Thanks very much. I am Hannah Fry, the badass. And today I'm asking the question: Is life really that complex? Now, I've only got nine minutes to try and provide you with an answer, so what I've done is split this neatly into two parts: part one: yes; and later on, part two: no. Or, to be more accurate: no?
Asanteni sana. Mimi ni Hannah Fry, shupavu. Na leo ninauliza maswali: Maisha ni magumu kweli? Sasa, nina dakika tisa tu kujaribu kuwapatia jibu, nilichofanya ni kugawa hili vizuri kwenye sehemu mbili: sehemu ya kwanza: ndio; na baadae, kwenye sehemu ya pili: hapana. Au, kuwa sahihi zaidi: hapana?
(Laughter)
(Kicheko)
So first of all, let me try and define what I mean by "complex." Now, I could give you a host of formal definitions, but in the simplest terms, any problem in complexity is something that Einstein and his peers can't do. So, let's imagine -- if the clicker works ... there we go. Einstein is playing a game of snooker. He's a clever chap, so he knows that when he hits the cue ball, he could write you an equation and tell you exactly where the red ball is going to hit the sides, how fast it's going and where it's going to end up. Now, if you scale these snooker balls up to the size of the solar system, Einstein can still help you. Sure, the physics changes, but if you wanted to know about the path of the Earth around the Sun, Einstein could write you an equation telling you where both objects are at any point in time. Now, with a surprising increase in difficulty, Einstein could include the Moon in his calculations. But as you add more and more planets, Mars and Jupiter, say, the problem gets too tough for Einstein to solve with a pen and paper. Now, strangely, if instead of having a handful of planets, you had millions of objects or even billions, the problem actually becomes much simpler, and Einstein is back in the game. Let me explain what I mean by this, by scaling these objects back down to a molecular level.
Kwanza kabisa, ngoja nijaribu kufafanua maana ya "magumu." Sasa, ninaweza kukupa umati wa fafanuzi zilizorasmi, ila kwa istilahi rahisi, tatizo lolote kwenye ugumu ni kitu ambacho Einstein na wenzake wameshindwa. Hivyo, tufikirie -- kama kibonyezeo kikikubali ... haya twende. Einstein anacheza mchezo wa snuka. Ni mwanaume mwerevu, anajua kua anapopiga mpira wa ishara, anaweza kukuandikia mlinganyo na kukuambia kabisa mpira mwekundu utaenda kugonga kwenye pande jinsi unavyoenda haraka na wapi utaishia. Kama ukirekebisha hii mipira ya snuka mpaka kwenye kipimo cha mfumo wa jua Einstein bado anaweza kukusaidia. Kweli, fizikia hubadilika, lakini kama ulitaka kujua kuhusu njia ya Dunia kuzunguka Jua, Einstein angekuandikia mlinganyo ukikuambia wapi vitu vyote viwili vilipo kwa mda wowote Sasa, na ongezeko la ugumu la kushangaza, Einstein angeweza kuongezea Mwezi kwenye hesabu. Lakini unapoongeza sayari zaidi na zaidi, Mars na Jupiter, mfano, tatizo linakua gumu sana kwa Einstein kutatua na kalamu na karatasi. Sasa, kiajabu, kama badala ya kua na sayari chache, unakua na mamilioni ya vitu au hata mabilioni, tatizo linakua kweli rahisi zaidi, na Einstein anarudi kwenye mchezo. Wacha nieleze ninachomaanisha na hili, kwa kurekebisha hivi vitu kua vidogo kwenye kiwango cha masi.
If you wanted to trace the erratic path of an individual air molecule, you'd have absolutely no hope. But when you have millions of air molecules all together, they start to act in a way which is quantifiable, predictable and well-behaved. And thank goodness air is well-behaved, because if it wasn't, planes would fall out of the sky. Now, on an even bigger scale, across the whole of the world, the idea is exactly the same with all of these air molecules. It's true that you can't take an individual rain droplet and say where it's come from or where it's going to end up. But you can say with pretty good certainty whether it will be cloudy tomorrow. So that's it. In Einstein's time, this is how far science had got. We could do really small problems with a few objects with simple interactions, or we could do huge problems with millions of objects and simple interactions. But what about everything in the middle?
Kama ulitaka kufuatilia njia mbalimbali za molekyuli binafsi ya hewa, ungekosa matumaini kabisa. Lakini kama una mamilioni ya molekyuli za hewa kwa pamoja. zinaanza kutenda kwa njia inayohesabika, inayotabirika na inayotenda vizuri. Ninashukuru sana hewa inatenda vizuri, kwa sababu isingekua hivyo, ndege zingeanguka kutoka angani. Sasa, kwenye marekebisho makubwa zaidi, katika dunia nzima, hilo wazo liko sawa kabisa na molekyuli nyingine zote za hewa. Ni kweli kua huwezi kuchukua tone moja la mvua na kusema lilipotoka au litaishia wapi. Lakini unaweza sema na uhakika mzuri sana kama kutakua na mawingu kesho. Basi ndio hivyo. Kwa wakati wa Einstein, hapa ndipo sayansi ilipofika. Tungeweza kufanya matatizo madogo kweli ya vitu vichache na miingiliano mirahisi, au tungefanya matatizo makubwa ya mamilioni ya vitu na miingiliano mirahisi. Lakini vipi kuhusu kila kitu katikati?
Well, just seven years before Einstein's death, an American scientist called Warren Weaver made exactly this point. He said that scientific methodology has gone from one extreme to another, leaving out an untouched great middle region. Now, this middle region is where complexity science lies, and this is what I mean by complex. Now, unfortunately, almost every single problem you can think of to do with human behavior lies in this middle region. Einstein's got absolutely no idea how to model the movement of a crowd. There are too many people to look at them all individually and too few to treat them as a gas. Similarly, people are prone to annoying things like decisions and not wanting to walk into each other, which makes the problem all the more complicated. Einstein also couldn't tell you when the next stock market crash is going to be. Einstein couldn't tell you how to improve unemployment. Einstein can't even tell you whether the next iPhone is going to be a hit or a flop. So to conclude part one: we're completely screwed. We've got no tools to deal with this, and life is way too complex.
Basi, miaka saba tu kabla ya kifo cha Einstein, mwanasayansi Mmarekani aitwae Warren Weaver alisema kabisa jambo hili. Alisema kua mbinu za kisayansi zimeenda kutoka kasi moja hadi nyingine, ikibakiza eneo kubwa la kati lisiloguswa. Sasa, hili eneo la kati ndipo ugumu wa sayansi ulipo, na hichi ndicho ninachomaanisha kwa ugumu. Sasa, bahati mbaya, karibia kila tatizo utakalo liwaza kuhusu tabia ya binadamu linakaa eneo hili la kati. Einstein hana wazo kabisa jinsi ya kuunda harakati ya umati. Kuna watu wengi sana wa kuwaangalia wote binafsi na wachache sana kuwatendea kama gesi. Vilevile, watu wanakabiliwa na vitu vinavyoudhi kama maamuzi na kutokutaka kugongana na kila mmoja, ambayo inaleta tatizo yote ambayo ni ngumu zaidi. Einstein asingeweza kukuambia lini kutakua na mgongano wa soko la hisa. Einstein hakuweza kukuambia jinsi ya kuboresha ajira. Einstein asingeweza kukuambia kama iPhone ijayo itakua kubwa au itatia fora Kuhitimisha sehemu ya kwanza: tuna matatizo kabisa. Hatuna vifaa vya kutendea kazi hili na maisha ni magumu sana.
But maybe there's hope, because in the last few years, we've begun to see the beginnings of a new area of science using mathematics to model our social systems. And I'm not just talking here about statistics and computer simulations. I'm talking about writing down equations about our society that will help us understand what's going on in the same way as with the snooker balls or the weather prediction. And this has come about because people have begun to realize that we can use and exploit analogies between our human systems and those of the physical world around us.
Lakini labda kuna matumaini, miaka michache iliyopita, tulianza kuona mwanzo wa eneo jipya la sayansi kutumia hisabati kuunda mifumo ya kijamii. Na siongelei tu kuhusu takwimu na uigaji wa kompyuta. Naongelea kuhusu kuandika chini milinganyo kuhusu jamii ambayo itatusaidia kuelewa kinachoendelea kwa njia sawa kama mipira ya snuka au utabiri wa hali ya hewa. Na hii imetokea kwa sababu watu wameanza kugundua kua tunaweza kutumia mifano kati ya mifumo yetu ya kibinadamu na zile za dunia ya halisi inayotuzunguka.
Now, to give you an example: the incredibly complex problem of migration across Europe. Actually, as it turns out, when you view all of the people together, collectively, they behave as though they're following the laws of gravity. But instead of planets being attracted to one another, it's people who are attracted to areas with better job opportunities, higher pay, better quality of life and lower unemployment. And in the same way as people are more likely to go for opportunities close to where they live already -- London to Kent, for example, as opposed to London to Melbourne -- the gravitational effect of planets far away is felt much less.
Sasa, kuwapa mfano: Ukubwa mkuu wa tatizo la uhamiaji katika Ulaya. Ukweli, inaonekana, unapoangalia watu wote pamoja, kwa pamoja, wanatenda kama vile wanafuata sheria za mvutano. Lakini badala ya sayari kuvutiana zenyewe kwa zenyewe, ni watu ambao wanavutiwa na maeneo yenye nafasi bora za ajira, malipo ya juu, ubora wa maisha na ukosefu wa ajira wa chini. Na kwa hali hio hio kama watu wanaelekea zaidi kwenda kwa nafasi karibu na wanapoishi tayari -- London mpaka Kent, kwa mfano, tofauti na London mpaka Melbourne -- matokeo ya mvutano wa sayari mbali sana unasikika kidogo sana.
So, to give you another example: in 2008, a group in UCLA were looking into the patterns of burglary hot spots in the city. Now, one thing about burglaries is this idea of repeat victimization. So if you have a group of burglars who manage to successfully rob an area, they'll tend to return to that area and carry on burgling it. So they learn the layout of the houses, the escape routes and the local security measures that are in place. And this will continue to happen until local residents and police ramp up the security, at which point, the burglars will move off elsewhere. And it's that balance between burglars and security which creates these dynamic hot spots of the city. As it turns out, this is exactly the same process as how a leopard gets its spots, except in the leopard example, it's not burglars and security, it's the chemical process that creates these patterns and something called "morphogenesis." We actually know an awful lot about the morphogenesis of leopard spots. Maybe we can use this to try and spot some of the warning signs with burglaries and perhaps, also to create better crime strategies to prevent crime. There's a group here at UCL who are working with the West Midlands police right now on this very question. I could give you plenty of examples like this, but I wanted to leave you with one from my own research on the London riots.
Hivyo, kuwapa mfano mwingine: mwaka 2008, kikundi cha UCLA kilikua kikiangalia ndani ya mitindo ya sehemu wizi upo kwa wingi mjini. Sasa, kitu kimoja kuhusu wezi ni hili wazo la uathirikaji wa kuendelea. Hivyo kama una kundi la wezi waliofaniikiwa kuiba kwenye eneo lako, wanapenda kurudi kwenye hilo eneo na kuendelea na wizi. Hivyo wanajifunza mipangilio ya nyumba, njia za kutorokea na kipimo cha ulinzi wa eneo uliopo sehemu hio. Na hii itaendelea kutokea mpaka wa kazi na polisi wa eneo hilo wasimamie ulinzi, ambapo ndipo, wezi watahamia kuelekea eneo lingine. Na ni huo usawa kati ya wezi na ulinzi unaotengeneza hizi sehemu kuu za wizi jijini zinazobadilika. Kama inavyoonekana, huu ni mfumo sawa kabisa ya jinsi chui anavyopata madoadoa, isipokua kwenye mfano wa chui, sio wezi na ulinzi, ni mchakato wa kemikali unaotengeneza huu utaratibu na kitu kinachoitwa "morphogenesis." Tunajua kabisa mambo mengi kuhusu morphogenesis ya madoa ya chui. Labda tunaweza kutumia hili kujaribu kuona baadhi ya alama za kuonya na wezi na pengine, kuunda pia mbinu bora za uhalifu kuzuia uhalifu. Kuna kikundi hapa UCL ambao wanafanya kazi na polisi wa West Midlands sasa hivi kwenye swali hili hasa. Nitawapa mifano mengi kama hii, lakini nilitaka kukuacha na moja ya tafiti zangu kwenye ghasia za London.
Now, you probably don't need me to tell you about the events of last summer, where London and the UK saw the worst sustained period of violent looting and arson for over twenty years. It's understandable that, as a society, we want to try and understand exactly what caused these riots, but also, perhaps, to equip our police with better strategies to lead to a swifter resolution in the future. Now, I don't want to upset the sociologists here, so I absolutely cannot talk about the individual motivations for a rioter, but when you look at the rioters all together, mathematically, you can separate it into a three-stage process and draw analogies accordingly.
Sasa, labda hauhitaji mimi nikuambie juu ya matukio ya kiangazi kilichopita, ambapo London na Uingereza waliona kipindi kibaya cha kuhimili cha uhalifu na uchomaji kwa zaidi ya miaka ishirini. Inaeleweka kua, kama jamii, tunataka kujaribu kuelewa nini kilichosababisha hizi ghasia, lakini pia, pengine, kuwapa polisi wetu mbinu bora kuongoza kwenye suluhisho nyepesi mbeleni. Sasa, sitaki kuwakasirisha wanasosholojia hapa, hivyo sitaweza kuongelea kabisa kuhusu hamasisho binafsi za mpinduzi, lakini unapoangalia wanamapinduzi kwa pamoja, kihisabati, unaweza kuitenga kwenye mchakato wa hatua tatu na kuweka mifano ipasavyo.
So, step one: let's say you've got a group of friends. None of them are involved in the riots, but one of them walks past a Foot Locker which is being raided, and goes in and bags himself a new pair of trainers. He texts one of his friends and says, "Come on down to the riots." So his friend joins him, and then the two of them text more of their friends, who join them, and text more of their friends and more and more, and so it continues. This process is identical to the way that a virus spreads through a population. If you think about the bird flu epidemic of a couple of years ago, the more people that were infected, the more people that got infected, and the faster the virus spread before the authorities managed to get a handle on events. And it's exactly the same process here.
Basi, hatua ya kwanza: tuseme una kikundi cha marafiki. Hakuna hata mmoja anaehusika na ghasia, lakini mmoja wao anapita mbele ya Foot Locker inayokua inavamiwa, na kuingia na kujinunulia viatu vipya vya mazoezi. Anaandika ujumbe kwa mmoja wa rafiki zake na kusema, "Njoo kwenye mgomo." Hivyo rafiki yake anamuunga, halafu wawili hao wanaandikia marafiki zao zaidi, ambao wanawaunga, na kuandikia marafiki zao zaidi na zaidi na zaidi, na hivyo inaendelea. Huu mchakato unafanana na jinsi kile kirusi kinavyosambaa kwenye umati. Ukifikiria kuhusu tatizo la mafua ya ndege miaka kadhaa iliopita, wagonjwa walivyoongezeka, watu walioambukizwa waliongezeka, na kadri kirusi kilivyosambaa kabla mamlaka hayajaweza kumudu kuweza kudhibiti matukio. Na ni mchakato huo huo hapa.
So let's say you've got a rioter, he's decided he's going to riot. The next thing he has to do is pick a riot site. Now, what you should know about rioters is that, um ... Oops, clicker's gone. There we go. What you should know about rioters is, they're not prepared to travel that far from where they live, unless it's a really juicy riot site.
Tusema umepata mpinduzi, ameamua atoke aende kwenye maandamano. Kinachofuata ni kuchagua mahali pa maandamano. Sasa, unachotakiwa kujua kuhusu wapinduzi ni kua, um ... Oops, kibonyezo kimetoweka. Sawa. Unachotakiwa kujua kuhusu wapinduzi ni, hawajajiandaa kusafiri mbali sana na wanapoishi, isipokuwa eneo zuri sana la ghasia.
(Laughter)
(Kicheko)
So you can see that here from this graph, with an awful lot of rioters having traveled less than a kilometer to the site that they went to. Now, this pattern is seen in consumer models of retail spending, i.e., where we choose to go shopping. So, of course, people like to go to local shops, but you'd be prepared to go a little bit further if it was a really good retail site. And this analogy, actually, was already picked up by some of the papers, with some tabloid press calling the events "Shopping with violence," which probably sums it up in terms of our research. Oh! -- we're going backwards.
Hivyo unaona hilo hapo kwenye hii grafu, lenye wapinduzi wengi waliosafiri chini ya kilomita kwenye mahali walipoenda. Sasa, huu mtindo unaonekana kwenye mifano ya watumiaji wa rejareja, yaani, tunapochagua kwenda kununua vitu. Hivyo, bila shaka, watu wanapenda kwenye maduka ya mtaani, lakini utakua umejiandaa kwenda mbali zaidi kidogo kama lilikua eneo zuri la maduka. Na huu mfano, kweli, ulikua tayari umechaguliwa na baadhi ya magazeti, na baadhi ya wachapa magazeti wakiita haya matukio "Ununuzi na fujo," ambayo pengine inaeleza kwa maneno ya utafiti wetu. Oh! -- tunaenda nyuma.
OK, step three. Finally, the rioter is at his site, and he wants to avoid getting caught by the police. The rioters will avoid the police at all times, but there is some safety in numbers. And on the flip side, the police, with their limited resources, are trying to protect as much of the city as possible, arrest rioters wherever possible and to create a deterrent effect. And actually, as it turns out, this mechanism between the two species, so to speak, of rioters and police, is identical to predators and prey in the wild. So if you can imagine rabbits and foxes, rabbits are trying to avoid foxes at all costs, while foxes are patrolling the space, trying to look for rabbits. We actually know an awful lot about the dynamics of predators and prey. We also know a lot about consumer spending flows. And we know a lot about how viruses spread through a population.
Sawa, hatua ya tatu. Mwishowe, mpinduzi yupo eneo lake, na anataka kuepuka kukamatwa na polisi. Wapinduzi wataepuka polisi muda wote, lakini kuna usalama kwenye wengi. Na kwa upande mwingine, polisi, na rasilimali zao kidogo, wanajaribu kulinda jiji kadri watakavyoweza, kukamata wapinduzi inapowezekana na kutengeneza athari za kizuizi. Na kweli, kama inavyoonekana, huu mfumo kati ya jamii hizi mbili, za wapinduzi na polisi, ni sawa na wawindaji na windo mbugani. Basi ukifikiria sungura na mbweha, sungura wanajaribu kuepuka mbweha kwa namna zote, wakati mbweha wanazunguka eneo, wakijaribu kutafuta sungura. Tunajua ukweli kiasi kikubwa kuhusu msukumo wa wawindaji na windo. Tunajua pia mengi kuhusu mtiririko wa tabia za watumiaji. Na tunajua mengi kuhusu jinsi virusi vinavyosamnaa kwenya umati.
So if you take these three analogies together and exploit them, you can come up with a mathematical model of what actually happened, that's capable of replicating the general patterns of the riots themselves. Now, once we've got this, we can almost use this as a petri dish and start having conversations about which areas of the city were more susceptible than others and what police tactics could be used if this were ever to happen again in the future. Even twenty years ago, modeling of this sort was completely unheard of. But I think that these analogies are an incredibly important tool in tackling problems with our society, and perhaps, ultimately improving our society overall.
Hivyo ukichukua hii mifano mitatu kwa pamoja na kuitumia, unaweza kutoka na mfano wa hisabati wa nini hasa kilichotokea, ambacho kinauwezo wa kurudia mitindo ya jumla ya ghasia zenyewe. Tunapojua hili, tunaweza tumia hili kama kisahani cha tafiti na kuanza kuwa na majadiliano kuhusu maeneo gani kwenye jiji ambayo yako hatarishi kuliko mengine na mbinu gani za polisi zinaweza tumika kama zingetokea tena huko mbeleni. Hata miaka ishirini iliyopita, mifano ya hii namna haikusikika kabisa. Lakini ninafikiri hii mifano ni kifaa kizuri na muhimu sana kwenye kupambana na matatizo ya jamii yetu, na pengine, mwishowe kuboresha jamii yetu kwa ujumla.
So, to conclude: life is complex, but perhaps understanding it need not necessarily be that complicated.
Hivyo, kuhitimisha, maisha ni magumu, lakini pengine kuyaelewa sio lazima kuwe kugumu hivyo.
Thank you.
Asanteni.
(Applause)
(Makofi)