 Last 7 days

covid19.iza.org covid19.iza.org

Explaining Governors’ Response to the COVID19 Pandemic in the United States. COVID19 and the Labor Market. (n.d.). IZA – Institute of Labor Economics. Retrieved August 8, 2020, from https://covid19.iza.org/publications/dp13137/

 Aug 2020

www.bbc.co.uk www.bbc.co.uk

BBC Radio 4—The Political School, Episode 1. (n.d.). BBC. Retrieved August 2, 2020, from https://www.bbc.co.uk/programmes/m000kv6v

 Jul 2020

osf.io osf.io

Andia, T., Mantilla, C., RodriguezLesmes, P., Criado, L., Gomez, J. S., Ortiz, S., Quintero, A., Rincón, H., & Romero, S. (2020). Mentioning anosmia improves how community pharmacies handle phone call requests during the COVID19 pandemic: An audit study in Colombia [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/s2z47


www.nature.com www.nature.com

Block, P., Hoffman, M., Raabe, I. J., Dowd, J. B., Rahal, C., Kashyap, R., & Mills, M. C. (2020). Social networkbased distancing strategies to flatten the COVID19 curve in a postlockdown world. Nature Human Behaviour, 4(6), 588–596. https://doi.org/10.1038/s4156202008986


www.sciencedirect.com www.sciencedirect.com

Argument Quality in Real World Argumentation. (2020). Trends in Cognitive Sciences, 24(5), 363–374. https://doi.org/10.1016/j.tics.2020.01.004

 Jun 2020

twitter.com twitter.com

ReconfigBehSci on Twitter: “Fellow behavioural scientists, I had a lightbulb moment yesterday. I suspect I might have been unusually slow here, and you all clocked this months ago, but thought I’d share nevertheless: I’ve lost count of how often I’ve been told ‘You only have a 1/100 chance of dying’” / Twitter. (n.d.). Twitter. Retrieved June 27, 2020, from https://twitter.com/scibeh/status/1276434777856446464


psyarxiv.com psyarxiv.com

Veltri, G. A., Prof, LupiáñezVillanueva, F., Folkvord, F., Theben, A., & Gaskell, G. (2020, April 29). The impact of online platform transparency of information on consumer’s choices. https://doi.org/10.31234/osf.io/htja5


psycnet.apa.org psycnet.apa.org

Attali, Y., Budescu, D., & ArieliAttali, M. (2020). An item response approach to calibration of confidence judgments. Decision, 7(1), 1–19. https://doi.org/10.1037/dec0000111


psycnet.apa.org psycnet.apa.org

Winman, A., Hansson, P., & Juslin, P. (2004). Subjective Probability Intervals: How to Reduce Overconfidence by Interval Evaluation. Journal of Experimental Psychology: Learning, Memory, and Cognition, 30(6), 1167–1175. https://doi.org/10.1037/02787393.30.6.1167


psycnet.apa.org psycnet.apa.org

Erev, I., Wallsten, T. S., & Budescu, D. V. (1994). Simultaneous over and underconfidence: The role of error in judgment processes. Psychological Review, 101(3), 519–527. https://doi.org/10.1037/0033295X.101.3.519

 May 2020

www.sjdm.org www.sjdm.org

[Jdmsociety] Decision on reopening economies. (n.d.). Retrieved April 21, 2020, from http://www.sjdm.org/mailarchive/jdmsociety/2020April/008496.html


psyarxiv.com psyarxiv.com

Dhami, M. K., & Mandel, D. R. (2020). UK and US policies for communicating probability in intelligence analysis: A review [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/kuyhb


psyarxiv.com psyarxiv.com

Moore, D. A., & Wallsten, T. S. (2020). Rolling Forecasts. https://doi.org/10.31234/osf.io/ryvg3


twitter.com twitter.com

Dr Muge Cevik on Twitter
Tags
 household
 friends
 asymptomatic
 age
 exposure
 family
 transmission reduction
 COVID19
 is:twitter
 environment
 infection rate
 lang:en
 high risk
 attack rate
 aged care facility
 public transport
 nursing home
 transmission dynamics
 contact tracing
 symptomatic
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 indoors
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threadreaderapp.com threadreaderapp.com

Thread by @taaltree: Antibody tests are coming online. Never before have humans needed to understand Bayes rule more. Let’s talk about why it’s critical NOT to a…. (n.d.). Retrieved April 21, 2020, from https://threadreaderapp.com/thread/1248467731545911296.html

 Apr 2020

psyarxiv.com psyarxiv.com

Mandel, D. R., Wallsten, T. S., & Budescu, D. (2020, April 20). NumericallyBounded Language Schemes Are Unlikely to Communicate Uncertainty Effectively. https://doi.org/10.31234/osf.io/9f6ev


math.stackexchange.com math.stackexchange.com

Therefore, En=2n+1−2=2(2n−1)
Simplified formula for the expected number of tosses (e) to get
n
consecutive heads(n≥1)
:$$e_n=2(2^n1)$$
For example, to get 5 consecutive heads, we've to toss the coin 62 times:
$$e_n=2(2^51)=62$$
We can also start with the longer analysis of the 5 scenarios:
 If we get a tail immediately (probability 1/2) then the expected number is e+1.
 If we get a head then a tail (probability 1/4), then the expected number is e+2.
 If we get two head then a tail (probability 1/8), then the expected number is e+2.
 If we get three head then a tail (probability 1/16), then the expected number is e+4.
 If we get four heads then a tail (probability 1/32), then the expected number is e+5.
 Finally, if our first 5 tosses are heads, then the expected number is 5.
Thus:
$$e=\frac{1}{2}(e+1)+\frac{1}{4}(e+2)+\frac{1}{8}(e+3)+\frac{1}{16}\\(e+4)+\frac{1}{32}(e+5)+\frac{1}{32}(5)=62$$
We can also generalise the formula to:
$$e_n=\frac{1}{2}(e_n+1)+\frac{1}{4}(e_n+2)+\frac{1}{8}(e_n+3)+\frac{1}{16}\\(e_n+4)+\cdots +\frac{1}{2^n}(e_n+n)+\frac{1}{2^n}(n) $$


www.cmu.edu www.cmu.edu

Fischhoff, B., de Bruin, W. B., Güvenç, Ü., Caruso, D., & Brilliant, L. (2006). Analyzing disaster risks and plans: An avian flu example. Journal of Risk and Uncertainty, 33(1–2), 131–149. https://doi.org/10.1007/s1116600601758

 Jan 2020

levels.io levels.io

My friend Marc again to the rescue. He suggested that since there was 10,000+ people RT'ing and following, I could just pick a random follower from my current total follower list (78,000 at this point), then go to their profile to check if they RT'd it and see. If they didn't, get another random follower and repeat, until you find someone. With 78,000 followers this should take about ~8 tries.
Technically he said it would be random among those who retweeted, but he's chose a much smaller subset of people who are BOTH following him and who retweeted it. Oops!
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 Dec 2019

en.wikipedia.org en.wikipedia.org

the exponential distribution is the probability distribution of the time between events in a Poisson point process, i.e., a process in which events occur continuously and independently at a constant average rate.


colinwalker.blog colinwalker.blog

Many people luck out like me, accidentally. We recognize what particular path to mastery we’re on, long after we actually get on it.
Far too many people luck out this way and we all perceive them as magically talented when in reality, they're no better than we, they just had better circumstances or were in the right place at the right time.

 Nov 2019

parenting.nytimes.com parenting.nytimes.com

How this phenomenon translates into absolute, rather than relative, risk, however, is a bit thorny. A large study published in 2018, for instance, found that among women who had children between 34 and 47, 2.2 percent developed breast cancer within three to seven years after they gave birth (among women who never had children, the rate was 1.9 percent). Over all, according to the American Cancer Society, women between 40 and 49 have a 1.5 percent chance of developing breast cancer.
The rates here are so low as to be nearly negligible on their face. Why bother reporting it?

 Mar 2019

complexityzoo.uwaterloo.ca complexityzoo.uwaterloo.ca

Special Complexity Zoo Exhibit: Classes of Quantum States and Probability Distributions 24 classes and counting! A whole new phylum of the Complexity kingdom has recently been identified. This phylum consists of classes, not of problems or languages, but of quantum states and probability distributions. Well, actually, infinite families of states and distributions, one for each number of bits n. Admittedly, computer scientists have been talking about the complexity of sampling from probability distributions for years, but they haven't tended to organize those distributions into classes designated by inscrutable sequences of capital letters. This needs to change.

 Feb 2019

paleorxiv.org paleorxiv.org

n they will share similar genes, but it 18is the phenotype –upon which selection acts –which is crucia
There two important things to note.
If the same genetic programme leads to two phenotypes because of the environment, this falls in the category of epigenetics. Epigenetic processes are usually not treelike, hence, poorly modelled by inferring a tree.
You implicitly assume (via your Rscript) that homoiologies (in a strict sense, i.e. parallelism) are rare and not beneficial (neutral). But if the homoiology is beneficial (i.e. positively selected for), it will be much more common in a clade of close relatives than the primitive phenotype (the symplesiomorphy). We can further assume that beneficial homoiologies will accumulate in the mostderived, advanced, specialised taxa, in the worst case (from the mainstream cladistic viewpoint) mimicking or even outcompeting synapomorphies. A simply thought example: let's say we have a monophylum (fide Hennig) with two sublineages, each sublineage defined by a single synapormorphy. Both sublineages radiate and invade in parallel a new niche (geographically separated from each other) and fix (evolve) a set of homoiologies in adaptation to that new niche. The members of both sublineages with the homoiologies will be resolved as one clade, a pseudomonophylum, supported by the homoiologies as pseudosynapomorphies. And the actual synapomorphies will be resolved as plesiomorphies or autapomorphies.
Without molecular (and sometime even with, many molecular trees are based on plastid in plants and mitochondria in animals, and both are maternally inherited, hence, geographically controlled) or ontologicalphysiological control it will be impossible to make a call what is derived (hence a potential homoiology) and what ancestral in a group of organisms sharing a relative recent common origin and a still similiar genetic programme.

 Oct 2018

chem.libretexts.org chem.libretexts.org

In contrast to his concept of a simple circular orbit with a fixed radius, orbitals are mathematically derived regions of space with different probabilities of having an electron.
In this case, the QM model allows for probabilistic radii, not fixed radii, and the quantization is the energy level. An electron with principal quantum number n = 2 will always have quantized energy corresponding to \( E = R(1/n^2) \), but the exact minimal and maximal radial distance from the nucleus is not specified as in the Bohr model of the atom. Similar to the Bohr model though, the most probable radial distance is quantifiable, and that is the radius the electron is most likely to inhabit, however it will be found elsewhere at other times.

 Sep 2017

thebulletin.org thebulletin.org

Terrorist use of an actual nuclear bomb is a lowprobability event
Low probability and high impact but not a black swan

 Feb 2017

static1.squarespace.com static1.squarespace.com

These two qualities, therefore, PROBABILITY and PLAUSIBILITY
This is an important set of terms to think through in terms of come to think about and with rhetoric.

CHAPTER VI
Chapter VII: General Audience Awareness
But, really, Mere Rhetoric has a nice (I'm assuming she's mostly on point here) summary of some of the concepts to follow.

 Jan 2017

static1.squarespace.com static1.squarespace.com

Hume considers the possibility that there is, indeed, complete relativism in this matter. But his purpose is to find ways to reduce or eliminate disagreement, to set a standard
A rhetorical concern dating back to at least Aristotle: how to decide upon things in the realm of the probable rather than the absolute.

 Nov 2016

journals.plos.org journals.plos.org

Finally, by assuming the nondetection of a species to indicate absence from a given grid cell, we introduced an extra level of error into our models. This error depends on the probability of false absence given imperfect detection (i.e., the probability that a species was present but remained undetected in a given grid cell [73]): the higher this probability, the higher the risk of incorrectly quantifying speciesclimate relationships [73].
This will be an ongoing challenge for species distribution modeling, because most of the data appropriate for these purposes is not collected in such a way as to allow the straightforward application of standard detection probability/occupancy models. This could potentially be addressed by developing models for detection probability based on species and habitat type. These models could be built on smaller/different datasets that include the required data for estimating detectability.

 Jul 2016

www.quantamagazine.org www.quantamagazine.org

hyperuniform distribution  Appears random at smaller scales, but more predictable at larger scales.

 Feb 2016

blog.cloudera.com blog.cloudera.com

Great explanation of 15 common probability distributions: Bernouli, Uniform, Binomial, Geometric, Negative Binomial, Exponential, Weibull, Hypergeometric, Poisson, Normal, Log Normal, Student's t, ChiSquared, Gamma, Beta.

 Jan 2016

blogs.scientificamerican.com blogs.scientificamerican.com

P(BE) = P(B) X P(EB) / P(E), with P standing for probability, B for belief and E for evidence. P(B) is the probability that B is true, and P(E) is the probability that E is true. P(BE) means the probability of B if E is true, and P(EB) is the probability of E if B is true.


phys.org phys.org

paradox of unanimity  Unanimous or nearly unanimous agreement doesn't always indicate the correct answer. If agreement is unlikely, it indicates a problem with the system.
Witnesses who only saw a suspect for a moment are not likely to be able to pick them out of a lineup accurately. If several witnesses all pick the same suspect, you should be suspicious that bias is at work. Perhaps these witnesses were cherrypicked, or they were somehow encouraged to choose a particular suspect.

 Oct 2015


Nearly all applications of probability to cryptography depend on the factor principle (or Bayes’ Theorem).
This is easily the most interesting sentence in the paper: Turing used Bayesian analysis for codebreaking during WWII.

 Oct 2013

rhetoric.eserver.org rhetoric.eserver.org

Now the propositions of Rhetoric are Complete Proofs, Probabilities, and Signs.
From chapter 2.


rhetoric.eserver.org rhetoric.eserver.org

the materials of enthymemes are Probabilities and Signs


rhetoric.eserver.org rhetoric.eserver.org

The premisses from which enthymemes are formed are "probabilities" and "signs"
