Modeling social information skills

In a modern economy, the most important resource consists in\ud human talent: competent, knowledgeable people. Locating the right person for\ud the task is often a prerequisite to complex problem-solving, and experienced\ud professionals possess the social skills required to find appropriate human\ud expertise. These skills can be reproduced more and more with specific\ud computer software, an approach defining the new field of social information\ud retrieval. We will analyze the social skills involved and show how to model\ud them on computer. Current methods will be described, notably information\ud retrieval techniques and social network theory. A generic architecture and its\ud functions will be outlined and compared with recent work. We will try in this\ud way to estimate the perspectives of this recent domain

The Origins of Human Swarm Problem Solving

Chapter 5 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 5 argues that the origins of human swarm problem solving can be traced back to group hunting which required rapid problem solving during the hunt, but also planning activities. Collective actions build on synchronization in the sense that every contribution from individual hunters mattered. Another milestone was the emergence of premodern trade, which enabled human groups to utilize informational diversity from non-kin and even strangers. Knowledge was shared in new ways through large gatherings and trade networks. The third major achievement was the establishment of the first democracy in ancient Athens with institutions such as the Assembly of the People, the Council of 500 and the People`s Court. These institutions let a large number of individuals engage in rapid problem solving in a formalized manner. Individuals from all over the Athenian territory met in the city to solve societal problems. These historical examples show that human swarm problem solving is also a story about our ability to solve problems in increasingly larger groups.publishedVersio

From Relational Data to Graphs: Inferring Significant Links using Generalized Hypergeometric Ensembles

The inference of network topologies from relational data is an important problem in data analysis. Exemplary applications include the reconstruction of social ties from data on human interactions, the inference of gene co-expression networks from DNA microarray data, or the learning of semantic relationships based on co-occurrences of words in documents. Solving these problems requires techniques to infer significant links in noisy relational data. In this short paper, we propose a new statistical modeling framework to address this challenge. It builds on generalized hypergeometric ensembles, a class of generative stochastic models that give rise to analytically tractable probability spaces of directed, multi-edge graphs. We show how this framework can be used to assess the significance of links in noisy relational data. We illustrate our method in two data sets capturing spatio-temporal proximity relations between actors in a social system. The results show that our analytical framework provides a new approach to infer significant links from relational data, with interesting perspectives for the mining of data on social systems.Comment: 10 pages, 8 figures, accepted at SocInfo201

Solving complex problems: Human identification and control of complex systems

Studying complex problem solving by means of computer-simulated scenarios has become one of the favorite themes of modern theorists in German-speaking countries who are concerned with the psychology of thinking. Following the pioneering work of Dietrich Doerner (University of Bamberg, FRG) in the mid-70s, many new scenarios have been developed and applied in correlational as well as in experimental studies (for a review see Funke, 1988). Instead of studying problem-solving behavior in restricted situations (like the "Tower of Hanoi" or "Cannibals and Missionaries"; cf. Greeno, 1974; Jeffries, Polson, & Razran, 1977), the new approach focuses on semantically rich domains that provide a touch of reality that has not inherent in the older research (see also Bhaskar & Simon, 1977). In the computer-administered scenario "LOHHAUSEN", for instance, subjects have to take over the regentship of a little town (Doerner, Kreuzig, Reither, & Staeudel, 1983). In other work, subjects take over the roles of a manager of a little shop (Putz-Osterloh, 1981), of an engineer in a developmental country (Reither, 1981), or of a pilot flying to the moon (Thalmaier, 1979). In general, the new approach deals with the exploration and control of complex and dynamic systems by human individuals. This chapter is divided into four main parts. First, I give a working definition of what I mean by "complex problem solving" and suggest how complex tasks can be profitably analyzed and compared to each other across domains. Second, I summarize recent research on complex problem solving, analyze the main streams of current research, and discuss the underlying principles and mechanisms uncovered so far. Also, I consider how people learn to solve complex problems and discuss expert-novice differences in complex problem solving. Third, I describe my own approach to studying complex problem solving in which it is conceptualized as a dynamic process of knowledge acquisition and of knowledge application. I briefly describe the so-called DYNAMIS project and the DYNAMIS shell for scenario, and report the results of some studies within this framework. Finally, I give perspectives for future research

The Intelligent Society

Chapter 15 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 15 concludes by describe two radically different future visions of the intelligent society. On one hand, instrumentarian intelligence assumes that algorithms tracking human behavior can predict human behavior more accurately than ever before. In western countries, this intelligence manifests itself in a new surveillance capitalism with companies like Google and Facebook constantly searching for behavioral surplus in both online and offline settings. In the political domain, instrumentarian intelligence seeks a reputation state built on a neobehavioristic governing model. The most prominent example is the nationwide social credit system in China that makes it possible to grade citizens on different behavioral indicators. In stark contrast, civic intelligence highlights a use of technology still controlled by the community and citizens in contrast to the dehumanizing aspects of instrumentarian intelligence. While machine intelligence also craves for informational diversity in its hunt for behavioral surplus, civic intelligence seeks a broader diversity that includes not only information, but also multicultural, cognitive, biological, and participatory diversity. The “fuel” of CI is people who are different from each other, with different interests and unique perspectives. Civic intelligence also builds on a strong knowledge commons and an open shared collective memory. It does not hide information to produce the best predictions, but it promotes complete transparency and individual empowerment. In contrast to instrumentarian intelligence, CI still lets human-to-human intelligence, and not the algorithms, be at the core of the human collective problem solving.publishedVersio

Critical and Instrumental Perspectives of Interdisciplinarity for Business Education

Home The Route Towards Global Sustainability Chapter Critical and Instrumental Perspectives of Interdisciplinarity for Business Education Flavio Martins, Luciana Cezarino & Lara Liboni Chapter First Online: 08 February 2023 110 Accesses Abstract Sustainable Development Education is considered one of the main vectors for a sustainability transition. Sustainability is a broad field, inherently occupied by different knowledge areas that drink from each other to address the complexity of safeguarding the biosphere for the current and future human and nonhuman inhabitants. With the advent of new frameworks addressing sustainable development under a wider and more detailed lens, such as Aichi Targets, Millennium Development Goals, and ultimately the Sustainable Development Goals, the call for interdisciplinarity becomes even more needed. Interdisciplinarity can be seen as combining methods, theoretical approaches, and epistemological perspectives in diverse working groups for problem-solving; interdisciplinarity can also assume a critical perspective, grounded on the real-world problem needs. We assume that critical and instrumental perspectives, combined in the higher education milieu, can be the answer for educating leaders that hold the theoretical repertoire and the practical competencies that enable them to be agents of changing realitie

Facilitating Protégé Career Development through Roles of Mentors in Software Companies

The success of protégé contribution in any organizationtoday depends more on the type of relationship that an organization establishes with the support of mentors. Research indicates that individuals who are mentoredhave an increased likelihood of career success as a result of the targeted developmental support they receive. Mentors serve as trusted and significant advisors,providing a sounding board for day-to-day issues encountered on the job and alternative perspectives on issues regarding both problem identification and problem-solving. Mentoring can take on different forms. There are many ways through which mentors can improve the effectiveness of the mentoring process. Qualities of mentors, skills of mentors and mentoring methods have already contributed toward protégé career development. In this paper, the author is making an effort to assist the human resource department on how protégé career development can be achieved through the new roles of the Mentors, in the near future. By practising new roles, mentors in organizations can facilitate healthy relationships between the levels of management and in turn, try to achieve individual goals and organizationalgoals

Crowdsourcing

Chapter 2 in the book 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 2 describes crowdsourcing, a process where problems are sent outside an organization to a large group of people—a crowd—who can help provide solutions. Online citizen science and online innovation contests are of particular interest because of their societal value. Within innovation, the two selected examples are from IdeaConnection and Climate Co-lab, two innovation intermediaries who host different types of online innovation contests. One of these contests, the IdeaRalley, represents an interesting new crowdsourcing method that allows hundreds of experts to participate in a one-week long intensive idea building process. In online citizen science, Zooniverse (e.g. Galaxy Zoo) and Foldit, are selected as two prominent, but contrasting examples. The online protein folding game Foldit stands out as a particularly successful project that show what amateur gamers can achieve. The game design combines human visual skills with computer power in solving protein-structure prediction problems by constructing three-dimensional structures. Most successful solutions are team performances or achievements made by the entire Foldit gaming community. All the examples in this chapter illustrate successful case stories, and the detailed analysis identify basic problem-solving mechanisms in crowdsourcing.publishedVersio