Hollywood as a model for academic research

Academia has a problem: the value, necessity, and practices of collaboration are increasing, but the system of giving credit is inadequate. In most fields, there are only 4 levels of credit:

  • None at all
  • “Our thanks to Jill for sharing her data.” (a note of thanks)
  • First Authorship (This is ambiguous: it may be alphabetical.)
  • Listed as another author

In contrast to this paucity, modern empirical paper writing has many roles. Here are a dozen roles. Not all of them are important on a single paper, but each of them is important in some papers.

  • Intellectual leadership.
    • Source of the original idea
  • Doing the writing
    • Writing various parts, e.g. literature review
    • Doing the grunt work on the stat analysis. (Writing and running the R code)
    • Doing the grunt work of finalizing for publication. (Much easier than it used to be!)
    • Dealing with revisions, exchanges with editors, etc.
  • Source of the data.
    • Funder of the data
  • Raised the funding;
    • Runs the lab where the authors are employed
    • Source of the money: usually an agency or foundation, but sometimes the contracting author is listed as a coauthor.

Continue reading


Is high-speed Internet pointless​? No.

A contributor to Dave Farber’s IP (“Important People” list) recently stated that 1 Megabit per second  (Mbps) is adequate bandwidth for consumers. This compares to “high speed Internet” which in the US is 20 Mbps or higher, and Korea where speeds over 50 Mbps are common.

My response: 1 Mbps is woefully low for any estimate of “useful bandwidth” to an individual, much less to a home. It’s risky to give regulators an any excuse to further ignore consumer desires for faster connections.  1 Mbps is too low by at least one order of magnitude, quite likely by three orders of magnitude, and conceivably by even more. I have written this note in an effort to squash the 1Mbps idea in case it gets “out into the world.”

The  claim that 1 Megabit per second is adequate:

>From: Brett Glass <brett@lariat.net>
>Date: Sun, Dec 31, 2017 at 2:14 PM

> The fact is that, according to neurophysiologists, the entire bandwidth of
> all of the human senses combined is about 1 Mbps. (Some place it slightly
> higher, at 1.25 Mbps.) Thus, to completely saturate all inputs to the human
> nervous system, one does not even need a T1 line – much less tens of megabits.
> And therefore, a typical household needs nowhere near 25 Mbps – even if they
> were all simultaneously immersed in high quality virtual reality. Even the

My response:

First, I don’t know where the 1Mbps number comes from, but a common number is the bandwidth of the optic nerve, which is generally assessed at around 10Mbps. See references.



An American Scientist article on “How the Retina Works” is available here.


Second, a considerable amount of pre-processing occurs in the retina and the layer under the retina, before reaching the optic nerve. These serve as the first layers of a neural network, and handle issues like edge detection.

Continue reading

Irrational tax cuts won’t raise long term economic growth

I recently received the following on a Dave Farber’s “Interesting People” list, a collection of techies with interest in Internet policy issues. Why discuss it now, since the tax bill has been passed? It is important for all to realize how much the Republicans in Washington no longer believe in basing their decisions on  reality  (“facts”). It is very hard to believe this, but the evidence is now overwhelming, and the consequences will continue to be grave. I wrote the following quick response.

It seems completely reasonable and even desirable to take actions such as lowering corp taxes,  lowering taxes on productivity and reducing regulation to get the economy growing at the 3-4%  range.

Continue reading

Some of my harder to find papers

Accelerated Learning by Experimentation


In most technologies and most industries, experiments play a central role in organizational learning as a source of knowledge and as a check before changes are implemented. There are four primary types of experiments: controlled, natural, ad-hoc, and evolutionary operation. This paper discusses factors that affect learning by experimentation and how they influence learning rates. In some cases, new ways of experimenting can create an order of magnitude improvement in the rate of learning. On the other hand, some situations are inherently hard to run experiments on, and therefore learning remains slow until basic obstacles are solved. Examples of experimentation are discussed in four domains: product development, manufacturing, consumer marketing, and medical trials.

Keywords: Learning, Experimentation

Full published version:    Bohn Accelerated Learning by Experimentation.    in Learning Curves: Theory, Models, and Applications  edited by Mohamad Y. Jaber, CRC Press, 2011.
Preprint version, through SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1640767.

This is a rewritten version of my 1987 paper about manufacturing. Although I never published that paper, it was very influential in the 1990s, including becoming the (plagiarized) framework of a book and several articles by Stefan Thomke.  This book chapter expands and extends the original concepts, including new material from Michael Lapré.

Fraudulent academic journals are growing

Gina Kolata in the NY Times has been running a good series of articles on fraudulent academic publishing. The basic business model is an unholy alliance between academics looking to enhance their resumes, and quick-buck internet sites. Initially, I thought these sites were enticing naive academics. But many academics are apparently willing participants, suggesting that it’s  easy to fool many promotion and award committees.

All but one academic in 10 who won a School of Business and Economics award had published papers in these journals. One had 10 such articles.

Continue reading

What snakes are growing in the Gardens of Technological Eden?

Two emerging technologies are revolutionizing industries, and will soon have big impacts on our health, jobs, entertainment, and entire lives. They are Artificial Intelligence, and Big Data. Of course, these have already had big effects in certain applications, but I expect that they will become even more important as they improve. My colleague Dr. James Short is putting together a conference called Data West at the San Diego Supercomputer Center, and I came up with a list of fears that might disrupt their emergence.

1) If we continue to learn that ALL large data repositories will be hacked from time to time (Experian; National Security Agency), what blowback will that create against data collection? Perhaps none in the US, but in some other countries, it will cause less willingness to allow companies to collect consumer data.

2) Consensual reality is unraveling, mainly as a result of deliberate, sophisticated, distributed, attacks. That should concern all of us as citizens. Should it also worry us as data users, or will chaos in public venues not leak over into formal data? For example, if information portals (YouTube, Facebook, etc.) are forced to take a more active role in censoring content, will advertisers care? Again, Europe may be very different. We can presume that any countermeasures will only be partly effective – the problem probably does not have a good technical solution.

3) Malware, extortion, etc. aimed at companies. Will this “poison the well” in general?

4) Malware, extortion, doxing, etc. aimed at Internet of Things users, such as household thermostats, security cameras, cars. Will this cause a backlash against sellers of these systems, or will people accept it as the “new normal.” So far, people have seemed willing to bet that it won’t affect them personally, but will that change. For example, what will happen when auto accidents are caused by deliberate but unknown parties who advertise their success? When someone records all conversations within reach of the Alexa box in the living room?

Each of these scenarios has at least a 20% chance of becoming common. At a minimum, they will require more spending on defenses. Will any become large enough to suppress entire applications of these new technologies?

I have not said anything about employment and income distribution. They may change for the worse over the next 20 years, but the causes and solutions won’t be simple, and I doubt that political pressure will become strong enough to alter technology evolution.