Jie Sun, Abd AlRahman AlMomani, Erik Bollt

 

Boolean functions and networks are commonly used in the modeling and analysis of complex biological systems, and this paradigm is highly relevant in other important areas in data science and decision making, such as in the medical field and in the finance industry. Automated learning of a Boolean network and Boolean functions, from data, is a challenging task due in part to the large number of unknowns (including both the structure of the network and the functions) to be estimated, for which a brute force approach would be exponentially complex. In this paper we develop a new information theoretic methodology that we show to be significantly more efficient than previous approaches. Building on the recently developed optimal causation entropy principle (oCSE), that we proved can correctly infer networks distinguishing between direct versus indirect connections, we develop here an efficient algorithm that furthermore infers a Boolean network (including both its structure and function) based on data observed from the evolving states at nodes. We call this new inference method, Boolean optimal causation entropy (BoCSE), which we will show that our method is both computationally efficient and also resilient to noise. Furthermore, it allows for selection of a set of features that best explains the process, a statement that can be described as a networked Boolean function reduced order model. We highlight our method to the feature selection in several real-world examples: (1) diagnosis of urinary diseases, (2) Cardiac SPECT diagnosis, (3) informative positions in the game Tic-Tac-Toe, and (4) risk causality analysis of loans in default status. Our proposed method is effective and efficient in all examples.

Source: arxiv.org

Thomas van Es
Adaptive Behavior

 

The free energy principle (FEP) is an information-theoretic approach to living systems. FEP characterizes life by living systems’ resistance to the second law of thermodynamics: living systems do not randomly visit the possible states, but actively work to remain within a set of viable states. In FEP, this is modelled mathematically. Yet, the status of these models is typically unclear: are these models employed by organisms or strictly scientific tools of understanding? In this article, I argue for an instrumentalist take on models in FEP. I shall argue that models used as instruments for knowledge by scientists and models as implemented by organisms to navigate the world are being conflated, which leads to erroneous conclusions. I further argue that a realist position is unwarranted. First, it overgenerates models and thus trivializes the notion of modelling. Second, even when the mathematical mechanisms described by FEP are implemented in an organism, they do not constitute a model. They are covariational, not representational in nature, and precede the social practices that have shaped our scientific modelling practice. I finally argue that the above arguments do not affect the instrumentalist position. An instrumentalist approach can further add to conceptual clarity in the FEP literature.

Source: journals.sagepub.com

Masaaki Inoue, Thong Pham, Hidetoshi Shimodaira

Journal of Informetrics
Volume 14, Issue 3, August 2020, 101042

 

• Transitivity and preferential attachment exist jointly in two co-authorship networks.

• Neither alone could describe the networks well.

• Their functional forms deviate substantially from the conventional power-law form.

• Transitivity greatly dominated preferential attachment in both networks.

Source: www.sciencedirect.com

Federico Battiston, Giulia Cencetti, Iacopo Iacopini, Vito Latora, Maxime Lucas, Alice Patania, Jean-Gabriel Young, Giovanni Petri

 

The complexity of many biological, social and technological systems stems from the richness of the interactions among their units. Over the past decades, a great variety of complex systems has been successfully described as networks whose interacting pairs of nodes are connected by links. Yet, in face-to-face human communication, chemical reactions and ecological systems, interactions can occur in groups of three or more nodes and cannot be simply described just in terms of simple dyads. Until recently, little attention has been devoted to the higher-order architecture of real complex systems. However, a mounting body of evidence is showing that taking the higher-order structure of these systems into account can greatly enhance our modeling capacities and help us to understand and predict their emerging dynamical behaviors. Here, we present a complete overview of the emerging field of networks beyond pairwise interactions. We first discuss the methods to represent higher-order interactions and give a unified presentation of the different frameworks used to describe higher-order systems, highlighting the links between the existing concepts and representations. We review the measures designed to characterize the structure of these systems and the models proposed in the literature to generate synthetic structures, such as random and growing simplicial complexes, bipartite graphs and hypergraphs. We introduce and discuss the rapidly growing research on higher-order dynamical systems and on dynamical topology. We focus on novel emergent phenomena characterizing landmark dynamical processes, such as diffusion, spreading, synchronization and games, when extended beyond pairwise interactions. We elucidate the relations between higher-order topology and dynamical properties, and conclude with a summary of empirical applications, providing an outlook on current modeling and conceptual frontiers.

Source: arxiv.org