Merge pull request #331 from Identitywoman/patch-41

Update 03-01-living-in-a-plural-world.md

contents/english/03-01-living-in-a-plural-world.md

@@ -76,7 +76,7 @@ If the defining idea of 19th century macrobiology (concerning advanced organisms
 
      Yet, whatever level of explanation is chosen, actors are almost always modeled as atomistically self-interested and planners as coherent, objective maximizers, rather than socially-embedded intersections of group affiliations.  The essence of understanding social phenomena as arising from a “network society” is to embrace this richness and build social systems, technologies, and policies that harness it, rather than viewing it as a distracting complication.  Such systems need, among other things, to explicitly account for the social nature of motivations, to empower a diversity of social groups, to anticipate and support social dynamism and evolution, to ground personal identity in social affiliations and group choices in collective, democratic participation and to facilitate the establishment and maintenance of social context facilitating community.  
 
-     While we do not have the space to review it in detail, a rich literature provides quantitative and social scientific evidence for the explanatory power of the pluralist perspective.  Studies of industrial dynamics, of social and behavioral psychology, of economic development, of organizational cohesion, and much else, have shown the central role of social relationships that create and harness diversity[^SocialDynamics].   Instead, we will pull out just one example that perhaps will be both the most surprising and most related to the scientific themes above: the evolution of scientific knowledge itself.
+     While we do not have the space to review it in detail, a rich literature provides quantitative and social scientific evidence for the explanatory power of the pluralist perspective [^Assemblage Theory]. Studies of industrial dynamics, of social and behavioral psychology, of economic development, of organizational cohesion, and much else, have shown the central role of social relationships that create and harness diversity[^SocialDynamics].   Instead, we will pull out just one example that perhaps will be both the most surprising and most related to the scientific themes above: the evolution of scientific knowledge itself.
 
      A growing interdisciplinary academic field of “Science of Science” (SciSci) studies the emergence of scientific knowledge as a complex system[^SciSciField].  It charts the emergence and proliferation of scientific fields, the sources of scientific novelty and progress, the strategies of exploration scientists choose, and the impact of social structure on intellectual advance.  Among other things, they find that, relative to the most efficient ways of discovering existing knowledge (in chemistry, as an example), scientific exploration is biased towards topics and connections related to social connections and previous publications within a field[^TopicBiasInScience]. It finds strong connections between research team size and diversity and the types of findings (risky and revolutionary v. normal science) developed and documents the increasingly dominant role of teams (as opposed to individual research) in modern science [^TeamScience]. The largest innovations tend to arise from a strong grounding in existing disciplines deployed in unusual and surprising combinations[^ScientificInnovation].  It illustrates that most incentive structures used in science (based e.g. on publication quality and citation count) create perverse incentives that limit scientific creativity and has helped produce new metrics that can complement and offset these biases, creating a more pluralistic incentive set [^ScienceMetrics]. 
 
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 [^RelationalReality]: https://www.theguardian.com/books/2022/sep/05/the-big-idea-why-relationships-are-the-key-to-existence
 [^MultilevelSelection] Wilson, David Sloan et al. “Multilevel Selection Theory and Major Evolutionary Transitions.” Current Directions in Psychological Science 17 (2008): 6 - 9.
 [^NeuroscienceComplexity]: Here are some examples of these properties in neuroscience: **Sensitivity**: In neuroscience, sensitivity refers to the ability of the brain to detect and respond to small changes in its environment. One example of sensitivity in the brain is the phenomenon of synaptic plasticity, which is the ability of synapses (connections between neurons) to change in strength in response to activity. This sensitivity allows the brain to adapt and learn from experience. **Chaos**: Chaos is a property of complex systems that exhibit unpredictable behavior even though they are deterministic. In neuroscience, chaos has been observed in the activity of neurons in the brain. For example, studies have shown that the firing patterns of individual neurons can be highly irregular and chaotic, with no discernible pattern or rhythm. This chaotic activity may play a role in information processing and communication within the brain. **Sensitivity and chaos together:** Sensitivity and chaos can also interact in the brain to produce complex and adaptive behavior. For example, studies have shown that the brain can exhibit sensitivity to small changes in sensory input, but this sensitivity can also lead to chaotic activity in neural networks. However, this chaotic activity can be controlled and harnessed to produce adaptive behavior, such as in the case of motor control and coordination. The brain's ability to integrate sensitivity and chaos in this way is a hallmark of its remarkable complexity and adaptability. 
+[^Assemblage Theory] In assemblage theory, as articulated by Manuel DeLanda, entities are understood as complex structures formed from the symbiotic relationship between heterogeneous components, rather than being reducible to their individual parts. Its central thesis is that people do not act exclusively by themselves, and instead human action requires complex socio-material interdependencies. DeLanda's perspective shifts the focus from inherent qualities of entities to the dynamic processes and interactions that give rise to emergent properties within networks of relations.  His book "A New Philosophy of Society: Assemblage Theory and Social Complexity" (2006) is a good starting point. 
 [^SocialDynamics] Page, S. E. (2007). The difference: How the power of diversity creates better groups, firms, schools, and societies. Princeton University Press.; Hidalgo, C. A. (2015). Why information grows: The evolution of order, from atoms to economies. Basic Books.; Acemoglu, D., & Linn, J. (2004). Market size in innovation: Theory and evidence from the pharmaceutical industry. The Quarterly Journal of Economics, 119(3), 1049-1090.; Mercier, H., & Sperber, D. (2017). The enigma of reason. Harvard University Press.; Pentland, A. (2014). Social physics: How good ideas spread—the lessons from a new science. Penguin. Putnam, R. D. (2000). Bowling alone: The collapse and revival of American community. Simon and Schuster. Granovetter, M. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1360-1380. Uzzi, B. (1997). Social structure and competition in interfirm networks: The paradox of embeddedness. Administrative Science Quarterly, 42(1), 35-67.; Burt, R. S. (1992). Structural holes: The social structure of competition. Harvard University Press.; McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27(1), 415-444.
 [^SciSciField]: See a summary in Fortunato et al. (2018)
 [^TopicBiasInScience]: Rzhetsky et al. 2015