1 00:00:08,139 --> 00:00:09,490 Hello again. 2 00:00:09,490 --> 00:00:15,900 In part 2 we are going to look at the characteristics of complex systems that we have come to understand 3 00:00:15,900 --> 00:00:17,560 through the theories of complexity. 4 00:00:17,560 --> 00:00:20,730 Let me remind you. 5 00:00:20,730 --> 00:00:25,349 What we looked at in the first video were the group at the top, 6 00:00:25,349 --> 00:00:26,749 connectivity, interdependence, 7 00:00:26,749 --> 00:00:28,449 feedback and emergence. 8 00:00:28,449 --> 00:00:35,449 We will built on those to look at the others, but let me first of all introduce you to some 9 00:00:35,460 --> 00:00:40,710 of the theories of complexity that have come from the natural sciences. 10 00:00:40,710 --> 00:00:46,070 Because they have all contributed to our deep understanding of complex systems. 11 00:00:46,070 --> 00:00:50,719 So that understanding has come from chemistry and physics, 12 00:00:50,719 --> 00:00:53,949 through evolutionary biology, autopoiesis, 13 00:00:53,949 --> 00:00:57,190 biology and cognition and chaos theory. 14 00:00:57,190 --> 00:01:01,100 And from economics based very much, to begin with, 15 00:01:01,100 --> 00:01:03,070 on the work of Brain Arthur. 16 00:01:03,070 --> 00:01:10,070 So what we will look at now is the contribution that these theories have made to help us develop 17 00:01:10,170 --> 00:01:15,630 and understand much further the four principles I discussed in the first video. 18 00:01:15,630 --> 00:01:20,119 So let's start with self organization. 19 00:01:20,119 --> 00:01:25,490 In biology this is an example of self organization, birds flocking. 20 00:01:25,490 --> 00:01:34,700 They do not have a particular leader, they know where they are flying and so on. 21 00:01:34,759 --> 00:01:39,100 But what does that actually mean in a human context. 22 00:01:39,180 --> 00:01:45,509 And this is where I think we need to keep making that distinction of what is appropriate, 23 00:01:45,509 --> 00:01:48,430 what is relevant in a human context. 24 00:01:48,430 --> 00:01:54,610 Because we cannot always take something from the natural sciences and apply them directly 25 00:01:54,610 --> 00:01:56,740 to a human system. 26 00:01:56,740 --> 00:02:00,829 So, self organization in a human context, 27 00:02:00,829 --> 00:02:09,800 is first of all something which is spontaneous, is a coming together that has not been pre 28 00:02:09,950 --> 00:02:15,000 thought and it's not directed or designed by someone outside the group. 29 00:02:15,090 --> 00:02:17,180 That is very, very important. 30 00:02:17,180 --> 00:02:18,849 Now let me give you an example. 31 00:02:18,849 --> 00:02:23,849 During the Arab Spring, there was a point when someone took a broom 32 00:02:23,849 --> 00:02:27,620 and went on to Tahrir square and started cleaning up the square, 33 00:02:27,620 --> 00:02:29,709 simply because the square needed cleaning. 34 00:02:29,709 --> 00:02:35,200 No one actually told that person, and of course when one person started others 35 00:02:35,200 --> 00:02:36,129 joined him. 36 00:02:36,129 --> 00:02:41,010 Now that was pure self-organization, it was spontaneous, 37 00:02:41,010 --> 00:02:46,400 there was a job that needed to be done, someone decided that they could do the job 38 00:02:46,400 --> 00:02:50,090 and it was not directed by anyone outside. 39 00:02:50,090 --> 00:02:54,239 And the group that eventually cleared the square were not directed. 40 00:02:54,239 --> 00:02:59,760 Now this is quite different from self-management. 41 00:02:59,760 --> 00:03:06,209 A self-organized group decides what needs to be done, 42 00:03:06,209 --> 00:03:08,409 how, and when. 43 00:03:08,409 --> 00:03:11,190 And it can be a great source of innovation. 44 00:03:11,190 --> 00:03:14,659 In self management we have something different. 45 00:03:14,659 --> 00:03:19,440 A senior manager would probably identify a particular group, 46 00:03:19,440 --> 00:03:25,099 would give them a particular objective, but then give them the freedom to address 47 00:03:25,099 --> 00:03:27,610 it in whichever way they want. 48 00:03:27,610 --> 00:03:30,690 Can you see the difference between the two? In the second one, 49 00:03:30,690 --> 00:03:35,189 the self-managed one, there is someone outside the group that actually 50 00:03:35,189 --> 00:03:40,489 directs the group what to do, not necessarily have to do it, 51 00:03:40,489 --> 00:03:41,140 but what to do. 52 00:03:41,140 --> 00:03:44,930 So it is not spontaneous and it is directed from outside. 53 00:03:44,930 --> 00:03:51,930 So let's keep that distinction in mind and go on to the next principle. 54 00:03:52,650 --> 00:03:58,010 The next principle is called exploration of the space of possibilities. 55 00:03:58,010 --> 00:04:02,939 What that means is it is simply that the system explores new options, 56 00:04:02,939 --> 00:04:09,129 different ways of working and relating, because it has found a particular constraint 57 00:04:09,129 --> 00:04:15,129 that will not allow it to fulfill a particular objective the way that it may have been preplanned. 58 00:04:19,400 --> 00:04:23,200 So for example, your grandmother is dying, 59 00:04:23,200 --> 00:04:28,320 you love her dearly, you really want to be there with her, 60 00:04:28,320 --> 00:04:30,710 but your flight is cancelled. 61 00:04:30,710 --> 00:04:33,789 What are you going to do? You can of course do absolutely nothing, 62 00:04:33,789 --> 00:04:35,390 that is one option. 63 00:04:35,390 --> 00:04:40,080 But if you really want to see her, you will explore the space of possibilities 64 00:04:40,080 --> 00:04:45,349 and find a way of getting there to actually be with your grandmother. 65 00:04:45,349 --> 00:04:51,479 And that is what complex systems do, they are very good at finding new ways of 66 00:04:51,479 --> 00:04:52,250 doing things. 67 00:04:52,250 --> 00:04:54,610 Now let me explain something else. 68 00:04:54,610 --> 00:05:00,440 This is a fitness landscape, we borrowed this idea from biology. 69 00:05:00,440 --> 00:05:11,400 And what it shows is that the very successful species has climbed to the very top of the highest peak. 70 00:05:11,580 --> 00:05:16,100 Because it has got a very successful strategy. 71 00:05:16,190 --> 00:05:22,880 Now imagine that as a strategy of a very successful company. 72 00:05:22,880 --> 00:05:29,880 It has that one very successful strategy that has put it to the very top and nothing else. 73 00:05:30,800 --> 00:05:38,500 What happens when the entire landscape actually changes? Because that fitness landscape does 74 00:05:38,599 --> 00:05:41,500 not stand still, it is moving all the time. 75 00:05:41,539 --> 00:05:46,810 If you know what a children's bouncing castle looks like, 76 00:05:46,810 --> 00:05:48,849 that's the way to imagine it. 77 00:05:48,849 --> 00:05:53,810 As the children jump up and down the bouncing castle it changes all the time, 78 00:05:53,810 --> 00:05:57,250 and that is how to imagine a fitness landscape. 79 00:05:57,250 --> 00:06:04,200 So if we cannot rely on one successful strategy, what is the answer? 80 00:06:04,600 --> 00:06:09,620 The answer is multiple micro-strategies. 81 00:06:09,620 --> 00:06:12,959 Let me explain why that is the case. 82 00:06:12,959 --> 00:06:19,959 We are encouraged to think of a single optimum strategy, 83 00:06:20,469 --> 00:06:27,409 but I would suggest that a single optimum strategy is neither possible, 84 00:06:27,409 --> 00:06:32,050 nor desirable in a changing or turbulent environment. 85 00:06:32,050 --> 00:06:37,370 Because it can only be optimal under one set of circumstances. 86 00:06:37,370 --> 00:06:41,520 When those circumstances change it is no longer optimal. 87 00:06:41,520 --> 00:06:48,120 So what do you do? And as I said, the answer here is while that one strategy 88 00:06:48,120 --> 00:06:54,080 is successful, you need to explore different micro-strategies, 89 00:06:54,080 --> 00:06:58,140 and then do different experiments in order to see what works. 90 00:06:58,140 --> 00:07:03,709 So when your big strategy fails, you have already worked out, 91 00:07:03,709 --> 00:07:06,099 experimented with alternatives. 92 00:07:06,099 --> 00:07:10,899 And this is of course absolutely essential for innovation. 93 00:07:10,899 --> 00:07:14,510 So that is the principle of exploration of the space of possibilities, 94 00:07:14,510 --> 00:07:21,190 and that is something that complex systems do very well indeed. 95 00:07:21,190 --> 00:07:24,500 The next principle is called co-evolution. 96 00:07:24,500 --> 00:07:29,700 Now, most of you will be familiar with the term evolution. 97 00:07:29,800 --> 00:07:39,800 But evolution happens within an ecosystem, with relation to other things. 98 00:07:40,050 --> 00:07:44,800 The principle I will explain to you is far more accurate than just evolution. 99 00:07:44,880 --> 00:07:51,240 In this case we have an example in biology and we look at bumblebees and the flowers 100 00:07:51,240 --> 00:07:52,899 that they pollinate. 101 00:07:52,899 --> 00:07:59,899 Now they have co-evolved so that both have become dependent on each other for survival. 102 00:08:00,890 --> 00:08:04,450 So this is the definition in biology. 103 00:08:04,450 --> 00:08:06,849 What does it mean in a social context. 104 00:08:06,849 --> 00:08:13,219 By the way it took us two years to actually understand what does co-evolution mean in 105 00:08:13,219 --> 00:08:15,899 a social ecosystem. 106 00:08:15,899 --> 00:08:22,190 And I will use the term ecosystem because I want to emphasize the fact that nothing 107 00:08:22,190 --> 00:08:28,820 evolves in isolation, it is part of a bigger picture. 108 00:08:28,820 --> 00:08:30,190 Let me give you an example. 109 00:08:30,190 --> 00:08:34,490 I take a decision or action that affects you. 110 00:08:34,490 --> 00:08:40,190 It affects you to such an extend that you have to change your behaviour in response 111 00:08:40,190 --> 00:08:42,340 to my decision or action. 112 00:08:42,340 --> 00:08:49,030 Now that is simple adaptation, what you are doing is adapting to changes 113 00:08:49,030 --> 00:08:51,030 in your environment. 114 00:08:51,030 --> 00:08:53,440 That's only half the story. 115 00:08:53,440 --> 00:08:58,510 However, if you change your decision or action in due 116 00:08:58,510 --> 00:09:07,500 course comes back and affects me to such an extent that I also have to change my behaviour, 117 00:09:07,620 --> 00:09:10,300 that's co-evolution. 118 00:09:10,340 --> 00:09:12,660 So let's look at the definition. 119 00:09:12,660 --> 00:09:20,400 The definition is reciprocal influence which changes the behaviour of the interacting entities, 120 00:09:20,450 --> 00:09:24,300 and it is a very, very powerful dynamic. 121 00:09:24,370 --> 00:09:34,700 Because what it means is that we cannot just think about the impact of the environment 122 00:09:34,790 --> 00:09:36,900 on individuals, on organizations, 123 00:09:36,900 --> 00:09:40,100 on societies, because the moment they start changing their 124 00:09:40,100 --> 00:09:45,580 behaviour, that behaviour will go back and influence 125 00:09:45,580 --> 00:09:48,770 the initiator of the change. 126 00:09:48,770 --> 00:09:55,770 So this is co-evolution which happens within a social eco-system. 127 00:09:56,700 --> 00:10:02,690 And the final one I want to discuss with you, because I think it brings all of the characteristics 128 00:10:02,690 --> 00:10:05,820 together is far-from-equilibrium. 129 00:10:05,820 --> 00:10:11,140 The original work, which was done on dissipative structures by 130 00:10:11,140 --> 00:10:18,140 Ilya Prigogine and with his co-workers Nicolis and Stengers, 131 00:10:18,520 --> 00:10:25,300 it won Ilya Prigogine the Nobel Prize, because he reinterpreted the second law of thermodynamics. 132 00:10:25,300 --> 00:10:31,000 We're not going into the second law of thermodynamics, what we're actually going to look at is what 133 00:10:31,070 --> 00:10:36,620 does far-from-equilibrium mean in a human context. 134 00:10:36,620 --> 00:10:40,440 And let me give you another example. 135 00:10:40,440 --> 00:10:45,820 As you may recognize there, you will see the two screens and again it 136 00:10:45,820 --> 00:10:51,490 is a global financial system when it tumbled down. 137 00:10:51,490 --> 00:10:54,340 So let me explain what happened there. 138 00:10:54,340 --> 00:11:05,900 When a system is pushed far-from-equilibrium it means that it can no longer carry on under 139 00:11:05,900 --> 00:11:10,120 its previous way of operating. 140 00:11:10,120 --> 00:11:20,400 You will see that the system is dynamically moving within a certain limit. 141 00:11:20,700 --> 00:11:25,690 But what happens is when there is a disturbance outside the system, 142 00:11:25,690 --> 00:11:32,690 that means it can no longer continue to function in its old way. 143 00:11:33,620 --> 00:11:36,820 This is called pushing the system far-from-equilibrium. 144 00:11:36,820 --> 00:11:43,820 In human terms it means that it has to change its norms, 145 00:11:44,060 --> 00:11:48,000 its organizational structure, its culture, etc. etc. 146 00:11:48,000 --> 00:11:51,710 But let us look at what this science actually tells us. 147 00:11:51,710 --> 00:11:56,090 There is a point in the second part of the diagram, 148 00:11:56,090 --> 00:12:00,000 and that is called a point of bifurcation. 149 00:12:00,000 --> 00:12:02,980 Bifurcation means splitting into two. 150 00:12:02,980 --> 00:12:09,980 But that is only a very great simplification of what actually happens. 151 00:12:10,180 --> 00:12:15,020 Because at that point, at that critical point, 152 00:12:15,020 --> 00:12:17,860 the system, the complex system, 153 00:12:17,860 --> 00:12:24,860 will explore its space of possibilities, will continually explore different options. 154 00:12:25,360 --> 00:12:30,710 Because if it doesn't find another way of operating it will die. 155 00:12:30,710 --> 00:12:36,610 So what that simple bifurcation shows is it will either create new order, 156 00:12:36,610 --> 00:12:41,430 remember at the very beginning I said that the complex system has the capacity to create 157 00:12:41,430 --> 00:12:44,800 new order, and this is what I mean. 158 00:12:44,800 --> 00:12:49,900 It uses all its characteristics to create something new. 159 00:12:49,900 --> 00:12:53,110 It could be a new structure, a new way of relating, 160 00:12:53,110 --> 00:12:56,170 a new way of organizing, a new culture, 161 00:12:56,170 --> 00:13:02,170 but it needs to do something completely different in order to survive. 162 00:13:02,170 --> 00:13:07,500 And if it cannot create that new order then it will die. 163 00:13:07,500 --> 00:13:13,810 But what happens at that point is very, very exciting. 164 00:13:13,810 --> 00:13:20,029 And let me also point out that even though we talk about a point, 165 00:13:20,029 --> 00:13:23,570 it could be a process, it could take days, 166 00:13:23,570 --> 00:13:28,500 months, years for that exploration to actually take place. 167 00:13:28,500 --> 00:13:35,900 So what happens is when a system is pushed far-from-equilibrium the following characteristics 168 00:13:35,900 --> 00:13:39,690 come into play to create the new order. 169 00:13:39,690 --> 00:13:46,190 It will self-organize, it will explore possible solutions, 170 00:13:46,190 --> 00:13:51,920 it will co-evolve, new structures will emerge, 171 00:13:51,920 --> 00:13:59,500 there will be a sense of coherence, but also the precise behaviour can neither 172 00:13:59,560 --> 00:14:02,700 be predicted, nor controlled. 173 00:14:02,700 --> 00:14:07,060 Now this is a very disturbing conclusion. 174 00:14:07,060 --> 00:14:11,300 Especially when we're looking at complex systems. 175 00:14:11,300 --> 00:14:17,960 When we're looking at complex systems that we actually want to design. 176 00:14:17,960 --> 00:14:25,600 And one thing I want to make very clear is to give you a distinction between complicated 177 00:14:25,600 --> 00:14:29,270 systems and complex systems. 178 00:14:29,270 --> 00:14:35,240 Complicated systems we can design, we can predict their behaviour and we can 179 00:14:35,240 --> 00:14:36,890 control their behaviour. 180 00:14:36,890 --> 00:14:43,370 Now these are systems for example like producing a glass. 181 00:14:43,370 --> 00:14:49,400 We know exactly what we're producing, but we cannot do these things with a complex system. 182 00:14:49,410 --> 00:14:56,410 We may try to design it, but we cannot quite predict the outcome. 183 00:14:56,710 --> 00:15:01,930 So the behaviour is not predictable, nor is it controlled. 184 00:15:01,930 --> 00:15:08,930 And in our third video on the challenges of managing complex systems we will then look 185 00:15:09,410 --> 00:15:15,750 at what is it that we can actually do if we cannot design, 186 00:15:15,750 --> 00:15:18,460 predict and control a complex system. 187 00:15:18,460 --> 00:15:19,520 Thank you.