Might social intelligence save Latin America from its governments in times of Covid-19?


Essay by Thamy Pogrebinschi: “…In such scenarios, it seems relevant to acknowledge the limits of the state to deal with huge and unpredictable challenges and thus the need to resort to civil society. State capacity cannot be built overnight, but social intelligence is an unlimited and permanently available resource. In recent years, digital technology has multiplied what has been long called social intelligence (Dewey) and is now more often known as collective intelligence (Lévy), the wisdom of crowds (Surowiecki), or democratic reason (Landemore).

Taken together, these concepts point to the most powerful tool available to governments facing hard problems and unprecedented challenges: the sourcing and sharing of knowledge, information, skills, resources, and data from citizens in order to address social and political problems.

The Covid-19 pandemic presents an opportunity to test the potential of social intelligence as fuel for processes of creative collaboration that may aid governments to reinvent themselves and prepare for the challenges that will remain after the virus is gone. By creative collaboration, I mean a range of forms of communication, action, and connection among citizens themselves, between citizens and civil society organizations (CSOs), and between the latter two and their governments, all with the common aim of addressing problems that affect all and that the state for various reasons cannot (satisfactorily) respond to alone.

While several Latin American countries have been stuck in the Covid-19 crisis with governments unable or unwilling to contain it or to reduce its damages, a substantial number of digital democratic innovations have been advanced by civil society in the past few months. These comprise institutions, processes, and mechanisms that rely on digital citizen participation as a means to address social and political problems – and, more recently, also problems of a humanitarian nature….

Between March 16 and July 1 of this year, at least 400 digital democratic innovations were created across 18 countries in Latin America with the specific aim of handling the Covid-19 crisis and mitigating its impact, according to recent data from the LATINNO project. These innovations are essentially mechanisms and processes in which citizens, with the aid of digital tools, are enabled to address social, political, and humanitarian problems related to the pandemic.

Citizens engage in and contribute to three levels of responses, which are based on information, connection, and action. About one-fourth of these digital democratic innovations clearly rely on crowdsourcing social intelligence.

The great majority of those digital innovations have been developed by CSOs. Around 75% of them have no government involvement at all, which is striking in a region known for implementing state-driven citizen participation as a result of the democratization processes that took place in the late 20th century. Civil society has stepped in in most countries, particularly where government responses were absent (Brazil and Nicaragua), slow (Mexico), insufficient due to lack of economic resources (Argentina) or infrastructure (Peru), or simply inefficient (Chile).

Based on these data from 18 Latin American countries, one can observe that digital democratic innovations address challenges posed by the Covid-19 outbreak in five main ways: first, generating verified information and reliable data; second, geolocating problems, needs, and demands; third, mobilizing resources, skills, and knowledge to address those problems, needs, and demands; fourth, connecting demand (individuals and organizations in need) and supply (individuals and organizations willing to provide whatever is needed); and fifth and finally, implementing and monitoring public policies and actions. In some countries, there is a sixth use that cuts across the other five: assisting vulnerable groups such as the elderly, women, children and youth, indigenous peoples, and Afro-descendants….(More)”

Designing Governance as Collective Intelligence


Paper by Hamed Khaledi: “This research models governance as a collective intelligence process, particularly as a collective design process. The outcome of this process is a solution to a problem. The solution can be a decision, a policy, a product, a financial plan, etc. The quality (value) of the outcome solution reflects the quality (performance) of the process. Using an analytical model, I identify five mediators (channels) through which, different factors and features can affect the quality of the outcome and thus the process. Based on this model, I propose an asymmetric response surface method that introduces factors to the experimental model considering their plausible effects.

As a proof of concept, I implemented a generic collective design process in a web application and measured the effects of several factors on its performance through online experiments. The results demonstrate the effectiveness of the proposed method. They also show that approval voting is significantly superior to plurality voting. Some studies assert that not the design process, but the designers drive the quality of the outcome. However, this study shows that the characteristics of the design process (e.g. voting schemes) as well as the designers (e.g. expertise and gender) can significantly affect the quality of the outcome. Hence, the outcome quality can be used as an indicator of the performance of the process. This enables us to evaluate and compare governance mechanisms objectively free from fairness criteria….(More)”.

German coronavirus experiment enlists help of concertgoers


Philip Oltermann at the Guardian: “German scientists are planning to equip 4,000 pop music fans with tracking gadgets and bottles of fluorescent disinfectant to get a clearer picture of how Covid-19 could be prevented from spreading at large indoor concerts.

As cultural mass gatherings across the world remain on hold for the foreseeable future, researchers in eastern Germany are recruiting volunteers for a “coronavirus experiment” with the singer-songwriter Tim Bendzko, to be held at an indoor stadium in the city of Leipzig on 22 August.

Participants, aged between 18 and 50, will wear matchstick-sized “contact tracer” devices on chains around their necks that transmit a signal at five-second intervals and collect data on each person’s movements and proximity to other members of the audience.

Inside the venue, they will also be asked to disinfect their hands with a fluorescent hand-sanitiser – designed to not just add a layer of protection but allow scientists to scour the venue with UV lights after the concerts to identify surfaces where a transmission of the virus through smear infection is most likely to take place.

Vapours from a fog machine will help visualise the possible spread of coronavirus via aerosols, which the scientists will try to predict via computer-generated models in advance of the event.

The €990,000 cost of the Restart-19 project will be shouldered between the federal states of Saxony and Saxony-Anhalt. The project’s organisers say the aim is to “identify a framework” for how larger cultural and sports events could be held “without posing a danger for the population” after 30 September….

To stop the Leipzig experiment from becoming the source of a new outbreak, signed-up volunteers will be sent a DIY test kit and have a swab at a doctor’s practice or laboratory 48 hours before the concert starts. Those who cannot show proof of a negative test at the door will be denied entry….(More)”.

Coronavirus: how the pandemic has exposed AI’s limitations


Kathy Peach at The Conversation: “It should have been artificial intelligence’s moment in the sun. With billions of dollars of investment in recent years, AI has been touted as a solution to every conceivable problem. So when the COVID-19 pandemic arrived, a multitude of AI models were immediately put to work.

Some hunted for new compounds that could be used to develop a vaccine, or attempted to improve diagnosis. Some tracked the evolution of the disease, or generated predictions for patient outcomes. Some modelled the number of cases expected given different policy choices, or tracked similarities and differences between regions.

The results, to date, have been largely disappointing. Very few of these projects have had any operational impact – hardly living up to the hype or the billions in investment. At the same time, the pandemic highlighted the fragility of many AI models. From entertainment recommendation systems to fraud detection and inventory management – the crisis has seen AI systems go awry as they struggled to adapt to sudden collective shifts in behaviour.

The unlikely hero

The unlikely hero emerging from the ashes of this pandemic is instead the crowd. Crowds of scientists around the world sharing data and insights faster than ever before. Crowds of local makers manufacturing PPE for hospitals failed by supply chains. Crowds of ordinary people organising through mutual aid groups to look after each other.

COVID-19 has reminded us of just how quickly humans can adapt existing knowledge, skills and behaviours to entirely new situations – something that highly-specialised AI systems just can’t do. At least yet….

In one of the experiments, researchers from the Istituto di Scienze e Tecnologie della Cognizione in Rome studied the use of an AI system designed to reduce social biases in collective decision-making. The AI, which held back information from the group members on what others thought early on, encouraged participants to spend more time evaluating the options by themselves.

The system succeeded in reducing the tendency of people to “follow the herd” by failing to hear diverse or minority views, or challenge assumptions – all of which are criticisms that have been levelled at the British government’s scientific advisory committees throughout the pandemic…(More)”.

Harnessing the collective intelligence of stakeholders for conservation


Paper by Steven Gray et al: ” Incorporating relevant stakeholder input into conservation decision making is fundamentally challenging yet critical for understanding both the status of, and human pressures on, natural resources. Collective intelligence (CI ), defined as the ability of a group to accomplish difficult tasks more effectively than individuals, is a growing area of investigation, with implications for improving ecological decision making. However, many questions remain about the ways in which emerging internet technologies can be used to apply CI to natural resource management. We examined how synchronous social‐swarming technologies and asynchronous “wisdom of crowds” techniques can be used as potential conservation tools for estimating the status of natural resources exploited by humans.

Using an example from a recreational fishery, we show that the CI of a group of anglers can be harnessed through cyber‐enabled technologies. We demonstrate how such approaches – as compared against empirical data – could provide surprisingly accurate estimates that align with formal scientific estimates. Finally, we offer a practical approach for using resource stakeholders to assist in managing ecosystems, especially in data‐poor situations….(More)”.

Collective intelligence, not market competition, will deliver the best Covid-19 vaccine


Els Torreele at StatNews: “…Imagine mobilizing the world’s brightest and most creative minds — from biotech and pharmaceutical industries, universities, government agencies, and more — to work together using all available knowledge, innovation, and infrastructure to develop an effective vaccine against Covid-19. A true “people’s vaccine” that would be made freely available to all people in all countries. That’s what an open letter by more than 140 world leaders and experts calls for.

Unfortunately, that is not how the race for a Covid-19 vaccine is being run. The rules of that game are oblivious to the goal of maximizing global health outcomes and access.

Despite a pipeline of more than 100 vaccine candidates reflecting massive public and private efforts, there exists no public-health-focused way to design or prioritize the development of the most promising candidates. Instead, the world is adopting a laissez-faire approach and letting individual groups and companies compete for marketing authorization, each with their proprietary vaccine candidate, and assume that the winner of that race will be the best vaccine to tackle the pandemic.

Science thrives, and technological progress is made, when knowledge is exchanged and shared freely, generating collective intelligence by building on the successes and failures of others in real time instead of through secretive competition. Regrettably, market logic has come to overtake medicinal product innovation, including the unproven premise that competition is an efficient way to advance science and deliver the best solutions for public health….(More)”.

Dynamic Networks Improve Remote Decision-Making


Article by Abdullah Almaatouq and Alex “Sandy” Pentland: “The idea of collective intelligence is not new. Research has long shown that in a wide range of settings, groups of people working together outperform individuals toiling alone. But how do drastic shifts in circumstances, such as people working mostly at a distance during the COVID-19 pandemic, affect the quality of collective decision-making? After all, public health decisions can be a matter of life and death, and business decisions in crisis periods can have lasting effects on the economy.

During a crisis, it’s crucial to manage the flow of ideas deliberatively and strategically so that communication pathways and decision-making are optimized. Our recently published research shows that optimal communication networks can emerge from within an organization when decision makers interact dynamically and receive frequent performance feedback. The results have practical implications for effective decision-making in times of dramatic change….

Our experiments illustrate the importance of dynamically configuring network structures and enabling decision makers to obtain useful, recurring feedback. But how do you apply such findings to real-world decision-making, whether remote or face to face, when constrained by a worldwide pandemic? In such an environment, connections among individuals, teams, and networks of teams must be continually reorganized in response to shifting circumstances and challenges. No single network structure is optimal for every decision, a fact that is clear in a variety of organizational contexts.

Public sector. Consider the teams of advisers working with governments in creating guidelines to flatten the curve and help restart national economies. The teams are frequently reconfigured to leverage pertinent expertise and integrate data from many domains. They get timely feedback on how decisions affect daily realities (rates of infection, hospitalization, death) — and then adjust recommended public health protocols accordingly. Some team members move between levels, perhaps being part of a state-level team for a while, then federal, and then back to state. This flexibility ensures that people making big-picture decisions have input from those closer to the front lines.

Witness how Germany considered putting a brake on some of its reopening measures in response to a substantial, unexpected uptick in COVID-19 infections. Such time-sensitive decisions are not made effectively without a dynamic exchange of ideas and data. Decision makers must quickly adapt to facts reported by subject-area experts and regional officials who have the relevant information and analyses at a given moment….(More)“.

Adaptive social networks promote the wisdom of crowds


Paper by Abdullah Almaatouq et al: “Social networks continuously change as new ties are created and existing ones fade. It is widely acknowledged that our social embedding has a substantial impact on what information we receive and how we form beliefs and make decisions. However, most empirical studies on the role of social networks in collective intelligence have overlooked the dynamic nature of social networks and its role in fostering adaptive collective intelligence. Therefore, little is known about how groups of individuals dynamically modify their local connections and, accordingly, the topology of the network of interactions to respond to changing environmental conditions. In this paper, we address this question through a series of behavioral experiments and supporting simulations. Our results reveal that, in the presence of plasticity and feedback, social networks can adapt to biased and changing information environments and produce collective estimates that are more accurate than their best-performing member. To explain these results, we explore two mechanisms: 1) a global-adaptation mechanism where the structural connectivity of the network itself changes such that it amplifies the estimates of high-performing members within the group (i.e., the network “edges” encode the computation); and 2) a local-adaptation mechanism where accurate individuals are more resistant to social influence (i.e., adjustments to the attributes of the “node” in the network); therefore, their initial belief is disproportionately weighted in the collective estimate. Our findings substantiate the role of social-network plasticity and feedback as key adaptive mechanisms for refining individual and collective judgments….(More)”.

Smart cities during COVID-19: How cities are turning to collective intelligence to enable smarter approaches to COVID-19.


Article by Peter Baeck and Sophie Reynolds: One of the most prominent examples of how technology and data is being used to empower citizens is happening in Seoul. Here the city has used its ‘citizens as mayors’ philosophy for smart cities; an approach which aims to equip citizens with the same real-time access to information as the mayor. Seoul has gone further than most cities in making information about the COVID-19 outbreak in the city accessible to citizens. Its dashboard is updated multiple times daily and allows citizens to access the latest anonymised information on confirmed patients’ age, gender and dates of where they visited and when, after developing symptoms. Citizens can access even more detailed information; down to visited restaurants and cinema seat numbers.

The goal is to provide citizens with the information needed to take precautionary measures, self-monitor and report if they start showing symptoms after visiting one of the “infection points.” To help allay people’s fears and reduce the stigma associated with businesses that have been identified as “infection points”, the city government also provides citizens with information about the nearest testing clinics and makes “clean zones” (places that have been disinfected after visits by confirmed patients) searchable for users.

In addition to national and institutional responses there are (at least) five ways collective intelligence approaches are helping city governments, companies and urban communities in the fight against COVID-19:

1. Open sharing with citizens about the spread and management of COVID-19:

Based on open data provided by public agencies, private sector companies are using the city as a platform to develop their own real-time dashboards and mobile apps to further increase public awareness and effectively disseminate disease information. This has been the case with Corona NOWCorona MapCorona 100m in Seoul, Korea – which allow people to visualise data on confirmed coronavirus patients, along with patients’ nationality, gender, age, which places the patient has visited, and how close citizens are to these coronavirus patients. Developer Lee Jun-young who created the Corona Map app, said he built it because he found that the official government data was too difficult to understand.

Meanwhile in city state Singapore, the dashboard developed by UpCode scrapes data provided by the Singapore Ministry of Health’s own dashboard (which is exceptionally transparent about coronavirus case data) to make it cleaner and easier to navigate, and vastly more insightful. For instance, it allows you to learn about the average recovery time for those infected.

UpCode is making its platform available for others to re-use in other contexts.

2. Mobilising community-led responses to tackle COVID-19

Crowdfunding is being used in a variety of ways to get short-term targeted funding to a range of worthy causes opened up by the COVID-19 crisis. Examples include helping to fundraise for community activities for those directly affected by the crisis, backing tools and products that can address the crisis (such as buying PPE) and pre-purchasing products and services from local shops and artists. A significant proportion of the UK’s 1,000 plus mutual aid initiatives are now turning to crowdfunding as a way to rapidly respond to the new and emerging needs occurring at the city-wide and hyperlocal (i.e. streets and neighbourhood) levels.

Aberdeen City Mutual Aid group set up a crowdfunded community fund to cover the costs of creating a network of volunteers across the city, as well as any expenses incurred at food shops, fuel costs for deliveries and purchasing other necessary supplies. Similarly, the Feed the Heroes campaign was launched with an initial goal of raising €250 to pay for food deliveries for frontline staff who are putting in extra hours at the Mater Hospital, Dublin during the coronavirus outbreak….(More)”.

How AI can help us harness our ‘collective intelligence’


Edd Gent at the BBC: “…There are already promising examples of how AI can help us better pool our unique capabilities. San Francisco start-up Unanimous AI has built an online platform that helps guide group decisions. They’ve looked to an unlikely place to guide their AI: the way honeybees make collective decisions.

“We went back to basics and said, ‘How does nature amplify the intelligence of groups?’,” says CEO Louis Rosenberg. “What nature does is form real-time systems, where the groups are interacting all at once together with feedback loops. So, they’re pushing and pulling on each other as a system, and converging on the best possible combination of their knowledge, wisdom, insight and intuition.”

Their Swarm AI platform presents groups with a question and places potential answers in different corners of their screen. Users control a virtual magnet with their mouse and engage in a tug of war to drag an ice hockey puck to the answer they think is correct. The system’s algorithm analyses how each user interacts with the puck – for instance, how much conviction they drag it with or how quickly they waver when they’re in the minority – and  uses this information to determine where the puck moves. That creates feedback loops in which each user is influenced by the choice and conviction of the others allowing the puck to end up at the answer best reflecting the collective wisdom of the group.

Several academic papers and high-profile clients who use the product back up the effectiveness of the Swarm AI platform. In one recent study, a group of traders were asked to forecast the weekly movement of several key stock market indices by trying to drag the puck to one of four answers — up or down by more than 4%, or up and down by less than 4%. With the tool, they boosted their accuracy by 36%.

Credit Suisse has used the platform to help investors forecast the performance of Asian markets; Disney has used it to predict the success of TV shows; and Unanimous has even partnered with Stanford Medical School to boost doctors’ ability to diagnose pneumonia from chest X-rays by 33%….(More)”

See also: Where and when AI and CI meet: exploring the intersection of artificial and collective intelligence towards the goal of innovating how we govern and Identifying Citizens’ Needs by Combining Artificial Intelligence (AI) and Collective Intelligence (CI).