Saving Climate Data (Part 6)

23 February, 2017

Scott Pruitt, who filed legal challenges against Environmental Protection Agency rules fourteen times, working hand in hand with oil and gas companies, is now head of that agency. What does that mean about the safety of climate data on the EPA’s websites? Here is an inside report:

• Dawn Reeves, EPA preserves Obama-Era website but climate change data doubts remain, InsideEPA.com, 21 February 2017.

For those of us who are backing up climate data, the really important stuff is in red near the bottom.

The EPA has posted a link to an archived version of its website from Jan. 19, the day before President Donald Trump was inaugurated and the agency began removing climate change-related information from its official site, saying the move comes in response to concerns that it would permanently scrub such data.

However, the archived version notes that links to climate and other environmental databases will go to current versions of them—continuing the fears that the Trump EPA will remove or destroy crucial greenhouse gas and other data.

The archived version was put in place and linked to the main page in response to “numerous [Freedom of Information Act (FOIA)] requests regarding historic versions of the EPA website,” says an email to agency staff shared by the press office. “The Agency is making its best reasonable effort to 1) preserve agency records that are the subject of a request; 2) produce requested agency records in the format requested; and 3) post frequently requested agency records in electronic format for public inspection. To meet these goals, EPA has re-posted a snapshot of the EPA website as it existed on January 19, 2017.”

The email adds that the action is similar to the snapshot taken of the Obama White House website.

For example, it says the page is “not the current EPA website” and that the archive includes “static content, such as webpages and reports in Portable Document Format (PDF), as that content appeared on EPA’s website as of January 19, 2017.”

It cites technical limits for the database exclusions. “For example, many of the links contained on EPA’s website are to databases that are updated with the new information on a regular basis. These databases are not part of the static content that comprises the Web Snapshot.” Searches of the databases from the archive “will take you to the current version of the database,” the agency says.

“In addition, links may have been broken in the website as it appeared” on Jan. 19 and those will remain broken on the snapshot. Links that are no longer active will also appear as broken in the snapshot.

“Finally, certain extremely large collections of content… were not included in the Snapshot due to their size” such as AirNow images, radiation network graphs, historic air technology transfer network information, and EPA’s searchable news releases.”

‘Smart’ Move

One source urging the preservation of the data says the snapshot appears to be a “smart” move on EPA’s behalf, given the FOIA requests it has received, and notes that even though other groups like NextGen Climate and scientists have been working to capture EPA’s online information, having it on EPA’s site makes it official.

But it could also be a signal that big changes are coming to the official Trump EPA site, and it is unclear how long the agency will maintain the archived version.

The source says while it is disappointing that the archive may signal the imminent removal of EPA’s climate site, “at least they are trying to accommodate public concerns” to preserve the information.

A second source adds that while it is good that EPA is seeking “to address the widespread concern” that the information will be removed by an administration that does not believe in human-caused climate change, “on the other hand, it doesn’t address the primary concern of the data. It is snapshots of the web text.” Also, information “not included,” such as climate databases, is what is difficult to capture by outside groups and is what really must be preserved.

“If they take [information] down” that groups have been trying to preserve, then the underlying concern about access to data remains. “Web crawlers and programs can do things that are easy,” such as taking snapshots of text, “but getting the data inside the database is much more challenging,” the source says.

The first source notes that EPA’s searchable databases, such as those maintained by its Clean Air Markets Division, are used by the public “all the time.”

The agency’s Office of General Counsel (OGC) Jan. 25 began a review of the implications of taking down the climate page—a planned wholesale removal that was temporarily suspended to allow for the OGC review.

But EPA did remove some specific climate information, including links to the Clean Power Plan and references to President Barack Obama’s Climate Action Plan. Inside EPA captured this screenshot of the “What EPA Is Doing” page regarding climate change. Those links are missing on the Trump EPA site. The archive includes the same version of the page as captured by our screenshot.

Inside EPA first reported the plans to take down the climate information on Jan. 17.

After the OGC investigation began, a source close to the Trump administration said Jan. 31 that climate “propaganda” would be taken down from the EPA site, but that the agency is not expected to remove databases on GHG emissions or climate science. “Eventually… the propaganda will get removed…. Most of what is there is not data. Most of what is there is interpretation.”

The Sierra Club and Environmental Defense Fund both filed FOIA requests asking the agency to preserve its climate data, while attorneys representing youth plaintiffs in a federal climate change lawsuit against the government have also asked the Department of Justice to ensure the data related to its claims is preserved.

The Azimuth Climate Data Backup Project and other groups are making copies of actual databases, not just the visible portions of websites.

Azimuth Backup Project (Part 4)

18 February, 2017

The Azimuth Climate Data Backup Project is going well! Our Kickstarter campaign ended on January 31st and the money has recently reached us. Our original goal was $5000. We got$20,427 of donations, and after Kickstarter took its cut we received $18,590.96. Next time I’ll tell you what our project has actually been doing. This time I just want to give a huge “thank you!” to all 627 people who contributed money on Kickstarter! I sent out thank you notes to everyone, updating them on our progress and asking if they wanted their names listed. The blanks in the following list represent people who either didn’t reply, didn’t want their names listed, or backed out and decided not to give money. I’ll list people in chronological order: first contributors first. Only 12 people backed out; the vast majority of blanks on this list are people who haven’t replied to my email. I noticed some interesting but obvious patterns. For example, people who contributed later are less likely to have answered my email yet—I’ll update this list later. People who contributed more money were more likely to answer my email. The magnitude of contributions ranged from$2000 to $1. A few people offered to help in other ways. The response was international—this was really heartwarming! People from the US were more likely than others to ask not to be listed. But instead of continuing to list statistical patterns, let me just thank everyone who contributed. Daniel Estrada Ahmed Amer Saeed Masroor Jodi Kaplan John Wehrle Bob Calder Andrea Borgia L Gardner Uche Eke Keith Warner Dean Kalahan James Benson Dianne Hackborn Walter Hahn Thomas Savarino Noah Friedman Eric Willisson Jeffrey Gilmore John Bennett Glenn McDavid Brian Turner Peter Bagaric Martin Dahl Nielsen Broc Stenman Gabriel Scherer Roice Nelson Felipe Pait Kenneth Hertz Luis Bruno Andrew Lottmann Alex Morse Mads Bach Villadsen Noam Zeilberger Buffy Lyon Josh Wilcox Danny Borg Krishna Bhogaonker Harald Tveit Alvestrand Tarek A. Hijaz, MD Jouni Pohjola Chavdar Petkov Markus Jöbstl Bjørn Borud Sarah G William Straub Frank Harper Carsten Führmann Rick Angel Drew Armstrong Jesimpson Valeria de Paiva Ron Prater David Tanzer Rafael Laguna Miguel Esteves dos Santos Sophie Dennison-Gibby Randy Drexler Peter Haggstrom Jerzy Michał Pawlak Santini Basra Jenny Meyer John Iskra Bruce Jones Māris Ozols Everett Rubel Mike D Manik Uppal Todd Trimble Federer Fanatic Forrest Samuel, Harmos Consulting Annie Wynn Norman and Marcia Dresner Daniel Mattingly James W. Crosby Jennifer Booth Greg Randolph Dave and Karen Deeter Sarah Truebe Tieg Zaharia Jeffrey Salfen Birian Abelson Logan McDonald Brian Truebe Jon Leland Nicole Sarah Lim James Turnbull John Huerta Katie Mandel Bruce Bethany Summer Heather Tilert Anna C. Gladstone Naom Hart Aaron Riley Giampiero Campa Julie A. Sylvia Pace Willisson Bangskij Peter Herschberg Alaistair Farrugia Conor Hennessy Stephanie Mohr Torinthiel Lincoln Muri Anet Ferwerda Hanna Michelle Lee Guiney Ben Doherty Trace Hagemann Ryan Mannion Penni and Terry O'Hearn Brian Bassham Caitlin Murphy John Verran Susan Alexander Hawson Fabrizio Mafessoni Anita Phagan Nicolas Acuña Niklas Brunberg Adam Luptak V. Lazaro Zamora Branford Werner Niklas Starck Westerberg Luca Zenti and Marta Veneziano Ilja Preuß Christopher Flint George Read Courtney Leigh Katharina Spoerri Daniel Risse Hanna Charles-Etienne Jamme rhackman41 Jeff Leggett RKBookman Aaron Paul Mike Metzler Patrick Leiser Melinda Ryan Vaughn Kent Crispin Michael Teague Ben Fabian Bach Steven Canning Betsy McCall John Rees Mary Peters Shane Claridge Thomas Negovan Tom Grace Justin Jones Jason Mitchell Josh Weber Rebecca Lynne Hanginger Kirby Dawn Conniff Michael T. Astolfi Kristeva Erik Keith Uber Elaine Mazerolle Matthieu Walraet Linda Penfold Lujia Liu Keith Samar Tareem Henrik Almén Michael Deakin Rutger Ockhorst Erin Bassett James Crook Junior Eluhu Dan Laufer Carl Robert Solovay Silica Magazine Leonard Saers Alfredo Arroyo García Larry Yu John Behemonth Eric Humphrey Svein Halvor Halvorsen Karim Issa Øystein Risan Borgersen David Anderson Bell III Ole-Morten Duesend Adam North and Gabrielle Falquero Robert Biegler Qu Wenhao Steffen Dittmar Shanna Germain Adam Blinkinsop John WS Marvin (Dread Unicorn Games) Bill Carter Darth Chronis Lawrence Stewart Gareth Hodges Colin Backhurst Christopher Metzger Rachel Gumper Mariah Thompson Falk Alexander Glade Johnathan Salter Maggie Unkefer Shawna Maryanovich Wilhelm Fitzpatrick Dylan “ExoByte” Mayo Lynda Lee Scott Carpenter Charles D, Payet Vince Rostkowski Tim Brown Raven Daegmorgan Zak Brueckner Christian Page Adi Shavit Steven Greenberg Chuck Lunney Adriel Bustamente Natasha Anicich Bram De Bie Edward L Gray Detrick Robert Sarah Russell Sam Leavin Abilash Pulicken Isabel Olondriz James Pierce James Morrison April Daniels José Tremblay Champagne Chris Edmonds Hans & Maria Cummings Bart Gasiewiski Andy Chamard Andrew Jackson Christopher Wright Crystal Collins ichimonji10 Alan Stern Alison W Dag Henrik Bråtane Martin Nilsson William Schrade  Saving Climate Data (Part 5) 6 February, 2017 There’s a lot going on! Here’s a news roundup. I will separately talk about what the Azimuth Climate Data Backup Project is doing. I’ll start with the bad news, and then go on to some good news. Tweaking the EPA website Scientists are keeping track of how Trump administration is changing the Environmental Protection Agency website, with before-and-after photos, and analysis: • Brian Kahn, Behold the “tweaks” Trump has made to the EPA website (so far), National Resources Defense Council blog, 3 February 2017. There’s more about “adaptation” to climate change, and less about how it’s caused by carbon emissions. All of this would be nothing compared to the new bill to eliminate the EPA, or Myron Ebell’s plan to fire most of the people working there: • Joe Davidson, Trump transition leader’s goal is two-thirds cut in EPA employees, Washington Post, 30 January 2017. If you want to keep track of this battle, I recommend getting a 30-day free subscription to this online magazine: Taking animal welfare data offline The Trump team is taking animal-welfare data offline. The US Department of Agriculture will no longer make lab inspection results and violations publicly available, citing privacy concerns: • Sara Reardon, US government takes animal-welfare data offline, Nature Breaking News, 3 Feburary 2017. Restricting access to geospatial data A new bill would prevent the US government from providing access to geospatial data if it helps people understand housing discrimination. It goes like this: Notwithstanding any other provision of law, no Federal funds may be used to design, build, maintain, utilize, or provide access to a Federal database of geospatial information on community racial disparities or disparities in access to affordable housing._ For more on this bill, and the important ways in which such data has been used, see: • Abraham Gutman, Scott Burris, and the Temple University Center for Public Health Law Research, Where will data take the Trump administration on housing?, Philly.com, 1 February 2017. The EDGI fights back The Environmental Data and Governance Initiative or EDGI is working to archive public environmental data. They’re helping coordinate data rescue events. You can attend one and have fun eating pizza with cool people while saving data: • 3 February 2017, Portland • 4 February 2017, New York City • 10-11 February 2017, Austin Texas • 11 February 2017, U. C. Berkeley, California • 18 February 2017, MIT, Cambridge Massachusetts • 18 February 2017, Haverford Connecticut • 18-19 February 2017, Washington DC • 26 February 2017, Twin Cities, Minnesota Or, work with EDGI to organize one your own data rescue event! They provide some online tools to help download data. I know there will also be another event at UCLA, so the above list is not complete, and it will probably change and grow over time. Keep up-to-date at their site: Scientists fight back The pushback is so big it’s hard to list it all! For now I’ll just quote some of this article: • Tabitha Powledge, The gag reflex: Trump info shutdowns at US science agencies, especially EPA, 27 January 2017. THE PUSHBACK FROM SCIENCE HAS BEGUN Predictably, counter-tweets claiming to come from rebellious employees at the EPA, the Forest Service, the USDA, and NASA sprang up immediately. At The Verge, Rich McCormick says there’s reason to believe these claims may be genuine, although none has yet been verified. A lovely head on this post: “On the internet, nobody knows if you’re a National Park.” At Hit&Run, Ronald Bailey provides handles for several of these alt tweet streams, which he calls “the revolt of the permanent government.” (That’s a compliment.) Bailey argues, “with exception perhaps of some minor amount of national security intelligence, there is no good reason that any information, data, studies, and reports that federal agencies produce should be kept from the public and press. In any case, I will be following the Alt_Bureaucracy feeds for a while.” NeuroDojo Zen Faulkes posted on how to demand that scientific societies show some backbone. “Ask yourself: “Have my professional societies done anything more political than say, ‘Please don’t cut funding?’” Will they fight?,” he asked. Scientists associated with the group_ 500 Women Scientists _donned lab coats and marched in DC as part of the Women’s March on Washington the day after Trump’s Inauguration, Robinson Meyer reported at the Atlantic. A wildlife ecologist from North Carolina told Meyer, “I just can’t believe we’re having to yell, ‘Science is real.’” Taking a cue from how the Women’s March did its social media organizing, other scientists who want to set up a Washington march of their own have put together a closed Facebook group that claims more than 600,000 members, Kate Sheridan writes at STAT. The #ScienceMarch Twitter feed says a date for the march will be posted in a few days. [The march will be on 22 April 2017.] The group also plans to release tools to help people interested in local marches coordinate their efforts and avoid duplication. At The Atlantic, Ed Yong describes the political action committee 314Action. (314=the first three digits of pi.) Among other political activities, it is holding a webinar on Pi Day—March 14—to explain to scientists how to run for office. Yong calls 314Action the science version of Emily’s List, which helps pro-choice candidates run for office. 314Action says it is ready to connect potential candidate scientists with mentors—and donors. Other groups may be willing to step in when government agencies wimp out. A few days before the Inauguration, the Centers for Disease Control and Prevention abruptly and with no explanation cancelled a 3-day meeting on the health effects of climate change scheduled for February. Scientists told Ars Technica’s Beth Mole that CDC has a history of running away from politicized issues. One of the conference organizers from the American Public Health Association was quoted as saying nobody told the organizers to cancel. I believe it. Just one more example of the chilling effect on global warming. In politics, once the Dear Leader’s wishes are known, some hirelings will rush to gratify them without being asked. The APHA guy said they simply wanted to head off a potential last-minute cancellation. Yeah, I guess an anticipatory pre-cancellation would do that. But then—Al Gore to the rescue! He is joining with a number of health groups—including the American Public Health Association—to hold a one-day meeting on the topic Feb 16 at the Carter Center in Atlanta, CDC’s home base. Vox’s Julia Belluz reports that it is not clear whether CDC officials will be part of the Gore rescue event. The Sierra Club fights back The Sierra Club, of which I’m a proud member, is using the Freedom of Information Act or FOIA to battle or at least slow the deletion of government databases. They wisely started even before Trump took power: • Jennifer A Dlouhy, Fearing Trump data purge, environmentalists push to get records, BloombergMarkets, 13 January 2017. Here’s how the strategy works: U.S. government scientists frantically copying climate data they fear will disappear under the Trump administration may get extra time to safeguard the information, courtesy of a novel legal bid by the Sierra Club. The environmental group is turning to open records requests to protect the resources and keep them from being deleted or made inaccessible, beginning with information housed at the Environmental Protection Agency and the Department of Energy. On Thursday [January 9th], the organization filed Freedom of Information Act requests asking those agencies to turn over a slew of records, including data on greenhouse gas emissions, traditional air pollution and power plants. The rationale is simple: Federal laws and regulations generally block government agencies from destroying files that are being considered for release. Even if the Sierra Club’s FOIA requests are later rejected, the record-seeking alone could prevent files from being zapped quickly. And if the records are released, they could be stored independently on non-government computer servers, accessible even if other versions go offline. Information Geometry (Part 16) 1 February, 2017 This week I’m giving a talk on biology and information: • John Baez, Biology as information dynamics, talk for Biological Complexity: Can it be Quantified?, a workshop at the Beyond Center, 2 February 2017. While preparing this talk, I discovered a cool fact. I doubt it’s new, but I haven’t exactly seen it elsewhere. I came up with it while trying to give a precise and general statement of ‘Fisher’s fundamental theorem of natural selection’. I won’t start by explaining that theorem, since my version looks rather different than Fisher’s, and I came up with mine precisely because I had trouble understanding his. I’ll say a bit more about this at the end. Here’s my version: The square of the rate at which a population learns information is the variance of its fitness. This is a nice advertisement for the virtues of diversity: more variance means faster learning. But it requires some explanation! The setup Let’s start by assuming we have $n$ different kinds of self-replicating entities with populations $P_1, \dots, P_n.$ As usual, these could be all sorts of things: • molecules of different chemicals • organisms belonging to different species • genes of different alleles • restaurants belonging to different chains • people with different beliefs • game-players with different strategies • etc. I’ll call them replicators of different species. Let’s suppose each population $P_i$ is a function of time that grows at a rate equal to this population times its ‘fitness’. I explained the resulting equation back in Part 9, but it’s pretty simple: $\displaystyle{ \frac{d}{d t} P_i(t) = f_i(P_1(t), \dots, P_n(t)) \, P_i(t) }$ Here $f_i$ is a completely arbitrary smooth function of all the populations! We call it the fitness of the ith species. This equation is important, so we want a short way to write it. I’ll often write $f_i(P_1(t), \dots, P_n(t))$ simply as $f_i,$ and $P_i(t)$ simply as $P_i.$ With these abbreviations, which any red-blooded physicist would take for granted, our equation becomes simply this: $\displaystyle{ \frac{dP_i}{d t} = f_i \, P_i }$ Next, let $p_i(t)$ be the probability that a randomly chosen organism is of the ith species: $\displaystyle{ p_i(t) = \frac{P_i(t)}{\sum_j P_j(t)} }$ Starting from our equation describing how the populations evolve, we can figure out how these probabilities evolve. The answer is called the replicator equation: $\displaystyle{ \frac{d}{d t} p_i(t) = ( f_i - \langle f \rangle ) \, p_i(t) }$ Here $\langle f \rangle$ is the average fitness of all the replicators, or mean fitness: $\displaystyle{ \langle f \rangle = \sum_j f_j(P_1(t), \dots, P_n(t)) \, p_j(t) }$ In what follows I’ll abbreviate the replicator equation as follows: $\displaystyle{ \frac{dp_i}{d t} = ( f_i - \langle f \rangle ) \, p_i }$ The result Okay, now let’s figure out how fast the probability distribution $p(t) = (p_1(t), \dots, p_n(t))$ changes with time. For this we need to choose a way to measure the length of the vector $\displaystyle{ \frac{dp}{dt} = (\frac{d}{dt} p_1(t), \dots, \frac{d}{dt} p_n(t)) }$ And here information geometry comes to the rescue! We can use the Fisher information metric, which is a Riemannian metric on the space of probability distributions. I’ve talked about the Fisher information metric in many ways in this series. The most important fact is that as a probability distribution $p(t)$ changes with time, its speed $\displaystyle{ \left\| \frac{dp}{dt} \right\|}$ as measured using the Fisher information metric can be seen as the rate at which information is learned. I’ll explain that later. Right now I just want a simple formula for the Fisher information metric. Suppose $v$ and $w$ are two tangent vectors to the point $p$ in the space of probability distributions. Then the Fisher information metric is given as follows: $\displaystyle{ \langle v, w \rangle = \sum_i \frac{1}{p_i} \, v_i w_i }$ Using this we can calculate the speed at which $p(t)$ moves when it obeys the replicator equation. Actually the square of the speed is simpler: $\begin{array}{ccl} \displaystyle{ \left\| \frac{dp}{dt} \right\|^2 } &=& \displaystyle{ \sum_i \frac{1}{p_i} \left( \frac{dp_i}{dt} \right)^2 } \\ \\ &=& \displaystyle{ \sum_i \frac{1}{p_i} \left( ( f_i - \langle f \rangle ) \, p_i \right)^2 } \\ \\ &=& \displaystyle{ \sum_i ( f_i - \langle f \rangle )^2 p_i } \end{array}$ The answer has a nice meaning, too! It’s just the variance of the fitness: that is, the square of its standard deviation. So, if you’re willing to buy my claim that the speed $\|dp/dt\|$ is the rate at which our population learns new information, then we’ve seen that the square of the rate at which a population learns information is the variance of its fitness! Fisher’s fundamental theorem Now, how is this related to Fisher’s fundamental theorem of natural selection? First of all, what is Fisher’s fundamental theorem? Here’s what Wikipedia says about it: It uses some mathematical notation but is not a theorem in the mathematical sense. It states: “The rate of increase in fitness of any organism at any time is equal to its genetic variance in fitness at that time.” Or in more modern terminology: “The rate of increase in the mean fitness of any organism at any time ascribable to natural selection acting through changes in gene frequencies is exactly equal to its genetic variance in fitness at that time”. Largely as a result of Fisher’s feud with the American geneticist Sewall Wright about adaptive landscapes, the theorem was widely misunderstood to mean that the average fitness of a population would always increase, even though models showed this not to be the case. In 1972, George R. Price showed that Fisher’s theorem was indeed correct (and that Fisher’s proof was also correct, given a typo or two), but did not find it to be of great significance. The sophistication that Price pointed out, and that had made understanding difficult, is that the theorem gives a formula for part of the change in gene frequency, and not for all of it. This is a part that can be said to be due to natural selection Price’s paper is here: • George R. Price, Fisher’s ‘fundamental theorem’ made clear, Annals of Human Genetics 36 (1972), 129–140. I don’t find it very clear, perhaps because I didn’t spend enough time on it. But I think I get the idea. My result is a theorem in the mathematical sense, though quite an easy one. I assume a population distribution evolves according to the replicator equation and derive an equation whose right-hand side matches that of Fisher’s original equation: the variance of the fitness. But my left-hand side is different: it’s the square of the speed of the corresponding probability distribution, where speed is measured using the ‘Fisher information metric’. This metric was discovered by the same guy, Ronald Fisher, but I don’t think he used it in his work on the fundamental theorem! Something a bit similar to my statement appears as Theorem 2 of this paper: • Marc Harper, Information geometry and evolutionary game theory. and for that theorem he cites: • Josef Hofbauer and Karl Sigmund, Evolutionary Games and Population Dynamics, Cambridge University Press, Cambridge, 1998. However, his Theorem 2 really concerns the rate of increase of fitness, like Fisher’s fundamental theorem. Moreover, he assumes that the probability distribution $p(t)$ flows along the gradient of a function, and I’m not assuming that. Indeed, my version applies to situations where the probability distribution moves round and round in periodic orbits! Relative information and the Fisher information metric The key to generalizing Fisher’s fundamental theorem is thus to focus on the speed at which $p(t)$ moves, rather than the increase in fitness. Why do I call this speed the ‘rate at which the population learns information’? It’s because we’re measuring this speed using the Fisher information metric, which is closely connected to relative information, also known as relative entropy or the Kullback–Leibler divergence. I explained this back in Part 7, but that explanation seems hopelessly technical to me now, so here’s a faster one, which I created while preparing my talk. The information of a probability distribution $q$ relative to a probability distribution $p$ is $\displaystyle{ I(q,p) = \sum_{i =1}^n q_i \log\left(\frac{q_i}{p_i}\right) }$ It says how much information you learn if you start with a hypothesis $p$ saying that the probability of the ith situation was $p_i,$ and then update this to a new hypothesis $q.$ Now suppose you have a hypothesis that’s changing with time in a smooth way, given by a time-dependent probability $p(t).$ Then a calculation shows that $\displaystyle{ \left.\frac{d}{dt} I(p(t),p(t_0)) \right|_{t = t_0} = 0 }$ for all times $t_0$. This seems paradoxical at first. I like to jokingly put it this way: To first order, you’re never learning anything. However, as long as the velocity $\frac{d}{dt}p(t_0)$ is nonzero, we have $\displaystyle{ \left.\frac{d^2}{dt^2} I(p(t),p(t_0)) \right|_{t = t_0} > 0 }$ so we can say To second order, you’re always learning something… unless your opinions are fixed. This lets us define a ‘rate of learning’—that is, a ‘speed’ at which the probability distribution $p(t)$ moves. And this is precisely the speed given by the Fisher information metric! In other words: $\displaystyle{ \left\|\frac{dp}{dt}(t_0)\right\|^2 = \left.\frac{d^2}{dt^2} I(p(t),p(t_0)) \right|_{t = t_0} }$ where the length is given by Fisher information metric. Indeed, this formula can be used to define the Fisher information metric. From this definition we can easily work out the concrete formula I gave earlier. In summary: as a probability distribution moves around, the relative information between the new probability distribution and the original one grows approximately as the square of time, not linearly. So, to talk about a ‘rate at which information is learned’, we need to use the above formula, involving a second time derivative. This rate is just the speed at which the probability distribution moves, measured using the Fisher information metric. And when we have a probability distribution describing how many replicators are of different species, and it’s evolving according to the replicator equation, this speed is also just the variance of the fitness! Biology as Information Dynamics 31 January, 2017 This is my talk for the workshop Biological Complexity: Can It Be Quantified? • John Baez, Biology as information dynamics, 2 February 2017. Abstract. If biology is the study of self-replicating entities, and we want to understand the role of information, it makes sense to see how information theory is connected to the ‘replicator equation’—a simple model of population dynamics for self-replicating entities. The relevant concept of information turns out to be the information of one probability distribution relative to another, also known as the Kullback–Leibler divergence. Using this we can get a new outlook on free energy, see evolution as a learning process, and give a clean general formulation of Fisher’s fundamental theorem of natural selection. For more, read: • Marc Harper, The replicator equation as an inference dynamic. • Marc Harper, Information geometry and evolutionary game theory. • Barry Sinervo and Curt M. Lively, The rock-paper-scissors game and the evolution of alternative male strategies, Nature 380 (1996), 240–243. • John Baez, Diversity, entropy and thermodynamics. • John Baez, Information geometry. The last reference contains proofs of the equations shown in red in my slides. In particular, Part 16 contains a proof of my updated version of Fisher’s fundamental theorem. Quantifying Biological Complexity 23 January, 2017 Next week I’m going to this workshop: Biological Complexity: Can It Be Quantified?, 1-3 February 2017, Beyond Center for Fundamental Concepts in Science, Arizona State University, Tempe Arizona. Organized by Paul Davies. I haven’t heard that any of it will be made publicly available, but I’ll see if there’s something I can show you. Here’s the schedule: Wednesday February 1st 9:00 – 9:30 am Paul Davies Brief welcome address, outline of the subject and aims of the meeting Session 1. Life: do we know it when we see it? 9:30 – 10:15 am: Chris McKay, “Mission to Enceladus” 10:15 – 10:45 am: Discussion 10:45– 11:15 am: Tea/coffee break 11:15 – 12:00 pm: Kate Adamala, “Alive but not life” 12:00 – 12:30 pm: Discussion 12:30 – 2:00 pm: Lunch Session 2. Quantifying life 2:00 – 2:45 pm: Lee Cronin, “The living and the dead: molecular signatures of life” 2:45 – 3:30 pm: Sara Walker, “Can we build a life meter?” 3:30 – 4:00 pm: Discussion 4:00 – 4:30 pm: Tea/coffee break 4:30 – 5:15 pm: Manfred Laubichler, “Complexity is smaller than you think” 5:15 – 5:30 pm: Discussion The Beyond Annual Lecture 7:00 – 8:30 pm: Sean Carroll, “Our place in the universe” Thursday February 2nd Session 3: Life, information and the second law of thermodynamics 9:00 – 9:45 am: James Crutchfield, “Vital bits: the fuel of life” 9:45 – 10:00 am: Discussion 10:00 – 10:45 pm: John Baez, “Information and entropy in biology” 10:45 – 11:00 am: Discussion 11:00 – 11:30 pm: Tea/coffee break 11:30 – 12:15 pm: Chris Adami, “What is biological information?” 12:15 – 12:30 pm: Discussion 12:30 – 2:00 pm: Lunch Session 4: The emergence of agency 2:00 – 2:45 pm: Olaf Khang Witkowski, “When do autonomous agents act collectively?” 2:45 – 3:00 pm: Discussion 3:00 – 3:45 pm: William Marshall, “When macro beats micro” 3:45 – 4:00 pm: Discussion 4:00 – 4:30 am: Tea/coffee break 4:30 – 5:15pm: Alexander Boyd, “Biology’s demons” 5:15 – 5:30 pm: Discussion Friday February 3rd Session 5: New physics? 9:00 – 9:45 am: Sean Carroll, “Laws of complexity, laws of life?” 9:45 – 10:00 am: Discussion 10:00 – 10:45 am: Andreas Wagner, “The arrival of the fittest” 10:45 – 11:00 am: Discussion 11:00 – 11:30 am: Tea/coffee break 11:30 – 12:30 pm: George Ellis, “Top-down causation demands new laws” 12:30 – 2:00 pm: Lunch Azimuth Backup Project (Part 3) 22 January, 2017 Along with the bad news there is some good news: • Over 380 people have pledged over$14,000 to the Azimuth Backup Project on Kickstarter, greatly surpassing our conservative initial goal of \$5,000.

• Given our budget, we currently aim at backing up 40 terabytes of data, and we are well on our way to this goal. You can see what we’ve done at Our Progress, and what we’re still doing at the Issue Tracker.

• I have gotten a commitment from Danna Gianforte, the head of Computing and Communications at U. C. Riverside, that eventually the university will maintain a copy of our data. (This commitment is based on my earlier estimate that we’d have 20 terabytes of data, so I need to see if 40 is okay.)

• I have gotten two offers from other people, saying they too can hold our data.

I’m hoping that the data at U. C. Riverside will be made publicly available through a server. The other offers may involve it being held ‘secretly’ until such time as it became needed; that has its own complementary advantages.

However, the interesting problem that confronts us now is: how to spend our money?

You can see how we’re currently spending it on our Budget and Spending page. Basically, we’re paying a firm called Hetzner for servers and storage boxes.

We could simply continue to do this until our money runs out. I hope that long before then, U. C. Riverside will have taken over some responsibilities. If so, there would be a long period where our money would largely pay for a redundant backup. Redundancy is good, but perhaps there is something better.

Two members of our team, Sakari Maaranen and Greg Kochanski, have thoughts on this matter which I’d like to share. Sakari posted his thoughts on Google+, while Greg posted his in an email which he’s letting me share here.

Please read these and offer us your thoughts! Maybe you can help us decide on the best strategy!

Sakari Maaranen

For the record, my views on our strategy of using the budget that the Azimuth Climate Data Backup Project now has.

People have contributed it to this effort specifically.

Some non-government entities have offered “free hosting”. Of course the project should take any and all free offers to host our data. Those would not be spending our budget however. And they are still paying for it, even if they offered it to us “for free”.

As far as it comes to spending, I think we should think in terms of 1) terabytemonths, and 2) sufficient redundancy, and do that as cost-efficiently as possible. We should not just dump the money to any takers, but think of the best bang for the buck. We owe that to the people who have contributed now.

For example, if we burn the cash quick to expensive storage, I would consider that a failure. Instead, we must plan for the best use of the budget towards our mission.

What we have promised to the people is that we back up and serve these data sets, by the money they have given to us. Let’s do exactly that.

We are currently serving the mission at approximately €0.006 per gigabytemonth at least for as long as we have volunteers to work for free. The cost could be slightly higher if we paid for professional maintenance, which should be a reasonable assumption if we plan for long term service. Volunteer work cannot be guaranteed forever, even if it works temporarily.

This is one view and the question is open to public discussion.﻿

Greg Kochanski

Some misc thoughts.

1) As I see it, we have made some promise of serving the data (“create a better interface for getting it”) which can be an expensive thing.

UI coding isn’t all that easy, and takes some time.

Beyond that, we’ve promised to back up the data, and once you say “backup”, you’ve also made an implicit promise to make the data available.

2) I agree that if we have a backup, it is a logical extension to take continuous backups, but I wouldn’t say it’s necessary.

Perhaps the way to think about it is to ask the question, “what do our donors likely want”?

3) Clearly they want to preserve the data, in case it disappears from the Federal sites. So, that’s job 1. And, if it does disappear, we need to make it available.

3a) Making it available will require some serving CPU, disk, and network. We may need to worry about DDOS attacks, thought perhaps we could get free coverage from Akamai or Google Project Shield.

3b) Making it available may imply paying some students to write Javascript and HTML to put up a front-end to allow people to access the data we are collecting.

Not all the data we’re collecting is in strictly servable form. Some of the databases, for example aren’t usefully servable in the form we collect, and we know some links will be broken because of missing pages, or because of wget’s design flaw.*

[* Wget stores http://a/b/c as a file, a/b/c, where a/b is a directory. Wget stores http://a/b as a file a/b, where a/b is a file.

Therefore, both cannot exist simultaneously on disk. If they do, wget drops one.]

Points 3 & 3a imply that we need to keep some money in the bank until either the websites are taken down, or we decide that the threat has abated. So, we need to figure out how much money to keep as a serving reserve. It doesn’t sound like UCR has committed to serve the data, though you could perhaps ask.

Beyond the serving reserve, I think we are free to do better backups (i.e. more than one data collection), and change detection.