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

Optimal incentives for collective intelligence

Collective intelligence is the ability of a group to perform more effectively than any individual alone. Diversity among group members is a key condition for the emergence of collective intelligence, but maintaining diversity is challenging in the face of social pressure to imitate one’s peers. Through an evolutionary game-theoretic model of collective prediction, we investigate the role that incentives may play in maintaining useful diversity. We show that market-based incentive systems produce herding effects, reduce information available to the group, and restrain collective intelligence. Therefore, we propose an incentive scheme that rewards accurate minority predictions and show that this produces optimal diversity and collective predictive accuracy. We conclude that real world systems should reward those who have shown accuracy when the majority opinion has been in error

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

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

Social Media and Collective Intelligence: Ongoing and Future Research Streams

The tremendous growth in the use of Social Media has led to radical paradigm shifts in the ways we communicate, collaborate, consume, and create information. Our focus in this special issue is on the reciprocal interplay of Social Media and Collective Intelligence. We therefore discuss constituting attributes of Social Media and Collective Intelligence, and we structure the rapidly growing body of literature including adjacent research streams such as Social Network Analysis, Web Science, and Computational Social Science. We conclude by making propositions for future research where in particular the disciplines of artificial intelligence, computer science, and information systems can substantially contribute to the interdisciplinary academic discourse

Folksonomies : Indexing and Retrieval in Web 2.0

One of the defining principles of Web 2.0 when it first emerged was that the collective intelligence of users should be harnessed in order to enrich services for that user community (O’Reilly, 2005). This so-called ‘network effect’ principle remains as central to the Web 2.0 thesis then as it does five years on (O’Reilly and Battelle, 2009). Folksonomies, or collaborative tagging systems, have become the epitome of the network effect; using collective intelligence to organise and retrieve information on the Web. In Folksonomies: indexing and retrieval in Web 2.0, author Isabella Peters explores the use of folksonomies in ‘collaborative information services’, a catch-all term used by Peters to encompass the heterogeneous nature of the Web 2.0 services that use tagging systems. The stated purpose of Folksonomies is to provide a degree of insight into folksonomy applications, as well as discuss their strengths, weaknesses and how their problems can be ameliorated by applying recognised information retrieval models and formal knowledge representation methods

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