TensionThe ability to see like a data structure afforded us the technology we have today. But it was built for and within a set of societal systems—and stories—that can’t cope with nebulosity. Worse still is the transitional era we’ve entered, in which overwhelming complexity leads more and more people to believe in nothing. That way lies madness. Seeing is a choice, and we need to reclaim that choice. However, we need to see things and do things differently, and build sociotechnical systems that embody this difference.This is best seen through a small example. In our jobs, many of us deal with interpersonal dynamics that sometimes overwhelm the rules. The rules are still there—those that the company operates by and laws that it follows—meaning there are limits to how those interpersonal dynamics can play out. But those rules are rigid and bureaucratic, and most of the time they are irrelevant to what you’re dealing with. People learn to work with and around the rules rather than follow them to the letter. Some of these might be deliberate hacks, ones that are known, and passed down, by an organization’s workers. A work-to-rule strike, or quiet quitting for that matter, is effective at slowing a company to a halt because work is never as routine as schedules, processes, leadership principles, or any other codified rules might allow management to believe.The tension we face is that on an everyday basis, we want things to be simple and certain. But that means ignoring the messiness of reality. And when we delegate that simplicity and certainty to systems—either to institutions or increasingly to software—they feel impersonal and oppressive. People used to say that they felt like large institutions were treating them like a number. For decades, we have literally been numbers in government and corporate data structures. BreakdownAs historian Jill Lepore wrote, we used to be in a world of mystery. Then we began to understand those mysteries and use science to turn them into facts. And then we quantified and operationalized those facts through numbers. We’re currently in a world of data—overwhelming, human-incomprehensible amounts of data—that we use to make predictions even though that data isn’t enough to fully grapple with the complexity of reality.How do we move past this era of breakdown? It’s not by eschewing technology. We need our complex socio-technical systems. We need mental models to make sense of the complexities of our world. But we also need to understand and accept their inherent imperfections. We need to make sure we’re avoiding static and biased patterns—of the sort that a state functionary or a rigid algorithm might produce—while leaving room for the messiness inherent in human interactions. Chapman calls this balance “fluidity,” where society (and really, the tech we use every day) gives us the disparate things we need to be happy while also enabling the complex global society we have today.

But that’s not the case for a computer, or a robot, or even a corporate food service, which can’t navigate the intricacies and uncertainties of the real world with the flexibility we expect of a person. And at an even larger scale, our societal systems, whether we’re talking about laws and governments or just the ways our employers expect us to get our jobs done, don’t have that flexibility built into them. We’ve seen repeatedly how breaking corporate or government operations into thousands of disparate, rigid contracts ends in failure.

Advocating for the great booksidea, then, could mean fighting against anti-intellectualism, antira-tionalism (i.e., the reliance on ideology), and “agnotology.”

Reflecting on this, I'm reminded of a pattern that has been evident since my days co-running Third Wave with Johannes: the adoption of Uncertainty Coping Strategies. Broadly speaking, these are various behaviors, products, and practices people employ to manage the strains of everyday reality. Our work has consistently identified a spectrum ranging from technological interventions like neuroimplants to the rise in mindfulness services.The staggering contrasts in how different socioeconomic groups address these pressures are well illustrated by the recent New York Times article.

This is one of the challenges of being reactive to the public mood, rather than shaping it. Donald Trump, too, launched his first presidential campaign by elevating arguments and rhetoric from right-wing media, but he also shaped what the media was talking about. DeSantis has largely followed the trends, and the trends shift.

when we step into uncertainty, our bodies respond physiologically and mentally.

“Biaya paling mahal hidup di Indonesia adalah biaya ketidakpastian (uncertainties),”

The Hawthorne effect is a type of human behavior reactivity

Note 9/8j says - "There is a note in the Zettelkasten that contains the argument that refutes the claims on every other note. But this note disappears as soon as one opens the Zettelkasten. I.e. it appropriates a different number, changes position (or: disguises itself) and is then not to be found. A joker." Is he talking about some hypothetical note? What did he mean by disappearing? Can someone please shed some light on what he really meant?

We analyzed URLs cited in Twitter messages before and after the temporary interruption of the vaccine development on September 9, 2020 to investigate the presence of low credibility and malicious information. We show that the halt of the AstraZeneca clinical trials prompted tweets that cast doubt, fear and vaccine opposition. We discovered a strong presence of URLs from low credibility or malicious websites, as classified by independent fact-checking organizations or identified by web hosting infrastructure features. Moreover, we identified what appears to be coordinated operations to artificially promote some of these URLs hosted on malicious websites.

We found that users who followed elites who made more false or inaccurate statements themselves shared news from lower-quality news outlets (as judged by both fact-checkers and politically-balanced crowds of laypeople), used more toxic language, and expressed more moral outrage.

We found that misinformation-exposure scores are significantly positively related to language toxicity (Fig. 3a; b = 0.129, 95% CI = [0.098, 0.159], SE = 0.015, t (4121) = 8.323, p < 0.001; b = 0.319, 95% CI = [0.274, 0.365], SE = 0.023, t (4106) = 13.747, p < 0.001 when controlling for estimated ideology) and expressions of moral outrage (Fig. 3b; b = 0.107, 95% CI = [0.076, 0.137], SE = 0.015, t (4143) = 14.243, p < 0.001; b = 0.329, 95% CI = [0.283,0.374], SE = 0.023, t (4128) = 14.243, p < 0.001 when controlling for estimated ideology). See Supplementary Tables 1, 2 for full regression tables and Supplementary Tables 3–6 for the robustness of our results.

The style is one that is now widely recognized as a tool of sowing doubt: the author just asked ‘reasonable’ questions, without making any evidence-based conclusions.Who is the audience of this story and who could potentially be targeted by such content? As Bratich argued, 9/11 represents a prototypical case of ‘national dissensus’ among American individuals, and an apparently legitimate case for raising concerns about the transparency of the US authorities13. It is indicative that whoever designed the launch of RT US knew how polarizing it would be to ask questions about the most painful part of the recent past.

In the Irish sample, the combined vaccine hesitant and resistant group differed most pronouncedly from the vaccine acceptance group on the following psychological variables: lower levels of trust in scientists (d = 0.51), health care professionals (d = 0.45), and the state (d = 0.31); more negative attitudes toward migrants (d’s ranged from 0.27 to 0.29); lower cognitive reflection (d = 0.25); lower levels of altruism (d’s ranged from 0.17 to 0.24); higher levels of social dominance (d = 0.22) and authoritarianism (d = 0.14); higher levels of conspiratorial (d = 0.21) and religious (d = 0.20) beliefs; lower levels of the personality trait agreeableness (d = 0.15); and higher levels of internal locus of control (d = 0.14).

The only negative to this method is that it may not ALWAYS work. If the data is faulty, or the link is inaccurately provided by the sender, Gmail won’t be able to recognise and include the unsubscribe button in Gmail.

You may find this link isn’t available straight away, after a few emails one should appear, this is a common technique with mailing list providers.

I often think back to MySpace’s downfall. In 2007, I penned a controversial blog post noting a division that was forming as teenagers self-segregated based on race and class in the US, splitting themselves between Facebook and MySpace. A few years later, I noted the role of the news media in this division, highlighting how media coverage about MySpace as scary, dangerous, and full of pedophiles (regardless of empirical evidence) helped make this division possible. The news media played a role in delegitimizing MySpace (aided and abetted by a team at Facebook, which was directly benefiting from this delegitimization work).

Trope, trope, trope, strung into a Gish Gallop.

Today, the people in politics who most often invoke the name of Jesus for their political causes tend to be the most merciless and judgmental, the most consumed by rage and fear and vengeance. They hate their enemies, and they seem to want to make more of them. They claim allegiance to the truth and yet they have embraced, even unwittingly, lies. They have inverted biblical ethics in the name of biblical ethics.

Your Alternator Is Not A ChargerYes, you read that right. An alternator is designed to MAINTAIN your car battery, not recharge it from a deeply discharged state. Its designed to run at only 35-50% of its fully rated output. When you use your cars alternator to recharge a dead battery, you force it to run at nearly 100% of its rated capacity and that overheats the alternator and dramatically shortens its life.

But this sounds like spreading fear and doubt when the Ruby parser has no such concepts :) {} always binds tightly to the call right next to it. This block {} will never go to using, unless it's rewritten as do ... end.

We observed an overall increase in the amount of negative information as it passed along the chain—known as the social amplification of risk.

The request might or might not eventually be acted upon, as it might be disallowed when processing actually takes place.

that's something that insect with a six-legged version is now it's the whole super organism oh well the ant colony is society that whole frame is actually the shadow of 01:18:52 what the evolutionary reality is which is that the ant colony is an organism not a super organism and the ants are tissues and so which level we prioritize or do we say no there's no a priori level ant is just i'm not even 01:19:05 gonna say there's anything out there called ants it's it's you how you're thinking about it or do we get lost or are we going to find a ladder in that multi-scale yeah well the latter is you know the 01:19:17 latter is active inference because it doesn't say active inference doesn't say make a make an internal model of the world that is accurate that actually accurately captures all the all the 01:19:30 details of the world of the universe that's not the point that's not what the mind does that's not the point the point is to act under uncertainty 01:19:41 given some useful model of the world act under uncertainty so that your fitness score improves and by fitness score here we essentially mean you know and anticipated uncertainty so i i would 01:19:55 very much like to be have some certainty that i'm going to be alive tomorrow and if it's freezing outside and i don't have a coat on uh you know that that becomes iffy so uh i'm going to be happy 01:20:06 if i'm going to be i'm going to go find a coat because it is going to reduce my uncertainty about survival over the next 24 hours but you can expand that you know outward right we we need to act the all organisms are acting under 01:20:19 uncertainty and and we can think about that as we can think about that um we can think from that perspective as a society of what are we doing and how do we measure 01:20:31 success well we're measuring success by acting under uncertainty and then re and then paying attention to what happens and then acting the same or differently or you know some other way or somehow some were 01:20:45 then choosing to act again in this cycle of act uh you know act uh process act process act process you know model act model act model that reminds me of course of the ooda 01:20:57 observe orient side act model and other sort of cyclic models of action and perception and then i would say that active inference provides a few nice little benefits over other phrasings of action and perception 01:21:10 qualitative and philosophical ones like inactivism as well as quantitative ones like cybernetics and other kinds of control theories so i totally agree this

This fundamental tension—between quality and quantity—is atension we share as knowledge workers. We also must producework to an extremely high standard, and we must do it fast,continuously, all year long. We are like sprinters who are also tryingto run a marathon.

It’s as if we need the gravitational pull of both worlds to keep us on track, locked on a good and righteous path. Without both worlds pulling on us, we would crash into one, or simply lose our way, hurtling through the universe on our own, intersecting nothing, helping no one.

Goodhart's law is an adage often stated as "When a measure becomes a target, it ceases to be a good measure".[1] It is named after British economist Charles Goodhart, who advanced the idea in a 1975 article on monetary policy in the United Kingdom:[2][3] .mw-parser-output .templatequote{overflow:hidden;margin:1em 0;padding:0 40px}.mw-parser-output .templatequote .templatequotecite{line-height:1.5em;text-align:left;padding-left:1.6em;margin-top:0}Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes.

we find that fixing fossil ages to the midpoint or a random point drawn from within the stratigraphic age range leads to biases in divergence time estimates, while sampling fossil ages leads to estimates that are similar to inferences that employ the correct ages of fossils. Second, we show a comparison using an empirical dataset of extant and fossil cetaceans, which confirms that different methods of handling fossil age uncertainty lead to large differences in estimated node ages

Stop trying to connect all the dots ahead of time. Embrace uncertainty and start doing. “You can’t connect the dots looking forward; you can only connect them looking backwards. So you have to trust that the dots will somehow connect in your future. You have to trust in something — your gut, destiny, life, karma, whatever. This approach has never let me down, and it has made all the difference in my life." - Steve Jobs

Before you go like “Wow!!!”, understand that the packages highlighted above take a lot into consideration when detecting timezones. This makes them slightly more accurate than Intl API alone.

Career decision making involves so much uncertainty that it’s easy to feel paralysed. Instead, make some hypotheses about which option is best, then identify key uncertainties: what information would most change your best guess?

think of your career as a series of experiments designed to help you learn about yourself and test out potentially great longer-term paths

In object-oriented programming, information hiding (by way of nesting of types) reduces software development risk by shifting the code's dependency on an uncertain implementation (design decision) onto a well-defined interface. Clients of the interface perform operations purely through it so if the implementation changes, the clients do not have to change.

If you have a better/simpler/"more official" solution, I'd still love to see it!

The Y-intercept of the SML is equal to the risk-free interest rate. The slope of the SML is equal to the market risk premium and reflects the risk return tradeoff at a given time: S M L : E ( R i ) = R f + β i [ E ( R M ) − R f ] {\displaystyle \mathrm {SML} :E(R_{i})=R_{f}+\beta _{i}[E(R_{M})-R_{f}]\,} where: E(Ri) is an expected return on security E(RM) is an expected return on market portfolio M β is a nondiversifiable or systematic risk RM is a market rate of return Rf is a risk-free rate

This abnormal extra return above the market's return at a given level of risk is what is called the alpha.

Capital asset pricing model

quantity beta (β)

If the fraction q {\displaystyle q} of a one-unit (e.g. one-million-dollar) portfolio is placed in asset X and the fraction 1 − q {\displaystyle 1-q} is placed in Y, the stochastic portfolio return is q x + ( 1 − q ) y {\displaystyle qx+(1-q)y} . If x {\displaystyle x} and y {\displaystyle y} are uncorrelated, the variance of portfolio return is var ( q x + ( 1 − q ) y ) = q 2 σ x 2 + ( 1 − q ) 2 σ y 2 {\displaystyle {\text{var}}(qx+(1-q)y)=q^{2}\sigma _{x}^{2}+(1-q)^{2}\sigma _{y}^{2}} . The variance-minimizing value of q {\displaystyle q} is q = σ y 2 / [ σ x 2 + σ y 2 ] {\displaystyle q=\sigma _{y}^{2}/[\sigma _{x}^{2}+\sigma _{y}^{2}]} , which is strictly between 0 {\displaystyle 0} and 1 {\displaystyle 1} . Using this value of q {\displaystyle q} in the expression for the variance of portfolio return gives the latter as σ x 2 σ y 2 / [ σ x 2 + σ y 2 ] {\displaystyle \sigma _{x}^{2}\sigma _{y}^{2}/[\sigma _{x}^{2}+\sigma _{y}^{2}]} , which is less than what it would be at either of the undiversified values q = 1 {\displaystyle q=1} and q = 0 {\displaystyle q=0} (which respectively give portfolio return variance of σ x 2 {\displaystyle \sigma _{x}^{2}} and σ y 2 {\displaystyle \sigma _{y}^{2}} ). Note that the favorable effect of diversification on portfolio variance would be enhanced if x {\displaystyle x} and y {\displaystyle y} were negatively correlated but diminished (though not eliminated) if they were positively correlated.

Similarly, a 1985 book reported that most value from diversification comes from the first 15 or 20 different stocks in a portfolio.[6]

This balancing act needs to take into account project complexity (size, distribution, etc.), uncertainty (risk, innovation need, etc.), and the cost of change at the project level and for each major component.

Disclaimer: I’m new to Svelte so this isn’t so much a recommendation as it is a “I guess this is a way to do it 🤷‍♂️”

The challenge is to find a way to live with uncertainty,