These are my recent Pin​board​.in links:

• Jonathan Lethem’s ‘Neote­nous Aes­thetic” — Mis­ter Bit — Wired​.it

  ‘Dur­ing the brief, but very inter­est­ing Q&A ses­sion, Lethem argued that inter­net cul­ture brought the “closet into the open”, that is, it gave ephemera, triv­i­al­i­ties, and every­day activ­i­ties “A new kind of vis­i­bil­ity”. “Peo­ple have always been pro­duc­ing weird stuff and have always been engag­ing in arcane activ­i­ties,” Lethem remarked. “What is really new is the fact the now we can see it. We can see it all. We can quan­tify what we do — or not do — online.” Lethem men­tioned the uncanny abil­ity to track, in real time, “how many books I am not sell­ing on Ama­zon”. “Real­ity has acquired a new level of mea­sur­a­bil­ity”. “The activ­i­ties we per­form in our dig­i­tal age are not nec­es­sar­ily new. What is new is that. We. Can. See. Them. All.”.’
  
  one-​​measures-​​a-​​circle inter­per­me­ation access local­ism

• Sex, Oil, and Video­tape | Mother Jones

  “Loom­ing over Saylor’s con­fronta­tion with Bolen­baugh was the EPA’s Sep­tem­ber 27 cleanup dead­line, and it appears that Enbridge and its con­trac­tors were feel­ing the pres­sure as it drew near. In early Sep­tem­ber, after the Michi­gan Mes­sen­ger pub­lished its exposé on the use of undoc­u­mented work­ers by Hall­mark Indus­trial, another group of work­ers employed by a dif­fer­ent Enbridge con­trac­tor came for­ward with detailed sto­ries of how they had been instructed to con­ceal oil at the same site. Work­ers would land on an island, they said, remove all veg­e­ta­tion, and then lay out absorbent pom-​​poms, all per EPA reg­u­la­tions. But once the top layer of oil was absorbed, they were instructed to rake dirt over the area to make it appear as though it had been dug out. One worker described his super­vi­sor show­ing him the process step-​​by-​​step, con­clud­ing with sprin­kling a thin layer of dirt on top. “He said, ‘There, now they can’t see it. It is clean,’” the worker told the Mes­sen­ger. Another worker described being told to cover pock­ets of oil with leaves and sticks. As a last step, such areas were cor­doned off with cau­tion tape.”
  
  oil­spill Kala­ma­zoo local whistle­blower

• [1204.4200] Dis­crete Dynam­i­cal Genetic Pro­gram­ming in XCS

  “A num­ber of rep­re­sen­ta­tion schemes have been pre­sented for use within Learn­ing Clas­si­fier Sys­tems, rang­ing from binary encod­ings to neural net­works. This paper presents results from an inves­ti­ga­tion into using a dis­crete dynam­i­cal sys­tem rep­re­sen­ta­tion within the XCS Learn­ing Clas­si­fier Sys­tem. In par­tic­u­lar, asyn­chro­nous ran­dom Boolean net­works are used to rep­re­sent the tra­di­tional condition-​​action pro­duc­tion sys­tem rules. It is shown pos­si­ble to use self-​​adaptive, open-​​ended evo­lu­tion to design an ensem­ble of such dis­crete dynam­i­cal sys­tems within XCS to solve a num­ber of well-​​known test problems.”
  
  genetic-​​programming learning-​​classifier-​​systems representation-​​theory design-​​patterns boolean-​​networks nudge-​​targets nice

• Why Is Darwin’s Tan­gled Bank Tan­gled? : 13.7: Cos­mos And Cul­ture : NPR

  Sad to hear him still phras­ing this sim­ple truth so obscurely: Not “Because, on the scale of mol­e­c­u­lar bind­ing site recog­ni­tion, say a few tens of angstroms in length, height and width and sev­eral other fea­tures such as polar­ity, van-​​der-​​Waal forces, and so on, there are far fewer effec­tively dif­fer­ent mol­e­c­u­lar shapes than there are kinds of mol­e­cules.“ … but “Because there are fewer sto­ries than there are facts.”
  
  oh-​​stu pragmatism-it-ain’t philosophy-​​of-​​science

• Math Notes | Futil­ity Closet

  So for finite sequences of dig­its, which sequences are such that the most right-​​truncated sub­strings are prime? Which are such that the most right-​​repeating exten­sions are prime?
  
  nudge-​​targets number-​​theory indirect-​​link

• Home — Scal­able and Mod­u­lar Archi­tec­ture for CSS

  “I’ve been ana­lyz­ing my process (and the process of those around me) and fig­ur­ing out how best to struc­ture code for projects on a larger scale. What I’ve found is a process that works equally well for sites small and large. Learn how to struc­ture your CSS to allow for flex­i­bil­ity and main­tain­abil­ity as your project and your team grows.”
  
  css tuto­r­ial best-​​practices graphic-​​design via-​​trek

• [1204.3678] Crowd Mem­ory: Learn­ing in the Collective

  “Crowd algo­rithms often assume work­ers are inex­pe­ri­enced and thus fail to adapt as work­ers in the crowd learn a task. These assump­tions fun­da­men­tally limit the types of tasks that sys­tems based on such algo­rithms can han­dle. This paper explores how the crowd learns and remem­bers over time in the con­text of human com­pu­ta­tion, and how more real­is­tic assump­tions of worker expe­ri­ence may be used when design­ing new sys­tems. We first demon­strate that the crowd can recall infor­ma­tion over time and dis­cuss pos­si­ble impli­ca­tions of crowd mem­ory in the design of crowd algo­rithms. We then explore crowd learn­ing dur­ing a con­tin­u­ous con­trol task. Recent sys­tems are able to dis­guise dynamic groups of work­ers as crowd agents to sup­port con­tin­u­ous tasks, but have not yet con­sid­ered how such agents are able to learn over time. We show, using a real-​​time gam­ing set­ting, that crowd agents can learn over time, and ‘remem­ber’ by pass­ing strate­gies from one gen­er­a­tion of work­ers to the next, despite high turnover rates in the work­ers com­pris­ing them. We con­clude with a dis­cus­sion of future research direc­tions for crowd mem­ory and learning.”
  
  crowd­sourc­ing learn­ing agent-​​based collective-​​intelligence mem­ory nudge-​​targets

• [0911.1582] Manip­u­lat­ing Tour­na­ments in Cup and Round Robin Competitions

  “In sports com­pe­ti­tions, teams can manip­u­late the result by, for instance, throw­ing games. We show that we can decide how to manip­u­late round robin and cup com­pe­ti­tions, two of the most pop­u­lar types of sport­ing com­pe­ti­tions in poly­no­mial time. In addi­tion, we show that find­ing the min­i­mal num­ber of games that need to be thrown to manip­u­late the result can also be deter­mined in poly­no­mial time. Finally, we show that there are sev­eral dif­fer­ent vari­a­tions of stan­dard cup com­pe­ti­tions where manip­u­la­tion remains polynomial.”
  
  algo­rithms eco­nom­ics game-​​theory nudge-​​targets

• Intro­duc­ing the Jour­nal of Dig­i­tal Human­i­ties — ProfHacker — The Chron­i­cle of Higher Education

  “If the con­tents of the inau­gural issue—which range from an essay argu­ing that human­ists need to under­stand and inter­pret quan­ti­ta­tive data to a review of the Word­Seer text analy­sis tool—fall out­side your usual schol­arly domain, then cer­tainly the journal’s edi­to­r­ial and pub­lish­ing appa­ra­tus will piqué your interest. As Dan Cohen explained in a sep­a­rate blog post, the jour­nal oper­ates under the model of catch­ing the good—of find­ing sub­stan­tive and valu­able dig­i­tal human­i­ties work “in what­ever for­mat, and wher­ever, it exists.” Blogs, pod­casts, Twit­ter con­ver­sa­tions, slideshows, and so on, these are all venues in which sig­nif­i­cant and, though I hate to use such an ungainly word, impact­ful work is being done. The reg­u­lar and guest edi­tors “catch” this work, and then pro­vide lay­ers of eval­u­a­tion and review before it appears in JDH.”
  
  digital-​​humanities jour­nal to-​​read two-​​cultures-​​only-​​one-​​of-​​which-​​can-​​write

• [1005.4159] The Com­plex­ity of Manip­u­lat­ing $k$-Approval Elections

  “An impor­tant prob­lem in com­pu­ta­tional social choice the­ory is the com­plex­ity of unde­sir­able behav­ior among agents, such as con­trol, manip­u­la­tion, and bribery in elec­tion sys­tems. These kinds of vot­ing strate­gies are often tempt­ing at the indi­vid­ual level but dis­as­trous for the agents as a whole. Cre­at­ing elec­tion sys­tems where the deter­mi­na­tion of such strate­gies is dif­fi­cult is thus an impor­tant goal. …”
  
  vot­ing game-​​theory design-​​patterns mechanism-​​design nudge-​​targets

• [0903.1147] Tetravex is NP-​​complete

  “Tetravex is a widely played one per­son com­puter game in which you are given $n^2$ unit tiles, each edge of which is labelled with a num­ber. The objec­tive is to place each tile within a $n$ by $n$ square such that all neigh­bour­ing edges are labelled with an iden­ti­cal num­ber. Unfor­tu­nately, play­ing Tetravex is com­pu­ta­tion­ally hard. More pre­cisely, we prove that decid­ing if there is a tiling of the Tetravex board is NP-​​complete. Decid­ing where to place the tiles is there­fore NP-​​hard. This may help to explain why Tetravex is a good puz­zle. This result com­pli­ments a num­ber of sim­i­lar results for one per­son games involv­ing tiling. For exam­ple, NP-​​completeness results have been shown for: the offline ver­sion of Tetris, KPlumber (which involves rotat­ing tiles con­tain­ing draw­ings of pipes to make a con­nected net­work), and short­est slid­ing puz­zle prob­lems. It raises a num­ber of open ques­tions. For exam­ple, is the infi­nite ver­sion Turing-​​complete? How do we gen­er­ate Tetravex prob­lems which are truly puz­zling as ran­dom NP-​​complete prob­lems are often sur­pris­ing easy to solve? Can we observe phase tran­si­tion behav­iour? What about the com­plex­ity of the prob­lem when it is guar­an­teed to have an unique solu­tion? How do we gen­er­ate puz­zles with unique solutions?”
  
  mathematical-​​recreations computational-​​complexity algo­rithms nudge-​​targets

• [1204.4286] Fair Allo­ca­tion With­out Trade

  “We con­sider the age-​​old prob­lem of allo­cat­ing items among dif­fer­ent agents in a way that is effi­cient and fair. Two papers, by Dolev et al. and Ghodsi et al., have recently stud­ied this prob­lem in the con­text of com­puter sys­tems. Both papers had sim­i­lar mod­els for agent pref­er­ences, but advo­cated dif­fer­ent notions of fair­ness. We for­mal­ize both fair­ness notions in eco­nomic terms, extend­ing them to apply to a larger fam­ily of util­i­ties. Not­ing that in set­tings with such util­i­ties effi­ciency is eas­ily achieved in mul­ti­ple ways, we study notions of fair­ness as cri­te­ria for choos­ing between dif­fer­ent effi­cient allo­ca­tions. Our tech­ni­cal results are algo­rithms for find­ing fair allo­ca­tions cor­re­spond­ing to two fair­ness notions: Regard­ing the notion sug­gested by Ghodsi et al., we present a polynomial-​​time algo­rithm that com­putes an allo­ca­tion for a gen­eral class of fair­ness notions, in which their notion is included. For the other, sug­gested by Dolev et al., we show that a com­pet­i­tive mar­ket equi­lib­rium achieves the desired notion of fair­ness, thereby obtain­ing a polynomial-​​time algo­rithm that com­putes such a fair allo­ca­tion and solv­ing the main open prob­lem raised by Dolev et al.”
  
  eco­nom­ics game-​​theory fair­ness algo­rithms phi­los­o­phy design-​​patterns

• Why is Esti­mat­ing so Hard? | 8^th Light

  “It turns out that we don’t know the pro­ce­dure. We haven’t got any clue to just how dif­fi­cult the pro­ce­dure is. We aren’t com­put­ers. We don’t fol­low pro­ce­dures. And so com­par­ing the com­plex­ity of the man­ual task, to the com­plex­ity of the pro­ce­dure is invalid. This is one of the rea­sons that esti­mates are so hard, and why we get them wrong so often. We look at a task that seems easy and esti­mate it on that basis, only to find that writ­ing down the pro­ce­dure is actu­ally quite intri­cate. We blow the esti­mate because we esti­mate the wrong thing.”
  
  esti­ma­tion agile-​​practices philosophy-​​of-​​engineering man­age­ment self-​​definition plan­ning

• [1204.4374] Higher Order City Voronoi Diagrams

  “We inves­ti­gate higher-​​order Voronoi dia­grams in the city met­ric. This met­ric is induced by quick­est paths in the L1 met­ric in the pres­ence of an accel­er­at­ing trans­porta­tion net­work of axis-​​parallel line segments. …”
  
  computational-​​geometry algo­rithms voronoi-​​diagrams diver­sity network-​​theory nudge-​​targets

• Topic mod­el­ing made just sim­ple enough. | The Stone and the Shell

  “Com­puter sci­en­tists make LDA seem com­pli­cated because they care about prov­ing that their algo­rithms work. And the proof is indeed brain-​​squashingly hard. But the prac­tice of topic mod­el­ing makes good sense on its own, with­out proof, and does not require you to spend even a sec­ond think­ing about “Dirich­let dis­tri­b­u­tions.” When the math is approached in a prac­ti­cal way, I think human­ists will find it easy, intu­itive, and empow­er­ing. This post focuses on LDA as short­hand for a broader fam­ily of “prob­a­bilis­tic” tech­niques. I’m going to ask how they work, what they’re for, and what their lim­its are.”
  
  text-​​processing clas­si­fi­ca­tion algo­rithms lovely two-​​cultures-​​only-​​one-​​of-​​which-​​can-​​write

• Math­e­mati­cians are Giraffe Hunters by Barry Mazur | berfrois

  “No won­der life (i.e., the thing that my once 10-​​year old niece referred to as “the thing that isn’t fair”) comes to us as a fil­i­gree of ash sto­ries. Walk­ing down the street past a cou­ple in con­ver­sa­tion, an over­heard mor­pheme, a mere glance at a wrongly but­toned rain­coat, sparks a nar­ra­tive in our imag­i­na­tion. Ask any ques­tion begin­ning with “why?” and the answer will surely be a story, or it will be embed­ded in a story. Or, at the very least, it will offer a tempt­ing thread for some story that you your­self will hold onto, embell­ish even, as you try to absorb the answer. We inter­po­late between such frag­ments. This is, for many of us, sim­ply the way we think. What about the “why ques­tions” in sci­ence, in logic, in math­e­mat­ics? We should acknowl­edge how they are often “what ques­tions” or “how ques­tions” in dis­guise. Or how they slide down into such ques­tions, as the ever-​​elusive, ever-​​illusory quest for an X that actu­ally causes a Y dis­solves. Some of the more sat­is­fy­ing answers to sci­en­tific “why” ques­tions involves deft rephras­ing. “Why is the sky blue?” is replaced by the ques­tion “what is the func­tion that describes scat­ter­ing ampli­tude as depen­dent on wave-​​length”?”
  
  math­e­mat­ics philosophy-​​of-​​mathematics sto­ry­telling prag­ma­tism theory-​​and-​​practice-​​sitting-​​in-​​a-​​tree what-​​is-​​it-​​good-​​for-​​hunh