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The Climate CoLab: Large scale model-based collaborative planning
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The Climate CoLab is a system to help thousands of people around the world collectively develop plans for what humans should do about global climate change. This paper shows how the system combines three design elements (model-based planning, on-line debates, and electronic voting) in a synergistic way. The paper also reports early usage experience showing that: (a) the system is attracting a continuing stream of new and returning visitors from all over the world, and (b) the nascent community can use the platform to generate interesting and high quality plans to address climate change. These initial results indicate significant progress towards an important goal in developing a collective intelligence system – the formation of a large and diverse community collectively engaged in solving a single problem.
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... In SOPDs, this issue is compounded due to potentially conflicting stakeholder opinions on both the nature of the problem and the direction towards solutions. As research on the Climate Co-Lab platform showed, developing a co-creation platform for climate change brings the challenge of dealing with the diversity of opinions, divisiveness, and polarization that may threaten the collaborations among participants (Introne et al., 2011). The diversity of actors can surface different, even contradictory opinions that make it even more challenging to identify, capture, and aggregate the knowledge and leverage their synergic or contrasting perspectives into a coherent problem-solving process (Siltaoja, 2014). ...
... The diversity of actors can surface different, even contradictory opinions that make it even more challenging to identify, capture, and aggregate the knowledge and leverage their synergic or contrasting perspectives into a coherent problem-solving process (Siltaoja, 2014). Specific design choices, such as integrating computer-supported argument mapping tools on digital platforms, can improve comprehension and retention and, thus, address the divisiveness and polarization that may threaten collaborations among diverse actors on SODPs (Introne et al., 2011). ...
In the digital age, interactions among heterogenous actors are increasingly mediated by digital platforms. While sustainability-oriented digital platforms (SODPs) have the potential to accelerate sustainability through increased connectivity, knowledge sharing, and co-creation, they also have a dark side, leading to unexpected tensions and paradoxical effects that may risk the creation of value for societal actors. Understanding how digital platforms can be designed to stimulate fruitful interactions among participants without succumbing to their paradoxical effects is still an unresolved puzzle. In this paper, we examine how the bright sides of SODPs can be brought to light while reducing their associated tensions and paradoxes through a qualitative study of micro-level knowledge integration interactions on an open platform to tackle food waste. Our analysis identified 11 distinct mechanisms and 3 main interactional patterns through which participants framed the scale of the sustainability problem, mobilized resources for their solutions, and generated breadth and diversity of knowledge on the platform to tackle the problem of food waste. Our study contributes to research on SOPDs by showing how participants can take on a “distributed brokering” role through their interactions on the platform. We also provide implications to policy-oriented practitioners regarding platform design choices to help manage known paradoxes and tensions of SOPDs.
... Therefore, bargaining as an element of interaction is characteristic of conflicting parties, and one of the ways to promote resource management in the energy community is through collective awareness-building platforms, through which innovative ways of citizen participation can be offered, while identifying their interests and giving them the opportunity to contribute to the solution of such sustainability issues. where a social dilemma occurs in an environment of many decision makers [24][25][26][27]. ...
Energy communities are widely studied from various perspectives, especially in the context of geopolitical events of recent years, when humanity is faced with the need for urgent solutions to promote climate change and alleviate the crisis of energy resources. Although citizens' interest in the use of renewable resources has gradually grown, energy policy support measures for more active participation of society in the implementation of energy efficiency measures are still being implemented with variable success, especially through mutual agreement. Serious games are a rapidly growing tool for awareness and collaboration on a single platform for gamers seeking solutions to energy resource optimization issues. The paper focuses on energy community versus energy use practices, trends, and intervention strategies in multifamily residential blocks, using serious gaming and direct user online feedback. This study uses a multi-player simulation tool to enable the modelling of scenarios for energy efficiency measures for apartment building block residents and energy community target goals for decision-making decisions. User experience and game mechanics were tested on a pre-selected group. The results indicate positive feedback, including a practical application for both energy community and professionals, and provide valuable recommendations for further research and improvement of the tool.
... Collaboratory (Colab) was used for all evaluations, a cloud service based on Jupyter Notebooks for distributing ML teaching and research [37]. The models, pre-processing processes, and measurements were all implemented using the Python programming language in the same Colab environment. ...
The exponential growth of Internet and network usage has necessitated heightened security measures to protect against data and network breaches. Intrusions, executed through network packets, pose a significant challenge for firewalls to detect and prevent due to the similarity between legitimate and intrusion traffic. The vast network traffic volume also complicates most network monitoring systems and algorithms. Several intrusion detection methods have been proposed, with machine learning techniques regarded as promising for dealing with these incidents. This study presents an Intrusion Detection System Based on Stacking Ensemble Learning base (Random Forest , Decision Tree, and k-Nearest-Neighbors). The proposed system employs pre-processing techniques to enhance classification efficiency and integrates seven machine learning algorithms. The stacking ensemble technique increases performance by incorporating three base models (Random Forest, Decision Tree, and k-Nearest-Neighbors) and a meta-model represented by the Logistic Regression algorithm. Evaluated using the UNSW-NB15 dataset, the proposed IDS gained an accuracy of 96.16% in the training phase and 97.95% in the testing phase, with precision of 97.78%, and 98.40% for taring and testing, respectively. The obtained results demonstrate improvements in other measurement criteria.
... While AI has been a subject established in science for over seven decades Rzepka and Berger 2018;Simon 1995), in recent years, it has received increasing attention in both research and practice (Bawack et al. 2019;de Vreede et al. 2020;Hinsen et al. 2022;Hofmann et al. 2021;Leal Filho et al. 2022;Pumplun et al. 2019;Rai 2020). AI is expected to disrupt the interplay between user, task, and technology (Maedche et al. 2019;Rzepka and Berger 2018) and the nature of work Iansiti and Lakhani 2020;Nascimento et al. 2018). This expectation is accompanied by many unrealistic expectations, and the timeless question of "[W]hat can AI do today?" ...
Crowdsourcing holds great potential: macro-task crowdsourcing can, for example, contribute to work addressing climate change. Macro-task crowdsourcing aims to use the wisdom of a crowd to tackle non-trivial tasks such as wicked problems. However, macro-task crowdsourcing is labor-intensive and complex to facilitate, which limits its efficiency, effectiveness, and use. Technological advancements in artificial intelligence (AI) might overcome these limits by supporting the facilitation of crowdsourcing. However, AI’s potential for macro-task crowdsourcing facilitation needs to be better understood for this to happen. Here, we turn to affordance theory to develop this understanding. Affordances help us describe action possibilities that characterize the relationship between the facilitator and AI, within macro-task crowdsourcing. We follow a two-stage, bottom-up approach: The initial development stage is based on a structured analysis of academic literature. The subsequent validation & refinement stage includes two observed macro-task crowdsourcing initiatives and six expert interviews. From our analysis, we derive seven AI affordances that support 17 facilitation activities in macro-task crowdsourcing. We also identify specific manifestations that illustrate the affordances. Our findings increase the scholarly understanding of macro-task crowdsourcing and advance the discourse on facilitation. Further, they help practitioners identify potential ways to integrate AI into crowdsourcing facilitation. These results could improve the efficiency of facilitation activities and the effectiveness of macro-task crowdsourcing.
... There are in-fact various types of CI platforms available on the web, however, the underlying objective of said platforms is always the same i.e., to connect individuals and to harness their collective intelligence for various purposes. On one side of the spectrum, CI platforms like (i) ClimateCoLab utilize the collective knowledge of individuals to solve complex effects of climate change [7], (ii) Tippanee uses collaborative knowledge sharing to improve the quality of textual content on the web [18] and (iii) Wikipedia exploits the wisdom of the crowd to build an online collaborative encyclopedia [12]; while on the other side of the spectrum organizations like InnoCentive, OpenIDEO, NESTA [24] and AccelerateEstonia (aE!) harness the collective innovation and knowledge of the crowd to tackle wicked societal issues with the help of experts. A more recent example of this is, InnoCentive's initiative to tackle the COVID-19 pandemic. ...
Building on the rising interest in online crowdsourcing platforms, and the ever-rising concerns over mental health issues worldwide; in this paper, we propose a novel citizen-oriented web-based Collective Intelligence (CI) platform called 'CommunityCare'. The platform is meant to be focused on end-users' communities and aims to empower its users by allowing them to work collectively when helping those suffering from mental health issues such as depression, anxiety, and stress. Through our work, we attempt to make two distinct contributions: first, we elucidate an abstract yet novel CI platform for mental health, that could enable citizen volunteers and medical practitioners to work together to help those suffering from psychological/behavioural issues; and second, we evaluate our previously proposed 'generic' CI framework by utilizing the said platform as a use case for the same. We describe the 'CommunityCare' platform through the four primary components of CI systems, namely: staffing, processes, goals and motivation.
Empowered by advancements in social media technologies, Collective Intelligence (CI) systems in recent decades have enabled effective and efficient mobilization and utilization of the skills and knowledge of crowds over the web. Unfortunately, even with the plethora of CI solutions available on the web, the development of CI systems remains an exhaustive and costly venture. Literature suggests that this is because there is a fundamental gap in our understanding of CI systems in general. This work addresses this gap, through a first-of-its-kind ‘generic’ CI framework and model, designed to empower researchers, developers, and stakeholders by enabling them to better understand existing and develop new CI platforms.
Civic problems are often too complex to solve through traditional top-down strategies. Various governments and civic initiatives have explored more community-driven strategies where citizens get involved with defining problems and innovating solutions. While certain people may feel more empowered, the public at large often does not have accessible, flexible, and meaningful ways to engage. Prior theoretical frameworks for public participation typically offer a one-size-fits-all model based on face-to-face engagement and fail to recognize the barriers faced by even the most engaged citizens. In this article, we explore a vision for open civic design where we integrate theoretical frameworks from public engagement, crowdsourcing, and design thinking to consider the role technology can play in lowering barriers to large-scale participation, scaffolding problem-solving activities, and providing flexible options that cater to individuals’ skills, availability, and interests. We describe our novel theoretical framework and analyze the key goals associated with this vision: (1) to promote inclusive and sustained participation in civics; (2) to facilitate effective management of large-scale participation; and (3) to provide a structured process for achieving effective solutions. We present case studies of existing civic design initiatives and discuss challenges, limitations, and future work related to operationalizing, implementing, and testing this framework.
In online discussion platforms, participants gather to discuss the effective approaches to solve the common issues they face. To facilitate these discussions proceeding smoothly and reaching consensus efficiently, human facilitators are introduced into these discussions. However, human facilitator-related problems such as human bias and scalability arise with the increasing sophistication of these online discussions. As a result, it becomes critical to find approaches that support facilitation in these online discussions. Towards this end, we propose a novel case-based reasoning framework to support online discussion facilitation. In the proposed framework (CBR), each discussion thread is styled using an issue-based information system (IBIS), where complex problems are modeled as argumentation processes among several stack holders. Upon identifying a new discussion case, facilitation suggestions are generated by retrieving the most similar facilitated case from the discussion case base. For the sake of experimental evaluation, we build an online facilitation dataset that contains the facilitation information from annotated real-world discussion data. The experimental results show the validity of the generated facilitation suggestions.
In many instances of online collaboration, ideation and deliberation about what to write happen separately from the synthesis of the deliberation into a cohesive document. However, this may result in a final document that has little connection to the discussion that came before. In this work, we present interleaved discussion and summarization, a process where discussion and summarization are woven together in a single space, and collaborators can switch back and forth between discussing ideas and summarizing discussion until it results in a final document that incorporates and references all discussion points. We implement this process into a tool called Wikum+ that allows groups working together on a project to create living summaries-artifacts that can grow as new collaborators, ideas, and feedback arise and shrink as collaborators come to consensus. We conducted studies where groups of six people each collaboratively wrote a proposal using Wikum+ and a proposal using a messaging platform along with Google Docs. We found that Wikum+'s integration of discussion and summarization helped users be more organized, allowing for light-weight coordination and iterative improvements throughout the collaboration process. A second study demonstrated that in larger groups, Wikum+ is more inclusive of all participants and more comprehensive in the final document compared to traditional tools.
With the rapid development of Internet, the online discussion system or social democratic system has become an important and effective vehicle for group decision-making support since it can continue collecting the opinions from the public at anytime. To reach a consensus in crowd-scale deliberation, the existing online discussion systems require an experienced human facilitator to navigate and guild the discussion. When human facilitator performs the required facilitation there are several issues such as heavy burden on decision-making, the 24/7 online facilitation, bias on the social issues, etc. To address these issues it is necessary and inevitable to explore intelligent facilitation. For this purpose, we propose a novel machine learning-based method for smart facilitation, in particular the intelligent consensus decision-making support (CDMS) for crowd-scale deliberation. After presenting an overview of the crowd-scale deliberation and the COLLAGREE, the paper details the proposed approach, a machine learning-based framework for CDMS in crowd-scale deliberation. To validate the developed methods the offline evaluation experiments were conducted with the online discussion platform, COLLAGREE. The preliminary experimental results obtained from offline validation demonstrated the feasibility and usefulness of the developed machine learning-based methods for CDMS.
There is no single definition for ‘preferential voting’ or ‘preference voting’ since the terms are used for a number of different election systems and groups of such systems. They can be synonymous with the single-transferable vote, the alternative vote, open-list proportional representation, or the group of all ranking methods. This article offers an overview of the various definitions and classifications of preferential voting and other terms used in the literature to describe it. It proposes a common understanding of preferential voting. I suggest that preferentiality ought to be one of the characteristics by which electoral systems are evaluated. All election systems are preferential, though to varying degrees. I offer a classification of numerous electoral schemes according to their preferentiality.
The successful emergence of on-line communities, such as open source software and Wikipedia, seems due to an effective combination of intelligent collective behavior and internet capabilities However, current internet technologies, such as forum, wikis and blogs appear to be less supportive for knowledge organization and consensus formation. In particular very few attempts have been done to support large, diverse, and geographically dispersed groups to systematically explore and come to decisions concerning complex and controversial systemic challenges. In order to overcome the limitations of current collaborative technologies, in this paper, we present a new large-scale collaborative platform based on argumentation mapping. To date argumentation mapping has been effectively used for small-scale, co-located groups. The main research questions this work faces are: can argumentation scale? Will large-scale argumentation outperform current collaborative technologies in collective problem solving and deliberation? We present some preliminary results obtained from a first field test of an argumentation platform with a moderate-sized (few hundreds) users community.
Earlier chapters have introduced the notion of “wicked problems” and the Issue Based Information System (IBIS) framework (Buckingham Shum, Chapter 1, van Bruggen Chapter 2), both of which derive from the work of Horst Rittel. Selvin (Chapter 7) proposes a generic framework for facilitated Computer Supported Argument Visualization (CSAV), and reports on case studies using the IBIS-based Compendium approach. Compendium itself is based on a facilitated CSAV approach called Dialog Mapping, the focus of this chapter. We begin by elaborating on the art and process of Dialog Mapping, before reporting on a particular business application, probably the longest-term case study available of CSAV adoption in an organization
This article was submitted without an abstract, please refer to the full-text PDF file.
This article was submitted without an abstract, please refer to the full-text PDF file.
Computer Supported Argument Visualization is attracting attention across education, science, public policy and business. More than ever, we need sense-making tools to help negotiate understanding in the face of multi-stakeholder, ill-structured problems. In order to be effective, these tools must support human cognitive and discursive processes, and provide suitable representations, services and user interfaces. Visualizing Argumentation is written by practitioners and researchers for colleagues working in collaborative knowledge media, educational technology and organizational sense-making. It will also be of interest to theorists interested in software tools which embody different argumentation models. Particular emphasis is placed on the usability and effectiveness of tools in different contexts.
Among the key features are:
- Case studies covering educational, public policy, business and scientific argumentation
- Expanded, regularly updated resources on the companion website:
www.VisualizingArgumentation.info
"The old leadership idea of "vision" has been transformed in the face of wicked problems in the new organizational landscape. In this excellent book we find a comprehensive yet practical guide for using visual methods to collaborate in the construction of shared knowledge. This book is essential for managers and leaders seeking new ways of navigating complexity and chaos in the workplace."
(Charles J. Palus, Ph.D, Center for Creative Leadership, Greensboro, North Carolina, USA)
The work described is the result of a study extending over the past few years by a chemist and a statistician. Development has come about mainly in answer to problems of determining optimum conditions in chemical investigations, but we believe that the methods will be of value in other fields where experimentation is sequential and the error fairly small.
Google. Wikipedia. Threadless. All are platinum exemplars of collective intelligence in action. Two of them are famous. The third is getting there. Each of the three helps demonstrate how large, loosely organized groups of people can work together electronically in surprisingly effective ways sometimes even without knowing that they are working together, as in the case of Google. In the authors' work at MIT's Center for Collective Intelligence, they have gathered nearly 250 examples of web-enabled collective intelligence. After examining these examples in depth, they identified a relatively small set of building blocks that are combined and recombined in various ways in different collective intelligence systems. This article offers a new framework for understanding those systems - and more important, for understanding how to build them. It identifies the underlying building blocks - the "genes" - that are at the heart of collective intelligence systems. It explores the conditions under which each gene is useful. And it begins to suggest the possibilities for combining and recombining these genes to not only harness crowds in general, but to harness them in just the way that your organization needs.