How to make the future more rational

Over a year into the pan­dem­ic, a lack of resources in sci­ence com­mu­ni­ca­tion, abuse of bad epis­temics and ques­tion­able glob­al gov­er­nance – such as vac­cine dis­tri­b­u­tion – are still result­ing in thou­sands of avoid­able deaths per day. Even in west­ern democ­ra­cies, politi­cians still strug­gle to under­stand the role of aerosol-based trans­mis­sion and, as such, vital improve­ments that could be eas­i­ly achieved by bet­ter ven­ti­la­tion. Mean­while, scep­ti­cism around vac­cines – whilst it may be get­ting bet­ter – remains as long-stand­ing col­lat­er­al dam­age caused by dis­or­der in the infor­ma­tion land­scape or info­dem­ic. 

Of all human traits, ratio­nal­i­ty is arguably one we cher­ish most because we con­sid­er it a defin­ing dis­tinc­tion between us and oth­er ani­mals. How­ev­er, this unfor­tu­nate­ly also comes with a recur­rent over­con­fi­dence in our abil­i­ty to be ratio­nal lead­ing us to trust our intu­ition, believe in our gut-feel­ing and lis­ten to com­mon-sense. All of which go against ratio­nal­i­ty. In addi­tion, nobody is born with ratio­nal­i­ty or inher­ent log­ic. Hence, it is up to soci­ety, through the accu­mu­la­tion of knowl­edge, to endow indi­vid­u­als with the abil­i­ty to think objec­tive­ly. As such, the future of humanity’s abil­i­ty to per­form col­lec­tive prob­lem solv­ing requires a scale-up of the num­ber of cit­i­zens well-equipped with exter­nal think­ing strate­gies that include log­ic and the sci­en­tif­ic method. 

Of all human char­ac­ter­is­tics, ratio­nal­i­ty is prob­a­bly the one we cher­ish most.

Copié !

A pos­si­ble first step could be to repair the pop­ulist impres­sion that our tech­no­log­i­cal­ly dri­ven era is too advanced for ‘non-pro­duc­tive’ arm­chair phi­los­o­phy. After all, the mod­ern com­put­er era was not insti­gat­ed by engi­neers try­ing to build a gad­get, but rather a group of philoso­phers who were lit­er­al­ly think­ing about think­ing. It was the foun­da­tion­al cri­sis in log­ic in the late 19^th Cen­tu­ry that led a series of philoso­phers and math­e­mati­cians to ques­tion the very act of “pro­cess­ing infor­ma­tion”. In doing so, they found use­ful loop­holes in log­ic and set the right ques­tions to which Kurt Gödel, Alan Tur­ing, Alon­zo Church and oth­ers would bring aware­ness – a pre­cur­sor to many things today, in the form of lap­tops and smart­phones¹.

Anoth­er use­ful step might be to stress how log­ic and the sci­en­tif­ic method, while still an end­less work in progress, can be viewed through two of their most impor­tant com­po­nents: deduc­tion and induc­tion. Sim­ply put, deduc­tion is “top-down” log­ic, mean­ing how to infer a con­clu­sion from a gen­er­al prin­ci­ple or a law. This includes launch­ing a space rock­et, cur­ing a well-known dis­ease, or apply­ing a law in court. Where­as induc­tion is “bot­tom-up” log­ic, that is, how to infer – based on obser­va­tions – the laws to explain how these obser­va­tions hap­pen. This may be describ­ing the laws of grav­i­ty, dis­cov­er­ing the cure for a new dis­ease or defin­ing a law for soci­ety to abide by. All of which require an induc­tive mindset.

Deduc­tive log­ic was his­tor­i­cal­ly the first to be estab­lished through algo­rithms. While today this term is pri­mar­i­ly asso­ci­at­ed with tech­nol­o­gy, it should be stressed that it was orig­i­nal­ly derived from the name of a thinker, Al Khwaraz­i­mi. He was most­ly try­ing to help lawyers by writ­ing step-by-step rules that they could apply to reach com­pa­ra­ble results². Far from being a tool to ren­der the deci­sion-mak­ing process obscure, algo­rithms, such as writ­ten law, were his­tor­i­cal­ly a tool for trans­paren­cy. We feel safer if we know we will be judged accord­ing to a well-defined rule or law, rather than accord­ing to the fluc­tu­at­ing mood of an autocrat.

Induc­tive process­es are hard­er than deduc­tive ones. Even though medieval thinkers such as Ibn Al Haytham (Alhazen), Jabir Ibn Hayan (Geber) and, of course, Galileo left ear­ly traces of progress in for­mal­is­ing the sci­en­tif­ic method used today, we still do not have a wide­ly adopt­ed algo­rithm for induc­tion as we do have for deduc­tion. Impor­tant attempts to pro­vide algo­rithms for induc­tion were made by Bayes and Laplace³. The lat­ter even pro­duced an impor­tant, yet high­ly over­looked “Philo­soph­i­cal Essay on Prob­a­bil­i­ties”, decades after for­mal­is­ing the laws of prob­a­bil­i­ties (in the form of a course giv­en at the then nascent Ecole Nor­male and Ecole poly­tech­nique). Read­ing Laplace’s essay today, one finds pio­neer­ing ideas about what can go wrong with induc­tion – some­thing mod­ern cog­ni­tive psy­chol­o­gists refer to as cog­ni­tive bias­es.

Once we look at the details, many cog­ni­tive bias­es fall under an exces­sive use of a deduc­tive mind­set in sit­u­a­tions where an induc­tive one is more appro­pri­ate. The most com­mon of which being con­fir­ma­tion bias: our brain would rather seek facts that prove the hypoth­e­sis it already has than to expend men­tal effort to go against it. There is also the oth­er (less com­mon) extreme, exces­sive rel­a­tivism, where we refuse any causal inter­pre­ta­tion, even when data jus­ti­fies an expla­na­tion more appro­pri­ate­ly than exist­ing alternatives. 

To com­pen­sate for the weak­ness­es of the human mind, sci­en­tists devised heuris­tics to bet­ter per­form induc­tion: test­ing a hypoth­e­sis, con­trolled exper­i­ments, ran­domised tri­als, mod­ern sta­tis­tics and so on. Bayes and Laplace went even fur­ther and gave us an algo­rithm to per­form induc­tion – the Bayes’ equa­tion. It can be used to show that first order log­ic, where state­ments are either true or false, is a spe­cial case of the laws of prob­a­bil­i­ty, where use­ful room is left for uncer­tain­ty. Whilst the lan­guage of deduc­tion is most­ly answer­ing with a pre-defined “because” to ques­tions start­ing with “why”, rig­or­ous induc­tion requires a more prob­a­bilis­tic analy­sis that adds a “how much” to weigh every dif­fer­ent pos­si­ble cause. 

To com­pen­sate for the weak­ness­es of the human mind and to make bet­ter use of induc­tion, sci­en­tists have devel­oped heuristics.

Copié !

Philoso­pher Daniel Den­nett⁴ describes some of our great­est sci­en­tif­ic and philo­soph­i­cal rev­o­lu­tions as “strange inver­sions of rea­son­ing”. Dar­win was able to invert the log­ic that com­plex beings (i.e., humans) did not nec­es­sar­i­ly need a more com­plex cre­ator to emerge. Tur­ing showed that com­plex infor­ma­tion pro­cess­ing does not need the agent (i.e., the com­put­er) per­form­ing it to be aware of any­thing oth­er than sim­ple mechan­i­cal log­i­cal instruc­tions. I would like to argue that what Den­nett calls strange inver­sions of rea­son­ing, are his­tor­i­cal moves from a deduc­tive (and some­what cre­ation­ist) frame­work to an induc­tive frame­work. The more com­plex the prob­lem, the less a “why” is use­ful and the more a “how much” is needed. 

While sci­en­tists were busy devis­ing log­ic and the sci­en­tif­ic method for the past mil­len­nia, the larg­er part of soci­ety realised the lim­its of the deduc­tive mind­set that comes with either autoc­ra­cy, where a monarch sets the rule, or theoc­ra­cy, where God, often a com­fort­able shield for the monarch, sets the rule. This led to the pro­gres­sive devel­op­ment of democ­ra­cy, where the aggre­ga­tion of opin­ions helps soci­ety per­form a bet­ter and more robust col­lec­tive induc­tion and, in prin­ci­ple, estab­lish more effec­tive rules. Yet, democ­ra­cy lies in the hope that a sig­nif­i­cant frac­tion of soci­ety is well-informed and act­ing in its own interest. 

Today, this assump­tion is at greater threat than it has ever been before. For the first time in human his­to­ry, we are pro­duc­ing infor­ma­tion dis­sem­i­na­tion tools that have the broad­cast pow­er of the most dystopi­an pro­pa­gan­da machine yet the fine-grained per­son­al­i­sa­tion fea­tures of indi­vid­ual door-to-door cam­paign­ing – for the bet­ter or for worse. The dig­i­tal tools we enjoy today are most­ly the out­come of automat­ing deduc­tion (pro­gram­ming), which most­ly hap­pened dur­ing the past cen­tu­ry. As we are enter­ing a new phase of automa­tion, which is this time data-dri­ven, it is impor­tant to stress that, beyond the gad­gets and the tech­nol­o­gy part, we are try­ing to auto­mate induc­tion, and, while doing so, bet­ter under­stand what induc­tion is and how to do it right. 

Keep­ing that in mind in how we design our cours­es on Data Sci­ence or com­mu­ni­cate the advances of arti­fi­cial intel­li­gence to the pub­lic, might hope­ful­ly help pro­duce a new gen­er­a­tion of cit­i­zens that are not only able to build or use these tools, but able to join the larg­er con­ver­sa­tion on the future of rea­son­ing. A con­ver­sa­tion in which induc­tion, deduc­tion, society’s diet of infor­ma­tion and appro­pri­ate col­lec­tive deci­sion-mak­ing are empow­ered, and not cor­rupt­ed by the very dig­i­tal tools that were invent­ed as mere side prod­ucts of the human endeav­our. Our endeav­our to under­stand and auto­mate what we cher­ish the most: our abil­i­ty to think.