Monopoly started out as a game to protest the worst preda­tory mo­nop­o­lists

The Woolwich Observer - - LIVING HERE - ABOUT THE AU­THORS Bill is a jour­nal­ist, Rich holds a doc­tor­ate in physics. To­gether the brothers bring you “Strange But True.” Send your ques­tions to strangetrue@com­puserve.com.

Q. Have you ever played the board game Monopoly? Did you know that it was orig­i­nally patented in 1904 with the ti­tle “Land­lord’s Game” and had two very dif­fer­ent sets of rules? And it was cre­ated by a woman.

A. El­iz­a­beth Magie “ac­tu­ally de­signed the game as a protest against the big mo­nop­o­lists of her time — peo­ple like An­drew Carnegie and John D. Rock­e­feller,” says Mary Pilon in “A Dif­fer­ent Point of View,” pub­lished by the Na­tional Women’s His­tory Mu­seum. At a time when poverty and squalor were ram­pant in the ur­ban cen­ters of the United States, Magie drew in­spi­ra­tion from “Progress and Poverty,” writ­ten by econ­o­mist Henry Ge­orge, who ad­vanced the idea of a “land value tax” that taxed only land as a way to help shift the tax bur­den to wealthy land­lords. So Magie cre­ated two sets of rules for her game: “an anti-mo­nop­o­list set in which all were re­warded when wealth was cre­ated, and a mo­nop­o­list set in which the goal was to cre­ate mo­nop­o­lies and dom­i­nate op­po­nents.”

It was this se­cond set that took hold, be­com­ing a folk favourite among North­east­ern in­tel­lec­tu­als, some elite col­lege cam­puses, and even an At­lantic City Quaker com­mu­nity that added its neigh­bour­hood prop­er­ties to the board and in­tro­duced mod­i­fi­ca­tions to make the game eas­ier to play. It was a ver­sion of this Quaker game that Charles Dar­row, the long-cred­ited “in­ven­tor” of Monopoly, played and even­tu­ally sold to Parker Brothers. Not long af­ter the deal with Dar­row, the com­pany pur­chased Magie’s Land­lord Game patent and two more of her game de­signs.

Q. How have stud­ies of so­cial net­works helped iden­tify ar­eas of gen­der in­equal­ity in the world?

A. Look­ing at the anonymized data of 1.4 bil­lion Face­book users in 217 coun­tries and re­gions, re­searchers cal­cu­lated the pro­por­tion of women and men ages 13-65, says Matthew Hut­son in “Sci­en­tific Amer­i­can” magazine. They found that cer­tain coun­tries, such as Afghanistan, had low fe­male-tomale us­age ra­tios, and they deemed this the “Face­book gen­der di­vide.” Bangladesh, Chad, South Su­dan and Ye­men also had un­usu­ally large Face­book gen­der di­vides.

When the team an­a­lyzed World Eco­nomic Fo­rum data on coun­tries’ gen­der equal­ity in terms of ed­u­ca­tion, eco­nomic op­por­tu­nity and health, they fur­ther dis­cov­ered that “the smaller a coun­try’s Face­book gen­der di­vide in 2015, the more eco­nomic gen­der equal­ity in­creased the fol­low­ing year. More­over, a par­tic­u­larly strong link emerged be­tween the Face­book gen­der di­vide and ed­u­ca­tional in­equal­ity.

As David Gar­cia, the study’s lead au­thor, con­cluded: “Face­book data could help pol­icy mak­ers es­ti­mate gen­der in­equal­ity in poor coun­tries and could track its evo­lu­tion on a daily ba­sis.”

Q. Ar­ti­fi­cial in­tel­li­gence is in the busi­ness of cloning. How so?

A. Voice cloning tech­nol­ogy has im­proved rapidly in re­cent years, with neu­ral net­works now able to mimic some­one’s voice with less than a minute’s worth of speech, says Edd Gent in “New Sci­en­tist” magazine. At China’s search en­gine gi­ant Baidu, re­searchers have de­vel­oped soft­ware ca­pa­ble of syn­the­siz­ing a copy of a voice based solely on hear­ing snatches of the orig­i­nal. “The best ver­sion needed 100 snip­pets, each no more than five sec­onds long… But one trained on just 10 snip­pets per­formed well enough to dupe a voice recog­ni­tion sys­tem more than 95% of the time, and hu­man eval­u­a­tors gave it 3.16 out of 4 for mimicry.”

The po­ten­tial for abuse is ob­vi­ous, since “most voice au­then­ti­ca­tion sys­tems — used to se­cure ev­ery­thing from bank­ing ser­vices to smart­phones — can be fooled….” But “the tech­nol­ogy could also cre­ate dig­i­tal du­pli­cate voices for peo­ple who have lost the abil­ity to talk,” re­searchers say. And dig­i­tal as­sis­tants, video game char­ac­ters or au­to­matic speech trans­la­tion ser­vices could also be per­son­al­ized.

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