Abstract: We have an intuitive understanding of what an individual is; however, this intuitive understanding is difficult to formalize in a way that makes sense from an evolutionary perspective. Consider two people: the first is home in bed with a bad ﬂu, the second is lying in a lounger next to a pool drinking beer. From one perspective, both of these people are missing work for the same ostensible reason: they are too “lazy” to get up and go to work. Yet we absolve the sick person of responsibility, since they are sick and it’s not their fault, but do assign blame to the one lounging around the pool and feel they are missing work because they are lazy. In other words, we do not assign responsibility to the sick person, but instead we assign responsibility to the ﬂu virus, which is not considered part of the “individual.” We consider the virus to be a separate entity, a parasite living within the person. On the other hand, for the pool lounger there is no separate entity to which we assign blame. The person is considered a single individual, and must shoulder all responsibility. This sense that the second person is an “individual” is challenged when we recognize that humans are not single organisms. We are actually complex communities that include follicle mites (Woolley 1988), skin and gut bacterial symbionts (Deth-lefsen, McFall-Ngai, & Relman 2007), and at a more basic level mitochondria, which can be considered to be endosymbionts of eukaryotic cells (Margulis 1970). The question then becomes, Why do we consider mitochondria and gut bacteria to be part of an individual human, but not a ﬂu virus? In this paper, I will argue that we make the distinction between what is and isn’t an individual based on an intuitive concept of shared evolutionary fate. That is, I will argue …
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).