Portland State University: “In 1906, Francis Galton was at a country fair where attendees had the opportunity to guess the weight of a dead ox. Galton took the guesses of 787 fair-goers and found that the average guess was only one pound off of the correct weight — even when individual guesses were off base.
This concept, known as “the wisdom of crowds” or “collective intelligence,” has been applied to many situations over the past century, from people estimating the number of jellybeans in a jar to predicting the winners of major sporting events — often with high rates of success. Whatever the problem, the average answer of the crowd seems to be an accurate solution.
But does this also apply to knowledge about systems, such as ecosystems, health care, or cities? Do we always need in-depth scientific inquiries to describe and manage them — or could we leverage crowds?
This question has fascinated Antonie J. Jetter, associate professor of Engineering and Technology Management for many years. Now, there’s an answer. A recent study, which was co-authored by Jetter and published in Nature Sustainability, shows that diverse crowds of local natural resource stakeholders can collectively produce complex environmental models very similar to those of trained experts.
For this study, about 250 anglers, water guards and board members of German fishing clubs were asked to draw connections showing how ecological relationships influence the pike stock from the perspective of the anglers and how factors like nutrients and fishing pressures help determine the number of pike in a freshwater lake ecosystem. The individuals’ drawings — or their so-called mental models — were then mathematically combined into a collective model representing their averaged understanding of the ecosystem and compared with the best scientific knowledge on the same subject.
The result is astonishing. If you combine the ideas from many individual anglers by averaging their mental models, the final outcomes correspond more or less exactly to the scientific knowledge of pike ecology — local knowledge of stakeholders produces results that are in no way inferior to lengthy and expensive scientific studies….(More)”.
Idea by Helena Rong and Juncheng Yang: “We propose an interactive design engagement platform which facilitates a continuous conversation between developers, designers and end users from pre-design and planning phases all the way to post-occupancy, adopting a citizen-centric and inclusive-oriented approach which would stimulate trust-building and invite active participation from end users from different age, ethnicity, social and economic background to participate in the design and development process. We aim to explore how collective intelligence through citizen engagement could be enabled by data to allow new collectives to emerge, confronting design as an iterative process involving scalable cooperation of different actors. As a result, design is a collaborative and conscious practice not born out of a single mastermind of the architect. Rather, its agency is reinforced by a cooperative ideal involving institutions, enterprises and single individuals alike enabled by data science….(More)”
Paper by Jean Marie Tshimula et al: “Over the years, there seems to be a unidirectional top-down approach to decision-making in providing social services to the masses. This has often led to poor uninformed decisions being made with outcomes which do not necessarily match needs. Similarly from the grassroots level, it has been challenging to give opinions that reach the governing authorities (decision-making organs). The government consequently sets targets geared towards addressing societal concerns, but which do not often achieve desired results where such government endeavors are not in harmony with societal needs.
With public opinions being heard and given consideration, societal needs can be better known and priorities set to address these concerns. This paper therefore presents a priority-based voting model for governments to collect public opinion data that bring suggestions to boost their endeavors in the right direction using crowdsourcing and big data analytics….(More)”.
About: “Dreamocracy is a think-and-do-tank that fosters collective intelligence / creativity for the common good through analysis, advice to organisations, and by developing and implementing innovative stakeholder management experiments.
Dreamocracy aims to contribute to democracy’s reinvention and future. As Harvard scholar Yascha Mounk stresses, democracy in many parts of the world is at risk of “deconsolidation.” Possible collapse is signalled by the convergence of people’s dissatisfaction with democracy; their willingness to consider non-democratic forms of government as possible alternatives; and the rise in populist parties, anti-system movements and demagogues in government.
In order to ensure a bright future for democracy in service to society, Dreamocracy believes collective intelligence done well is essential to address the following three terms of our proposed “trust-in-government equation”:
TRUST = Process legitimacy + Output legitimacy + Emotions legitimacy….(More)”.
Paper by Feijuan He et al: “Collective intelligence (CI) refers to the intelligence that emerges at the macro-level of a collection and transcends that of the individuals. CI is a continuously popular research topic that is studied by researchers in different areas, such as sociology, economics, biology, and artificial intelligence. In this survey, we summarize the works of CI in various fields. First, according to the existence of interactions between individuals and the feedback mechanism in the aggregation process, we establish CI taxonomy that includes three paradigms: isolation, collaboration and feedback. We then conduct statistical literature analysis to explain the differences among three paradigms and their development in recent years. Second, we elaborate the types of CI under each paradigm and discuss the generation mechanism or theoretical basis of the different types of CI. Third, we describe certain CI-related applications in 2019, which can be appropriately categorized by our proposed taxonomy. Finally, we summarize the future research directions of CI under each paradigm. We hope that this survey helps researchers understand the current conditions of CI and clears the directions of future research….(More)”
Paper by Lorenzo Barberis Canonico, Christopher Flathmann, Dr. Nathan McNeese: “There is an ever-growing literature on the power of prediction markets to harness “the wisdom of the crowd” from large groups of people. However, traditional prediction markets are not designed in a human-centered way, often restricting their own potential. This creates the opportunity to implement a cognitive science perspective on how to enhance the collective intelligence of the participants. Thus, we propose a new model for prediction markets that integrates human factors, cognitive science, game theory and machine learning to maximize collective intelligence. We do this by first identifying the connections between prediction markets and collective intelligence, to then use human factors techniques to analyze our design, culminating in the practical ways with which our design enables artificial intelligence to complement human intelligence….(More)”.
A Talk By Seth Lloyd at The Edge: “We haven’t talked about the socialization of intelligence very much. We talked a lot about intelligence as being individual human things, yet the thing that distinguishes humans from other animals is our possession of human language, which allows us both to think and communicate in ways that other animals don’t appear to be able to. This gives us a cooperative power as a global organism, which is causing lots of trouble. If I were another species, I’d be pretty damn pissed off right now. What makes human beings effective is not their individual intelligences, though there are many very intelligent people in this room, but their communal intelligence….(More)”.
Report by Andrew Zahuranec, Andrew Young and Stefaan G. Verhulst: “Around the world, public leaders are seeking new ways to better understand the needs of their citizens, and subsequently improve governance, and how we solve public problems. The approaches proposed toward changing public engagement tend to focus on leveraging two innovations. The first involves artificial intelligence (AI), which offers unprecedented abilities to quickly process vast quantities of data to deepen insights into public needs. The second is collective intelligence (CI), which provides means for tapping into the “wisdom of the crowd.” Both have strengths and weaknesses, but little is known on how the combination of both could address their weaknesses while radically transform how we meet public demands for more responsive governance.
Today, The GovLab is releasing a new report, Identifiying Citizens’ Needs By Combining AI and CI, which seeks to identify and assess how institutions might responsibly experiment in how they engage with citizens by leveraging AI and CI together.
The report, authored by Stefaan G. Verhulst, Andrew J. Zahuranec, and Andrew Young, builds upon an initial examination of the intersection of AI and CI conducted in the context of the MacArthur Foundation Research Network on Opening Governance. …
The report features five in-depth case studies and an overview of eight additional examples from around the world on how AI and CI together can help to:
- Anticipate citizens’ needs and expectations through cognitive insights and process automation and pre-empt problems through improved forecasting and anticipation;
- Analyze large volumes of citizen data and feedback, such as identifying patterns in complaints;
- Allow public officials to create highly personalized campaigns and services; or
- Empower government service representatives to deliver relevant actions….(More)”.
Tooran Alizadeh, Somwrita Sarkar and Sandy Burgoyne in Cities: “Social media and online communication have changed the way citizens engage in all aspects of lives from shopping and education, to how communities are planned and developed. It is no longer one-way or two- way communication. Instead, via networked all-to-all communication channels, our citizens engage on urban issues in a complex and more connected way than ever before. So government needs new ways to listen to its citizens. The paper comprises three components. Firstly, we build on the growing discussions in the literature focused on smart cities, on one hand, and social media research, on the other, to capture the diversity of citizen voices and better inform decision-making. Secondly, with the support of the Australian Federal Government and in collaboration with the local government partners, we collect citizen voices from Twitter on selected urban projects. Thirdly, we present preliminary findings in terms of quantity and quality of publicly available online data representing citizen concerns on the urban matters. By analyzing the sentiments of the citizen voices captured online, clustering them into topic areas, and then reevaluating citizen’s sentiments within each cluster, we elaborate the scope and value of technologically-enabled opportunities in terms of enabling participatory local government decision making processes….(More)”.
EU report by Rene Van Bavel et al: “Recognising that advances in behavioural, decision and social sciences demonstrate that we are not purely rational beings, this report brings new insights into our political behaviour and this understanding have the potential to address some of the current crises in our democracies. Sixty experts from across the globe working in the fields of behavioural and social sciences as well as the humanities, have contributed to the research that underpins this JRC report that calls upon evidence-informed policymaking not to be taken for granted. There is a chapter dedicated to each key finding which outlines the latest scientific thinking as well as an overview of the possible implications for policymaking. The key findings are:
- Misperception and Disinformation: Our thinking skills are challenged by today’s information environment and make us vulnerable to disinformation. We need to think more about how we think.
- Collective Intelligence: Science can help us re-design the way policymakers work together to take better decisions and prevent policy mistakes.
- Emotions: We can’t separate emotion from reason. Better information about citizens’ emotions and greater emotional literacy could improve policymaking.
- Values and Identities drive political behaviour but are not properly understood or debated.
- Framing, Metaphor and Narrative: Facts don’t speak for themselves. Framing, metaphors and narratives need to be used responsibly if evidence is to be heard and understood.
- Trust and Openness: The erosion of trust in experts and in government can only be addressed by greater honesty and public deliberation about interests and values.
- Evidence-informed policymaking: The principle that policy should be informed by evidence is under attack. Politicians, scientists and civil society need to defend this cornerstone of liberal democracy….(More)”