Challenges in Complex Systems Science

FuturICT foundations are social science, complex systems science, and ICT. The main concerns and challenges in the science of complex systems in the context of FuturICT are laid out in this paper with special emphasis on the Complex Systems route to Social Sciences. This include complex systems having: many heterogeneous interacting parts; multiple scales; complicated transition laws; unexpected or unpredicted emergence; sensitive dependence on initial conditions; path-dependent dynamics; networked hierarchical connectivities; interaction of autonomous agents; self-organisation; non-equilibrium dynamics; combinatorial explosion; adaptivity to changing environments; co-evolving subsystems; ill-defined boundaries; and multilevel dynamics. In this context, science is seen as the process of abstracting the dynamics of systems from data. This presents many challenges including: data gathering by large-scale experiment, participatory sensing and social computation, managing huge distributed dynamic and heterogeneous databases; moving from data to dynamical models, going beyond correlations to cause-effect relationships, understanding the relationship between simple and comprehensive models with appropriate choices of variables, ensemble modeling and data assimilation, modeling systems of systems of systems with many levels between micro and macro; and formulating new approaches to prediction, forecasting, and risk, especially in systems that can reflect on and change their behaviour in response to predictions, and systems whose apparently predictable behaviour is disrupted by apparently unpredictable rare or extreme events. These challenges are part of the FuturICT agenda

Prediction Markets:A literature review 2014

In recent years, Prediction Markets gained growing interest as a forecasting tool among researchers as well as practitioners, which resulted in an increasing number of publications. In order to track the latest development of research, comprising the extent and focus of research, this article provides a comprehensive review and classification of the literature related to the topic of Prediction Markets. Overall, 304 relevant articles, published in the timeframe from 2007 through 2013, were identified and assigned to a herein presented classification scheme, differentiating between descriptive works, articles of theoretical nature, application-oriented studies and articles dealing with the topic of law and policy. The analysis of the research results reveals that more than half of the literature pool deals with the application and actual function tests of Prediction Markets. The results are further compared to two previous works published by Zhao, Wagner and Chen (2008) and Tziralis and Tatsiopoulos (2007a). The article concludes with an extended bibliography section and may therefore serve as a guidance and basis for further research. (250 WORDS

Self-organization in Communicating Groups: the emergence of coordination, shared references and collective intelligence\ud

The present paper will sketch the basic ideas of the complexity paradigm, and then apply them to social systems, and in particular to groups of communicating individuals who together need to agree about how to tackle some problem or how to coordinate their actions. I will elaborate these concepts to provide an integrated foundation for a theory of self-organization, to be understood as a non-linear process of spontaneous coordination between actions. Such coordination will be shown to consist of the following components: alignment, division of labor, workflow and aggregation. I will then review some paradigmatic simulations and experiments that illustrate the alignment of references and communicative conventions between communicating agents. Finally, the paper will summarize the preliminary results of a series of experiments that I devised in order to observe the emergence of collective intelligence within a communicating group, and interpret these observations in terms of alignment, division of labor and workflow

Harnessing Collaborative Technologies: Helping Funders Work Together Better

This report was produced through a joint research project of the Monitor Institute and the Foundation Center. The research included an extensive literature review on collaboration in philanthropy, detailed analysis of trends from a recent Foundation Center survey of the largest U.S. foundations, interviews with 37 leading philanthropy professionals and technology experts, and a review of over 170 online tools.The report is a story about how new tools are changing the way funders collaborate. It includes three primary sections: an introduction to emerging technologies and the changing context for philanthropic collaboration; an overview of collaborative needs and tools; and recommendations for improving the collaborative technology landscapeA "Key Findings" executive summary serves as a companion piece to this full report

Decision Support Markets: An Innovative Business Intelligence Tools

The spirit of business intelligence lies at the heart of information markets. This paper advocates the use of information markets as business intelligence tools for decision support. It highlights the market promising potentials, and the business need for collective intelligence in decision making. Market forecasts can reduce uncertainty surrounding business decisions and improve the quality of input into the decision making process, and thus allow managers to make better informed decisions. Further, this paper presents a heuristic for traders’ forecasts of events probabilities. This heuristic extracts additional information from market transactions by incorporating posted, but not yet executed trades. Utilizing pending transactions is hypothesized to generate a more accurate representation of traders’ beliefs than other commonly used measures. It might also boost decision makers’ confidence in the market forecasts, and as a result, encourages them to embrace it, and make the best out of this invaluable tool

Collective Intelligence: The Wisdom and Foolishness of Deliberating Groups

The rise of the Internet and social media reignites interest in collective intelligence. We frame collective intelligence as follows: (1) Simple aggregation of individual opinion is a poor substitute for reasoned opinion by collectives (i.e., deliberation) except in limited circumstances. (2) What constitutes an intelligent decision on complex matters requires approximations to the ideal of what is intelligent. There is no “gold standard” for intelligent decisions. (3) If collective deliberation is to be useful, then its outcomes must be improved decisions—in short, intelligent outcomes. (4) Deliberation can lead to more intelligent outcomes when opinion, knowledge, and judgment within a collective is diverse and this diversity is expressed. (5) The trends within emerging media toward increasingly narrow, partisan sources of information, toward selective exposure and avoidance, and toward balkanization of collectives will depress the possibilities of collective intelligence that emerging media would on their surface seem to enhance

Collective innovation

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management, 2007.Includes bibliographical references (p. 178-179).The ability to innovate sits at the heart of an organization's ability to succeed in a competitive environment. An organization can innovate by improving existing products, services, or processes or by generating new products, services, or processes. Achieving successful, repeated organizational innovation, however, is a significant challenge. The hurdles to such innovation run the gamut from psychological to structural to procedural. Managers can fall victim to myopia and other human level challenges. Organizational processes, structures, and values can short circuit innovation as well. Given these challenges, we posit that an innovation strategy embracing the concepts of collective intelligence and openness may enable organizations to surmount these hurdles. We refer to this approach as Collective Innovation and define it as a connected, open, and collaborative process that generates, develops, prioritizes, and executes new ideas. To develop our argument, we surveyed literature from a wide array of disciplines including economics, organizational behavior, social psychology, and organizational change.(cont.) We begin this thesis by drawing a connection between the economic theories of Adam Smith and Ronald Coase and research into the changing workplace by Thomas Malone. We then introduce the concepts of collective intelligence and openness, core tenets of Collective Innovation. After introducing Collective Innovation, we examine its place in the history of innovation strategy. Next, we outline and describe the four stages of the Collective Innovation process. Having dealt mainly in theory, we then turn to the application of Collective Innovation and the myriad challenges that managers will face when attempting to implement such a strategy. Keeping in mind these challenges, we outline four ways in which organizations might use Collective Innovation to power the exploration-side of their operations. Finally, we revisit several remaining questions before concluding our analysis.by Alex Slawsby [and] Carlos Rivera.M.B.A

Blockchain Based Prediction Markets

Prediction markets are a form of collective intelligence that leverage market mechanisms to incentivise large numbers of individuals to make forecasts about future uncertain events. Since their origin in the 1980’s, they have been the subject of a small but steady stream of academic research. Proponents suggest that they have several advantages over comparable information aggregation mechanisms such as polls or expert groups. More recently the rise of blockchain, cryptocurrencies and decentralised finance (DeFi) has excited new interest in prediction markets. The characteristics of this triad of technologies has particular resonances with prediction markets. This research identifies the potential impact of blockchain technology on prediction market design and performance with a view to informing a research agenda to investigate those potential impacts

Prediction Markets: A Systematic Review and Meta-Analysis

Prediction markets (PM) have drawn considerable attention in recent years as a tool for forecasting events. Studies surveying and examining relevant the trends of PM using traditional approaches have been reported in the literature. However, research using meta-analysis to review Prediction markets systems is very limited in Management Information System (MIS). This paper aimed to fill this gap by using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method to study Prediction markets trends over the past decades. Our results are as follows. First, we find that shows that more than 64% of academic studies on Prediction markets are published in top journals such as Journal of the Association for Information Systems, Journal of Consumer Research and Information Systems Research. Second, we showed that Prediction markets applications can be can be divided into two groups: internal use PMS and general public usage. Finally, our significant meta-analysis result show that on average prediction markets is 79% more accurate than alternative forecast methods based