Stefaan Verhulst

Excerpt of book by Geoff Mulgan on Social Innovation: How Societies Find the Power to Change: “…There are very few thinkers who have changed how we see the world, and even fewer who have changed how we think about how we see the world. Mary Douglas was one of the very rare exceptions. Her field was culture, but she was as unlike the stereotypical cultural academic as one could imagine. A devout Catholic whose late husband was head of research at Conservative Central Office, she used the decades after she passed retirement age in an extraordinary flowering of enquiry that provided striking insights in fields as diverse as the study of the Old Testament and the politics of climate change.

She was a rare example of a public intellectual whose theoretical apparatus allowed her to think in original ways about almost any topic—for example, in her ideas on enclaves, the small groups which at their most extreme become terrorist cells. Where others emphasize their strengths, she emphasized their weaknesses: how prone they are to splits and sectarianism, and how hard it is for their founders to impose and enforce rules. To survive, they create around themselves what she called a ‘wall of virtue’—the sense that they alone uphold justice, while all around them are suspect. Yet the very thing that binds them together encourages individuals to compete to demonstrate their own virtue and the failings of their peers. The only thing that can override this fragility is fear of the outside world—and so sects, whether political or religious, peaceful or violent, feed off the fear and hostility of states and societies, using it to reinforce their own solidarity and their own sense of virtue. The implication is clear, and challenging, for Western governments: in the long term, defeating terrorism depends on ratcheting fear down, not up, and on dismantling the “walls of virtue” rather than attacking them head on with declarations of war….

In each of these fields Douglas’s work set in motion new schools of thought. Perhaps the most fertile of all of these is now being used to make sense of why so many well-intentioned policies fail, and why some others succeed even though they appear to work less well on paper. Her starting point is a deceptively simple framework which she repeatedly used to make sense of organizations and societies. It is a framework which should be part of the mental furniture of any educated person, as basic as the laws of supply and demand in economics, or the laws of thermodynamics.

Any culture, she argues, can be mapped on two dimensions. On one axis is what she calls the “grid”—the extent to which behaviors and rules are defined and differentiated, for example by public rules deciding who can do what according to their age, race, gender or qualifications. Examples of a high grid would be a traditional corporation, a traditional agrarian society or families with clear demarcations of roles and times (when to eat, when to go to bed). On the other axis is what she calls “group”—the extent to which people bond with each other and divide the world into insiders and outsiders. The more people do with a group of other people, the more they experience testing trials, or the more difficult the group is to get into, the higher the sense of group belonging will be.

These two dimensions come together to provide a simple two-by-two matrix: high grid and high group mean hierarchy; low grid and low group mean individualism; high group and low grid lead to egalitarianism; and low group and high grid result in fatalism. This very simple model has turned out to be a powerful tool for understanding social relations and for making sense of how people see the world. We may like to believe that we choose and shape our own beliefs, but Douglas, drawing on the work of Émile Durkheim and others, suggests that it’s much easier to understand societies by turning that assumption on its head: societies and institutions think through us much more than the other way around….(More)”.

Book by Gary M. Shiffman: “How do we understand illicit violence? Can we prevent it? Building on behavioral science and economics, this book begins with the idea that humans are more predictable than we like to believe, and this ability to model human behavior applies equally well to leaders of violent and coercive organizations as it does to everyday people. Humans ultimately seek survival for themselves and their communities in a world of competition. While the dynamics of ‘us vs. them’ are divisive, they also help us to survive. Access to increasingly larger markets, facilitated through digital communications and social media, creates more transnational opportunities for deception, coercion, and violence. If the economist’s perspective helps to explain violence, then it must also facilitate insights into promoting peace and security. If we can approach violence as behavioral scientists, then we can also better structure our institutions to create policies that make the world a more secure place, for us and for future generations….(More)”.

Chapter by Kabir C. Sen: “The twenty first century has seen the advent of technical advances in storage, transmission and analysis of information. This has had a profound impact on the field of medicine. However, notwithstanding these advances, various obstacles remain in the world regarding the improvement of human lives through the provision of better health care. The obstacles emanate from the demand (i.e., the problem) as well as the supply (i.e., the solution) side. In some cases, the nature of the problems might not have been correctly identified. In others, a solution to a problem could be known only to a small niche of the global population. Thus, from the demand perspective, the variety of health care issues can range from the quest for a cure for a rare illness to the inability to successfully implement verifiable preventive measures for a disease that affects pockets of the global population. Alternatively, from the supply perspective, the approach to a host of health issues might vary because of fundamental differences in both medical philosophies and organizational policies.

In many instances, effective solutions to health care problems are lacking because of inadequate global knowledge about the particular disease. Alternatively, in other cases, a solution might exist but the relevant knowledge about it might only be available to selected pockets of the global medical community. Sometimes, the barriers to the transfer of knowledge might have their root causes in ignorance or prejudice about the initiator of the cure or solution. However, the advent of information technology has now provided an opportunity for individuals located at different geographical locations to collaborate on solutions to various problems. These crowdsourcing projects now have the potential to extract the “wisdom of crowds” for tackling problems which previously could not be solved by a group of experts (Surowiecki, 2014). Anecdotal evidence suggests that crowdsourcing has achieved some success in providing solutions for a rare medical disease (Arnold, 2014). This chapter discusses crowdsourcing’s potential to solve medical problems by designing a framework to evaluate its promises and suggest recommended future paths of actions….(More)”.

Paper by Lion Hirth: “Power system modeling is data intensive. In Europe, electricity system data is often available from sources such as statistical offices or system operators. However, it is often unclear if these data can be legally used for modeling, and in particular if such use infringes intellectual property rights. This article reviews the legal status of power system data, both as a guide for data users and for data publishers.

It is based on interpretation of the law, a review of the secondary literature, an analysis of the licenses used by major data distributors, expert interviews, and a series of workshops. A core finding is that in many cases the legality of current practices is doubtful: in fact, it seems likely that modelers infringe intellectual property rights quite regularly. This is true for industry analysis but also academic researchers. A straightforward solution is open data – the idea that data can be freely used, modified, and shared by anyone for any purpose. To be open, it is not sufficient for data to be accessible free of cost, it must also come with an open data license, the most common types of which are also reviewed in this paper….(More)”.

The Economist: “Faster, cheaper, better—technology is one field many people rely upon to offer a vision of a brighter future. But as the 2020s dawn, optimism is in short supply. The new technologies that dominated the past decade seem to be making things worse. Social media were supposed to bring people together. In the Arab spring of 2011 they were hailed as a liberating force. Today they are better known for invading privacy, spreading propaganda and undermining democracy. E-commerce, ride-hailing and the gig economy may be convenient, but they are charged with underpaying workers, exacerbating inequality and clogging the streets with vehicles. Parents worry that smartphones have turned their children into screen-addicted zombies.

The technologies expected to dominate the new decade also seem to cast a dark shadow. Artificial intelligence (ai) may well entrench bias and prejudice, threaten your job and shore up authoritarian rulers (see article). 5g is at the heart of the Sino-American trade war. Autonomous cars still do not work, but manage to kill people all the same. Polls show that internet firms are now less trusted than the banking industry. At the very moment banks are striving to rebrand themselves as tech firms, internet giants have become the new banks, morphing from talent magnets to pariahs. Even their employees are in revolt.

The New York Times sums up the encroaching gloom. “A mood of pessimism”, it writes, has displaced “the idea of inevitable progress born in the scientific and industrial revolutions.” Except those words are from an article published in 1979. Back then the paper fretted that the anxiety was “fed by growing doubts about society’s ability to rein in the seemingly runaway forces of technology”.

Today’s gloomy mood is centred on smartphones and social media, which took off a decade ago. Yet concerns that humanity has taken a technological wrong turn, or that particular technologies might be doing more harm than good, have arisen before. In the 1970s the despondency was prompted by concerns about overpopulation, environmental damage and the prospect of nuclear immolation. The 1920s witnessed a backlash against cars, which had earlier been seen as a miraculous answer to the affliction of horse-drawn vehicles—which filled the streets with noise and dung, and caused congestion and accidents. And the blight of industrialisation was decried in the 19th century by Luddites, Romantics and socialists, who worried (with good reason) about the displacement of skilled artisans, the despoiling of the countryside and the suffering of factory hands toiling in smoke-belching mills….(More)”.

UCL Institute for Innovation and Public Purpose: “…The 21st century is becoming increasingly defined by the need to respond to major issues facing society, the environment around us and the possibility of developing a prosperous equal economy. Sometimes referred to as ‘grand challenges’, these include climate change, ageing societies, preventative healthcare, and generating sustainable growth for the benefit of all.

Innovation has not just a rate but also a direction. How that direction is set — not just by the government but by different actors and socio-political forces — is a key aspect of IIPP’s work. But how should we decide which direction? We use the concept of public value as a way to think about which direction innovation and industrial policy takes. Public value is value that is created collectively for a public purpose — this requires citizens to engage in defining purpose, nurturing capabilities and capacities, assess the value created, and ensure that societal value is distributed equitably…(More)”.

Michael Mehaffy at Public Square: “Urbanists have long been drawing lessons from other disciplines, including sociology, environmental psychology and ecology. Now there are intriguing new lessons being offered by a perhaps surprising field: brain science. But to explore the story of those lessons, we’ll have to start first with genetics.

Few developments in the sciences have had the impact of the revolutionary discoveries in genetics, and in particular, what is called the “genome”—the totality of the complex pattern of genetic information that produces the proteins and other structures of life. By getting a clearer picture of the workings of this evolving, generative structure, we gain dramatic new insights on disease processes, on cellular mechanisms, and on the ultimate wonders of life itself. In a similar way, geneticists now speak of the “proteome”—the no less complex structure of proteins and their workings that generate tissues, organs, signaling molecules, and other element of complex living processes.

An important characteristic of both the genome and the proteome is that they work as totalities, with any one part potentially interacting with any other. In that sense, they are immense interactive networks, with the pattern of connections shaping the interactions, and in turn being shaped by them through a process of self-organization. Proteins produce other proteins; genes switch on other genes. In this way, the structure of our bodies evolves and adapts to new conditions—new infections, new stresses, new environments. Our bodies “learn.”

It turns out that something very similar goes on in the brain. We are born with a vastly complex pattern of connections between our neurons, and these go on to change after birth as we experience new environments and learn new skills and concepts. Once again, the totality of the pattern is what matters, and the ways that different parts of the brain get connected (or disconnected) to form new patterns, new ideas and pictures of the world.  

Following the naming precedent in genetics, this complex neural structure is now being called the “connectome” (because it’s a structure that’s similar to a “genome”). The race is on to map this structure and its most important features. (Much of this work is being advanced by the NIH’s Human Connectome Project.)

What do these insights have to do with cities? As Steven Johnson noted in his book Emergence, there is more in common between the two structures than might appear. There is good reason to think that, as with brains, a lot of what happens in cities has more to do with the overall pattern of connections, and less to do with particular elements….(More)”.

As Jane Jacobs pointed out over half a century ago, the city is a kind of “intricate ballet” of people interacting, going about their plans, and shaping the life of the city, from the smallest scales to the largest. This intricate pattern is complex, but it’s far from random. As Jacobs argued, it exhibits a high degree of order — what she called “organized complexity.”

Paper by Gall, A. et al: “Fear of technology has a bad reputation. It is often seen as irrational, unfounded and hostile to innovation. However, the relationship between fear and technology is far more complex than this common cliché. To highlight this multidimensional relationship of fear and technology, we created the term “tech-fear”. The aim of this special issue, focusing on the US, Japan, and Germany, is to show to what extent fear has historically influenced the development, design, social acceptance and use of technology. But it also makes clear that the history of fear benefits when it turns to the subject of technology since tech-fear has been an essential factor in the history of fear and has strongly influenced concepts and ways of dealing with fear in a wide variety of contexts….(More)”.

Compendium developed by Andrew Reamer: “The E.M. Kauffman Foundation has asked the George Washington Institute of Public Policy (GWIPP) to prepare a compendium of federal sources of data on self-employment, entrepreneurship, and small business development. The Foundation believes that the availability of useful, reliable federal data on these topics would enable robust descriptions and explanations of entrepreneurship trends in the United States and so help guide the development of effective entrepreneurship policies.

Achieving these ends first requires the identification and detailed description of available federal datasets, as provided in this compendium. Its contents include:

• An overview and discussion of 18 datasets from four federal agencies, organized by two categories and five subcategories.
• Tables providing information on each dataset, including:
  • scope of coverage of self-employed, entrepreneurs, and businesses;
  • data collection methods (nature of data source, periodicity, sampling frame, sample size);
  • dataset variables (owner characteristics, business characteristics and operations, geographic areas);
  • Data release schedule; and
  • Data access by format (including fixed tables, interactive tools, API, FTP download, public use microdata samples [PUMS], and confidential microdata).

For each dataset, examples of studies, if any, that use the data source to describe and explain trends in entrepreneurship.
The author’s aim is for the compendium to facilitate an assessment of the strengths and weaknesses of currently available federal datasets, discussion about how data availability and value can be improved, and implementation of desired improvements…(More)”

Paul J. Zak at Harvard Business Review: “…About a decade ago, in an effort to understand how company culture affects performance, I began measuring the brain activity of people while they worked. The neuroscience experiments I have run reveal eight ways that leaders can effectively create and manage a culture of trust. I’ll describe those strategies and explain how some organizations are using them to good effect. But first, let’s look at the science behind the framework.

What’s Happening in the Brain

Back in 2001 I derived a mathematical relationship between trust and economic performance. Though my paper on this research described the social, legal, and economic environments that cause differences in trust, I couldn’t answer the most basic question: Why do two people trust each other in the first place? Experiments around the world have shown that humans are naturally inclined to trust others—but don’t always. I hypothesized that there must be a neurologic signal that indicates when we should trust someone. So I started a long-term research program to see if that was true….

How to Manage for Trust

Through the experiments and the surveys, I identified eight management behaviors that foster trust. These behaviors are measurable and can be managed to improve performance.

Recognize excellence.

The neuroscience shows that recognition has the largest effect on trust when it occurs immediately after a goal has been met, when it comes from peers, and when it’s tangible, unexpected, personal, and public. Public recognition not only uses the power of the crowd to celebrate successes, but also inspires others to aim for excellence. And it gives top performers a forum for sharing best practices, so others can learn from them….(More)”.