Designing for Collective Intelligence and Community Resilience on Social Networks

The popularity and ubiquity of social networks has enabled a new form of decentralised online collaboration: groups of users gathering around a central theme and working together to solve problems, complete tasks and develop social connections. Groups that display such ‘organic collaboration’ have been shown to solve tasks quicker and more accurately than other methods of crowdsourcing. They can also enable community action and resilience in response to different events, from casual requests to emergency response and crisis management. However, engaging such groups through formal agencies risks disconnect and disengagement by destabilising motivational structures. This paper explores case studies of this henomenon, reviews models of motivation that can help design systems to harness these groups and proposes a framework for lightweight engagement using existing platforms and social networks

Information Systems for “Wicked Problems” – Proposing Research at the Intersection of Social Media and Collective Intelligence

The objective of this commentary is to propose some fruitful research direction built upon the reciprocal interplay of social media and collective intelligence. We focus on “wicked problems” — a class of what Introne et al. 2013 call “problems for which no single computational formulation of the problem is sufficient, for which different stakeholders do not even agree on what the problem really is, and for which there are no right or wrong answers, only answers that are better or worse from different points of view”. We argue that information systems research in particular can aid in designing appropriate systems due to benefits derived from the combined perspectives of both social media and collective intelligence. We document the relevance and timeliness of social media and collective intelligence for business and information systems engineering, pinpoint needed functionality of information systems for wicked problems, describe related research challenges, highlight prospective suitable methods to tackle those challenges, and review examples of initial results

Designing Human-Centered Collective Intelligence

Human-Centered Collective Intelligence (HCCI) is an emergent research area that seeks to bring together major research areas like machine learning, statistical modeling, information retrieval, market research, and software engineering to address challenges pertaining to deriving intelligent insights and solutions through the collaboration of several intelligent sensors, devices and data sources. An archetypal contextual CI scenario might be concerned with deriving affect-driven intelligence through multimodal emotion detection sources in a bid to determine the likability of one movie trailer over another. On the other hand, the key tenets to designing robust and evolutionary software and infrastructure architecture models to address cross-cutting quality concerns is of keen interest in the “Cloud” age of today. Some of the key quality concerns of interest in CI scenarios span the gamut of security and privacy, scalability, performance, fault-tolerance, and reliability. I present recent advances in CI system design with a focus on highlighting optimal solutions for the aforementioned cross-cutting concerns. I also describe a number of design challenges and a framework that I have determined to be critical to designing CI systems. With inspiration from machine learning, computational advertising, ubiquitous computing, and sociable robotics, this literature incorporates theories and concepts from various viewpoints to empower the collective intelligence engine, ZOEI, to discover affective state and emotional intent across multiple mediums. The discerned affective state is used in recommender systems among others to support content personalization. I dive into the design of optimal architectures that allow humans and intelligent systems to work collectively to solve complex problems. I present an evaluation of various studies that leverage the ZOEI framework to design collective intelligence

Information Systems for “Wicked Problems” - Research at the Intersection of Social Media and Collective Intelligence

The objective of this commentary is to propose fruitful research directions built upon the reciprocal interplay of social media and collective intelligence. We focus on “wicked problems” – a class of problems that Introne et al. (Künstl. Intell. 27:45–52, 2013) call “prob- lems for which no single computational formulation of the problem is suffi- cient, for which different stakeholders do not even agree on what the prob- lem really is, and for which there are no right or wrong answers, only answers that are better or worse from differ- ent points of view”. We argue that in- formation systems research in partic- ular can aid in designing appropriate systems due to benefits derived from the combined perspectives of both so- cial media and collective intelligence. We document the relevance and time- liness of social media and collective in- telligence for business and information systems engineering, pinpoint needed functionality of information systems for wicked problems, describe related re- search challenges, highlight prospec- tive suitable methods to tackle those challenges, and review examples of initial results

Modeling and Mathematical Analysis of Swarms of Microscopic Robots

The biologically-inspired swarm paradigm is being used to design self-organizing systems of locally interacting artificial agents. A major difficulty in designing swarms with desired characteristics is understanding the causal relation between individual agent and collective behaviors. Mathematical analysis of swarm dynamics can address this difficulty to gain insight into system design. This paper proposes a framework for mathematical modeling of swarms of microscopic robots that may one day be useful in medical applications. While such devices do not yet exist, the modeling approach can be helpful in identifying various design trade-offs for the robots and be a useful guide for their eventual fabrication. Specifically, we examine microscopic robots that reside in a fluid, for example, a bloodstream, and are able to detect and respond to different chemicals. We present the general mathematical model of a scenario in which robots locate a chemical source. We solve the scenario in one-dimension and show how results can be used to evaluate certain design decisions.Comment: 2005 IEEE Swarm Intelligence Symposium, Pasadena, CA June 200

Intelligent Contributions

Chapter 11 in Cultural-historical perspectives on collective intelligence In the era of digital communication, collective problem solving is increasingly important. Large groups can now resolve issues together in completely different ways, which has transformed the arts, sciences, business, education, technology, and medicine. Collective intelligence is something we share with animals and is different from machine learning and artificial intelligence. To design and utilize human collective intelligence, we must understand how its problem-solving mechanisms work. From democracy in ancient Athens, through the invention of the printing press, to COVID-19, this book analyzes how humans developed the ability to find solutions together. This wide-ranging, thought-provoking book is a game-changer for those working strategically with collective problem solving within organizations and using a variety of innovative methods. It sheds light on how humans work effectively alongside machines to confront challenges that are more urgent than what humanity has faced before. This title is also available as Open Access on Cambridge Core.Chapter 11 address how contributions are combined in different ways when designing CI. One approach utilize many different perspectives on the same work, like in collective work on the same Wikipedia article. Multidisciplinary innovation teams also include a diversity of perspectives in creative problem solving. Second, contributions can be combined under the assumption that the golden middle way is the best solution. One example is the identification of a quantitative middle point, such as an average, that provide the most accurate solution if contributions are diverse. Another strategy is to find the middle way by developing a balanced representation of all sides like in collective argument mapping. In addition, the middle way can identify commonalities, like the online environment vTaiwan that let the crowd find consensual statements in political conflicts. A third approach scales up the number of contributions in the search for an unexpected solution. Many breakthrough ideas happen at the outskirts of a field. Online innovation contests aim to bring in creative outsiders or unknown others by inviting anyone to join. Furthermore, most of the contributions in CI-projects build on a modularization strategy that split a complex challenge into many smaller subtasks.publishedVersio

Modelling collective learning in design

In this paper, a model of collective learning in design is developed in the context of team design. It explains that a team design activity uses input knowledge, environmental information, and design goals to produce output knowledge. A collective learning activity uses input knowledge from different agents and produces learned knowledge with the process of knowledge acquisition and transformation between different agents, which may be triggered by learning goals and rationale triggers. Different forms of collective learning were observed with respect to agent interactions, goal(s) of learning, and involvement of an agent. Three types of links between team design and collective learning were identified, namely teleological, rationale, and epistemic. Hypotheses of collective learning are made based upon existing theories and models in design and learning, which were tested using a protocol analysis approach. The model of collective learning in design is derived from the test results. The proposed model can be used as a basis to develop agent-based learning systems in design. In the future, collective learning between design teams, the links between collective learning and creativity, and computational support for collective learning can be investigated

Advances towards a General-Purpose Societal-Scale Human-Collective Problem-Solving Engine

Human collective intelligence has proved itself as an important factor in a society's ability to accomplish large-scale behavioral feats. As societies have grown in population-size, individuals have seen a decrease in their ability to activeily participate in the problem-solving processes of the group. Representative decision-making structures have been used as a modern solution to society's inadequate information-processing infrastructure. With computer and network technologies being further embedded within the fabric of society, the implementation of a general-purpose societal-scale human-collective problem-solving engine is envisioned as a means of furthering the collective-intelligence potential of society. This paper provides both a novel framework for creating collective intelligence systems and a method for implementing a representative and expertise system based on social-network theory.Comment: Collective Problem Solving Theory and Social-Network algorith