the block with an extended coffee break where we expect that the proposed ideas trigger discussions around each participant’s field of study and how to apply it in their data.

The second block is very hands-on and is intended for juniors, but seniors interested in getting actively involved in the calculations are welcome as well. We present, discuss and write code. Participants are engaged in coding exercises and discussing practical applications. This block emphasizes practical skills and real-world problem-solving. We use different libraries, and we deploy statistical software especially designed to tackle the presented problems

The minicourse is generally designed for any scientific career. We use mostly physics examples, but the material will be useful and insightful for any other field with hard scientific research. We will try to adapt and discuss the problems within the participants’ fields of research.

Participants are expected to have taken courses in algebra and analysis, be familiar with multi-dimensional vectors and expressions, have some knowledge of probability and statistics, and be prepared for non-trivial abstract reasoning and thinking. Juniors, in addition, are expected to have some knowledge of Python.

There is no registration fee and limited funds are available for local expenses.

Lecturer:

Ezequiel Alvarez (ICAS-UNSAM, Argentina)
Organizer:

Rogério Rosenfeld (IFT-UNESP/ICTP-SAIFR, Brazil)

Apply  at: www.ictp-saifr.org

Analogies for modeling belief dynamics

Henrik Olsson, Mirta Galesic

Trends in Cognitive Sciences

Belief dynamics has an important role in shaping our responses to natural and societal phenomena, ranging from climate change and pandemics to immigration and conflicts. Researchers often base their models of belief dynamics on analogies to other systems and processes, such as epidemics or ferromagnetism. Similar to other analogies, analogies for belief dynamics can help scientists notice and study properties of belief systems that they would not have noticed otherwise (conceptual mileage). However, forgetting the origins of an analogy may lead to some less appropriate inferences about belief dynamics (conceptual baggage). Here, we review various analogies for modeling belief dynamics, discuss their mileage and baggage, and offer recommendations for using analogies in model development.

Read the full article at: www.sciencedirect.com

Noisy Circumnutations Facilitate Self-Organized Shade Avoidance in Sunflowers

Chantal Nguyen, Imri Dromi, Ahron Kempinski, Gabriella E. C. Gall, Orit Peleg, and Yasmine Meroz
Phys. Rev. X 14, 031027

Circumnutations are widespread in plants and typically associated with exploratory movements; however, a quantitative understanding of their role remains elusive. In this study we report, for the first time, the role of noisy circumnutations in facilitating an optimal growth pattern within a crowded group of mutually shading plants. We revisit the problem of self-organization observed for sunflowers, mediated by shade response interactions. Our analysis reveals that circumnutation movements conform to a bounded random walk characterized by a remarkably broad distribution of velocities, covering 3 orders of magnitude. In motile animal systems such wide distributions of movement velocities are frequently identified with enhancement of behavioral processes, suggesting that circumnutations may serve as a source of functional noise. To test our hypothesis, we developed a Langevin-type parsimonious model of interacting growing disks, informed by experiments, successfully capturing the characteristic dynamics of individual and multiple interacting plants. Employing our simulation framework we examine the role of circumnutations in the system, and find that the observed breadth of the velocity distribution represents a sharp transition in the force-noise ratio, conferring advantageous effects by facilitating exploration of potential configurations, leading to an optimized arrangement with minimal shading. These findings represent the first report of functional noise in plant movements and establish a theoretical foundation for investigating how plants navigate their environment by employing computational processes such as task-oriented processes, optimization, and active sensing. Since plants move by growing, space and time are coupled, and dynamics of self-organization lead to emergent 3D patterns. As such, this system provides conceptual insight for other interacting growth-driven systems such as fungal hyphae, neurons and self-growing robots, as well as active matter systems where agents interact with past trajectories of their counterparts, such as stigmergy in social insects. This foundational insight has implications in statistical physics, ecological dynamics, agriculture, and even swarm robotics.

Read the full article at: link.aps.org