Hourglass Emergence

Jessica Flack
Santa Fe Institute

Postponed event
Contact: cgg@unam.mx

Abstract:
In this talk, I will discuss (briefly) three key efforts in our work--collective computation + coarse-graining, hourglass emergence, and the information theory of individuality. I will conclude with a discussion of a new research direction--which I am provisionally calling "channel switching" that they collectively imply. This will be a big picture talk.

Just Machine Learning

Tina Eliassi-Rad
Northeastern University in Boston

Thursday, December 16 at 1:00 PM Mexico Time
Contact: cgg@unam.mx

Abstract:
Risk assessment is a popular task when machine learning is used for automated decision-making. For example, Jack’s risk of defaulting on a loan is 8, Jill’s is 2; Ed’s risk of recidivism is 9, Peter’s is 1. We know that this task definition comes with impossibility results for group fairness, where one cannot simultaneously satisfy desirable probabilistic measures of fairness. I will highlight recent findings related to these impossibility results [1]. Next, I will present work on how machine learning can be used to generate aspirational data (i.e., data that are free from biases present in real-world data). Such data are useful for recognizing sources of unfairness in machine learning models besides biased data [2]. If time permits, I will discuss the steps necessary to measure our algorithmically infused societies [3].
[1] http://fitelson.org/exploring_impossibility.pdf
[2] http://www.eliassi.org/papers/davidliu-aies2021.pdf
[3] https://rdcu.be/cnvVp

Speaker Bio:
Tina Eliassi-Rad is a Professor of Computer Science at Northeastern University in Boston, Massachusetts. She is also a core faculty member at the Network Science Institute and at the Institute for Experiential AI, both at Northeastern. Prior to joining Northeastern, Tina was an Associate Professor of Computer Science at Rutgers University; and before that she was a Member of Technical Staff and Principal Investigator at Lawrence Livermore National Laboratory. Tina earned her Ph.D. in Computer Sciences (with a minor in Mathematical Statistics) at the University of Wisconsin-Madison. Her research is at the intersection of data mining, machine learning, and network science. She has over 100 peer-reviewed publications (including a few best paper and best paper runner-up awards); and has given over 200 invited talks and 14 tutorials. Tina's work has been applied to personalized search on the World-Wide Web, statistical indices of large- scale scientific simulation data, fraud detection, mobile ad targeting, cyber situational awareness, and ethics in machine learning. Her algorithms have been incorporated into systems used by the government and industry (e.g., IBM System G Graph Analytics) as well as open-source software (e.g., Stanford Network Analysis Project). In 2017, Tina served as the program co-chair for the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (a.k.a. KDD, which is the premier conference on data mining) and as the program co-chair for the International Conference on Network Science (a.k.a. NetSci, which is the premier conference on network science). In 2020, she served as the program co-chair for the International Conference on Computational Social Science (a.k.a. IC2S2, which is the premier conference on computational social science). Tina received an Outstanding Mentor Award from the Office of Science at the US Department of Energy in 2010; became a Fellow of the ISI Foundation in Turin Italy in 2019; and was named one of the 100 Brilliant Women in AI Ethics for 2021. Also in 2021, she was selected as an external faculty at the Santa Fe Institute and at the Vermont Complex Systems Center.

The dynamics of urban systems

Denise Pumain
University Paris I Pantheon-Sorbonne

Thursday, December 02 at 1:00 PM Mexico Time
Contact: cgg@unam.mx

Speaker Bio: https://orcid.org/0000-0002-0730-4563

Modeling individual cognitive and behavioral trajectories from large-scale social media data

Johan Bollen
Indiana University

Thursday, November 04 at 3:00 PM Mexico Time
Contact: cgg@unam.mx

Abstract:
Millions of individuals leave behind detailed longitudinal traces that reflect their behavioral, cognitive, and emotional states on social media. Advances in machine learning, artificial intelligence, and natural language processing now allow us to detect and track these traces over time to construct longitudinal digital phenotypes for millions of individuals. This is a particularly powerful approach in mental health where disorders and risk factors vary extensively between individuals, often presenting with complex, variegated, and time-evolving symptomologies. In my talk I will provide an overview of my lab's research projects, in particular our efforts to better understand the relations between social media use and mental health. I will discuss 2 research projects in particular: detecting the lexical traces of cognitive distortions in online language and leveraging social network structure to understand the effects of social media on our well-being. I will discuss the potential pitfalls of this approach, and reflect on its considerable potential in bringing about a better understanding of the factors that shape human well-being.

Speaker Bio: https://homes.luddy.indiana.edu/jbollen/

The stochastic thermodynamics of computation

David Wolpert
Santa Fe Institute

Thursday, October 21 at 1:00 PM Mexico Time
Contact: cgg@unam.mx

Abstract:
One of the major resource requirements of computers—ranging from biological cells to human brains to high-performance digital computers—is the energy used to run them. Those energy requirements of performing a computation have been a long-standing focus of research in statistical physics, going back (at least) to the early work of Landauer and colleagues. However, one of the most prominent aspects of computers is that they are inherently non-equilibrium systems. They are also often quite small, far from the thermodynamic limit. Unfortunately, the research by Landauer and co-workers was grounded in the statistical physics of the 20th century, which could not properly address the thermodynamics of non-equilibrium, nanoscale systems. Fortunately, recent revolutionary breakthroughs in stochastic thermodynamics have overcome the limitations of 20th century statistical physics. We can now analyze arbitrarily off-equilibrium systems, of arbitrary size. Here I show how to apply these recent breakthroughs to analyze the thermodynamics of computation. Specifically, I present formulas for the thermodynamic costs of implementing (loop-free) digital circuits, of implementing Turing machines, and of implementing multipartite processes like the interacting organelles in a cell.

https://davidwolpert.weebly.com/

Initial Progress on the Science of Science

Dashun Wang
Northwestern University

Thursday, October 07 at 1:00 PM Mexico Time
Contact: cgg@unam.mx

Abstract:
The increasing availability of large-scale datasets that trace the entirety of the scientific enterprise, have created an unprecedented opportunity to explore scientific production and reward. Parallel developments in data science, network science, and artificial intelligence offer us powerful tools and techniques to make sense of these millions of data points. Together, they tell a complex yet insightful story about how scientific careers unfold, how collaborations contribute to discovery, and how scientific progress emerges through a combination of multiple interconnected factors. These opportunities—and challenges that come with them—have fueled the emergence of a multidisciplinary community of scientists that are united by their goals of understanding science. These practitioners of the science of science use the scientific methods to study themselves, examine projects that work as well as those that fail, quantify the patterns that characterize discovery and invention, and offer lessons to improve science as a whole. In this talk, I’ll highlight some examples of research in this area, hoping to illustrate the promise of science of science as well as its limitations.

Speaker Bio:
Dashun Wang is an Associate Professor of Management and Organizations at the Kellogg School of Management, and (by courtesy) the McCormick School of Engineering, Northwestern University. At Kellogg, he is the Founding Director of the Center for Science of Science and Innovation (CSSI). He is also a core faculty at the Northwestern Institute on Complex Systems (NICO). His current research focus is on Science of Science, a quest to turn the scientific methods and curiosities upon science itself, hoping to use and develop tools from complexity sciences and artificial intelligence to broadly explore the opportunities for innovation and promises of prosperity offered by the recent data explosion in science. His research has been published repeatedly in journals like Nature and Science, and has been featured in virtually all major global media outlets, including The New York Times, Wall Street Journal, The Economist, Bloomberg, Financial Times, The Today Show, Harvard Business Review, The Atlantic, World Economic Forum, Forbes, The Guardian, The Washington Post, and The Boston Globe, among others. Dashun is a recipient of multiple awards for his research and teaching, including the AFOSR Young Investigator award, Poets & Quants Best 40 Under 40 Professors, Complex Systems Society’s Junior Scientific Award, Network Science Society’s Erdos-Renyi Award, Thinkers50 Radar List 2021, and more. His first book, The Science of Science, coauthored with Albert-Laszlo Barabasi, was published in March 2021.

Opinion Models and Social Influence on Networks

Mason Porter
Department of Mathematics, UCLA

Thursday, September 02 at 1:00 PM Mexico Time
Contact: cgg@unam.mx

Abstract:
From the spreading of diseases and memes to the development of opinions and social influence, dynamical processes are influenced heavily by the networks on which they occur. In this talk, I'll discuss social influence and opinion models on networks. I'll present a few types of models --- including threshold models of social contagions, voter models that coevolve with network structure, and bounded-confidence models with continuous opinions --- and illustrate how such processes are affected by the networks on which they occur. I'll also connect these models to opinion polarization and the development of echo chambers in online social networks.

Speaker Bio:
Mason Porter is a professor in the Department of Mathematics at UCLA. He earned a B.S. in Applied Mathematics from Caltech in 1998 and a Ph.D. from the Center for Applied Mathematics at Cornell University in 2002. He held postdoctoral positions at Georgia Tech, the Mathematical Sciences Research Institute, and Caltech. He joined the faculty at University of Oxford in 2007 and moved to UCLA in 2016. Mason is a Fellow of the American Mathematical Society, American Physical Society, and Society for Industrial and Applied Mathematics. In recognition of his mentoring of undergraduate researchers, Mason won the 2017 Council on Undergraduate Research (CUR) Faculty Mentoring Award in the Advanced Career Category in the Mathematics and Computer Science Division. Thus far, 24 students have completed their doctoral degrees under Mason's mentorship, and Mason has alsomentored several postdocs, more than 30 Masters students, and more than 90 undergraduate students on research projects. Mason's research interests lie in theory and (rather diverse) applications of networks, complex systems, and nonlinear systems.
Homepage: https://www.math.ucla.edu/~mason/
Blog: http://masonporter.blogspot.com

Can Mutual Imitation Generate Open-Ended Evolution?

Takashi Ikegami
University Tokio

Wednesday, June 23 at 6:00 PM Mexico Time
Contact: cgg@unam.mx

Abstract:
We only find open-ended evolution (OEE) in the development of human technology or in the evolution of life itself. The research on OEE at ALIFE aims to discover a mechanism that generates OEE automatically in a computer or machine. A potential mechanism and the conditions required have been discussed in three previous workshops. In this study, we propose and discuss man--machine interaction experiments as a new OEE mechanism. The pertinent definition of OEE here is whether we can continue to create new movements that are distinguishable to us. We consider the development of body movement patterns generated when Alter3 androids imitate each other and when Alter3 androids and humans imitate each other. We use UMAP contraction and transfer entropy to measure these changes and demonstrate that man--machine communication is far more dynamic and complex than the machine--machine interaction. We discuss how human subjects can engender OEE via communication with the android.

The Space of Possible Minds

Philip Ball

Thursday, May 27 at 1:00 PM
Contact: cgg@unam.mx

Abstract:
In 1984 computer scientist Aaron Sloman published a paper called “The structure of the space of possible minds.” It called for systematic thinking about the vague yet intuitive notion of mind, which was capable of admitting into the conversation what we had then learnt about animal cognition and artificial intelligence. Almost four decades later, we are in a fair better position to examine Sloman’s proposal: to consider what kinds of minds can exist within the laws of physics, to compare those we already recognize (including the diversity of human minds), and to speculate about the possibilities for artificial “mind design”. In this talk I will explore this question, looking at our current understanding of the functions and capabilities of biological minds, what this might imply for efforts to create artificial “minds”, and what the implications are for ideas about consciousness, agency and free will.

Speaker Bio:
Philip Ball is a freelance writer and author, and worked for many years as an editor of Nature. His many books include Critical Mass (which won the 2005 Aventis Science Books prize), Beyond Weird and How to Grow a Human. His next book, The Book of Minds, will be published in early 2022.

Terraforming ecosystems with synthetic biology

Ricard Solé
ICREA-Complex Systems Lab UPF-IBE & Santa Fe Institute

Thursday, May 27 at 1:00 PM
Contact: cgg@unam.mx

Abstract:
Our planet is experiencing an accelerated process of change associated with a variety of anthropogenic phenomena. The future of this transformation is uncertain, but there is general agreement about its negative unfolding that might threaten our own survival. Furthermore, the pace of the expected changes is likely to be abrupt: catastrophic shifts might be the most likely outcome of this ongoing, apparently slow process. Although different strategies for geo-engineering the planet have been advanced, none seem likely to safely revert the large-scale problems associated to carbon dioxide accumulation or ecosystem degradation. An alternative possibility considered here is inspired in the rapidly growing potential for engineering living systems. It would involve designing synthetic organisms capable of reproducing and expanding to large geographic scales with the goal of achieving a long-term or a transient restoration of ecosystem-level homeostasis. Such a regional or even planetary-scale engineering would have to deal with the complexity of our biosphere. It will require not only a proper design of organisms but also understanding their place within ecological networks and their evolvability. This is a likely future scenario that will require integration of ideas coming from currently weakly connected domains, including synthetic biology, ecological and genome engineering, evolutionary theory, climate science, biogeography and invasion ecology, among others.

Speaker Bio:
ICREA research professor at Pompeu Fabra University (UPF), head of the Complex Systems Laboratory of the Institute of Evolutionary Biology (IBE), external professor of Santa Fe Institute (New Mexico, USA) and ECLT fellow. His academic training combines biology and physics, the discipline in which he did his PhD, and he has focused his career as a researcher on complex systems: from the evolutionary dynamics of viruses and synthetic biology to the great evolutionary transitions, the emergence of cognition and the terraformation of ecosystems. His work has been awarded with James McDonnell and ERC Advanced Grants.

Why AI is Harder Than We Think

Melanie Mitchell
Santa Fe Institute & Portland State University

Friday, May 14 at 1:00 PM
Contact: cgg@unam.mx

Abstract:
Since its beginning in the 1950s, the field of artificial intelligence has cycled several times between periods of optimistic predictions and massive investment (“AI Spring”) and periods of disappointment, loss of confidence, and reduced funding (“AI Winter”). Even with today’s seemingly fast pace of AI breakthroughs, the development of long-promised technologies such as self-driving cars and housekeeping robots has turned out to be much harder than we thought.
One reason for these repeating cycles is a lack of understanding of the nature and complexity of intelligence itself. In this talk I will discuss some fallacies in common assumptions made by AI researchers, which can lead to overconfident predictions about the field. I will also speculate on what is needed for the grand challenge of making AI systems more robust, general, and adaptable—in short, more intelligent.

Speaker Bio:
Melanie Mitchell is the Davis Professor of Complexity at the Santa Fe Institute, and Professor of Computer Science (currently on leave) at Portland State University. Her current research focuses on conceptual abstraction, analogy-making, and visual recognition in artificial intelligence systems. Melanie is the author or editor of six books and numerous scholarly papers in the fields of artificial intelligence, cognitive science, and complex systems. Her book Complexity: A Guided Tour (Oxford University Press) won the 2010 Phi Beta Kappa Science Book Award and was named by Amazon.com as one of the ten best science books of 2009. Her latest book is Artificial Intelligence: A Guide for Thinking Humans (Farrar, Straus, and Giroux).

Complexity Science – From philosophical foundations to applications in climate and social science

Karoline Wiesner
University of Potsdam & Complexity Science Hub Vienna

Thursday, April 29 at 1:00 PM
Contact: cgg@unam.mx

Abstract:
Many people might not bother to define complexity, thinking that we know it when we see it. Scientists should not afford such luxury. I will provide a compact but comprehensive overview of the different ways that systems can be complex, offering an aggregate definition. I will discuss the role of complexity measures, and why complexity cannot be captured by a single number. This work was done in collaboration with James Ladyman, published with Yale University Press in 2020. At the other end of the spectrum of complexity science is the application to real-world problems. I will present two examples from recent work. The project 'Aiding the mitigation of and adaptation to climate change using the tools of complexity science' was done in collaboration with the Green Climate Fund, founded by the UN members in 2014. Equally, political systems are more and more focus of computational and mathematical investigations. I will present conceptual work on the stability of democracy, a collaboration with an international and interdisciplinary group of scientists.

Short Bio:
Karoline Wiesner is Professor of Complexity Science at the University of Potsdam, Germany, and External Faculty at the Complexity Science Hub Vienna. With a PhD in physics from Uppsala University, Sweden, and postdoctoral research fellowships at the Santa Fe Institute and the University of California, Davis, she joined the faculty at the University of Bristol in 2007. In 2021 she joined the University of Potsdam, Germany, as full professor. Karoline has held Visiting Research Professorships at the Potsdam Institute for Climate Impact Research and at the Institute for Theoretical Physics at Lund University. Karoline is an internationally recognized expert on applied information theory and on the foundations of complexity science. Her recent book on the subject, published by Yale University Press, was one of Nature's "best science picks" in 2020. Her scholarly work has appeared in Nature Communications, IEEE, and Royal Society journals, as well as journals of physics, chemistry, computer science, and philosophy.

Computational Epidemiology at the time of COVID-19

Alessandro Vespignani
Network Science Institute at Northeastern University

Friday, April 9 at 1:00 PM
Contact: cgg@unam.mx

Abstract:
The data science revolution is finally enabling the development of large-scale data-driven models that provide real- or near-real-time forecasts and risk analysis for infectious disease threats. These models also provide rationales and quantitative analysis to support policy-making decisions and intervention plans. At the same time, the non-incremental advance of the field presents a broad range of challenges: algorithmic (multiscale constitutive equations, scalability, parallelization), real-time integration of novel digital data streams (social networks, participatory platform, human mobility etc.). I will review and discuss recent results and challenges in the area, and focus on ongoing work aimed at responding to the COVID-19 pandemic.

Short Bio:
Alessandro Vespignani is the Director of the Network Science Institute and Sternberg Family Distinguished University Professor at Northeastern University. He is a professor with interdisciplinary appointments in the College of Computer and Information Science, College of Science, and the Bouvé College of Health Sciences. Dr. Vespignani's work focuses on statistical and numerical simulation methods to model spreading phenomena, including the realistic and data-driven computational modeling of biological, social, and technological systems. For several years his work has focused on the spreading of infectious diseases, working closely with the CDC and the WHO.

The curious human

Danielle S. Bassett
University of Colorado Boulder & Santa Fe Institute

Thursday, March 25 at 1:00 PM
Contact: cgg@unam.mx

Abstract:
The human mind is curious. It is strange, remarkable, and mystifying; it is eager, probing, questioning. Despite its pervasiveness and its relevance for our well-being, scientific studies of human curiosity that bridge both the organ of curiosity and the object of curiosity remain in their infancy. In this talk, I will integrate historical, philosophical, and psychological perspectives with techniques from applied mathematics and statistical physics to study individual and collective curiosity. In the former, I will evaluate how humans walk on the knowledge network of Wikipedia during unconstrained browsing. In doing so, we will capture idiosyncratic forms of curiosity that span multiple millennia, cultures, languages, and timescales. In the latter, I will consider the fruition of collective curiosity in the building of scientific knowledge as encoded in Wikipedia. Throughout, I will make a case for the position that individual and collective curiosity are both network building processes, providing a connective counterpoint to the common acquisitional account of curiosity in humans.

Short Bio:
Prof. Bassett is the J. Peter Skirkanich Professor at the University of Pennsylvania, with appointments in the Departments of Bioengineering, Electrical & Systems Engineering, Physics & Astronomy, Neurology, and Psychiatry. Bassett is also an external professor of the Santa Fe Institute. Bassett is most well-known for blending neural and systems engineering to identify fundamental mechanisms of cognition and disease in human brain networks. Bassett is currently writing a book for MIT Press entitled Curious Minds, with co-author Perry Zurn Professor of Philosophy at American University. Bassett received a B.S. in physics from Penn State University and a Ph.D. in physics from the University of Cambridge, UK as a Churchill Scholar, and as an NIH Health Sciences Scholar. Following a postdoctoral position at UC Santa Barbara, Bassett was a Junior Research Fellow at the Sage Center for the Study of the Mind. Bassett has received multiple prestigious awards, including American Psychological Association's ‘Rising Star’ (2012), Alfred P Sloan Research Fellow (2014), MacArthur Fellow Genius Grant (2014), Early Academic Achievement Award from the IEEE Engineering in Medicine and Biology Society (2015), Harvard Higher Education Leader (2015), Office of Naval Research Young Investigator (2015), National Science Foundation CAREER (2016), Popular Science Brilliant 10 (2016), Lagrange Prize in Complex Systems Science (2017), Erdos-Renyi Prize in Network Science (2018), OHBM Young Investigator Award (2020), AIMBE College of Fellows (2020). Bassett is the author of more than 300 peer-reviewed publications, which have garnered over 27,000 citations, as well as numerous book chapters and teaching materials. Bassett is the founding director of the Penn Network Visualization Program, a combined undergraduate art internship and K-12 outreach program bridging network science and the visual arts. Bassett’s work has been supported by the National Science Foundation, the National Institutes of Health, the Army Research Office, the Army Research Laboratory, the Office of Naval Research, the Department of Defense, the Alfred P Sloan Foundation, the John D and Catherine T MacArthur Foundation, the Paul Allen Foundation, the ISI Foundation, and the Center for Curiosity.

Nearly-optimal prediction of missing links in networks

Aaron Clauset

Abstract:
Predicting missing links in networks is a fundamental task in network analysis and modeling. However, current link prediction algorithms exhibit wide variations in their accuracy, and we lack a general understanding of which methods work better in which contexts. In this talk, I'll describe a novel meta-learning solution to this problem, which makes predictions that appear to be nearly optimal by learning to combine three classes of prediction methods: community detection algorithms, structural features like degrees and triangles, and network embeddings. We evaluate 203 component methods individually and in stacked generalization on (i) synthetic data with known structure, for which we analytically calculate the optimal link prediction performance, and (ii) a large corpus of 548 structurally diverse networks from social, biological, technological, information, economic, and transportation domains. Across settings, supervised stacking nearly always performs best and produces nearly-optimal performance on synthetic networks. Moreover, we show that accuracy saturates quickly, and near-optimal predictions typically requires only a handful of component methods. Applied to real data, we quantify the utility of each method on different types of networks, and then show that the difficulty of predicting missing links varies considerably across domains: it is easiest in social networks and hardest in technological networks. I'll close with forward-looking comments on the limits of predictability for missing links in complex networks and on the utility of stacked generalizations for achieving them.

Joint work with Amir Ghasemian, Homa Hosseinmardi, Aram Galstyan, and Edoardo Airoldi.

Short Bio:
Aaron Clauset is an Associate Professor in the Department of Computer Science and the BioFrontiers Institute at the University of Colorado Boulder, and is External Faculty at the Santa Fe Institute. He received a PhD in Computer Science, with distinction, from the University of New Mexico, a BS in Physics, with honors, from Haverford College, and was an Omidyar Fellow at the prestigious Santa Fe Institute. In 2016, he was awarded the Erdos-Renyi Prize in Network Science, and since 2017, he has been a Deputy Editor responsible for the Social, Computing, and Interdisciplinary Sciences at Science Advances.
Clauset is an internationally recognized expert on network science, data science, and machine learning for complex systems. His work has appeared in many prestigious scientific venues, including Nature, Science, PNAS, SIAM Review, Science Advances, Nature Communications, AAAI, and ICDM. His work has also been covered in the popular press by Quanta Magazine, the Wall Street Journal, The Economist, Discover Magazine, Wired, the Boston Globe and The Guardian.