Tag: information

• Discussing some motivations behind a particle physics FAQ

  First, there is information. From information, people distill knowledge, and from knowledge, wisdom. Information is available on a lot of topics and in varying levels of detail. Knowledge on topics is harder to find – and even more hard is wisdom. This is because knowledge and wisdom require work (to fact-check and interpret) on information and knowledge, respectively. And people can be selective on what they choose to work on. One popular consequence of such choices is that most people are more aware of business information, business knowledge and business wisdom than they are of scientific information, scientific knowledge and scientific wisdom. This graduated topical awareness reflects in how we produce and consume the news.

  

  News articles written on business issues rarely see fit to delve into historical motivations or explainer-style elucidations because the audience is understood to be better aware of what business is about. Business information and knowledge are widespread and so is, to some extent, business wisdom, and articles can take advantage of conclusions made in each sphere, jumping between them to tease out more information, knowledge and wisdom. On the other hand, articles written on some topics of science – such as particle physics – have to start from the informational level before wisdom can be presented. This places strong limits on how the article can be structured or even styled.

  There are numerous reasons for why this is so, especially for topics like particle physics, which I regularly (try to) write on. I’m drawn toward three of them in particular: legacy, complexity and pacing. Legacy is the size of the body of work that is directly related to the latest developments in that work. So, the legacy of the LHC stretches back to include the invention of the cyclotron in 1932 – and the legacy of the Higgs boson stretches back to 1961. Complexity is just that but becomes more meaningful in the context of pacing.

  A consequence of business developments being reported on fervently is that there is at least some (understandable) information in the public domain about all stages of the epistemological evolution. In other words, the news reports are apace of new information, new knowledge, new wisdom. With particle physics, they aren’t – they can’t be. The reports are separated by some time, according to when the bigger developments occurred, and in the intervening span of time, new information/knowledge/wisdom would’ve arisen that the reports will have to accommodate. And how much has to be accommodated can be exacerbated by the complexity of what has come before.

  

  But there is a catch here – at least as far as particle physics is concerned because it is in a quandary these days. The field is wide open because physicists have realised two things: first, that their theoretical understanding of physics is far, far ahead of what their experiments are capable of (since the 1970s and 1980s); second, that there are inconsistencies within the theories themselves (since the late 1990s). Resolving these issues is going to take a bit of time – a decade or so at least (although we’re likely in the middle of such a decade) – and presents a fortunate upside to communicators: it’s a break. Let’s use it to catch up on all that we’ve missed.

  The break (or a rupture?) can also be utilised for what it signifies: a gap in information/knowledge. All the information/knowledge/wisdom that has come before is abruptly discontinued at this point, allowing communicators to collect them in one place, compose them and disseminate them in preparation for whatever particle physics will unearth next. And this is exactly what motivated me to write a ‘particle physics FAQ’, published on The Wire, as something anyone who’s graduated from high-school can understand. I can’t say if it will equip them to read scientific papers – but it will definitely (and hopefully) set them on the road to asking more questions on the topic.

  2016.03.21

• Credibility on the web

  There are a finite number of sources from which anyone receives information. The most prominent among them are media houses (incl. newspapers, news channels, radio stations, etc.) and scientific journals (at least w.r.t. the subjects I work with).

  Seen one way, these establishments generate the information that we receive. Without them, stories would remain localized, centralized, away from the ears that could accord them gravity.

  Seen another way, these establishments are also motors: sans their motive force, information wouldn’t move around as it does, although this is assuming that they don’t mess with the information itself.

  With more such “motors” in the media mix, the second perspective is becoming the norm of things. Even if information isn’t picked up by one house, it could be set sailing through a blog or a CJ initiative. The means through which we learn something, or stumble upon it for that matter, are growing to be more overlapped, lines crossing each others’ paths more often.

  Veritably, it’s a maze. In such a labyrinthine setup, the entity that stands to lose the most is faith of a reader/viewer/consumer in the credibility of the information received.

  In many cases, with a more interconnected web – the largest “supermotor” – the credibility of one bit of information is checked in one location, by one entity. Then, as it moves around, all following entities inherit that credibility-check.

  For instance, on Wikipedia, credibility is established by citing news websites, newspaper/magazine articles, journals, etc. Jimmy Wales’ enterprise doesn’t have its own process of verification in place. Sure, there are volunteers who almost constantly police its millions of pages, but all they can do is check if the citation is valid, and if there are any contrarious reports, too, to the claims being staked.

  One way or another, if a statement has appeared in a publication, it can be used to have the reader infer a fact.

  In this case, Wikipedia has inherited the credibility established by another entity. If the verification process had failed in the first place, the error would’ve been perpetrated by different motors, each borrowing from the credibility of the first.

  Moreover, the more strata that the information percolates through, the harder it will be to establish a chain of accountability.

  *

  My largest sources of information are:

  1. Wikipedia
  2. Journals
  3. Newspapers
  4. Blogs

  (The social media is just a popular aggregator of news from these sources.)

  

  Wikipedia cites news reports and journal articles.

  News reports are compiled with the combined efforts of reporters and editors. Reporters verify the information they receive by checking if it’s repeated by different sources under (if possible) different circumstances. Editors proofread the copy and are (or must remain) sensitive to factual inconsistencies.

  Journals have the notorious peer-reviewing mechanism. Each paper is subject to a thorough verification process intended to wean out all mistakes, errors, information “created” by lapses in the scientific method, and statistical manipulations and misinterpretations.

  Blogs borrow from such sources and others.

  Notice: Even in describing the passage of information through these ducts, I’ve vouched for reporters, editors, and peer-reviews. What if they fail me? How would I find out?

  *

  The point of this post was to illustrate

  1. The onerous yet mandatory responsibility that verifiers of information must assume,
  2. That there aren’t enough of them, and
  3. That there isn’t a mechanism in place that periodically verifies the credibility of some information across its lifetime.

  How would you ensure the credibility of all the information you receive?

  2012.09.08

• The Markovian Mind

  In many ways, human engagement with information happens in such a manner that, with the accumulation of information over time, the dataset constructed out of the latest volume of information has the strongest relationship of any kind with the consecutively next dataset – a Markovian trait.

  At any point of time, the future state of the dataset is determined solely by its present one. In other words, with a discrete understanding, its n^th state is dependant solely on its (n – i)^th state, where ‘i’ is a cardinal index. Upon a failure to quantify its (n + i)^th state, there is no certain state that we know the dataset will intend to assume.

  At the same time, given its limited historic dependency, the past’s bearing on the state of the dataset is continuous but constantly depreciating (asymptotically tending to zero): the correlation index between the (n + i)^th state for increasing i with the (n – k)^th decreases for increasing k (for all k = i).*

  Over time, if the information-dataset could be quantized through a set of state variables, S, then there will be a characteristic function, φ(n), which would describe the slope of the correlation index’s curve at (i, k). Essentially, the evolution of S will be as a Markov chain whereas φ(n) will be continuous, rendering (i, k) random and memoryless.

  (*For k = i, (n – k) = (n – i). However, for a given set of state variables S, which evolve as a Markov chain, the devolution that k tracks and the evolution that i tracks will be asymmetric, necessitating two different indices to describe the two degrees of freedom.)

  2012.08.28