Azimuth on Google Plus (Part 5)

Happy New Year! I’m back from Laos. Here are seven items, mostly from the Azimuth Circle on Google Plus:

1) Phil Libin is the boss of a Silicon Valley startup. When he’s off travelling, he uses a telepresence robot to keep an eye on things. It looks like a stick figure on wheels. Its bulbous head has two eyes, which are actually a camera and a laser. On its forehead is a screen, where you can see Libin’s face. It’s made by a company called Anybots, and it costs just $15,000.


I predict that within my life we’ll be using things like this to radically cut travel costs and carbon emissions for business and for conferences. It seems weird now, but so did telephones. Future models will be better to look at. But let’s try it soon!

• Laura Sydell No excuses: robots put you in two places at once, Weekend Edition Saturday, 31 December 2011.

Bruce Bartlett and I are already planning for me to use telepresence to give a lecture on mathematics and the environment at Stellenbosch University in South Africa. But we’d been planning to use old-fashioned videoconferencing technology.

Anybots is located in Mountain View, California. That’s near Google’s main campus. Can anyone help me set up a talk on energy and the environment at Google, where I use an Anybot?

(Or, for that matter, anywhere else around there?)

2) A study claims to have found a correlation between weather and the day of the week! The claim is that there are more tornados and hailstorms in the eastern USA during weekdays. One possible mechanism could be that aerosols from car exhaust help seed clouds.


I make no claims that this study is correct. But at the very least, it would be interesting to examine their use of statistics and see if it’s convincing or flawed:

• Thomas Bell and Daniel Rosenfeld, Why do tornados and hailstorms rest on weekends?, Journal of Geophysical Research 116 (2011), D20211.

Abstract. This study shows for the first time statistical evidence that when anthropogenic aerosols over the eastern United States during summertime are at their weekly mid-week peak, tornado and hailstorm activity there is also near its weekly maximum. The weekly cycle in summertime storm activity for 1995–2009 was found to be statistically significant and unlikely to be due to natural variability. It correlates well with previously observed weekly cycles of other measures of storm activity. The pattern of variability supports the hypothesis that air pollution aerosols invigorate deep convective clouds in a moist, unstable atmosphere, to the extent of inducing production of large hailstones and tornados. This is caused by the effect of aerosols on cloud drop nucleation, making cloud drops smaller and hydrometeors larger. According to simulations, the larger ice hydrometeors contribute to more hail. The reduced evaporation from the larger hydrometeors produces weaker cold pools. Simulations have shown that too cold and fast-expanding pools inhibit the formation of tornados. The statistical observations suggest that this might be the mechanism by which the weekly modulation in pollution aerosols is causing the weekly cycle in severe convective storms during summer over the eastern United States. Although we focus here on the role of aerosols, they are not a primary atmospheric driver of tornados and hailstorms but rather modulate them in certain conditions.

Here’s a discussion of it:

• Bob Yirka, New research may explain why serious thunderstorms and tornados are less prevalent on the weekends, PhysOrg, 22 December 2011.

3) And if you like to check how people use statistics, here’s a paper that would be incredibly important if its findings were correct:

• Joseph J. Mangano and Janette D. Sherman, An unexpected mortality increase in the United States follows arrival of the radioactive plume from Fukushima: is there a correlation?, International Journal of Health Services 42 (2012), 47–64.

The title has a question mark in it, but it’s been cited in very dramatic terms in many places, for example this video entitled “Peer reviewed study shows 14,000 U.S. deaths from Fukushima”:

Starting at 1:31 you’ll see an interview with one of the paper’s authors, Janette Sherman.

14,000 deaths in the US due to Fukushima? Wow! How did they get that figure? This quote from the paper explains how:

During weeks 12 to 25 [after the Fukushima disaster began], total deaths in 119 U.S. cities increased from 148,395 (2010) to 155,015 (2011), or 4.46 percent. This was nearly double the 2.34 percent rise in total deaths (142,006 to 145,324) in 104 cities for the prior 14 weeks, significant at p < 0.000001 (Table 2). This difference between actual and expected changes of +2.12 percentage points (+4.46% – 2.34%) translates to 3,286 “excess” deaths (155,015 × 0.0212) nationwide. Assuming a total of 2,450,000 U.S. deaths will occur in 2011 (47,115 per week), then 23.5 percent of deaths are reported (155,015/14 = 11,073, or 23.5% of 47,115). Dividing 3,286 by 23.5 percent yields a projected 13,983 excess U.S. deaths in weeks 12 to 25 of 2011.

Hmm. Can you think of some potential problems with this analysis?

In the interview, Janette Sherman also mentions increased death rates of children in British Columbia. Here’s the evidence the paper presents for that:

Shortly after the report [another paper by the authors] was issued, officials from British Columbia, Canada, proximate to the northwestern United States, announced that 21 residents had died of sudden infant death syndrome (SIDS) in the first half of 2011, compared with 16 SIDS deaths in all of the prior year. Moreover, the number of deaths from SIDS rose from 1 to 10 in the months of March, April, May, and June 2011, after Fukushima fallout arrived, compared with the same period in 2010. While officials could not offer any explanation for the abrupt increase, it coincides with our findings in the Pacific Northwest.

4) For the first time in 87 years, a wild gray wolf was spotted in California:

• Stephen Messenger, First gray wolf in 80 years enters California, Treehugger, 29 December 2011.

Researchers have been tracking this juvenile male using a GPS-enabled collar since it departed northern Oregon. In just a few weeks, it walked some 730 miles to California. It was last seen surfing off Malibu. Here is a photograph:

5) George Musser left the Centre for Quantum Technologies and returned to New Jersey, but not before writing a nice blog article explaining how the GRACE satellite uses the Earth’s gravitational field to measure the melting of glaciers:

• George Musser, Melting glaciers muck up Earth’s gravitational field, Scientific American, 22 December 2011.

6) The American Physical Society has started a new group: a Topical Group on the Physics of Climate! If you’re a member of the APS, and care about climate issues, you should join this.

7) Finally, here’s a cool picture taken in the Gulf of Alaska by Kent Smith:

He believes this was caused by fresher water meeting more salty water, but it doesn’t sounds like he’s sure. Can anyone figure out what’s going on? The foam where the waters meet is especially intriguing.

34 Responses to Azimuth on Google Plus (Part 5)

  1. I saw your Google+ post and came here to read #3. I do not have the expertise to critique the report, but I would think, as a layman, that past deviations should be researched to see how often such spikes happen (if at all) and how that data affects the statistical significance of the figures from 2011.

    I would be very interested in seeing the critique of someone more knowledgeable.

    • John Baez says:

      Yes, Chris: any well-trained scientist without an axe to grind would have done some statistics to estimate the probability that this increase in deaths was a random fluctuation! Not doing this was irresponsible—and it was also irresponsible for the journal editors to publish a paper that didn’t do this!

      Instead, the authors simply assumed the fluctuation was nonrandom and divided their figure of 3286 ‘excess deaths’ by 0.235 (the fraction of deaths recorded by the Center for Disease Control) to get a figure of 13,983 ‘excess deaths’.

      It’s very easy to be more careful than this. Roko Mijic took at try at it here. I need to check his calculation, but he’s estimating that this fluctuation was 1.5 times as big as the standard deviation of the observed data.

      If so, this would mean there’s a 6.7% probability that an increase in deaths of the amount they saw was due to chance. The usual standard for a ‘statistically significant’ result is that there’s a 5% or smaller probability that it’s due to chance. From what I hear, medical journals usually don’t publish results that fail to meet this standard.

      Here I should add some caveats:

      1) When I say “if so, this would mean”, I’m assuming the rate of deaths per week is normally distributed—that is, distributed according to a Gaussian, or ‘bell curve’. People commonly assume this, just because it’s convenient, but one really should check. The data is available, so we can check. If the distribution has ‘fat tails’, it’s more likely for large fluctuations to occur.

      2) The 5% figure for statistical significance is purely arbitrary; in looking for the Higgs boson they’re waiting for a vastly smaller probability that the results could be due to chance before they announce an official result! Instead of 1.5 standard deviations, they want 5. This is typical in particle physics, where standards are much higher, because particle physics is… umm… err… so much more important than public health?

      3) There are various ways the authors might be ‘cherry picking’ the data to get a result that looks more significant than it is. For example, they count excess deaths from weeks 12 to 25 after the Fukushima incident. Why then? Did they pick this interval just because there were a lot of excess deaths then? This would be a statistical sin called significance chasing. But usually people who engage in this sin take the trouble to estimate the probability that their result is due to chance!

  2. nad says:

    concerning 3) :
    what seems immediately problematic by looking at the above cited snippet (I didn’t look into the paper) is that she looks at total deaths. The before-Fukushima increase in total deaths in cities is probably – at least in part – due to population fluctuation in cities. The post-Fukushima double-increase may similarily be due to that, that is the increase may be for example due to population changes for economic reasons and eventually even be a typical seasonal change in city population.
    It is also confusing that she looks at total deaths in 119 cities and then in 104 cities and that the period after Fukushima is related to the year 2010, but that may be a typo, so I guess to say much more one would need to look into the paper.

  3. John Baez says:

    Over on Google+, Roko Mijic wrote:

    From the paper:

    “During weeks 12 to 25, total deaths in 119 U.S. cities increased from 148,395 (2010) to 155,015 (2011), or 4.46 percent. This was nearly double the 2.34 percent rise in total deaths (142,006 to 145,324) in 104 cities for the prior 14 weeks, significant at p < 0.000001"

    So the absolute death increase that this result is based upon is (4.46%-2.34%)*148,395 = 3146 deaths.

    Now, I wonder how big the variance in death figures over a 13 week period is? Since I don’t have the time to chase up data on deaths over a 13 week period for the past 10 years, I don’t know. But there is a way to get a lower bound on it. Conveniently, they provide a table of deaths in the individual weeks 12-25. For all the weeks in 2011 and 2010 combined, the standard deviation of those weekly figures is 556 deaths. Multiplying by the square root of 13, we get an estimate of the standard deviation of the sum of 13 weeks. And the answer is: 2006 deaths.

    3146/2006 ~ 1.5, so this would be a 1.5 sigma result. Unless I have made a mistake. Somebody please check.

    • John Baez says:

      I never got around to checking this, but here’s a blog entry documenting some other serious mistakes in the paper by Joseph J. Mangano and Janette D. Sherman:

      • Michael Moyer, Are babies dying in the Pacific Northwest due to Fukushima? A look at the numbers, Observations blog, Scientific American, 21 June 2011.

      In particular, they claim a spike in infant mortality in the Pacific Northwest after Fukushima, but Moyer examined the data and got this graph:

      Read the blog entry to understand exactly what the graph means. But here’s the main point: Mangano and Sherman compare the 4 weeks right before Fukushima—the green dots—to the weeks right after it, and claim there’s a big increase in deaths!

      This is called ‘cherry-picking the data’. The blue line is Moyer’s least-squares linear fit.

      • nad says:

        Actually this reminds me of a study which investigated cancer cases in Marin county. In the study they looked at childhood Leukemia cases in seven counties in the San Francisco bay area and found:

        no evidence of a non-random spatial pattern of childhood leukemia among six of these counties. The data from San Francisco County, however, produce a moderately small significance probability (0.08)

        The study did not convince me. There is a nuclear dumb site in the water near San Francisco, near Marin county. And in a blog post I described that by boldly looking at the cancer registry (links in the blogpost) that:

        one can actually observe spatial pecularities for Marin county, i.e. the five year death counts of cancer for Marin county seem to be increased for: skin cancer, breast cancer and Leukemia (where no difference between adult/non-adult had been made). (I looked only at death counts in order to avoid errors from over-diagnostization).

        This is of course in no way a statistically sound analysis, but it looked to me as if one should look at this more carefully if this hasn’t been done in the meantime. Maybe someone could make a Sage notebook for computing statistical significance.

      • John Baez says:

        Nad wrote:

        Maybe someone could make a Sage notebook for computing statistical significance.

        That would be good to try! Alas, I’m not a sage when it comes to Sage. And you may be biased against nuclear power—this Freudian slip is a hint:

        There is a nuclear dumb site …

        • nad says:

          And you may be biased against nuclear power—this Freudian slip is a hint:

          I am currently against using nuclear power for commercial power generation and who knows eventually this may partially be a reason for the freudian slip. However I have rational reasons for this opinion and I tried to explain them for example here . Moreover a computation can be checked and discussed. In particular I didn’t say that I would do the calculations.

          And who knows – people like for example Barry Brook (I might be wrong, but I think I saw him around the Azimuth blog) would eventually like to jump in, do the investigation and document it thoroughly? Barry Brook did great charts on his blog. And as I understood by looking for example at this
          report he can’t be called biased against nuclear power.

        • nad says:

          And apart from the Marin County example, there could eventually be other locations which may be interesting for a study about correlations between nuclear waste sites and cancer.

          If you want you could e.g. have a look at statistics over here (they have some local differentiation in there) and compare them with sites mentioned in this article (where I don’t know where to find the list of the sites) (see also this post on our blog about the issue.)

        • nad says:

          Somehow it seems one hyperlink didn’t appear in the above comment, so I repeat:

          And apart from the Marin County example, there could eventually be other locations which may be interesting for a study about correlations between nuclear waste sites and cancer.

          If you want you could e.g. have a look at statistics over here (they have some local differentiation in there) and compare them with sites mentioned in this article (where I don’t know where to find the list of the sites) (see also this post on our blog about the issue.)

    • John Baez says:

      On the Azimuth Forum, Graham Jones wrote:

      Roko Mijic said this on Google+:

      Now, I wonder how big the variance in death figures over a 13 week period is? Since I don’t have the time to chase up data on deaths over a 13 week period for the past 10 years, I don’t know. But there is a way to get a lower bound on it. Conveniently, they provide a table of deaths in the individual weeks 12-25. For all the weeks in 2011 and 2010 combined, the standard deviation of those weekly figures is 556 deaths. Multiplying by the square root of 13, we get an estimate of the standard deviation of the sum of 13 weeks. And the answer is: 2006 deaths.

      I just read this on the blog and I’m replying here.

      This is not a good way to estimate the standard deviation of the weekly figures. The paper is about differences between various 14 week periods. If you combine the data for two of them, including the allegedly unusual one, your estimate for the standard deviation will likely too high, since it will be increased by differences between periods. Even if you use the periods separately, and ignore the period after Mar 2011, there may be seasonal effects within the periods which boost the estimated standard deviation. (There certainly are such effects around Xmas.)

      Like Roko Mijic, I can’t be bothered to download more data. It is not difficult to do, but it is too like my day job. Using the data from the paper, I think the best way to estimate the standard deviation, allowing for seasonal effects, is to take the two ‘before’ periods (Dec 2009 – Mar 2010 and Dec 2010 – Mar 2011), subtract corresponding weeks, and estimate the standard deviation of the differences, and then divide by sqrt(2). I get 332 for the sd of the weekly figures.

      Next, I simulated data from a normal with mean 11000 and sd 332, and found out how often the method in the paper produced a result which is more unusual than the value of the statistic they calculated in Appendix Table 4. This happens about 20% of the time for a two-sided result or 10% for a one-sided result.

      Here is my R code for you to check.

      x2010spring <- c(11010, 11097, 11075, 10712, 10940, 10549, 10637, 10389,
      10491, 10352, 9894, 10781, 10178, 10290)

      x2011spring <- c(12137, 11739, 12052, 10928, 10743, 10826, 11251, 11300,
      11132, 10839, 9538, 10770, 10981, 10779)

      x2010winter <- c(10323, 7942, 8288, 11557, 11299, 10110, 10832,
      10524, 9877, 9802, 10198, 10586, 10699, 9969)

      x2011winter <- c(10702, 8339, 8194, 11804, 10775, 10689, 10420,
      10295, 10700, 10952, 10762, 10779, 10639, 10274)

      par(mfrow=c(2,1))
      maxd <- max(x2010spring, x2011spring, x2010winter, x2011winter)
      mind <- min(x2010spring, x2011spring, x2010winter, x2011winter)
      plot(c(x2010winter,x2010spring), ylim=c(mind, maxd))
      plot(c(x2011winter,x2011spring), ylim=c(mind, maxd))

      esd <- sd(x2010winter-x2011winter)/sqrt(2)

      n <- 0
      for (i in 1:10000) {
      w0 <- sum(rnorm(14, mean=11000, sd=esd))
      w1 <- sum(rnorm(14, mean=11000, sd=esd))
      s0 <- sum(rnorm(14, mean=11000, sd=esd))
      s1 <- sum(rnorm(14, mean=11000, sd=esd))

      O <- s1/s0
      E <- w1/w0
      mean1 <- sqrt(s1)^-1 * O
      mean2 <- sqrt(s0)^-1 * E
      X 5.7) { n <- n+1 }
      cat (X, "\n")
      }

      n

  4. phorgyphynance says:

    What is “the Azimuth Circle on Google Plus”? Is there a link?

    It seems RSS is dying (or at least the writing is on the wall) and I’ll be forced to learn Google+.

    • John Baez says:

      Google Plus is a social network vaguely like Facebook. People can share information either to everyone who is a member of Google Plus, or to people in chosen “circles”. I have a circle called “Azimuth” for people who are interested in getting lots of news tidbits about energy production, enviromental issues, and the like. I also post lots of things to everyone on Google Plus, or to my “Mathematicians” circle, etcetera—typically between 2 and 20 a day.

      Is there a link?

      Yes, but I’m pretty sure things posted on Google Plus are only visible to people who are members of Google Plus. If you become a member and learn the basics of how it works, my stuff is incredibly easy to find and I’ll gladly add you to the Azimuth Circle. But not everyone is or wants to be a member of Google Plus, which is one reason I post some (but not all) of the Azimuth Circle stuff in blog articles here as well.

      • phorgyphynance says:

        I created a Google+ account a while ago when you mentioned it on the Azimuth Forum. So far you’re the only person in my circle, but Azimuth does not show up in any searches. I wonder if there’s some setting you need to set to make Azimuth appear in searches?

        Since it seems I’m talking to myself on the Azimuth Forum these days, I’d be interested in joining the Azimuth Circle however that is done.

      • John Baez says:

        Phorgyphynance wrote:

        I wonder if there’s some setting you need to set to make Azimuth appear in searches?

        As I said, I need to add you to my Azimuth Circle for you to see the stuff I post there. I did it right now, so you can take a look. I also added you to my Physicists and Mathematicians circles.

        I’d be very happy if Google+ allowed me to create a circle which other people could choose to add themselves to, but it doesn’t work that way (yet). So, I periodically announce that anyone who wants to join, can. Counting you, 186 people have.

        • reperiendi says:

          There’s a kludge that almost does what you want. Type something like #AzimuthScience at the bottom of each of your postings, and Google+ will turn it into a link. People can click on it to see all posts with that word in them—and since AzimuthScience is something uncommon, the list of posts will probably consist exclusively of yours or references to yours. People can also save that search so it’s easier to get to.

          Subscriptions are a highly-requested feature and will definitely be coming, probably this year, but in the meantime, I think that’s the least work for you.

        • reperiendi says:

          P.S. To use the kludge, you’d have to mark the posts as public, which you may not want to do.

        • John Baez says:

          Unfortunately the whole reason for the Azimuth Circle is that I don’t want to make these posts public—since I don’t want to bother all the people who have me in their circles with this material, which is sometimes technical and dry.

          My theory is that 9263 people have me in their circles because they expect I’ll provide fun, exciting posts. I think this is a good thing, so I want to make my public posts fun and exciting to a large audience. I post more technical things only to circles of people who I think will be interested.

          A subscription system, where other people get to decide what stuff of mine they read, would be very helpful.

        • nad says:

          Occupy Mountain View.

        • John Baez says:

          My Azimuth posts on Google+ are now visible to everyone who is a member of Google+: just type “Azimuth” in the search bar on Google+, and add the page “Azimuth” to your circles!

          The image for this page consists of agave plants and roses on a blue sky, just like the top of this blog.

  5. Nick says:

    A few naive observations on 7:

    It does appear to be a front, meaning a region where properties change rapidly over small distances, distinguished by fluids of two different densities.

    We also notice that the darker water on the right appears to have gravity waves of some order ( I’m not sure about the scale of the photo) whereas the fluid on the left appears to be undisturbed, by comparison.

    Surface gravity waves will generate a mean flow, known as Stokes drift. If the current generated on the RHS of the picture is different than that on the LHS, shear instabilities would occur. i.e. Kelvin-Helmholtz, see here (not sure how to embed links)

    http://en.wikipedia.org/wiki/Kelvin%E2%80%93Helmholtz_instability

    This instability would lead to turbulent mixing and hence the foam.

    If the scales are large enough geostrophic considerations must come into play and I’m not sure if instabilities could be generated in this fashion.

    It’s late here in so I’ll leave it at this but I’ll look up some relevant papers tomorrow.

    • John Baez says:

      Cool! Thanks for tackling this puzzle! You’re making me want to learn more fluid dynamics. What you’re doing looks like ‘forensic fluid dynamics’.

    • I dunno, I’ve seen similar things from airplanes frequently. I’ve always assumed that these were depth changes, e.g. end of continental shelf. Last I looked carefully, things like this are visible throughout Google Earth. (Oh and BTW, off-topic, but in the Pacific, there are thousands of atolls one can zoom in on, with sub-meter resolution, so one can see individual waves breaking on them. Fascinating. Don’t remember, maybe one can see similar dropoffs near these also.) The point is, whatever the explanation, its quite common.

      • Nick says:

        Linas,

        That is an excellent point. If we look at the left hand side of http://ngadventure.typepad.com/.a/6a00e55031d3a3883401538f0bbccd970b-500wi we see a region that could be considered vaguely similar to the picture posted above.

        However, IMO what we’re observing is not due to topographically forced wave breakers. The white water, or foam, does not seem pronounced enough nor does it have the characteristic shape of that due to a breaker. With out really knowing the scales involved it makes it difficult to say anything with certainty.

        Of course, all of this is just hand waving, or as John kindly put it, ‘forensic fluid dynamics’.

    • James Borger says:

      Hi John. I saw something like this once while sailing just outside of the San Francisco Bay. We couldn’t come up with any good explanations, but the next day someone talked to someone else who said it can happen at an ebb tide. Then all the slightly muddy, slightly fresher water pours out of the bay in something like a cone that you can see. That felt like the right explanation, and it’s similar to the glacier-melt explanation in the photo above. I don’t know if there’s a nearby bay with strong ebb and flood tides to produce a noticeable stream. If I remember the line I saw was much straighter, so perhaps not.

    • John Baez says:

      This blog entry purports to explain the unmixed waters Ken Smith photographed. Thanks to Jane Shevtsov for pointing this out!

  6. Philip Gibbs says:

    I don’t think it is enough just to look at random fluctuations in death rates. The figures can be affected by systematic influences, such as the weather. Death rates are noticeably higher during the winter than the summer so it is not unreasonable to suppose that something like a bad spell of weather compared to the previous year could account for it. Other causes could be changes in health care policy or an outbreak of flu. These things need to be ruled out before any other conclusion can be drawn.

    In any case, how could the nuclear accident cause so many deaths so quickly? You might expect some cancer deaths but these do not happen in such a short time for low doses, do they? If they did someone would have noticed the alarming increase in the rate of fast cancer deaths.

    • John Baez says:

      Philip wrote:

      I don’t think it is enough just to look at random fluctuations in death rates. The figures can be affected by systematic influences, such as the weather. Death rates are noticeably higher during the winter than the summer so it is not unreasonable to suppose that something like a bad spell of weather compared to the previous year could account for it.

      Right. They do at least compare the deaths one year with the deaths during the corresponding weeks in the previous year, which would tend to eliminate seasonal changes. But you’re right: the weather is not the same every year!

      You’re making we want to look at some graphs of death rates, month by month or even day by day. I think one would need to do this (and some more technical ‘exploratory data analysis’) before having any sense whether the supposed upswing in deaths after Fukushima looks like a real thing.

      In any case, how could the nuclear accident cause so many deaths so quickly? You might expect some cancer deaths but these do not happen in such a short time for low doses, do they? If they did someone would have noticed the alarming increase in the rate of fast cancer deaths.

      If you listen to the interview on the video, you’ll see Janette Sherman thinks low-level radiation hurts people and can kill infants and the elderly in ways other than cancer. That’s why they consider the supposed rise in sudden infant death syndrome in British Columbia to be notable:

      Shortly after the report [another paper by the authors] was issued, officials from British Columbia, Canada, proximate to the northwestern United States, announced that 21 residents had died of sudden infant death syndrome (SIDS) in the first half of 2011, compared with 16 SIDS deaths in all of the prior year. Moreover, the number of deaths from SIDS rose from 1 to 10 in the months of March, April, May, and June 2011, after Fukushima fallout arrived, compared with the same period in 2010. While officials could not offer any explanation for the abrupt increase, it coincides with our findings in the Pacific Northwest.

  7. Badumna says:

    Concerning (2):

    In the plot, why is the data displayed twice, over two weeks rather than one? Is this intended to provide some kind of false emphasis?

  8. John Baez says:

    Hey! I got a reply from the folks at Anybot, and they said they’re willing to provide a robot for me to give a talk at Google! I just need to pick a date. This is going to be really fun. I hope folks at Google can videotape this and make it available for everyone to see.

    • Lee Bloomquist says:

      I wonder how the Anybot would work in the “ultimatum game” from experimental economics. Some time ago at the Society for the Quantitative Analysis of Behavior we presented a poster for my former company on how we saw changes in the game based just on the physical situation (different distances, different physical orientations– variables previously studied in environmental psychology that seemed to be significant). We ran the test with a university lab. Even these subtle differences in the game could change it from “cooperative” to “competitive,” where players in the former got more money from the game. Next steps were intended to include comparing these physical differences between people in the same room to various technological and virtual variations of “presence” in the game. On re-runs of the whole experiment, pure chance would have produced the pattern we observed only about 1/200 times. If the Anybot company were to sponsor this kind of research and show desired results, maybe they could get a tax credit for their customers—just like the office furniture industry obtained tax credits from the federal government for their customers based on much less scientific arguments about the productivity associated with office furniture. The role of Azimuth math and physics might be to include factors accounting for the situation and based on that, refine von Neumann and Morgenstern’s theorems in “Theory of Games and Economic Behavior” about perfect competition, which only occurs in games between two people. (Basically neither player has anybody else with which to form a coalition, to result in say three against one. When only two play the game, the competition is “perfect.”) John, when you’re using the Anybot to give your talk at Google, would it be possible to move it around—get closer to a person, change its orientation to a person? The previous results from environmental psychology say that distances of more than 15 feet from the audience are associated with very formal types of occasions. While distances between people of less than five feet, but no physical contact, are associated with more friendly, informal kinds of interactions. Orientation between two people of ninety degrees was associated with increased communication, for example two people at the corners of a table looking together at a source of information on the table. To which– one could add results like the above from the ultimatum game.

  9. […] adressed in my previous post about mini nuclear reactors went a little further on the blog Azimuth (here, here and here). Connected with the Azimuth blog is the socalled Azimuth project and the Azimuth […]

  10. Norman Stone says:

    Hi John,

    The foam in the picture #7 could be forming ice, in the encounter between colder salt water and just above freezing fresh water.While the ice could be forming sub-surface, the lower density of ice would ensure that it all floated to the surface along the *linear interface* where both water masses meet the air, and any ice that wandered into either body of water would quickly melt– it’s a dynamic formation!

    But I suppose the same argument can be applied to any reaction that occurs along the interface, and produces a low-density product.

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