The complexity paradigm for studying human communication: a summary and integration of two fields

There are more things in heaven and earth, Horatio, than are dreamt of in your philosophy. Hamlet (Act 1, Scene 5). This popular quote from Hamlet might be recast for the field of communication as “There are more things in science than are dreamt of in our philosophies”. This article will review several new and strange ideas from complexity science about how the natural world is organized and how we can go about researching it. These strange ideas, (e.g., deterministic, but unpredictable systems) resonate with many communication phenomena that our field has traditionally had difficulty studying. By reviewing these areas, we hope to add a new, compelling and useful way to think about science that goes beyond the current dominant philosophy of science employed in communication. Though the concepts reviewed here are difficult and often appear at odds with the dominant paradigm; they are not. Instead, this approach will facilitate research on problems of communication process and interaction that the dominant paradigm has struggled to study. Specifically, this article explores the question of process research in communication by reviewing three major paradigms of science and then delving more deeply into the most recent: complexity science. The article provides a broad overview of many of the major ideas in complexity science and how these ideas can be used to study many of the most difficult questions in communication science. It concludes with suggestions going forward for incorporating complexity science into communication

Social learning in practice: A review of lessons, impacts and tools for climate change

The aim of this report is to provide a detailed review of documented social learning processes for climate change and natural resource management as described in peer-reviewed literature. Particular focus is on identifying (1) lessons and principles, (2) tools and approaches, (3) evaluation of social learning, as well as (4) concrete examples of impacts that social learning has contributed to. This paper has sought to contribute to reflections on the role that social learning might play and the impacts it might have in supporting decision making on climate change, agriculture and food security. Understanding social learning is important if we wish to respond effectively to increasingly complex and “wicked” problems such as climate change; to break down barriers between producers and users of research, and increase the capacity of organisations to learn. This study, conducted on behalf of the Climate Change Agriculture and Food Security (CCAFS) program of the CGIAR, offers a range of framings and evidence of successful social learning approaches. It reflects on how this evidence relates to the existing change areas already being pursued by the CCAFS programme and on the gaps that are revealed through an analysis of a bounded set of literature

Towards a more efficient and cost-sensitive extreme learning machine: A state-of-the-art review of recent trend

In spite of the prominence of extreme learning machine model, as well as its excellent features such as insignificant intervention for learning and model tuning, the simplicity of implementation, and high learning speed, which makes it a fascinating alternative method for Artificial Intelligence, including Big Data Analytics, it is still limited in certain aspects. These aspects must be treated to achieve an effective and cost-sensitive model. This review discussed the major drawbacks of ELM, which include difficulty in determination of hidden layer structure, prediction instability and Imbalanced data distributions, the poor capability of sample structure preserving (SSP), and difficulty in accommodating lateral inhibition by direct random feature mapping. Other drawbacks include multi-graph complexity, global memory size, one-by-one or chuck-by-chuck (a block of data), global memory size limitation, and challenges with big data. The recent trend proposed by experts for each drawback is discussed in detail towards achieving an effective and cost-sensitive mode

Novel analysis and modelling methodologies applied to pultrusion and other processes

Often a manufacturing process may be a bottleneck or critical to a business. This thesis focuses on the analysis and modelling of such processest, to both better understand them, and to support the enhancement of quality or output capability of the process. The main thrusts of this thesis therefore are: To model inter-process physics, inter-relationships, and complex processes in a manner that enables re-exploitation, re-interpretation and reuse of this knowledge and generic elements e.g. using Object Oriented (00) & Qualitative Modelling (QM) techniques. This involves the development of superior process models to capture process complexity and reuse any generic elements; To demonstrate advanced modelling and simulation techniques (e.g. Artificial Neural Networks(ANN), Rule-Based-Systems (RBS), and statistical modelling) on a number of complex manufacturing case studies; To gain a better understanding of the physics and process inter-relationships exhibited in a number of complex manufacturing processes (e.g. pultrusion, bioprocess, and logistics) using analysis and modelling. To these ends, both a novel Object Oriented Qualitative (Problem) Analysis (OOQA) methodology, and a novel Artificial Neural Network Process Modelling (ANNPM) methodology were developed and applied to a number of complex manufacturing case studies- thermoset and thermoplastic pultrusion, bioprocess reactor, and a logistics supply chain. It has been shown that these methodologies and the models developed support capture of complex process inter-relationships, enable reuse of generic elements, support effective variable selection for ANN models, and perform well as a predictor of process properties. In particular the ANN pultrusion models, using laboratory data from IKV, Aachen and Pera, Melton Mowbray, predicted product properties very well

European governance challenges in bio-engineering : making perfect life : bio-engineering (in) the 21st century : final report

In the STOA project Making Perfect Life four fields were studied of 21st century bio-engineering: engineering of living artefacts, engineering of the body, engineering of the brain, and engineering of intelligent artefacts. This report describes the main results of the project. It shows how developments in the four fields of bio-engineering are shaped by two megatrends: "biology becoming technology" and "technology becoming biology". These developments result in a broadening of the bio-engineering debate in our society. The report addresses the long term views that are inspiring this debate and discusses a multitude of ethical, legal and social issues that arise from bio-engineering developments in the fields described. Against this background four specific developments are studied in more detail: the rise of human genome sequencing, the market introduction of neurodevices, the capturing by information technology of the psychological and physiological states of users, and the pursuit of standardisation in synthetic biology. These developments are taken in this report as a starting point for an analysis of some of the main European governance challenges in 21st century bio-engineering

European governance challenges in bio-engineering : making perfect life : bio-engineering (in) the 21st century : final report

In the STOA project Making Perfect Life four fields were studied of 21st century bio-engineering: engineering of living artefacts, engineering of the body, engineering of the brain, and engineering of intelligent artefacts. This report describes the main results of the project. It shows how developments in the four fields of bio-engineering are shaped by two megatrends: "biology becoming technology" and "technology becoming biology". These developments result in a broadening of the bio-engineering debate in our society. The report addresses the long term views that are inspiring this debate and discusses a multitude of ethical, legal and social issues that arise from bio-engineering developments in the fields described. Against this background four specific developments are studied in more detail: the rise of human genome sequencing, the market introduction of neurodevices, the capturing by information technology of the psychological and physiological states of users, and the pursuit of standardisation in synthetic biology. These developments are taken in this report as a starting point for an analysis of some of the main European governance challenges in 21st century bio-engineering

Applying innovation system concepts in agricultural research for development: a learning module

This learning module is expected to have multiple uses. One, a source material for trainings that could be organized at different levels, and two, as reference document to upgrade the knowledge of staff of partner organizations about innovation systems approach and applications. The design of the learning module includes guidance notes for potential trainers including learning purpose and objectives for each session; description of the session structure (including methods, techniques, time allocation to each activity); power point presentations, presentation text, exercise handouts, worksheets, and additional reading material. There are also evaluation forms and recommended bibliography for use by future facilitators. The module has been prepared in the style of a source book and it assumes that the reader is familiar with the concepts, procedures and tools used in participatory research approaches. Users can pick and choose the sessions/idea/tools/concepts that are most relevant and appropriate in specific contexts and for specific purposes. This is work in progress. The module is being continually refined and updated, based on application of the concept and tools in the project and elsewhere and, lessons learned in the process. Case studies will be prepared to supplement this module. Therefore, IPMS would like to encourage users of this learning module to actively provide feedback, including suggestions on how it can be improved

Predictors of Loneliness

This thesis explores how interpersonal behavioural patterns, internal working models, personality traits, and positive and negative emotional characteristics interact and impact human behaviour. Part one consists of a systematic review and a meta-analysis exploring the relationship between Attachment and Personality through an examination of the literature in English that examine this relationship. Personality was operationalised through the Big Five model, and Attachment through the standard three Attachment styles. 15 different analyses were conducted in order to explore all the possible combinations of the three Attachment styles and the five Personality traits. The literature was scrutinised through a thorough quality assessment and risk of biases assessment. Part two is an empirical research paper exploring different predictors of Loneliness through the prism of interpersonal behavioral patterns, and an internal working model. These were explored through Attachment, Interpersonal problems, Compassion, and Shame. The aim of this study was to understand better how maladaptive patterns of emotional and behavioural functions can lead an individual to be and feel lonely. This is a quantitative study utilising a battery of five different measures with data from 92 participants. Part three is a critical appraisal offering a reflection on both preceding parts. It emphasizes an overview of the whole process and ends with a reflection on the conclusions of both studies. During the reflection issues regarding Loneliness and better care for clients are raised

Learning Representations of Social Media Users

User representations are routinely used in recommendation systems by platform developers, targeted advertisements by marketers, and by public policy researchers to gauge public opinion across demographic groups. Computer scientists consider the problem of inferring user representations more abstractly; how does one extract a stable user representation - effective for many downstream tasks - from a medium as noisy and complicated as social media? The quality of a user representation is ultimately task-dependent (e.g. does it improve classifier performance, make more accurate recommendations in a recommendation system) but there are proxies that are less sensitive to the specific task. Is the representation predictive of latent properties such as a person's demographic features, socioeconomic class, or mental health state? Is it predictive of the user's future behavior? In this thesis, we begin by showing how user representations can be learned from multiple types of user behavior on social media. We apply several extensions of generalized canonical correlation analysis to learn these representations and evaluate them at three tasks: predicting future hashtag mentions, friending behavior, and demographic features. We then show how user features can be employed as distant supervision to improve topic model fit. Finally, we show how user features can be integrated into and improve existing classifiers in the multitask learning framework. We treat user representations - ground truth gender and mental health features - as auxiliary tasks to improve mental health state prediction. We also use distributed user representations learned in the first chapter to improve tweet-level stance classifiers, showing that distant user information can inform classification tasks at the granularity of a single message.Comment: PhD thesi

Learning Representations of Social Media Users

User representations are routinely used in recommendation systems by platform developers, targeted advertisements by marketers, and by public policy researchers to gauge public opinion across demographic groups. Computer scientists consider the problem of inferring user representations more abstractly; how does one extract a stable user representation - effective for many downstream tasks - from a medium as noisy and complicated as social media? The quality of a user representation is ultimately task-dependent (e.g. does it improve classifier performance, make more accurate recommendations in a recommendation system) but there are proxies that are less sensitive to the specific task. Is the representation predictive of latent properties such as a person's demographic features, socioeconomic class, or mental health state? Is it predictive of the user's future behavior? In this thesis, we begin by showing how user representations can be learned from multiple types of user behavior on social media. We apply several extensions of generalized canonical correlation analysis to learn these representations and evaluate them at three tasks: predicting future hashtag mentions, friending behavior, and demographic features. We then show how user features can be employed as distant supervision to improve topic model fit. Finally, we show how user features can be integrated into and improve existing classifiers in the multitask learning framework. We treat user representations - ground truth gender and mental health features - as auxiliary tasks to improve mental health state prediction. We also use distributed user representations learned in the first chapter to improve tweet-level stance classifiers, showing that distant user information can inform classification tasks at the granularity of a single message.Comment: PhD thesi