> And the article is written in the most neutral tone possible,
I disagree: Instead the article is written with lots of adjectives instead of numerical quantities.
E.g., in the title of the article, there is "Plunged".
I object to "Plunged": When I was in college in math and physics, in grad school for my Ph.D. in pure/applied math, and taught optimization in an MBA program, I never used plunged. When I taught college calculus, I did a lot with derivatives that showed quantities increasing or decreasing but never used plunged. In my opinion, that word is intended to give the article, say, an emotional kick instead of
> the most neutral tone possible.
Other uses of adjectives I don't like include
"sinking,
surging,
easing,
shrinking",
etc. When I pleased the highly concerned BoD and, thus, saved FedEx with some revenue projections from the differential equation
y'(t) = k y(t) (b - y(t))
I presented a clear graph with clear numerical quantities and never used any adjectives, e.g., surged. Uh, here t was time, in days, y(t) was revenue at time t, b was the maximum revenue from earlier estimates of the market size, and k was, as is common in calculus, a constant of proportionality, and y'(t) is the calculus first derivative, that is, the rate of growth of revenue in revenue per day. So, the assumption is that at each time t revenue will grow directly proportional to current number of happy customers and also the current number target customers not yet customers. Then, sure, the growth curve will be a lazy S approaching the maximum market size b asymptotically from below. The BoD cared.
I can't get anything meaningful from those adjectives. Instead I want to see numerical data, and for those uses of those adjective, the numerical data graphed over time.
Such uses of adjectives remind me of short stories in high school English class and a children's movie where a teacher assures that class
"adjectives are important in ANY writing".
The article has
> In 2019, the average cost to send a container across the Pacific to the U.S. West Coast was $1,500.
I doubt that anyone can do much with that one number $1,500. But a graph of the rates, or even the average rates in some version of average, from 2019, or some years before, to the present should be good progress understanding how rates have changed.
And still better would be, say, a good fitting regression model with independent variables from promising, common macro economic variables and variables particular to the container shipping industry -- that might let us make more sense out of rate variations, that is, identify causes (for a loose definition of cause) and make some progress predicting rates in the future. Gee, last time I heard, even Microsoft's spreadsheet software Excel would do the arithmetic for regression models!
Once a world class mathematician specializing in stochastic processes reviewed my work predicting the expected number of surviving US missile firing submarines in a scenario of global nuclear war limited to sea. So, I had a model and some real data and gave expected values over time, including in a graph. The mathematician had an objection and I reminded him of how to make an application of the strong law of large numbers for bounded, independent, identically distributed (i.i.d.) random variables, and he agreed with me with "that's a good way to look at it". So, with some more analysis, it should be possible to make some useful predictions of shipping rates over time -- the article could be improved by doing such.
For the "uncertainty" mentioned in the article, my Ph.D. dissertation was in best decision making over time under uncertainty, stochastic optimal control, and that might be relevant to customers of the container shipping industry. The application I made at the time had to do with varying oil prices -- hmm ...! So, the article could be improved not just mentioning uncertainty but making progress on doing something about it.
Costas Paris, "Ocean Shipping Rates Have Plunged 60% This Year", WSJ, Sept. 5, 2022 7:03 am ET, https://www.wsj.com/articles/ocean-shipping-rates-have-plung...
For
> There's a graph, starting in Feb 2020
I saw no graph.
For
> And the article is written in the most neutral tone possible,
I disagree: Instead the article is written with lots of adjectives instead of numerical quantities.
E.g., in the title of the article, there is "Plunged".
I object to "Plunged": When I was in college in math and physics, in grad school for my Ph.D. in pure/applied math, and taught optimization in an MBA program, I never used plunged. When I taught college calculus, I did a lot with derivatives that showed quantities increasing or decreasing but never used plunged. In my opinion, that word is intended to give the article, say, an emotional kick instead of
> the most neutral tone possible.
Other uses of adjectives I don't like include
"sinking, surging, easing, shrinking",
etc. When I pleased the highly concerned BoD and, thus, saved FedEx with some revenue projections from the differential equation
y'(t) = k y(t) (b - y(t))
I presented a clear graph with clear numerical quantities and never used any adjectives, e.g., surged. Uh, here t was time, in days, y(t) was revenue at time t, b was the maximum revenue from earlier estimates of the market size, and k was, as is common in calculus, a constant of proportionality, and y'(t) is the calculus first derivative, that is, the rate of growth of revenue in revenue per day. So, the assumption is that at each time t revenue will grow directly proportional to current number of happy customers and also the current number target customers not yet customers. Then, sure, the growth curve will be a lazy S approaching the maximum market size b asymptotically from below. The BoD cared.
I can't get anything meaningful from those adjectives. Instead I want to see numerical data, and for those uses of those adjective, the numerical data graphed over time.
Such uses of adjectives remind me of short stories in high school English class and a children's movie where a teacher assures that class
"adjectives are important in ANY writing".
The article has
> In 2019, the average cost to send a container across the Pacific to the U.S. West Coast was $1,500.
I doubt that anyone can do much with that one number $1,500. But a graph of the rates, or even the average rates in some version of average, from 2019, or some years before, to the present should be good progress understanding how rates have changed.
And still better would be, say, a good fitting regression model with independent variables from promising, common macro economic variables and variables particular to the container shipping industry -- that might let us make more sense out of rate variations, that is, identify causes (for a loose definition of cause) and make some progress predicting rates in the future. Gee, last time I heard, even Microsoft's spreadsheet software Excel would do the arithmetic for regression models!
Once a world class mathematician specializing in stochastic processes reviewed my work predicting the expected number of surviving US missile firing submarines in a scenario of global nuclear war limited to sea. So, I had a model and some real data and gave expected values over time, including in a graph. The mathematician had an objection and I reminded him of how to make an application of the strong law of large numbers for bounded, independent, identically distributed (i.i.d.) random variables, and he agreed with me with "that's a good way to look at it". So, with some more analysis, it should be possible to make some useful predictions of shipping rates over time -- the article could be improved by doing such.
For the "uncertainty" mentioned in the article, my Ph.D. dissertation was in best decision making over time under uncertainty, stochastic optimal control, and that might be relevant to customers of the container shipping industry. The application I made at the time had to do with varying oil prices -- hmm ...! So, the article could be improved not just mentioning uncertainty but making progress on doing something about it.