Identifying Urban Areas by Combining Human Judgment and Machine Learning: An Application to India

Curated on Posted on by Stefaan Verhulst

Paper by Virgilio Galdo, Yue Li and Martin Rama: “This paper proposes a methodology for identifying urban areas that combines subjective assessments with machine learning, and applies it to India, a country where several studies see the official urbanization rate as an under-estimate. For a representative sample of cities, towns and villages, as administratively defined, human judgment of Google images is used to determine whether they are urban or rural in practice. Judgments are collected across four groups of assessors, differing in their familiarity with India and with urban issues, following two different protocols. The judgment-based classification is then combined with data from the population census and from satellite imagery to predict the urban status of the sample.

The Logit model, and LASSO and random forests methods, are applied. These approaches are then used to decide whether each of the out-of-sample administrative units in India is urban or rural in practice. The analysis does not find that India is substantially more urban than officially claimed. However, there are important differences at more disaggregated levels, with ?other towns? and ?census towns? being more rural, and some southern states more urban, than is officially claimed. The consistency of human judgment across assessors and protocols, the easy availability of crowd-sourcing, and the stability of predictions across approaches, suggest that the proposed methodology is a promising avenue for studying urban issues….(More)”.

The Future of Minds and Machines

Curated on Posted on by Stefaan Verhulst

Report by Aleksandra Berditchevskaia and Peter Baek: “When it comes to artificial intelligence (AI), the dominant media narratives often end up taking one of two opposing stances: AI is the saviour or the villain. Whether it is presented as the technology responsible for killer robots and mass job displacement or the one curing all disease and halting the climate crisis, it seems clear that AI will be a defining feature of our future society. However, these visions leave little room for nuance and informed public debate. They also help propel the typical trajectory followed by emerging technologies; with inevitable regularity we observe the ascent of new technologies to the peak of inflated expectations they will not be able to fulfil, before dooming them to a period languishing in the trough of disillusionment.[1]

There is an alternative vision for the future of AI development. By starting with people first, we can introduce new technologies into our lives in a more deliberate and less disruptive way. Clearly defining the problems we want to address and focusing on solutions that result in the most collective benefit can lead us towards a better relationship between machine and human intelligence. By considering AI in the context of large-scale participatory projects across areas such as citizen science, crowdsourcing and participatory digital democracy, we can both amplify what it is possible to achieve through collective effort and shape the future trajectory of machine intelligence. We call this 21st-century collective intelligence (CI).

In The Future of Minds and Machines we introduce an emerging framework for thinking about how groups of people interface with AI and map out the different ways that AI can add value to collective human intelligence and vice versa. The framework has, in large part, been developed through analysis of inspiring projects and organisations that are testing out opportunities for combining AI & CI in areas ranging from farming to monitoring human rights violations. Bringing together these two fields is not easy. The design tensions identified through our research highlight the challenges of navigating this opportunity and selecting the criteria that public sector decision-makers should consider in order to make the most of solving problems with both minds and machines….(More)”.

Wisdom of stakeholder crowds in complex social–ecological systems

Curated on Posted on by Stefaan Verhulst

Paper by Payam Aminpour et al: “Sustainable management of natural resources requires adequate scientific knowledge about complex relationships between human and natural systems. Such understanding is difficult to achieve in many contexts due to data scarcity and knowledge limitations.

We explore the potential of harnessing the collective intelligence of resource stakeholders to overcome this challenge. Using a fisheries example, we show that by aggregating the system knowledge held by stakeholders through graphical mental models, a crowd of diverse resource users produces a system model of social–ecological relationships that is comparable to the best scientific understanding.

We show that the averaged model from a crowd of diverse resource users outperforms those of more homogeneous groups. Importantly, however, we find that the averaged model from a larger sample of individuals can perform worse than one constructed from a smaller sample. However, when averaging mental models within stakeholder-specific subgroups and subsequently aggregating across subgroup models, the effect is reversed. Our work identifies an inexpensive, yet robust way to develop scientific understanding of complex social–ecological systems by leveraging the collective wisdom of non-scientist stakeholders…(More)”.

Incentive Competitions and the Challenge of Space Exploration

Curated on Posted on by Stefaan Verhulst

Article by Matthew S. Williams: “Bill Joy, the famed computer engineer who co-founded Sun Microsystems in 1982, once said, “No matter who you are, most of the smartest people work for someone else.” This has come to be known as “Joy’s Law” and is one of the inspirations for concepts such as “crowdsourcing”.

Increasingly, government agencies, research institutions, and private companies are looking to the power of the crowd to find solutions to problems. Challenges are created and prizes offered – that, in basic terms, is an “incentive competition.”

The basic idea of an incentive competition is pretty straightforward. When confronted with a particularly daunting problem, you appeal to the general public to provide possible solutions and offer a reward for the best one. Sounds simple, doesn’t it?

But in fact, this concept flies in the face of conventional problem-solving, which is for companies to recruit people with knowledge and expertise and solve all problems in-house. This kind of thinking underlies most of our government and business models, but has some significant limitations….

Another benefit to crowdsourcing is the way it takes advantage of the exponential growth in human population in the past few centuries. Between 1650 and 1800, the global population doubled, to reach about 1 billion. It took another one-hundred and twenty years (1927) before it doubled again to reach 2 billion.

However, it only took fifty-seven years for the population to double again and reach 4 billion (1974), and just fifteen more for it to reach 6 billion. As of 2020, the global population has reached 7.8 billion, and the growth trend is expected to continue for some time.

This growth has paralleled another trend, the rapid development of new ideas in science and technology. Between 1650 and 2020, humanity has experienced multiple technological revolutions, in what is a comparatively very short space of time….(More)”.

The wisdom of crowds: What smart cities can learn from a dead ox and live fish

Curated on Posted on by Stefaan Verhulst

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)”.

Collective Intelligence in City Design

Curated on Posted on by Stefaan Verhulst

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)”

Sifting for Deeper Insights from Public Opinion: Towards Crowdsourcing and Big Data for Project Improvement

Curated on Posted on by Stefaan Verhulst

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)”.

Dreamocracy – Collective Intelligence for the Common Good

Curated on Posted on by Stefaan Verhulst

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)”.

Collective Intelligence: A Taxonomy and Survey

Curated on Posted on by Stefaan Verhulst

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)”

The Wisdom of the Market: Using Human Factors to Design Prediction Markets for Collective Intelligence

Curated on Posted on by Stefaan Verhulst

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)”.