TEDxUCL
Hannah Fry: Is life really that complex?
Hannah Fry: Maisha ni magumu kweli?
Filmed:
Readability: 4.5
819,007 views
Kanuni zinaweza kutabiri eneo la mapinduzi yajayo? Kwenye haya maongezi mazuri, mwanahisabati Hannah Fry anaonyesha jinsi tabia tata za kijamii zinaweza kuchunguzwa na pengine kutabiriwa kupitia mifano ya ishara halisi, kama mitindo ya madoa ya chui au mgawanyo wa wawindaji na windo mbugani.
Hannah Fry - Complexity theorist
Hannah Fry researches the trends in our civilization and ways we can forecast its future. Full bio
Hannah Fry researches the trends in our civilization and ways we can forecast its future. Full bio
Double-click the English transcript below to play the video.
00:10
Thanks very much.
0
24
1157
Asanteni sana.
00:11
I am Hannah Fry, the badass.
1
1205
1848
Mimi ni Hannah Fry, shupavu.
00:13
And today I'm asking the question:
2
3077
1680
Na leo ninauliza maswali:
00:14
Is life really that complex?
3
4781
1756
Maisha ni magumu kweli?
00:16
Now, I've only got nine minutes
to try and provide you with an answer,
to try and provide you with an answer,
4
6561
3325
Sasa, nina dakika tisa tu
kujaribu kuwapatia jibu,
kujaribu kuwapatia jibu,
00:19
so what I've done
is split this neatly into two parts:
is split this neatly into two parts:
5
9910
2716
nilichofanya ni kugawa hili vizuri
kwenye sehemu mbili:
kwenye sehemu mbili:
00:22
part one: yes;
6
12650
2353
sehemu ya kwanza: ndio;
00:25
and later on, part two: no.
7
15027
2528
na baadae, kwenye sehemu ya pili: hapana.
00:27
Or, to be more accurate: no?
8
17579
2544
Au, kuwa sahihi zaidi: hapana?
00:30
(Laughter)
9
20147
1204
(Kicheko)
00:31
So first of all, let me try and define
what I mean by "complex."
what I mean by "complex."
10
21375
3006
Kwanza kabisa, ngoja nijaribu kufafanua
maana ya "magumu."
maana ya "magumu."
00:34
Now, I could give you
a host of formal definitions,
a host of formal definitions,
11
24405
2441
Sasa, ninaweza kukupa
umati wa fafanuzi zilizorasmi,
umati wa fafanuzi zilizorasmi,
00:36
but in the simplest terms,
12
26870
1253
ila kwa istilahi rahisi,
00:38
any problem in complexity is something
that Einstein and his peers can't do.
that Einstein and his peers can't do.
13
28147
4899
tatizo lolote kwenye ugumu ni kitu
ambacho Einstein na wenzake wameshindwa.
ambacho Einstein na wenzake wameshindwa.
00:43
So, let's imagine --
if the clicker works ... there we go.
if the clicker works ... there we go.
14
33070
3240
Hivyo, tufikirie -- kama kibonyezeo
kikikubali ... haya twende.
kikikubali ... haya twende.
00:46
Einstein is playing a game of snooker.
15
36334
2103
Einstein anacheza mchezo wa snuka.
00:48
He's a clever chap, so he knows
that when he hits the cue ball,
that when he hits the cue ball,
16
38461
3479
Ni mwanaume mwerevu, anajua kua
anapopiga mpira wa ishara,
anapopiga mpira wa ishara,
00:51
he could write you an equation
17
41964
1441
anaweza kukuandikia mlinganyo
00:53
and tell you exactly where the red ball
is going to hit the sides,
is going to hit the sides,
18
43429
3128
na kukuambia kabisa mpira mwekundu
utaenda kugonga kwenye pande
utaenda kugonga kwenye pande
00:56
how fast it's going
and where it's going to end up.
and where it's going to end up.
19
46581
2439
jinsi unavyoenda haraka
na wapi utaishia.
na wapi utaishia.
00:59
Now, if you scale these snooker balls
up to the size of the solar system,
up to the size of the solar system,
20
49044
3469
Kama ukirekebisha hii mipira ya snuka
mpaka kwenye kipimo cha mfumo wa jua
mpaka kwenye kipimo cha mfumo wa jua
01:02
Einstein can still help you.
21
52537
1959
Einstein bado anaweza kukusaidia.
01:04
Sure, the physics changes,
22
54520
1245
Kweli, fizikia hubadilika,
01:05
but if you wanted to know about
the path of the Earth around the Sun,
the path of the Earth around the Sun,
23
55789
3282
lakini kama ulitaka kujua kuhusu
njia ya Dunia kuzunguka Jua,
njia ya Dunia kuzunguka Jua,
01:09
Einstein could write you an equation
24
59095
1733
Einstein angekuandikia mlinganyo
01:10
telling you where both objects are
at any point in time.
at any point in time.
25
60852
2643
ukikuambia wapi vitu vyote viwili
vilipo kwa mda wowote
vilipo kwa mda wowote
01:13
Now, with a surprising
increase in difficulty,
increase in difficulty,
26
63519
2204
Sasa, na ongezeko la
ugumu la kushangaza,
ugumu la kushangaza,
01:15
Einstein could include
the Moon in his calculations.
the Moon in his calculations.
27
65747
2452
Einstein angeweza kuongezea
Mwezi kwenye hesabu.
Mwezi kwenye hesabu.
01:18
But as you add more and more planets,
Mars and Jupiter, say,
Mars and Jupiter, say,
28
68223
3067
Lakini unapoongeza sayari zaidi na zaidi,
Mars na Jupiter, mfano,
Mars na Jupiter, mfano,
01:21
the problem gets too tough for Einstein
to solve with a pen and paper.
to solve with a pen and paper.
29
71314
3764
tatizo linakua gumu sana kwa Einstein
kutatua na kalamu na karatasi.
kutatua na kalamu na karatasi.
01:25
Now, strangely, if instead of having
a handful of planets,
a handful of planets,
30
75102
2843
Sasa, kiajabu, kama badala ya kua
na sayari chache,
na sayari chache,
01:27
you had millions of objects
or even billions,
or even billions,
31
77969
2672
unakua na mamilioni ya vitu
au hata mabilioni,
au hata mabilioni,
01:30
the problem actually becomes much simpler,
32
80665
2271
tatizo linakua kweli rahisi zaidi,
01:32
and Einstein is back in the game.
33
82960
1922
na Einstein anarudi kwenye mchezo.
01:34
Let me explain what I mean by this,
34
84906
1846
Wacha nieleze ninachomaanisha na hili,
01:36
by scaling these objects back down
to a molecular level.
to a molecular level.
35
86776
3294
kwa kurekebisha hivi vitu kua vidogo
kwenye kiwango cha masi.
kwenye kiwango cha masi.
01:40
If you wanted to trace the erratic path
of an individual air molecule,
of an individual air molecule,
36
90094
3747
Kama ulitaka kufuatilia njia mbalimbali
za molekyuli binafsi ya hewa,
za molekyuli binafsi ya hewa,
01:43
you'd have absolutely no hope.
37
93865
1842
ungekosa matumaini kabisa.
01:45
But when you have millions
of air molecules all together,
of air molecules all together,
38
95731
2711
Lakini kama una mamilioni ya
molekyuli za hewa kwa pamoja.
molekyuli za hewa kwa pamoja.
01:48
they start to act in a way
which is quantifiable, predictable
which is quantifiable, predictable
39
98466
3877
zinaanza kutenda kwa njia
inayohesabika, inayotabirika
inayohesabika, inayotabirika
01:52
and well-behaved.
40
102367
1170
na inayotenda vizuri.
01:53
And thank goodness air is well-behaved,
41
103561
1885
Ninashukuru sana hewa inatenda vizuri,
01:55
because if it wasn't,
planes would fall out of the sky.
planes would fall out of the sky.
42
105470
2910
kwa sababu isingekua hivyo,
ndege zingeanguka kutoka angani.
ndege zingeanguka kutoka angani.
01:58
Now, on an even bigger scale,
across the whole of the world,
across the whole of the world,
43
108404
3064
Sasa, kwenye marekebisho makubwa zaidi,
katika dunia nzima,
katika dunia nzima,
02:01
the idea is exactly the same
with all of these air molecules.
with all of these air molecules.
44
111492
3122
hilo wazo liko sawa kabisa
na molekyuli nyingine zote za hewa.
na molekyuli nyingine zote za hewa.
02:04
It's true that you can't take
an individual rain droplet
an individual rain droplet
45
114638
2918
Ni kweli kua huwezi kuchukua
tone moja la mvua
tone moja la mvua
02:07
and say where it's come from
or where it's going to end up.
or where it's going to end up.
46
117580
2785
na kusema lilipotoka
au litaishia wapi.
au litaishia wapi.
02:10
But you can say with pretty good certainty
47
120389
2034
Lakini unaweza sema na uhakika mzuri sana
02:12
whether it will be cloudy tomorrow.
48
122447
1813
kama kutakua na mawingu kesho.
02:14
So that's it.
49
124284
1158
Basi ndio hivyo.
02:15
In Einstein's time,
this is how far science had got.
this is how far science had got.
50
125466
2683
Kwa wakati wa Einstein,
hapa ndipo sayansi ilipofika.
hapa ndipo sayansi ilipofika.
02:18
We could do really small problems
with a few objects
with a few objects
51
128173
3613
Tungeweza kufanya matatizo madogo
kweli ya vitu vichache
kweli ya vitu vichache
02:21
with simple interactions,
52
131810
1275
na miingiliano mirahisi,
02:23
or we could do huge problems
with millions of objects
with millions of objects
53
133109
2602
au tungefanya matatizo makubwa
ya mamilioni ya vitu
ya mamilioni ya vitu
02:25
and simple interactions.
54
135735
1323
na miingiliano mirahisi.
02:27
But what about everything in the middle?
55
137082
1937
Lakini vipi kuhusu kila kitu katikati?
02:29
Well, just seven years
before Einstein's death,
before Einstein's death,
56
139043
2693
Basi, miaka saba tu
kabla ya kifo cha Einstein,
kabla ya kifo cha Einstein,
02:31
an American scientist called
Warren Weaver made exactly this point.
Warren Weaver made exactly this point.
57
141760
3658
mwanasayansi Mmarekani aitwae
Warren Weaver alisema kabisa jambo hili.
Warren Weaver alisema kabisa jambo hili.
02:35
He said that scientific methodology
has gone from one extreme to another,
has gone from one extreme to another,
58
145442
3624
Alisema kua mbinu za kisayansi
zimeenda kutoka kasi moja hadi nyingine,
zimeenda kutoka kasi moja hadi nyingine,
02:39
leaving out an untouched
great middle region.
great middle region.
59
149090
3071
ikibakiza eneo kubwa
la kati lisiloguswa.
la kati lisiloguswa.
02:42
Now, this middle region
is where complexity science lies,
is where complexity science lies,
60
152185
2699
Sasa, hili eneo la kati
ndipo ugumu wa sayansi ulipo,
ndipo ugumu wa sayansi ulipo,
02:44
and this is what I mean by complex.
61
154908
2259
na hichi ndicho ninachomaanisha kwa ugumu.
02:47
Now, unfortunately, almost
every single problem you can think of
every single problem you can think of
62
157191
3525
Sasa, bahati mbaya, karibia
kila tatizo utakalo liwaza
kila tatizo utakalo liwaza
02:50
to do with human behavior
63
160740
1222
kuhusu tabia ya binadamu
02:51
lies in this middle region.
64
161986
2069
linakaa eneo hili la kati.
02:54
Einstein's got absolutely no idea
how to model the movement of a crowd.
how to model the movement of a crowd.
65
164079
4290
Einstein hana wazo kabisa
jinsi ya kuunda harakati ya umati.
jinsi ya kuunda harakati ya umati.
02:58
There are too many people
to look at them all individually
to look at them all individually
66
168393
2801
Kuna watu wengi sana
wa kuwaangalia wote binafsi
wa kuwaangalia wote binafsi
03:01
and too few to treat them as a gas.
67
171218
1872
na wachache sana kuwatendea kama gesi.
03:03
Similarly, people are prone
to annoying things like decisions
to annoying things like decisions
68
173114
3356
Vilevile, watu wanakabiliwa
na vitu vinavyoudhi kama maamuzi
na vitu vinavyoudhi kama maamuzi
03:06
and not wanting to walk into each other,
69
176494
2014
na kutokutaka kugongana na kila mmoja,
03:08
which makes the problem
all the more complicated.
all the more complicated.
70
178532
2613
ambayo inaleta tatizo
yote ambayo ni ngumu zaidi.
yote ambayo ni ngumu zaidi.
03:11
Einstein also couldn't tell you
71
181169
1703
Einstein asingeweza kukuambia
03:12
when the next stock market crash
is going to be.
is going to be.
72
182896
2349
lini kutakua na mgongano
wa soko la hisa.
wa soko la hisa.
03:15
Einstein couldn't tell you
how to improve unemployment.
how to improve unemployment.
73
185269
2764
Einstein hakuweza kukuambia
jinsi ya kuboresha ajira.
jinsi ya kuboresha ajira.
03:18
Einstein can't even tell you
74
188057
1401
Einstein asingeweza kukuambia
03:19
whether the next iPhone
is going to be a hit or a flop.
is going to be a hit or a flop.
75
189482
3382
kama iPhone ijayo
itakua kubwa au itatia fora
itakua kubwa au itatia fora
03:22
So to conclude part one:
we're completely screwed.
we're completely screwed.
76
192888
2606
Kuhitimisha sehemu ya kwanza:
tuna matatizo kabisa.
tuna matatizo kabisa.
03:25
We've got no tools to deal with this,
and life is way too complex.
and life is way too complex.
77
195518
4500
Hatuna vifaa vya kutendea kazi hili
na maisha ni magumu sana.
na maisha ni magumu sana.
03:30
But maybe there's hope,
78
200042
1796
Lakini labda kuna matumaini,
03:31
because in the last few years,
79
201862
1534
miaka michache iliyopita,
03:33
we've begun to see the beginnings
of a new area of science
of a new area of science
80
203420
3837
tulianza kuona mwanzo wa
eneo jipya la sayansi
eneo jipya la sayansi
03:37
using mathematics
to model our social systems.
to model our social systems.
81
207281
3027
kutumia hisabati
kuunda mifumo ya kijamii.
kuunda mifumo ya kijamii.
03:40
And I'm not just talking here
about statistics and computer simulations.
about statistics and computer simulations.
82
210332
3484
Na siongelei tu kuhusu
takwimu na uigaji wa kompyuta.
takwimu na uigaji wa kompyuta.
03:43
I'm talking about writing down
equations about our society
equations about our society
83
213840
2996
Naongelea kuhusu kuandika chini
milinganyo kuhusu jamii
milinganyo kuhusu jamii
03:46
that will help us understand
what's going on
what's going on
84
216860
2069
ambayo itatusaidia kuelewa
kinachoendelea
kinachoendelea
03:48
in the same way as with the snooker balls
or the weather prediction.
or the weather prediction.
85
218953
3215
kwa njia sawa kama mipira ya snuka
au utabiri wa hali ya hewa.
au utabiri wa hali ya hewa.
03:52
And this has come about
because people have begun to realize
because people have begun to realize
86
222192
2842
Na hii imetokea kwa sababu
watu wameanza kugundua
watu wameanza kugundua
03:55
that we can use and exploit analogies
87
225058
2343
kua tunaweza kutumia mifano
03:57
between our human systems
and those of the physical world around us.
and those of the physical world around us.
88
227425
3942
kati ya mifumo yetu ya kibinadamu
na zile za dunia ya halisi inayotuzunguka.
na zile za dunia ya halisi inayotuzunguka.
04:01
Now, to give you an example:
89
231938
1464
Sasa, kuwapa mfano:
04:03
the incredibly complex problem
of migration across Europe.
of migration across Europe.
90
233426
3454
Ukubwa mkuu wa tatizo la
uhamiaji katika Ulaya.
uhamiaji katika Ulaya.
04:06
Actually, as it turns out, when you view
all of the people together,
all of the people together,
91
236904
3332
Ukweli, inaonekana, unapoangalia
watu wote pamoja,
watu wote pamoja,
04:10
collectively, they behave as though
they're following the laws of gravity.
they're following the laws of gravity.
92
240260
4043
kwa pamoja, wanatenda kama vile
wanafuata sheria za mvutano.
wanafuata sheria za mvutano.
04:14
But instead of planets
being attracted to one another,
being attracted to one another,
93
244327
3128
Lakini badala ya sayari kuvutiana
zenyewe kwa zenyewe,
zenyewe kwa zenyewe,
04:17
it's people who are attracted
to areas with better job opportunities,
to areas with better job opportunities,
94
247479
4210
ni watu ambao wanavutiwa na
maeneo yenye nafasi bora za ajira,
maeneo yenye nafasi bora za ajira,
04:21
higher pay, better quality of life
and lower unemployment.
and lower unemployment.
95
251713
4015
malipo ya juu, ubora wa maisha
na ukosefu wa ajira wa chini.
na ukosefu wa ajira wa chini.
04:25
And in the same way as people
are more likely to go for opportunities
are more likely to go for opportunities
96
255752
3528
Na kwa hali hio hio kama watu
wanaelekea zaidi kwenda kwa nafasi
wanaelekea zaidi kwenda kwa nafasi
04:29
close to where they live already --
London to Kent, for example,
London to Kent, for example,
97
259304
3035
karibu na wanapoishi tayari --
London mpaka Kent, kwa mfano,
London mpaka Kent, kwa mfano,
04:32
as opposed to London to Melbourne --
98
262363
1792
tofauti na London mpaka Melbourne --
04:34
the gravitational effect of planets
far away is felt much less.
far away is felt much less.
99
264179
4136
matokeo ya mvutano wa sayari
mbali sana unasikika kidogo sana.
mbali sana unasikika kidogo sana.
04:38
So, to give you another example:
100
268997
2067
Hivyo, kuwapa mfano mwingine:
04:41
in 2008, a group in UCLA
were looking into the patterns
were looking into the patterns
101
271088
4225
mwaka 2008, kikundi cha UCLA
kilikua kikiangalia ndani ya mitindo
kilikua kikiangalia ndani ya mitindo
04:45
of burglary hot spots in the city.
102
275337
2712
ya sehemu wizi upo kwa wingi mjini.
04:48
Now, one thing about burglaries
is this idea of repeat victimization.
is this idea of repeat victimization.
103
278073
5519
Sasa, kitu kimoja kuhusu wezi ni
hili wazo la uathirikaji wa kuendelea.
hili wazo la uathirikaji wa kuendelea.
04:53
So if you have a group of burglars
who manage to successfully rob an area,
who manage to successfully rob an area,
104
283616
4237
Hivyo kama una kundi la wezi
waliofaniikiwa kuiba kwenye eneo lako,
waliofaniikiwa kuiba kwenye eneo lako,
04:57
they'll tend to return to that area
and carry on burgling it.
and carry on burgling it.
105
287877
3790
wanapenda kurudi kwenye hilo eneo
na kuendelea na wizi.
na kuendelea na wizi.
05:01
So they learn the layout of the houses,
106
291691
2856
Hivyo wanajifunza mipangilio ya nyumba,
05:04
the escape routes
107
294571
1694
njia za kutorokea
05:06
and the local security measures
that are in place.
that are in place.
108
296289
3004
na kipimo cha ulinzi wa eneo
uliopo sehemu hio.
uliopo sehemu hio.
05:09
And this will continue to happen
109
299317
1685
Na hii itaendelea kutokea
05:11
until local residents and police
ramp up the security,
ramp up the security,
110
301026
3181
mpaka wa kazi na polisi wa
eneo hilo wasimamie ulinzi,
eneo hilo wasimamie ulinzi,
05:14
at which point, the burglars
will move off elsewhere.
will move off elsewhere.
111
304231
2771
ambapo ndipo, wezi watahamia
kuelekea eneo lingine.
kuelekea eneo lingine.
05:17
And it's that balance
between burglars and security
between burglars and security
112
307026
2808
Na ni huo usawa kati
ya wezi na ulinzi
ya wezi na ulinzi
05:19
which creates these dynamic
hot spots of the city.
hot spots of the city.
113
309858
3037
unaotengeneza hizi sehemu kuu
za wizi jijini zinazobadilika.
za wizi jijini zinazobadilika.
05:22
As it turns out,
this is exactly the same process
this is exactly the same process
114
312919
3544
Kama inavyoonekana,
huu ni mfumo sawa kabisa
huu ni mfumo sawa kabisa
05:26
as how a leopard gets its spots,
115
316487
2242
ya jinsi chui anavyopata madoadoa,
05:28
except in the leopard example,
it's not burglars and security,
it's not burglars and security,
116
318753
2936
isipokua kwenye mfano wa chui,
sio wezi na ulinzi,
sio wezi na ulinzi,
05:31
it's the chemical process
that creates these patterns
that creates these patterns
117
321713
3465
ni mchakato wa kemikali
unaotengeneza huu utaratibu
unaotengeneza huu utaratibu
05:35
and something called "morphogenesis."
118
325202
1995
na kitu kinachoitwa "morphogenesis."
05:37
We actually know an awful lot
about the morphogenesis of leopard spots.
about the morphogenesis of leopard spots.
119
327221
4256
Tunajua kabisa mambo mengi kuhusu
morphogenesis ya madoa ya chui.
morphogenesis ya madoa ya chui.
05:41
Maybe we can use this to try and spot
some of the warning signs with burglaries
some of the warning signs with burglaries
120
331501
4644
Labda tunaweza kutumia hili kujaribu kuona
baadhi ya alama za kuonya na wezi
baadhi ya alama za kuonya na wezi
05:46
and perhaps, also to create
better crime strategies to prevent crime.
better crime strategies to prevent crime.
121
336169
4107
na pengine, kuunda pia
mbinu bora za uhalifu kuzuia uhalifu.
mbinu bora za uhalifu kuzuia uhalifu.
05:50
There's a group here at UCL
122
340300
1572
Kuna kikundi hapa UCL
05:51
who are working with
the West Midlands police right now
the West Midlands police right now
123
341896
2825
ambao wanafanya kazi na
polisi wa West Midlands sasa hivi
polisi wa West Midlands sasa hivi
05:54
on this very question.
124
344745
1641
kwenye swali hili hasa.
05:56
I could give you
plenty of examples like this,
plenty of examples like this,
125
346410
2915
Nitawapa mifano mengi
kama hii,
kama hii,
05:59
but I wanted to leave you
with one from my own research
with one from my own research
126
349349
2643
lakini nilitaka kukuacha
na moja ya tafiti zangu
na moja ya tafiti zangu
06:02
on the London riots.
127
352016
1166
kwenye ghasia za London.
06:03
Now, you probably
don't need me to tell you
don't need me to tell you
128
353206
2015
Sasa, labda hauhitaji
mimi nikuambie
mimi nikuambie
06:05
about the events of last summer,
129
355245
1567
juu ya matukio ya kiangazi kilichopita,
06:06
where London and the UK saw
the worst sustained period
the worst sustained period
130
356836
3030
ambapo London na Uingereza waliona
kipindi kibaya cha kuhimili
kipindi kibaya cha kuhimili
06:09
of violent looting and arson
131
359890
1526
cha uhalifu na uchomaji
06:11
for over twenty years.
132
361440
1613
kwa zaidi ya miaka ishirini.
06:13
It's understandable that, as a society,
we want to try and understand
we want to try and understand
133
363077
3287
Inaeleweka kua, kama jamii,
tunataka kujaribu kuelewa
tunataka kujaribu kuelewa
06:16
exactly what caused these riots,
134
366388
1794
nini kilichosababisha hizi ghasia,
06:18
but also, perhaps, to equip our police
with better strategies
with better strategies
135
368206
3885
lakini pia, pengine, kuwapa polisi wetu
mbinu bora
mbinu bora
06:22
to lead to a swifter
resolution in the future.
resolution in the future.
136
372115
3781
kuongoza kwenye suluhisho
nyepesi mbeleni.
nyepesi mbeleni.
06:25
Now, I don't want to upset
the sociologists here,
the sociologists here,
137
375920
2356
Sasa, sitaki kuwakasirisha
wanasosholojia hapa,
wanasosholojia hapa,
06:28
so I absolutely cannot talk about
the individual motivations for a rioter,
the individual motivations for a rioter,
138
378300
4857
hivyo sitaweza kuongelea kabisa
kuhusu hamasisho binafsi za mpinduzi,
kuhusu hamasisho binafsi za mpinduzi,
06:33
but when you look at
the rioters all together,
the rioters all together,
139
383181
2168
lakini unapoangalia
wanamapinduzi kwa pamoja,
wanamapinduzi kwa pamoja,
06:35
mathematically, you can separate it
into a three-stage process
into a three-stage process
140
385373
3208
kihisabati, unaweza kuitenga kwenye
mchakato wa hatua tatu
mchakato wa hatua tatu
06:38
and draw analogies accordingly.
141
388605
1975
na kuweka mifano ipasavyo.
06:40
So, step one: let's say
you've got a group of friends.
you've got a group of friends.
142
390604
3177
Basi, hatua ya kwanza: tuseme
una kikundi cha marafiki.
una kikundi cha marafiki.
06:43
None of them are involved in the riots,
143
393805
1875
Hakuna hata mmoja anaehusika na ghasia,
06:45
but one of them walks past
a Foot Locker which is being raided,
a Foot Locker which is being raided,
144
395704
3682
lakini mmoja wao anapita mbele ya
Foot Locker inayokua inavamiwa,
Foot Locker inayokua inavamiwa,
06:49
and goes in and bags himself
a new pair of trainers.
a new pair of trainers.
145
399410
2513
na kuingia na kujinunulia
viatu vipya vya mazoezi.
viatu vipya vya mazoezi.
06:51
He texts one of his friends and says,
"Come on down to the riots."
"Come on down to the riots."
146
401947
4089
Anaandika ujumbe kwa mmoja wa rafiki
zake na kusema, "Njoo kwenye mgomo."
zake na kusema, "Njoo kwenye mgomo."
06:56
So his friend joins him,
147
406060
1421
Hivyo rafiki yake anamuunga,
06:57
and then the two of them text
more of their friends, who join them,
more of their friends, who join them,
148
407505
3157
halafu wawili hao wanaandikia
marafiki zao zaidi, ambao wanawaunga,
marafiki zao zaidi, ambao wanawaunga,
07:00
and text more of their friends
149
410686
1581
na kuandikia marafiki zao zaidi
07:02
and more and more, and so it continues.
150
412291
2374
na zaidi na zaidi, na hivyo inaendelea.
07:04
This process is identical to the way
that a virus spreads through a population.
that a virus spreads through a population.
151
414689
4583
Huu mchakato unafanana na jinsi
kile kirusi kinavyosambaa kwenye umati.
kile kirusi kinavyosambaa kwenye umati.
07:09
If you think about the bird flu epidemic
of a couple of years ago,
of a couple of years ago,
152
419296
3100
Ukifikiria kuhusu tatizo la mafua ya
ndege miaka kadhaa iliopita,
ndege miaka kadhaa iliopita,
07:12
the more people that were infected,
the more people that got infected,
the more people that got infected,
153
422420
3303
wagonjwa walivyoongezeka,
watu walioambukizwa waliongezeka,
watu walioambukizwa waliongezeka,
07:15
and the faster the virus spread
154
425747
1588
na kadri kirusi kilivyosambaa
07:17
before the authorities managed
to get a handle on events.
to get a handle on events.
155
427359
3141
kabla mamlaka hayajaweza kumudu
kuweza kudhibiti matukio.
kuweza kudhibiti matukio.
07:20
And it's exactly the same process here.
156
430988
2515
Na ni mchakato huo huo hapa.
07:23
So let's say you've got a rioter,
he's decided he's going to riot.
he's decided he's going to riot.
157
433527
3276
Tusema umepata mpinduzi,
ameamua atoke aende kwenye maandamano.
ameamua atoke aende kwenye maandamano.
07:26
The next thing he has to do
is pick a riot site.
is pick a riot site.
158
436827
2535
Kinachofuata ni kuchagua
mahali pa maandamano.
mahali pa maandamano.
07:30
Now, what you should know
about rioters is that, um ...
about rioters is that, um ...
159
440274
3624
Sasa, unachotakiwa kujua
kuhusu wapinduzi ni kua, um ...
kuhusu wapinduzi ni kua, um ...
07:33
Oops, clicker's gone. There we go.
160
443922
1642
Oops, kibonyezo kimetoweka.
Sawa.
Sawa.
07:35
What you should know about rioters is,
they're not prepared to travel
they're not prepared to travel
161
445588
3344
Unachotakiwa kujua kuhusu wapinduzi ni,
hawajajiandaa kusafiri
hawajajiandaa kusafiri
07:38
that far from where they live,
162
448956
1451
mbali sana na wanapoishi,
07:40
unless it's a really juicy riot site.
163
450431
1852
isipokuwa eneo zuri sana la ghasia.
07:42
(Laughter)
164
452307
1075
(Kicheko)
07:43
So you can see that here from this graph,
165
453406
2069
Hivyo unaona hilo hapo kwenye hii grafu,
07:45
with an awful lot of rioters
having traveled less than a kilometer
having traveled less than a kilometer
166
455499
3391
lenye wapinduzi wengi
waliosafiri chini ya kilomita
waliosafiri chini ya kilomita
07:48
to the site that they went to.
167
458914
1679
kwenye mahali walipoenda.
07:50
Now, this pattern is seen
in consumer models of retail spending,
in consumer models of retail spending,
168
460617
4909
Sasa, huu mtindo unaonekana
kwenye mifano ya watumiaji wa rejareja,
kwenye mifano ya watumiaji wa rejareja,
07:55
i.e., where we choose to go shopping.
169
465550
2309
yaani, tunapochagua kwenda kununua vitu.
07:57
So, of course, people like
to go to local shops,
to go to local shops,
170
467883
2922
Hivyo, bila shaka, watu wanapenda
kwenye maduka ya mtaani,
kwenye maduka ya mtaani,
08:00
but you'd be prepared
to go a little bit further
to go a little bit further
171
470829
2592
lakini utakua umejiandaa kwenda
mbali zaidi kidogo
mbali zaidi kidogo
08:03
if it was a really good retail site.
172
473445
2116
kama lilikua eneo zuri la maduka.
08:05
And this analogy, actually, was already
picked up by some of the papers,
picked up by some of the papers,
173
475585
3442
Na huu mfano, kweli, ulikua tayari
umechaguliwa na baadhi ya magazeti,
umechaguliwa na baadhi ya magazeti,
08:09
with some tabloid press calling the events
"Shopping with violence,"
"Shopping with violence,"
174
479051
3262
na baadhi ya wachapa magazeti wakiita
haya matukio "Ununuzi na fujo,"
haya matukio "Ununuzi na fujo,"
08:12
which probably sums it up
in terms of our research.
in terms of our research.
175
482337
2788
ambayo pengine inaeleza
kwa maneno ya utafiti wetu.
kwa maneno ya utafiti wetu.
08:15
Oh! -- we're going backwards.
176
485673
1476
Oh! -- tunaenda nyuma.
08:19
OK, step three.
177
489730
1456
Sawa, hatua ya tatu.
08:21
Finally, the rioter is at his site,
178
491210
1817
Mwishowe, mpinduzi yupo eneo lake,
08:23
and he wants to avoid
getting caught by the police.
getting caught by the police.
179
493051
4572
na anataka kuepuka
kukamatwa na polisi.
kukamatwa na polisi.
08:27
The rioters will avoid
the police at all times,
the police at all times,
180
497647
2701
Wapinduzi wataepuka
polisi muda wote,
polisi muda wote,
08:30
but there is some safety in numbers.
181
500372
2094
lakini kuna usalama kwenye wengi.
08:32
And on the flip side, the police,
with their limited resources,
with their limited resources,
182
502490
3061
Na kwa upande mwingine, polisi,
na rasilimali zao kidogo,
na rasilimali zao kidogo,
08:35
are trying to protect
as much of the city as possible,
as much of the city as possible,
183
505575
2579
wanajaribu kulinda jiji
kadri watakavyoweza,
kadri watakavyoweza,
08:38
arrest rioters wherever possible
184
508178
2013
kukamata wapinduzi inapowezekana
08:40
and to create a deterrent effect.
185
510215
2041
na kutengeneza athari za kizuizi.
08:45
And actually, as it turns out,
186
515510
1491
Na kweli, kama inavyoonekana,
08:47
this mechanism between the two species,
so to speak, of rioters and police,
so to speak, of rioters and police,
187
517025
4623
huu mfumo kati ya jamii hizi mbili,
za wapinduzi na polisi,
za wapinduzi na polisi,
08:51
is identical to predators
and prey in the wild.
and prey in the wild.
188
521672
2649
ni sawa na wawindaji
na windo mbugani.
na windo mbugani.
08:54
So if you can imagine rabbits and foxes,
189
524345
2197
Basi ukifikiria sungura na mbweha,
08:56
rabbits are trying to avoid
foxes at all costs,
foxes at all costs,
190
526566
2750
sungura wanajaribu kuepuka
mbweha kwa namna zote,
mbweha kwa namna zote,
08:59
while foxes are patrolling the space,
trying to look for rabbits.
trying to look for rabbits.
191
529340
3687
wakati mbweha wanazunguka eneo,
wakijaribu kutafuta sungura.
wakijaribu kutafuta sungura.
09:03
We actually know an awful lot
about the dynamics of predators and prey.
about the dynamics of predators and prey.
192
533051
3354
Tunajua ukweli kiasi kikubwa
kuhusu msukumo wa wawindaji na windo.
kuhusu msukumo wa wawindaji na windo.
09:06
We also know a lot about
consumer spending flows.
consumer spending flows.
193
536429
4979
Tunajua pia mengi kuhusu
mtiririko wa tabia za watumiaji.
mtiririko wa tabia za watumiaji.
09:11
And we know a lot about
how viruses spread through a population.
how viruses spread through a population.
194
541432
3163
Na tunajua mengi kuhusu
jinsi virusi vinavyosamnaa kwenya umati.
jinsi virusi vinavyosamnaa kwenya umati.
09:14
So if you take these three analogies
together and exploit them,
together and exploit them,
195
544619
3033
Hivyo ukichukua hii mifano mitatu
kwa pamoja na kuitumia,
kwa pamoja na kuitumia,
09:17
you can come up with a mathematical
model of what actually happened,
model of what actually happened,
196
547676
3236
unaweza kutoka na mfano wa
hisabati wa nini hasa kilichotokea,
hisabati wa nini hasa kilichotokea,
09:20
that's capable of replicating
the general patterns
the general patterns
197
550936
2404
ambacho kinauwezo wa kurudia
mitindo ya jumla
mitindo ya jumla
09:23
of the riots themselves.
198
553364
1343
ya ghasia zenyewe.
09:25
Now, once we've got this,
we can almost use this as a petri dish
we can almost use this as a petri dish
199
555678
3086
Tunapojua hili, tunaweza tumia
hili kama kisahani cha tafiti
hili kama kisahani cha tafiti
09:28
and start having conversations
200
558788
1623
na kuanza kuwa na majadiliano
09:30
about which areas of the city
were more susceptible than others
were more susceptible than others
201
560435
3139
kuhusu maeneo gani kwenye jiji
ambayo yako hatarishi kuliko mengine
ambayo yako hatarishi kuliko mengine
09:33
and what police tactics could be used
202
563598
1877
na mbinu gani za polisi zinaweza tumika
09:35
if this were ever to happen
again in the future.
again in the future.
203
565499
2307
kama zingetokea tena
huko mbeleni.
huko mbeleni.
09:37
Even twenty years ago, modeling
of this sort was completely unheard of.
of this sort was completely unheard of.
204
567830
4003
Hata miaka ishirini iliyopita, mifano
ya hii namna haikusikika kabisa.
ya hii namna haikusikika kabisa.
09:41
But I think that these analogies
are an incredibly important tool
are an incredibly important tool
205
571857
4444
Lakini ninafikiri hii mifano
ni kifaa kizuri na muhimu sana
ni kifaa kizuri na muhimu sana
09:46
in tackling problems with our society,
206
576325
2491
kwenye kupambana na matatizo ya
jamii yetu,
jamii yetu,
09:48
and perhaps, ultimately improving
our society overall.
our society overall.
207
578840
3406
na pengine, mwishowe kuboresha
jamii yetu kwa ujumla.
jamii yetu kwa ujumla.
09:52
So, to conclude: life is complex,
208
582270
2389
Hivyo, kuhitimisha, maisha ni magumu,
09:54
but perhaps understanding it need not
necessarily be that complicated.
necessarily be that complicated.
209
584683
3357
lakini pengine kuyaelewa sio
lazima kuwe kugumu hivyo.
lazima kuwe kugumu hivyo.
09:58
Thank you.
210
588064
1158
Asanteni.
09:59
(Applause)
211
589246
1386
(Makofi)
ABOUT THE SPEAKER
Hannah Fry - Complexity theoristHannah Fry researches the trends in our civilization and ways we can forecast its future.
Why you should listen
Hannah Fry completed her PhD in fluid dynamics in early 2011 with an emphasis on how liquid droplets move. Then, after working as an aerodynamicist in the motorsport industry, she began work on an interdisciplinary project in complexity sciences at University College London. Hannah’s current research focusses on discovering new connections between mathematically described systems and human interaction at the largest scale.
Fry is the author of the TED Book, The Mathematics of Love: Patterns, Proofs, and the Search for the Ultimate Equation.
Hannah Fry | Speaker | TED.com