Complex systems can behave in unpredictable ways and cause a lot of trouble. But it doesn’t have to be like this. Their behavior depends on the interactions between the system components, the strength of these interactions, and the institutional settings. Consequently, for a complex system to work well, it is important to understand the factors that drive its dynamics. In physics, many phenomena have been understood in terms of forces, which can be measured by suitable procedures. In a similar way, the success or failure of socio-economic systems depends on hidden forces, too. Thanks to new data about our world we can now measure the forces driving socio-economic change. This will allow us to act more successfully in future.
Societies around the world are suffering from financial crises, crime, conflicts, wars, and revolutions. These “societal ills” do not occur by chance, but for a reason. The fact that they are happening time and again proves that there are hidden causes, which we haven’t understood sufficiently well. This is why we keep failing to cope with these problems. In future, however, we will be able to understand societal problems and cure them. This will be akin to the discovery of the X-ray by Wilhelm Conrad Röntgen (1845–1923), which helped to reveal the causes of many diseases and to cure billions of people.1
Given that we are now living in a Big Data age, will we soon be able to answer all our questions and find the best possible course of action in every situation? Of course, it’s far from clear that this dream will ever come true. However, the growing amount of data about our world will certainly allow us to measure the hidden forces behind our global technological, social, economic and environmental systems, just as microscopes and telescopes enabled us to discover and understand the micro- and macro-cosmos—from cells to stars—in the past. Similarly to how we have built elementary particle accelerators to discover the forces that keep our world together, we can now create “socioscopes” to reveal the principles that make our society succeed or fail.
3.1 Measuring the World 2.0
It’s a sad but well-known fact that the loss of control over a system often results from a lack of knowledge about the rules governing it. Therefore, it is important that we learn to measure and understand the hidden “forces” determining changes in the world around us. This will eventually put us in a position where we can harness these forces to overcome systemic instability and create complex dynamical systems with particular structures, properties and functions.
Remember that some of the greatest discoveries in human history were made by measuring the world. We have discovered new continents and cultures. We have reached out to the skies and explored our universe to discover black holes, dark matter, and new worlds. Now, the Internet is offering entirely new ways to quantify what’s happening on our Earth. By analyzing the sentiment of blogs, Facebook posts, or tweets, we can visualize human emotions such as happiness.2 Furthermore, it is possible to get a picture of the social, economic, and political “climate”, by identifying the subjects that people publicly discuss.3 By mining data on the Web, we can also map social and economic indicators. This includes quantities such as the gross domestic product per capita4 or the levels of violence or crime,5 highly resolved according to geographic location and time.6 It is even feasible to digitally re-construct our three-dimensional world based on the photos that people upload on platforms such as flicker.7
Illustration of scientific productivity and impact. (Reproduced from Mazloumian et al. [4] with kind permission of the Springer Nature Publishing Group.)
3.2 Monitoring the Flu and Other Diseases
Pandemics are a major threat to humanity. Some of them have killed millions of people. The Spanish flu in 1918 was a shocking example of this. In fact, such pandemics are expected to happen time and again because viruses keep mutating, such that our immune systems might be unprepared. For instance, the world was caught by surprise by the Ebola outbreak, and recently by COVID-19.
To contain the spread of epidemics, the World Health Organization (WHO) is continuously monitoring emerging diseases. It takes about two weeks to collect the data from all the hospitals in the world, meaning that each overview of the current situation is two weeks out of date. However, Google Flu Trends pioneered an approach called “nowcasting”, which was celebrated as major success of Big Data analytics at that time. It was claimed that it is possible to estimate the number of infections in real-time, based on the search queries of Google users. The underlying idea was that queries such as “I have a headache” or “I don’t feel well” or “I have a fever”, and so on, might indicate that the user has the flu. While this makes a lot of sense, the Google Flu approach was recently found to be unreliable, partly because Google constantly changes its search algorithms and also because advertisements bias people’s behavior.13
3.3 Flu Prediction Better Than Google
Fortunately, a model using much less data than Google Flu can be applied to analyze how a disease spreads, namely by augmenting data of infections with a model based on air travel data. Dirk Brockmann and I found this approach in 2012/13. About ten years back, Dirk started to investigate the spread of diseases by analyzing the time and geographic location of infections using computer simulations. He also analyzed the paths of dollar bills in his famous “Where is George?” study.14 But when visualizing the spatio-temporal spread of epidemics, the patterns looked frustratingly chaotic and unpredictable. The relationship between the arrival time of a new disease as a function of the distance from the place where it originated was so scattered that it was hard to make much sense of the data. Eventually, however, it became clear that this problem resulted from the high volume of passenger air travel. Thus, Dirk had the idea to define an “effective distance”, based on the volume of travel between airports in the world, and to study the spread of disease as a function of this alternative measure of distance.15 In effective distance, two airports such as New York City and Frankfurt are close to each other because of the large passenger flows connecting them, while two nearby cities without any direct flights between them might be largely separated.16
Dirk Brockmann and I started to collaborate in 2011, when Germany was witnessing the spread of the deadly, food-borne EHEC epidemic. I got in touch with Dirk and suggested that we could combine a model of the spread of epidemics with a model of food supply chains. In this way, we wanted to identify the location where the disease originated, which was unknown at that time. Unfortunately, we could not obtain proper supply chain data then. But our discussion triggered a number of important ideas. In particular, the research activities shifted from predicting the spread of diseases toward detecting the locations where they originate.
In fact, when analyzing empirical data of infections as a function of effective distance from the perspective of all airports worldwide, we found that the most circular spreading pattern identifies the most likely origin of the disease. More importantly, however, once the location of origin of a disease is known, one can use the circular spreading dynamics as a function of effective distance to predict the order in which cities will be hit by a pandemic.17 This helps to put medical drugs (such as immunization shots) and doctors in place where they are most effective in countering the impact and spread of the disease.
When Ebola broke out, Dirk furthermore used the method discussed above to make early predictions about possible cases in other countries. This helped to inform international preparations to contain the virus.18 However, I would also like to highlight here the fantastic research teams of Alessandro Vespignani and Vittoria Colizza, both partners of the FuturICT initiative. To predict the spread of diseases, they have built a very detailed and sophisticated simulator. Whenever a disease breaks out, this simulator can be used to test the effectiveness of countermeasures and inform policy-makers around the world.19 It was found, for example, that closing down some airline connections can only delay the spread of the disease, while the best way for industrialized countries to protect themselves from diseases such as Ebola is to spend their money on fighting the disease in those countries that are suffering from the disease first.20
3.4 Creating a Planetary Nervous System as a Citizen Web
Very soon, we will not only have maps, which aggregate data from the past and represent them as a function of space, time or network interdependencies. We will also have systems, which deliver real-time answers. We will be able to ask questions, which trigger tailored measurements to answer them. “How is the traffic on Oxford Street in London?” “How is the weather in Moscow?” “How could investment decisions and consumer choices be affected?” “How happy are people in Sydney today, and how much money will they spend in shops?” “What worries people in Paris at the moment?” “How many people are up between 3 am and 4 am on Sunday nights around Manhattan’s Central Square, and is it worth selling pizza at that time?” “How noisy is it in the part of town I am considering to move to?” “What’s the rate of flu infections in the region where I wish to spend my holidays?” “Where are the road holes in my city located?” “When did we have the last significant earthquake within a range of 500 km?” Answers to questions like these would help us to be more aware of the world around us, to make better decisions, and act more effectively. But how will we get all this real-time information?
The sensor networks, on which the “Internet of Things” is based, will enable us to perform real-time measurements of almost everything. They can be used to build a “Planetary Nervous System” (PNS), an intelligent information platform proposed by the FuturICT project (http://www.futurict.eu).21 The In fact, my team has started to develop such an information platform, called Nervousnet.22 Nervousnet would harness the power of the Internet of Things for everyone’s benefit and would be built and managed in a participatory way, as a “Citizen Web”.23 Similar to OpenStreetMap, we wanted to develop this system together with an emerging network of volunteers, who are committed to developing the project further.
This collaborative approach would give citizens control over their personal data, in accordance with their right of informational self-determination, and create new opportunities for everyone. Nervousnet would not only offer the possibility to contribute to the measurement of our world, in order to jointly create something like a real-time data Wikipedia. Nervousnet would also establish a social mining paradigm, where users are given freedom and incentives to collect, share and use data in ways that do not aim to undermine privacy. Appendix 3.1 provides further information on the platform. With your help, it may very well become a cornerstone of the public information infrastructure of the emergent digital society. So why don’t you join us in building the Nervousnet platform or in measuring the world around us?24
3.5 Sociophysics: Revealing the Hidden Forces Governing Our Society
But it takes more than data to understand the world and its problems. Measuring, analyzing and visualizing data is just the first step, because data mining alone may not lead to a good understanding of unstable system dynamics, which produces most of the good and bad surprises in the world. In order to help us when we really need it, we must find explanatory models, which can predict situations that haven’t occurred before and cannot be understood by extrapolation.
In fact, some “social mechanisms” influence human behavior in a similar way to how the gravitational force determines planetary motion25. Just think of the way social norms determine our roles and behaviors.26 The scientific approach of “agent-based simulations” aims to formalize these rules and turn them into computer codes. Complementary, the research field of sociophysics tries to express the corresponding interactions and their outcomes using mathematical formulas.27 In this chapter, I will particularly discuss the powerful concept of “social forces”, which enables researchers to understand the link between micro-level interactions of individuals and the often unexpected macro-level outcomes in socio-economic systems.
The concept of forces is one of the main pillars of physics. In order to discover it, the old geocentric worldview, in which the Earth was assumed to be the center of the universe, first had to be replaced by the heliocentric worldview, which recognized that all the planets in our solar system revolve around the sun. Later, the new understanding of the planetary system allowed Isaac Newton (1642–1727) to interpret measurement data about the planets in a new way, which led him to formulate a simple and plausible model of planetary motion based on the concept of gravitational forces. Now, most concepts used in modern physics are formulated in terms of forces and the way they influence the world. The predictive power of these models is striking and has been impressively demonstrated by the Apollo moon shot and every single satellite launch.
Another foundation of the success of physics is the tradition of building instruments to measure the forces that would otherwise be imperceptible to our senses. This has enabled physicists to explore a vast array of questions, spanning from the early stages of our universe to the exploration of elementary particles and the study of fundamental processes in biological cells. Therefore, the next logical frontier of science is to build “Socioscopes” that can reveal the hidden forces behind the dynamics of socio-economic systems. In this way, we will eventually learn to understand the counterintuitive behaviors of complex dynamical systems.28 I believe that we will soon be able to diagnose emergent societal problems such as financial crashes, crime, or wars before they happen.29 This will empower us to avoid or mitigate these problems similarly to the way medical diagnostic instruments have helped us to prevent or cure diseases. Isn’t that an exciting prospect?
3.6 Social Forces Between Pedestrians
Illustration of the various “social forces” acting upon a pedestrian (I would like to thank Mehdi Moussaid for providing this graphic.)
This “social force model” assumes that the acceleration, deceleration an directional changes of pedestrians can be approximated by the sum of a number of different forces, each of which captures a specific desire or “interaction effect”. For example, each pedestrian likes to move with a certain desired speed into a preferred direction of motion. This can be represented by a simple “driving force”, which captures how the person’s velocity is gradually adapted. Moreover, each pedestrian seeks to avoid collisions and to respect a certain personal “territory” of others. This is reflected by a “repulsive interaction force” between pedestrians which increases with proximity. Repulsive interactions with walls or streets can be described by similar forces. The attraction of tourist sites and the tendency for friends and family members to stay together can be represented by “attractive forces”.31 Finally, a random force may be used to reflect the individual behavioral variability.
Despite its simplicity, computer simulations of this model match many empirically observed phenomena surprisingly well. For example, it is possible to understand the emergence of river-like flow patterns through a standing crowd of people, the wave-like progression of individuals waiting in queues, or the lower density of people on a dance floor compared to the surrounding spectators watching them.32
3.7 Self-Organization of Unidirectional Lanes in Pedestrian Counter-Flows
Illustration of the formation of lanes of uniform walking direction in pedestrian counterflows (Reproduced from Helbing [18], with kind permission of Springer Publishers.)
While it seems as if the “invisible hand” is at work here, we can actually explain how social order is created and how collectively desirable outcomes occur from local interactions: whenever an encounter between two pedestrians occurs, the repulsive interaction force between them pushes the pedestrians a bit to the side. These interactions are more frequent between opposite directions of motion, due to the higher relative velocity. This is the main reason why people walking in opposing directions tend to separate into lanes of unidirectional flow. To explain the phenomenon, we don’t need to assume that pedestrians prefer to walk on a certain side of the street.34 In conclusion, complexity science can explain the formation of lanes of pedestrians as a so called “symmetry breaking” phenomenon, which occurs when a mixture of different directions of motion becomes unstable.
3.8 Walking Through a “Wall” of People Without Stopping
Illustration of the phenomenon of “stripe formation” in two crossing pedestrian flows (adapted from Helbing et al. [21]. Reproduced with kind permission of INFORMS.)
3.9 Measuring Forces
In physics, forces are experimentally determined by measuring the trajectories of particles, especially changes in their speed and direction of motion. It would be natural to do this for pedestrians, too. At the time when we developed the social force model for pedestrians, I could not imagine that it would ever be possible to measure social forces experimentally. But a few years later, we actually managed to do this. In around 2006, the advent of powerful video camera and processing technologies put my former Ph.D. student, Anders Johansson, into the position to detect and analyze the trajectories of pedestrians from filmed footage. Using this data, we adapted the parameters of the social force model in such a way that it optimally reproduced the trajectories of the observed pedestrians.36 In 2006/07, similar tracking methods became essential for the analysis of dense pedestrian flows and the avoidance of crowd disasters.37
Later, in 2008, Mehdi Moussaid and Guy Theraulaz set up a pedestrian experiment in Toulouse, France, under well-controlled lab conditions.38 This finally allowed us to perform data-driven modeling. While before, we had to make assumptions about the functional form of pedestrian interactions, it then became possible to determine the functional dependencies directly from the wealth of tracking data generated by the pedestrian experiment. After fitting the social force model to individual pedestrian data, it was finally used to simulate the flows of many pedestrians. To our excitement, the computer simulations yielded a surprisingly accurate prediction of the pedestrian flows observed in a wide pedestrian walkway.
So, pedestrian modeling can be considered a great success of sociophysics. One can say that, over time, pedestrian studies have turned from a social to a natural science, bringing theoretical, computational, experimental and data-driven approaches together. This has even led to practical and surprising lessons for the design of pedestrian facilities and for the planning of large-scale public events such as the annual pilgrimage in and around Mecca, as we will discuss below.
3.10 Most Pedestrian Facilities Are Inefficient
Illustration of conventional and improved elements of pedestrian facilities (adapted from Helbing [25]. Reproduction with kind permission of Springer Publishers.)
In the case of busy bi-directional pedestrian flows, the efficiency of motion can be improved by a series of pillars in the middle. These pillars help to stabilize the interface between the opposite flow directions, thereby reducing disturbances. The effectiveness of the design becomes particularly clear in subway tunnels, where pedestrians move both ways and pillars exist for static reasons.
Finally, an obstacle in the middle of a pedestrian intersection may also improve the flow. When Peter Molnar and I discovered this, it took us some time to understand this unexpected finding. Eventually we noticed that, at intersections, many different collective patterns of motion can emerge, for example, clockwise or counter-clockwise rotary flows, or oscillatory patterns of the crossing flows. The problem is that the different collective patterns of motion conflict with each other, so that none of them are stable. Putting a column in the center increases the likelihood of rotary flows and thereby increases the overall efficiency of pedestrian traffic. The flow can be improved even more by replacing a four-way intersection by four intersections of bidirectional flows, which can be achieved by placing railings in suitable locations. This encourages a rotary flow pattern, which greatly reduces disturbances.
3.11 Crowd Disasters
Unfortunately, pedestrian flow doesn’t always self-organize in an efficient way. Sometimes, terrible crowd disasters happen, and hundreds of people may be injured or killed, even when everyone has peaceful intentions and does not behave aggressively. How is this possible?
When I got interested in the problem in 1999, crowd disasters were often regarded as “acts of God” similar to natural disasters that are beyond human control. However, the root cause of the breakdown of social order in crowds is similar to the reason behind “phantom traffic jams”. If the density of people gets too high, the flow of pedestrians becomes unstable. The resulting crowd dynamics can be uncontrollable for individual people and even for hundreds of security guards. Nevertheless, crowd disasters can be avoided, if their causes are well enough understood and proper preparations made.
Crowd disasters have happened since at least Roman times. That’s why building codes were developed for stadiums, as exemplified by the Coliseum in Rome. The Coliseum had 76 numbered entrances and could accommodate between 50,000 and 73,000 visitors, who would exit through the same gate through which they had entered. These rules and the generous provision of exits meant that the Coliseum could be evacuated within five minutes. Modern stadiums, which generally have a smaller number of exits, can rarely match this performance.
Despite the frequent and tragic occurrences of crowd disasters in the past, they continue to happen due to common misperceptions. Media reports often suggest that crowd disasters occur when a crowd panics, causing a stampede in which people are crushed or trampled. Therefore, crowd disasters are claimed to be the result of unreasonable or aggressive behavior, with some individuals pushing others relentlessly as they try to escape. But why would people panic? My colleague Keith Still put it like this:
“People don’t die because they panic, they panic because they die.”
In fact, studies that I conducted with Illes Farkas, Tamas Vicsek, Mehdi Moussaid, Guy Theraulaz and others revealed that many crowd disasters have physical rather than psychological causes.39 They may occur even if everybody behaves reasonably and tries not to harm anyone else. Therefore, the common view that crowd disasters are mostly a result of panic is outdated. I don’t negate that people are in a danger to be crushed when the inflow of people into a spatially constrained area exceeds the outflow for an extended period of time. Certainly, a high density crowd can become life-threatening under such conditions, as more and more people accumulate in too little space. However, most of the time crowd disasters occur for a different reason.
Time-lapse photograph of stop-and-go flows in dense pedestrian crowds. (Reproduced from Helbing and Johansson [29], with kind permission of Springer Publishers.)
The accelerated videos showed some striking phenomena. First, we observed an unexpected, sudden transition from smooth pedestrian flows to stop-and-go flows (see the long-term photograph in Fig. 3.6).41 In contrast to highway traffic, however, these stop-and-go waves were previously unknown and unlikely to result from delayed adaptation. Eventually, we discovered that these waves were caused by a competition of too many pedestrians for too few gaps in the crowd, i.e. by a coordination problem.42 The stop-and-go movement emerged when the overall flow suddenly dropped to lower values, similarly to the capacity drop phenomenon in vehicle traffic. As a consequence, the outflow from the area drastically decreased, while the inflow stayed the same. Thus, the density increased quickly, but while this certainly created a dangerous situation, it was not the ultimate cause of the tragedy!
Illustration of the phenomenon of crowd turbulence under extremely crowded conditions. (Reproduced from Helbing and Johansson [29], with kind permission of Springer Publishers.)
It was just a matter of time until someone lost balance, stumbled, and fell to the ground. This produced a “hole” in the crowd, which unbalanced the forces acting on the surrounding people, because the counter-force from where the person stood before was missing. Therefore, the surrounding people tended to fall on top of those who had previously fallen or they were forced to step on them. The situation ended with many people piled up on top of each other, suffocating the people on the ground. Similar observations were made in other crowd incidents, such as the Love Parade disaster in Duisburg, Germany, for example.44
3.12 Countering Crowd Disasters
Can we use the above knowledge to avoid crowd disasters in the future? The answer is yes! Some years back, together with a team of various colleagues, I got temporarily involved in a project aiming to improve the flow of pedestrians during the annual Muslim pilgrimage to Mecca. We were asked to find a better way of organizing the crowd movements around the Jamarat Bridge, a focal point of the pilgrimage, where thousands of pilgrims had died in the past due to a number of tragic crowd disasters. How could one avoid them?
This was a challenge that was not simply related to technical matters such as crowd densities. We also needed to take dozens of religious, political, historical, cultural, financial and ethical factors into account. Our previous experience of modeling crowds led us to propose a range of measures including the counting of crowds through a newly developed video analysis tool, the implementation of time schedules for groups of pilgrims, re-routing strategies for crowded situations, contingency plans for possible incidents, an awareness program informing pilgrims in advance about the procedures during the Hajj, and an improved information system to guide millions of pilgrims who spoke 200 different languages.45 As many of the proposals had been implemented, the Hajj (in 1427H) was indeed performed safely without any incident in 2007, 46 after which I turned to other projects. The main success principles were to avoid crossing and counter-flows, and to suitably adapt to real-time information gathered.
Since then, the principle of providing real-time feedback to crowds has become a trend. An interesting example is an app to improve the safety of mass events, which was developed by Paul Lukowicz, a member of the FuturICT project, and a number of other scientists such as Ulf Blanke.47 By using this app, festivalgoers at a number of festivals in London, Vienna and Zurich voluntarily provided GPS data about their locations, which was used to determine their speeds and directions. This data was then returned to the festivalgoers to give them a picture of the areas which they should better avoid due to over-crowding.
3.13 Forces Describing Opinion Formation and Other Behaviors
Is the usefulness of the concept of social forces restricted to pedestrian flows (and traffic flows48), or can it be applied to various other kinds of social phenomena such as crime and conflict as well? The success of force models in describing pedestrian flows is related to the fact that pedestrians are moving continuously in space. Therefore, the dynamics of a pedestrian can be represented by an equation of motion, which states that the change of his/her spatial position over time is given by their velocity. An additional equation expresses that the change in velocity over time (i.e. the acceleration) can be modeled by a sum of forces. But can we also understand how people form opinions or other behavioral changes based on social forces? Surprisingly, the answer is “yes”, if the changes in opinion are more or less gradual on a continuous opinion scale or in a continuous opinion space.49 Otherwise, generalized models would have to be used, which exist as well.50
After formulating the social force model for pedestrians in Göttingen, Germany, in 1990, I joined the team of Professor Wolfgang Weidlich (1931–2015) at the University of Stuttgart, Germany. He was probably the only physicists working on socio-economic modeling at that time. So, Professor Weidlich might be seen as grandfather of sociophysics.51 When I joined his team, my plan at this time was to learn how to model opinion formation and decision-making. Since my work on pedestrians, I had the idea that both individual and collective human behavior could be understood as a result of social forces, and I formulated a corresponding theory (see Appendix 3.2).
Interestingly, it is possible to develop social force models for migration, too, if one assumes that people relocate within a certain (not too large) geographic range. A model that I formulated in 2009 examines “success-driven migration”.52 According to this, individuals try to avoid locations where they expect bad outcomes and are attracted to locations that appear to be favorable. Bad neighborhoods (in which people were uncooperative) were found to have a repulsive effect, whereas good neighborhoods (where people were cooperative) attracted migrants. It is even possible to calculate the direction and strength of this repulsion and attraction effects, i.e. the forces which imply the average direction and speed of motion in a certain location.
In general, a great advantage of using the concept of “social forces” is that it can help us to develop a better idea of the complex processes underlying social change. Movements towards a subject or object are reflected by attractive forces, while movements away from a subject or object are reflected by repulsive forces. It is also important to recognize that such forces may not be attributable to individuals, but rather to groups of individuals, companies or institutions. In other words, social forces may be a collective effect. Group dynamics or “group think”, as a result of the emergence of a particular group identity is probably a good example for this. Here, a collective “group” perspective emerges from the interactions of individuals, which in turn changes their characteristic opinions and behaviors. In fact, the theory of social milieus posits that the behavior of individuals is largely influenced by their social environment (Helbing et al. [40]). Very soon, it will be possible to quantify the underlying forces and to derive mathematical formulas for them. But what is more powerful, physical or social forces?
3.14 Culture: More Persistent Than Steel
It has often been claimed that civilizations were born out of war, and that the world is ultimately ruled by those with the greatest military power. However, I don’t buy this. Even though war certainly played a role in establishing the modern world, I believe the main mechanisms underlying the spreading of civilization are migration and the exchange of goods and ideas. Today, the Internet can certainly advance civilizations in ways that don’t need to be paid by human lives.
But what is the basis of civilizations? It’s culture, and to a large extent, culture is the result of numerous sets of rules, such as social conventions, values, norms, roles, and routines.53 These rules determine the success or failure of societies and guide their evolution. Just take religious values for instance, which can determine the behavior of millions of people for thousands of years. It is not an exaggeration, therefore, to say that culture is more persistent than steel54 and probably also more relevant to the success of civilizations than weapons.55 In other words, social forces can be stronger than physical forces. A good example of this is ancient Greek culture, which spread to the Roman occupants because it was more advanced.
3.15 Reducing Conflict
While we all learned about physical forces at school, very few people have an explicit understanding of the social forces, which determine the behavior of socio-economic systems. This has to change, if we want to overcome or at least mitigate the problems we are faced with. Conflicts, wars and revolutions can be understood as a result of social forces, too. Certain forces can destabilize systems and cause them to disintegrate. There are at least three types of conflict situations: (1) An encounter (say, between two countries) causes losses on both sides. This might be avoided by increasing the awareness of the likely outcomes of such an encounter in advance. (2) The encounter is beneficial to one party but unfavorable to the other, and causes damage overall. Here, the second party needs to be protected from exploitation (e.g. through solidarity from third parties, or by separating the disputing parties). (3) The encounter is advantageous for one side and undesirable for the other, but the overall outcome is positive. In such situations, the benefits can be redistributed to make the interaction beneficial for both sides, i.e. it’s possible to align interests to create a win-win situation.56
Would it also be possible to actually measure the forces creating conflict? Yes, I think one can build a ConflictMap, which illustrates regional and international tensions and explains how they come about. In fact, when working in my team, Thomas Chadefaux mined millions of news articles over a period of more than 100 years and performed a sentiment analysis for words indicating conflict. This allowed him to quantify the level of tension between countries in the world. Moreover, he could show that the level of tension could be used to predict the likelihood of outbreaks of war within a 6–12 months time period.57 Such advance warning signals might give politicians enough time to engage in diplomacy to peacefully resolve the tensions before it’s too late. Our analyses also revealed how tension spreads from one country to the next, destabilizing a huge region, as it happened after the war in Iraq. This might also have produced fertile ground for the rise of the Islamic State (IS).
3.16 What We Can Learn from Jerusalem
Another data-driven study analyzed a problem that has worried the world for many decades: the conflict in the Middle East. Why haven’t we been able to end this conflict yet? A classical Big Data approach, even if we knew the trajectories of all the bullets shot, couldn’t really reveal the causes of this conflict. Nevertheless, it is possible to understand the roots of the conflict. A few years ago, I initiated a study with Ravi Bhavnani, Dan Miodownik, Maayan Mor, and Karsten Donnay, which lead to an empirically grounded, agent-based model.58 Our model suggests that intercultural distance is the main driver of the conflict.
A further analysis reveals that violent events are correlated with each other.59 So, there is a responsive dynamic, whereby each side retaliates for previous attacks by the other side.60 For example, Palestinians retaliate against Israeli violence and vice versa. What does this tell us? Basically, both sides punish each other for violence that they suffered before. It seems that each party tries to send the message: “Stop being violent to us or you will have to pay a high price!” From the point of view of rational choice theory, this should stop the chain of violence. As one event triggers another, usually bigger one, or even several, the conflict becomes increasingly costly for both sides over time. However, rather than creating peace, a deadly spiral of violence sets in. An Israeli documentary film entitled “The Gatekeepers”,61 which interviewed previous secret service chiefs, came to a remarkable conclusion: “We have won every battle, but we are losing the war.” In other words, it doesn’t pay off to be violent, quite the opposite!
So why does such retaliation cause an escalation rather than a calming of hostilities? This occurs because both sides think their actions are right. In fact, they are applying sanctioning mechanisms that are intended to create social order, but these mechanisms are only suited for a mono-cultural context.
3.17 Punishment Doesn’t Always Work
To understand the problem better, we must ask the question: “Why do we punish others?” This has a simple reason: we have learned that punishment can establish and stabilize social norms. Therefore, who doesn’t follow our norms is usually sanctioned. But such punishment is only effective, if it is accepted by the punished party. Otherwise this party will strike back and inflict revenge, which escalates the conflict. Therefore, it is important to recognize that punishment is only effective if people share the same values, norms and culture.
In multi-cultural settings, punishment is often not suited to create social order. Under such circumstances, however, it might be possible to reduce the level of conflict by physically separating the opposing parties so that they live in different areas.62 Another option is to develop a culture of tolerance, understanding and respect. In fact, as we will see later, there are many social mechanisms that foster social order, such as reputation systems, for example. I am confident, therefore, that a deeper understanding of the mechanisms and forces producing conflicts will eventually help us to overcome or mitigate them. In a multi-cultural world, I would strongly recommend to move away from a punitive culture. Instead, it seems more promising to engage with each other in a differentiated, reputation-based culture in which diversity is welcomed.63 This brings us to another important set of invisible factors, which determines the success of societies, namely “social capital”.
3.18 Why “Social Capital” Is so Terribly Important
Most of us have probably heard the proverb that “money makes the world go round”, but there are other, intangible factors that matter. For example, human capital (like education) can boost individual careers, and social capital can act like a catalyst of socio-economic success. But what is social capital? I define it as everything that results from interactions within a social network which could potentially (be used to) create benefits. Examples include cooperativeness, public safety, a culture of punctuality, reputation, trust, power, and respect. However, while our own actions influence our social capital, we can’t fully control it. This is in contrast to money. In many cases, we can’t buy social capital (or only to a limited extent), but social capital creates added value.
Moreover, we cannot automatically generate a certain amount of social capital by doing certain things. Similarly to reputation and respect, these things are given to us by others. They depend on the effects of social interactions. Note that the amount of social capital within a system also determines its resilience or failure. Social capital influences both the probability and extent of damage. This became clear to me at a seminar of ETH Zurich’s Risk Center,64 when we discussed the disproportional effect of large disasters on public opinion. Plane crashes and terror attacks, for example, matter a lot to people, while they seem to feel less threatened by everyday risks such as car accidents or fatalities caused by smoking. Therefore, it is often believed that “size matters”, in the sense that large disasters make people respond irrationally or even in panic.
However, having studied the phenomenon of panic for some time, I came to a different conclusion. People realize that the damage is not just physical in nature. Social capital can be damaged, too. For example, a large-scale disaster often reduces public trust in the risk management of companies or public authorities, particularly if it was caused by unprofessional conduct or corruption. While people care about such things, no insurance company covers damage to social capital.
Hence, we must protect social capital similarly to how we protect economic capital or our environment. Social capital can be destroyed or exploited, but this should be prevented. In order to do this, we must learn to measure social capital and to quantify its value. Quantifying the value of our environment also helped to protect it.
3.19 Trust and Power
To stress the importance of social capital, it is important to acknowledge that the financial crisis resulted from a loss of trust. Banks did not trust other banks anymore and did not want to lend out money; customers did not trust their banks anymore and emptied their bank accounts; banks did not want to give loans to companies anymore; people did not want to invest in financial derivatives anymore—the list goes on. In the end, the resulting financial meltdown cost an estimated $15 trillion at least.65 So, trust is highly valuable and when it erodes, the economic losses are tremendous. To give another example, the recent loss of trust in US cloud storage companies due to the revelations concerning mass surveillance by the National Security Agency (NSA) has substantially reduced their business volume.66
Trust is also the basis of power and legitimacy. When I studied in Göttingen, Germany, a deadly car accident caused by a mistake of the police triggered a large public outcry and massive demonstrations. This was the first time when I noticed that public institutions can easily lose public support. In other words, they can easily lose social capital. Trust is eroded whenever authorities do something that is contrary to the moral beliefs of the public. I made a similar observation in Zurich, Switzerland, when many people complained about the policies of the migration office. During this time, the windows of the migration office were repeatedly smashed in, but when the director was replaced, the problem disappeared.
Illustration of how power results from trust and transparency
In other words, both legitimacy and power are contingent on the authorities doing what the people regard to be “the right thing”. If people withdraw their support, authority and power vanish. While the state can purchase weapons and, with this, acquire destructive power, constructive power depends on the trust and support of the people. While weapons might create fear, this is not a good substitute for genuine legitimacy. As the situation becomes increasingly unacceptable, more and more people will lose their fear, and start to resist the previously respected authorities. Some may even be willing to sacrifice their own lives. Remember that many extremists and terrorists previously led normal family lives. But even the passiveness of citizens can make a country fail within just a few years. This could be observed, for example, in the former German Democratic Republic, which had a horrible surveillance system. For these reasons, I am convinced that trust is the only sustainable basis of power and social order.67
3.20 Appendix 1: Nervousnet: A Decentralized Digital Nervous System
The open standards of the World Wide Web (WWW) have unleashed a digital economy worth many billion dollars, and participatory projects such as Linux, Wikipedia and OpenStreetMap have created opportunities for everyone. Therefore, it makes a lot of sense to build a Planetary Nervous System, which uses the sensor networks behind the Internet of Things, including those in our smartphones, to measure the world around us and build a data commons together. The question is how to do this while respecting privacy and minimizing misuse. It is time to learn this now.
The Nervousnet project has started to work on such an open and participatory, distributed information platform for real-time data.68 Nervousnet is an open source project, which believes in the importance of privacy, informational self-determination and trust. If you download the Nervousnet app to your smartphone, you can choose to turn about 10 different sensors separately on or off, such as the accelerometer, light or noise sensors. You can measure data about your environment for yourself (kept on your smartphone) or share it with others (as decided by yourself). External sensors for “smart home” and other applications can be added (for example, weather or health sensors). To maximize informational self-control, the user can also determine the recording rate, and potentially the storage time after which the data will be deleted. Shared data are anonymized. In addition, we are working on data encryption and plan to add a personal data store,69 which will allow you to determine what kind of data you want to share with whom, for what purpose, and for what period of time.
Nervousnet would be run as a Citizen Web, built and managed by its users. It would allow all developers to add measurement procedures and apps. For example, you may run games, scientific measurement projects, or business applications on top of the Nervousnet platform. So, anyone could add data-driven services and products. For security and conceptual reasons (such as scalability and fault-tolerance), Nervousnet would be based on distributed data and control. To promote responsible use, Nervousnet would integrate reputation, qualification, and community-based self-governing mechanisms, determining accessible sensors, data volume and functionality. All this is intended to catalyze a novel information, innovation and production ecosystem to create societal benefits, business opportunities, and new jobs.
Nervousnet would offer five main functionalities. First, it would empower us to collectively measure the world around us in real time. For example, it would allow us to quantify external effects of our interactions with the environment and others (such as noise and emissions, but also economic, social and immaterial value created). Such measurements could help to create a circular economy and more sustainable systems.
Second, Nervousnet would help to create awareness about the problems and opportunities around us. It would warn us of the side effects of certain decisions and actions (e.g. the amount of CO2 emissions produced) and support us in identifying and implementing better alternatives (e.g. how to use public transport comfortably).
Third, the Nervousnet data stream would establish something like a “CERN for society”. It would allow us to reveal the hidden regularities and forces underlying socio-economic change. This would create the knowledge to establish a Global System Science,70 which is needed to master our future in a highly complex and interdependent world.
Fourth, the combination of this knowledge with real-time data would enable us to build self-organizing systems, using real-time feedbacks. With the right kinds of interactions, a complex dynamical system could create a huge variety of self-stabilizing structures, properties and functions in a way that is self-organized and enormously efficient. For example, self-controlled traffic lights, which flexibly respond to local vehicle flows, can dramatically reduce urban congestion compared to the classical, centralized control approach.71 By using the Internet of Things or Internet of Everything, as some people say, many production processes could benefit as well (often summarized under the label “industry 4.0”).
Fifth, Nervousnet would support a network of distributed artificial and human intelligence. Digital assistants would support us not only in everyday situations, but also in bringing knowledge, ideas, and resources efficiently together. By creating such “collective intelligence”, we would be able to master the combinatorial complexity of our increasingly interdependent world much better.
3.21 Appendix 2: Social Fields and Social Forces
When I worked on the social force model, I soon discovered a book by Kurt Levin (1890–1947) on the concept of the “social field”.72 I immediately liked his idea, even though a behavioral and theoretical foundation of the concept was missing. So I decided to develop this foundation in my Ph.D. thesis in 1992.73 This involved deriving Boltzmann-like and Boltzmann-Fokker-Planck equations using behavioral assumptions.74
These equations contain a quantity describing a systematic motion in behavioral space, which can be interpreted as a “social force” and is often expressed as the slope of a “social field”. This social field can be imagined like a mountain range in behavioral space, where the steepest slope in a given location determines the social force that a person is subject to. This social force indicates the expected size and direction of the behavioral change. “Valleys” in the social field correspond to social norms. If someone complies with a norm, the social force is zero, but a deviation from the social norm will cause a social force (just as in real-life situations).
Note, however, that the “mountain range” discussed above (and its corresponding social field) is variable in time. It changes as a result of the behavioral changes of others. Therefore, while the social field influences the behavior of a person, at the same time, it is modified by that person’s behavior and the behavior of others. In other words, social norms may change over time as a result of social interactions.