47 Matching Annotations
  1. Mar 2025
  2. Jan 2025
    1. system reflexivity

      for - definition - system reflexivity (Moore et al., 2018) - the capacity to see the complexity and mobilize the agency in a system, while deeply engaging with diversity across multiple scales - SOURCE - paper - Reflexivity as a transformative capacity for sustainability science: introducing a critical systems approach - Lazurko et al. - 2025, Jan 10

    2. individual reflexivity is rarely traced through to a collective influence on the broader transdisciplinary research process

      for - adjacency - individual reflexivity is rarely traced to a collective influence - Indyweb provenance - SOURCE - paper - Reflexivity as a transformative capacity for sustainability science: introducing a critical systems approach - Lazurko et al. - 2025, Jan 10

      adjacency - between - individual reflexivity is rarely traced to a collective influence - Indyweb provenance - adjacency relationship - Indyweb provenance can allow granular tracing of individual contributions to collective knowledge work - so can assist in the use of reflexivity in transdisciplinary work

    3. Reflexivity has been explored on a collective societal level, for example through Ulrich Beck's work on reflexive modernization wherein the unintended consequences of simple modernity motivate a reflexive turn across society, including to science itself: ‘science itself is deconstructed by means of science’

      for - further research - Ulrich Beck's research on unintended consequences of simple modernity - SOURCE - paper - Reflexivity as a transformative capacity for sustainability science: introducing a critical systems approach - Lazurko et al. - 2025, Jan 10

    4. In the context of transformative transdisciplinary research, such reflexive processes are meant to open-up epistemic and solution spaces that elevate marginalized perspectives and challenge the status quo.

      for - adjacency - reflexive processes elevate marginalized perspectives and challenge status quo - diversity of Indyweb perspectival knowing - mitigates progress traps that emerge from myopism - SOURCE - paper - Reflexivity as a transformative capacity for sustainability science: introducing a critical systems approach - Lazurko et al. - 2025, Jan 10

    5. avigating the diverse and sometimes conflicting perspectives of researchers and participants in transdisciplinary processes raises challenges

      for - adjacency - challenges of harmonizing multiple perspectives - SRG complexity mapping - Deep Humanity - embedded in Indyweb - intrinsic perspectival knowing - facilitates high resolution perspectival complementarity - SOURCE - paper - Reflexivity as a transformative capacity for sustainability science: introducing a critical systems approach - Lazurko et al. - 2025, Jan 10

    6. Transdisciplinary sustainability science is increasingly applied to study transformative change. Yet, transdisciplinary research involves diverse actors who hold contrasting and sometimes conflicting perspectives and worldviews. Reflexivity is cited as a crucial capacity for navigating the resulting challenges

      for - adjacency - reflexivity - tool for transdisciplinary research - indyweb - people-centered interpersonal information architecture - mindplex - concept spaces - perspectival knowing - life situatedness - SRG transdisciplinary complexity mapping tool - SOURCE - paper - Reflexivity as a transformative capacity for sustainability science: introducing a critical systems approach - Lazurko et al. - 2025, Jan 10

      adjacency - between - reflexivity - tool for transdisciplinary research - indyweb - people-centered, interpersonal information architecture - mindplex - concept space - perspectival knowing - life situatedness - SRG transdisciplinary complexity mapping tool - adjacency relationship - This paper is interesting from the perspective of development of the Indyweb because there, - the people-centered, interpersonal information architecture intrinsically explicates perspectival knowing and life-situatedness - Indyweb can embed an affordance that is a meta function applied to an indyvidual's mindplex that - surfaces and aspectualizes the perspective and worldview salient to the research - The granular information that embeds an indyvidual's perspectives and worldviews is already there in the indyvidual's rich mindplex

    7. as crucial dimensions are left unacknowledged

      for - in other words - remain implicit instead of made explicit - SOURCE - paper - Reflexivity as a transformative capacity for sustainability science: introducing a critical systems approach - Lazurko et al. - 2025, Jan 10

    8. for - paper - Reflexivity as a transformative capacity for sustainability science: introducing a critical systems approach - Lazurko et al. - 2025, Jan 10

    Tags

    Annotators

    URL

  3. Dec 2024
  4. Sep 2024
    1. Therefore, similar to Ribes et al. in their study of domain [113], the epistemic positions we propose aim to provide conceptual tools for reasoning about different styles of organizing creativity-oriented research practices in HCI.

      David Ribes' work explores the definition of domain in computing and data science; offers insight into how studying domains helps organize computational systems.

  5. Jul 2024
    1. A critique on the Mass Media... The problem is that they want the Mass Media system to operate on the code of "True/False" rather than "Known/Unknown"... But if it were to be so, it would not be Mass Media anymore, but rather the Science System.

      For Mass Media to be Mass Media it needs to be concerned with selection and filtering, to condense and make known, not to present "all the facts". Sure, they need to be concerned with truth to a certain degree, but it's not the primary priority.


      This is a reflection based on my knowledge of Luhmann's theory of society as functionally differentiated systems; as explained by Hans-Georg Moeller (Carefree Wandering) on YouTube.

  6. Apr 2024
    1. Either system canbe s tart ed with a small li stof captions and be increasedscientifically.

      Scientific principles had bled so thoroughly into both culture and business that even advertising for filing systems in business in the 1930 featured their ability to be used and expanded scientifically.

  7. Jul 2023
  8. Jun 2023
  9. Apr 2023
  10. Jan 2023
    1. A term recommended by Eve regarding an interdisciplinary approach that accounts for multiple feedback loops within complex systems. Need to confer complex systems science to see if ADHD is already addressed in that domain.

  11. Sep 2022
    1. When we talk about air in a room, we can describe it by listing the properties of each and every molecule, or we speak in coarse-grained terms about things like temperature and pressure. One description is more "fundamental," in that its regime of validity is wider; but both have a regime of validity, and as long as we are in that regime, the relevant concepts have a perfectly good claim to "existing."

      Another way of saying this is that temperature and pressure are emergent properties of the more fundamental properties of the molecules of air.

      The problem with applying this to free will, though, is that unlike temperature, we have no way to measure free will. If we can't measure it, I am quite comfortable in denying this analogy.

  12. Apr 2022
  13. Mar 2022
    1. As Professor Rangi Mātāmua, a Māoriastronomy scholar, explains:Look at what our ancestors did to navigate here—you don’t do that onmyths and legends, you do that on science. I think there is empiricalscience embedded within traditional Māori knowledge ... but what they didto make it meaningful and have purpose is they encompassed it withincultural narratives and spirituality and belief systems, so it wasn’t just seenas this clinical part of society that was devoid of any other connection toour world, it was included into everything. To me, that cultural elementgives our science a completely new and deep and rich layer of meaning
  14. Nov 2021
  15. Sep 2021
  16. Jun 2020
  17. May 2020
  18. Nov 2019
  19. Sep 2019
  20. Jan 2016
    1. Stupid models are extremely useful. They are usefulbecause humans are boundedly rational and because language is imprecise. It is often only by formalizing a complex system that we can make progress in understanding it. Formal models should be a necessary component of the behavioral scientist’s toolkit. Models are stupid, and we need more of them.

      Formal models are explicit in the assumptions they make about how the parts of a system work and interact, and moreover are explicit in the aspects of reality they omit.

      -- Paul Smaldino

    2. Microeconomic models based on rational choice theory are useful for developing intuition, and may even approximate reality in a fewspecial cases, but the history of behavioral economics shows that standard economic theory has also provided a smorgasbord of null hypotheses to be struck down by empirical observation.
    3. Where differences between conditions are indicated, avoid the mistake of running statistical analyses as if you were sampling from a larger population.

      You already have a generating model for your data – it’s your model. Statistical analyses on model data often involve modeling your model with a stupider model. Don’t do this. Instead, run enough simulations to obtain limiting distributions.

    4. A model’s strength stemsfromits precision.

      I have come across too many modeling papers in which the model – that is, the parts, all their components, the relationships between them, and mechanisms for change – is not clearly expressed. This is most common with computational models (such as agent-based models), which can be quite complicated, but also exists in cases of purely mathematical models.

    5. However, I want to be careful not to elevate modelers above those scientists who employ other methods.

      This is important for at least two reasons, the first and foremost of which is that science absolutely requires empirical data. Those data are often painstaking to collect, requiring clever, meticulous, and occasionally tedious labor. There is a certain kind of laziness inherent in the professional modeler, who builds entire worlds from his or her desk using only pen, paper, and computer. Relatedly, many scientists are truly fantastic communicators, and present extremely clear theories that advance scientific understanding without a formal model in sight. Charles Darwin, to give an extreme example, laid almost all the foundations of modern evolutionary biology without writing down a single equation.

    6. Ultimately,the theory has been shown to be incorrect, and has been epistemically replaced by the theory of General Relativity. Nevertheless, the theory is able to make exceptionally good approximations of gravitational forces –so good that NASA’s moon missions have relied upon them.

      General Relativity may also turn out to be a "dumb model". https://twitter.com/worrydream/status/672957979545571329

    7. Table 1.Twelve functions served by false models. Adapted with permissionfrom Wimsatt

      Twelve good uses for dumb models, William Wimsatt (1987).

    8. To paraphrase Gunawardena (2014), a model is a logical engine for turning assumptions into conclusions.

      By making our assumptions explicit, we can clearly assess their implied conclusions. These conclusions will inevitably be flawed, because the assumptions are ultimately incorrect or at least incomplete. By examining how they differ from reality, we can refine our models, and thereby refine our theories and so gradually we might become less wrong.

    9. the stupidity of a model is often its strength. By focusing on some key aspects of a real-world system(i.e., those aspectsinstantiated in the model), we can investigate how such a system would work if, in principle, we really couldignore everything we are ignoring. This only sounds absurd until one recognizes that, in our theorizing about the nature of reality –both as scientists and as quotidianhumans hopelessly entangled in myriad webs of connection and conflict –weignore thingsall the time.
    10. The generalized linear model, the work horse ofthe social sciences, models data as being randomly drawn from a distribution whose mean varies according to some parameter. The linear model is so obviously wrong yet so useful that the mathematical anthropologist Richard McElreathhas dubbed it “the geocentric model of applied statistics,”in reference to the Ptolemaic model of the solar system that erroneously placed the earth rather than the sun at the center but nevertheless produced accurate predictions of planetary motion as they appeared in the night sky(McElreath 2015).

      A model that approximates some aspect of reality can be very useful, even if the model itself is flat-out wrong.

      But on the other hand, we can't accept approximation of reality as hard proof that a model is correct.

    11. Unfortunately, my own experience working with complex systems and working among complexity scientistssuggests that we are hardly immune to such stupidity. Consider the case of Marilyn Vos Savantand the Monty Hall problem.

      Many people, including some with training in advanced mathematics, contradicted her smugly. But a simple computer program that models the situation can demonstrate her point.

      2/3 times, your first pick will be wrong. Every time that happens, the door Monty didn't open is the winner. So switching wins 2/3 times.

      http://marilynvossavant.com/game-show-problem/

    12. Mitch Resnick, in his book Turtles, Termites, and Traffic Jams, details his experiences teaching gifted high school students about the dynamics of complex systems using artificial life models (Resnick 1994). He showed them how organized behavior could emerge when individualsresponded only to local stimuli using simple rules, without the need for a central coordinating authority. Resnick reports that even after weeks spent demonstrating the principles of emergence,using computer simulations that the students programmed themselves, many students still refused to believe that what they were seeing could really work without central leadership.