complexity rules

Harold Jarche

We live and work in a complex system. Simple, traditional linear models do not work in complex systems. Complexity is not complicated, nor linear — “In complex systems, the last thing that happened is almost never informative about what’s coming next.”

subject matter networks

Harold Jarche

We live in a networked world. Is it even possible for one person to have sufficient expertise to understand a complex situation such as this pandemic? So do we rely on one subject matter expert or rather a subject matter network ? One expert is merely a node in a network.

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Understanding complexity

Harold Jarche

Thinking of complex adaptive systems as merely complicated entities that can be regulated like machines can lead to disaster, as Niall Ferguson shows in his recent book. Human systems are complex. Today’s large, complicated organizations are now facing complex business environments that require agility in simultaneously learning and working. This lack of understanding is the major barrier to success in the network era. complexity

Transforming learning through analytics

George Siemens

Data, big data, analytics, and visualization are significant trends in education. There is much to be alarmed about with analytics, including the mechanization of teaching, learning, and assessment. Additionally, the data and analytics that are easy to collect and conduct risk becoming a simple veneer over the complexities of learning and cognition. Or, as Gardner Campbell states : “Current NGLC/NCLB paradigms create great risk of analytics-generated edu-hell.”

Social Physics - Reinventing Analytics to Better Predict Human Behaviors

Irving Wladawsky-Berger

Properly used, social learning made a big difference. “The traders who had the right balance and diversity of ideas in their network - meaning that their social learning was neither too sparse nor too dense - had a return on investment that was 30% higher than the returns of both the isolated traders and those in the herd. Social physics first emerged over 200 years ago as an attempt to understand society and human behavior using laws similar to those of the physical sciences.

Notes On "Global Guidelines: Ethics in Learning Analytics"

Stephen Downes: Half an Hour

In my post referencing the ICDE's Global Guidelines: Ethics in Learning Analytics yesterday I said: This document summarizes the considerations of an ICDE working group on learning analytics. The presumption throughout is that a global ethic in learning analytics is needed, can be known, and comprehends the issues in this document. Definition and purpose of the use of Learning Analytics Where is data drawn from? What data must be included in learning analytics?

Organize for Complexity

Harold Jarche

” Well I think Niels has answered much of that question himself, in his recent book Organize for Complexity. We call this “disciplined practice” Fads like business analytics, knowledge management, and big data will never make organizations fit for complexity. So what does Organize for Complexity cover? Complex markets require decentralization, combined with market-like coordination. Books complexity ConnectedEnterprise Wirearchy

The Competitive Value of Data: From Analytics to Machine Learning

Irving Wladawsky-Berger

The competitive concerns surrounding these various platforms are closely linked to the concept of network effects. We’re now seeing the emergence of another kind of network effect. Business analytics predates the big data era. Beyond business analytics, new data science methods could now be used to extract actionable knowledge from all that data, that is, knowledge to help make better decisions and predictions.

The Science of Complex Systems

Irving Wladawsky-Berger

When I look back over my long, relatively eclectic career, complex systems have been a common theme in all the activities I’ve been involved in. It started in the 1960s, when I was an undergraduate and graduate student at the University of Chicago majoring in physics, - the study of complex natural systems. The research for my thesis was focused on the highly complex world of atoms and molecules.

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Digital Learning Research Network (dLRN)

George Siemens

I’m happy to announce the formation of the digital Learning Research Network (dLRN), funded by a $1.6m With dLRN, our goals are to: Increase the impact of existing research in solving complex organizational and systems-level learning challenges. Build on existing research in learning sciences, online, blended, and distance learning, as well as data mining and learning analytics. Higher education is digitizing.

Future of work is complex, implicit and intangible

Harold Jarche

But as organizations, markets, and society become networked, intangibles create more of our value and this is much more difficult to measure. With the increasing complexity that networks bring, implicit knowledge-sharing becomes more important as well, but this is often ignored by both training and knowledge management programs. The new workplace of intangible assets is a complex environment , and one where traditional analytical methods no longer work.

In a complex society

Harold Jarche

In the network era, it’s all merging. That means engaging in social media and learning how to learn in a network. In complexity, we have to think about emergent practices, which means jumping in and immersing ourselves in the environment in order to start making sense of it. An external, analytical approach will tell us little. Networks and complexity are the defining characteristics of our “work&# places today.

Growing Up in a Complex World

Irving Wladawsky-Berger

The primary challenge emerging out of the CEO conversations was complexity - the fact that CEOs now operate in a world that is substantially more volatile, uncertain and complex. Inheriting a Complex World: Future Leaders Envision Sharing the Planet invited graduate and undergraduate students to participate in a Web-based survey between October 2009 and January 2010. They grew up in a complex world. This generation of students doesn’t need to be told what a network is.

Network Learning: Working Smarter

Harold Jarche

We need to re-think workplace learning for a networked society. Formal, structured learning plays only a small role in getting things done in the networked workplace. Making sense of information, both personally and in networks, is becoming a key part of work. Social learning is about getting things done in networks. Network Learning. Network learning is an individual, disciplined process by which we make sense of information, observations and ideas.

Ethical Codes and Learning Analytics

Stephen Downes: Half an Hour

Abstract The growth and development of learning analytics has placed a range of new capacities into the hands of educational institutions. The ethics of learning analytics will therefore need to be developed on criteria specific to education.

Irving Wladawsky-Berger: Reflections on Complex Systems

Irving Wladawsky-Berger

Home Archives Subscribe « The Web and the Long, "Soft" War | Main | The Life Cycle of a Business » April 16, 2007 Reflections on Complex Systems Last week I attended the Almaden Institute , a meeting held annually at IBMs Almaden Research Center , located in Silicon Valley. The theme of this years Institute was Navigating Complexity: Doing More with Less , and featured a number of talks and panels on a variety of themes relating to complex systems.

Ten IT Predictions for the Post-Pandemic New Normal

Irving Wladawsky-Berger

T hrough 2023, half of enterprises’ hybrid workforce, business automation, and other transformation efforts will be delayed or will fail due to a shortage of IT and analytics talent with the right skills.

The Current State of Cloud Computing

Irving Wladawsky-Berger

Applications suffer latencies in cloud provider’s networks. Latency is generally the result of routing access to cloud networks through in-house data centers in an attempt to improve security. Rigid and brittle infrastructures choke on the data required for sophisticated analytics.”.

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Beyond Institutions Personal Learning in a Networked World

Stephen Downes: Half an Hour

The talk is called "Beyond Institutions Personal Learning in a Networked World" and I want to begin with a story that came across the wires recently and I thought was very appropriate for this venue. We''re told there will be analytics and data-driven management. It''s rather more like a mesh network, in which each starling is reacting only to the seven starlings around it. Complexity, Cause, and Murmurations It''s interesting. Reclaimed learning is network learning.

Beyond Assessment ? Recognizing Achievement in a Networked World

Stephen Downes: Half an Hour

This talk is basically what he said, except he said it in the French way with complex theories, while I''m going to give it in the prickly, analytical, English kind of Canadian, dry presentation. A language is something much more dynamic, much more behavioral and complex ?? The complexities matter." It''s a complex, messy ?? The Achievement Standards Network is offering “open access to machine?readable Incredibly complex process, but easy to do.

The role of informal social networks in building organizational creativity and innovation

Trends in the Living Networks

For the last decade I have examined and applied social network analysis in and across organizations, for example in large professional firms , technology purchase decision-making , high-performance personal networks , and other applications. The more time you spend with the analysis of social networks in organizations and those firms that have applied the techniques, the more evident the power of these approaches. Use social network analysis to uncover creative leadership.

The State of AI Adoption - High Performers Show the Way

Irving Wladawsky-Berger

A common theme throughout the report was that the same players who were leaders in the earlier waves of digitization and analytics were the early leaders in the AI wave.

AI Is Mostly About Business Value, Not Technology

Irving Wladawsky-Berger

A few months ago, Babson College professor Tom Davenport convened a virtual meeting of chief data and analytic officers (CDAO) from a variety of industries to discuss how to best achieve a return on investments (ROI) in AI. “We

After Years of Promise and Hype, Is AI Once More Failing to Deliver?

Irving Wladawsky-Berger

One of deep learning’s key features is that, unlike classic analytic methods, there’s no asymptotic data size limit beyond which it stops improving.

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The AI Factory: A New Kind of Digital Operating Model

Irving Wladawsky-Berger

Operating models are often quite complex, encompassing the activities of thousands of people, lots of processes, advanced technologies and millions of lines of code.

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Online Community Purpose Checklist | Full Circle Associates

Nancy White

A network may look to create many weak connections and amplify the flow of ideas and information, but have very little interest in being a community or group.) Find people and create connections (social networking)?

Becoming a Data-Driven Business: Challenges and Opportunities

Irving Wladawsky-Berger

A few weeks ago, McKinsey published The age of analytics: Competing in a data-driven world , a comprehensive report on the state of big data, and in particular, on the challenges and opportunities a company faces as it strives to become a data-driven business. Big data ,- including analytics , data science , artificial intelligence and related information-based technologies - are now everywhere. “Is

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Deep Learning: Is it Approaching a Wall?

Irving Wladawsky-Berger

Deep learning is a powerful statistical technique for classifying patterns using large training data sets and multi-layer AI neural networks. Each successive layer in a multi-layer network uses the output from the previous layer as input. “In The data requirements for deep learning are substantially different from those of other analytic methods in a number of dimensions. The performance of traditional analytics tends to plateau as the data set size increases.

The Emergence of Industry 4.0

Irving Wladawsky-Berger

In its scale, scope and complexity, what I consider to be the fourth industrial revolution is unlike anything humankind has experienced before…”. which networks a wide range of new technologies to create value.”. The digitization of product and service offerings by adding smart sensors, communication devices, and data analytics tools to create smart digital products and smart services. Data analytics and digital trust are the foundations. “Data fuels Industry 4.0

My Old Online Facilitation Workshop Materials | Full Circle Associates

Nancy White

Online Facilitation Course Curriculum Rev Aug 2002 Share and Enjoy: 13 responses so far 13 Responses to “My Old Online Facilitation Workshop Materials&# # uberVU - social comments on 30 Dec 2009 at 10:24 am Social comments and analytics for this post… This post was mentioned on Twitter by NancyWhite: Setting my old online facilitation curriculum into the wild [link]. #

25-10-3

Harold Jarche

“Scientists at Rensselaer Polytechnic Institute have found that when just 10 percent of the population holds an unshakable belief, their belief will always be adopted by the majority of the society … An important aspect of the finding is that the percent of committed opinion holders required to shift majority opinion does not change significantly regardless of the type of network in which the opinion holders are working. ” — Analytics in HR. Communities Complexit

Globalization in Transition

Irving Wladawsky-Berger

McKinsey argues that globalization is in transition rather than faltering, as reflected in the title of its report - Globalization in Transition: the Future of Trade and Value Chains. “The 1990s and 2000s saw the expansion of complex value chains spanning the globe. Complex Systems Economic Issues Future of Work Innovation Management and Leadership Political Issues Services Innovation Society and Culture Technology and Strategy

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A Framework for Building AI Capabilities

Irving Wladawsky-Berger

Just about every process can become digital aware, networked and smart. It’s led to the development of AI algorithms that are first trained with lots and lots of sample inputs, and then subsequently applied to complex problems like language translation, natural language processing, real time fraud detection, personalized marketing and advertising, and so on. After decades of promise and hype, artificial intelligence has finally reached a tipping point of market acceptance.

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The Business Value of Resilience

Irving Wladawsky-Berger

At the same time, the Internet’s universal reach has led to increasingly powerful network effects and to a new kind of platform business dynamics. Moreover, the larger the network, the more data is available to customize offerings to user preferences and better match supply and demand, further increasing the platform’s global reach and overall efficiency. Biological systems have long been an inspiration in the study of complex systems.

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Beyond Machine Learning: Capturing Cause-and-Effect Relationships

Irving Wladawsky-Berger

Deep learning is a powerful statistical technique for classifying patterns using large training data sets and multi-layer AI neural networks. Each successive layer in a multi-layer network uses the output from the previous layer as input. Machine learning is a statistical modelling technique, like data mining and business analytics , which finds and correlates patterns between inputs and outputs without necessarily capturing their cause-and-effect relationships.

Human-AI Decision Systems

Irving Wladawsky-Berger

In 2014 he published Social Physics : How Social Networks Can Make Us Smarter. In modern societies, decision systems provide a leader the ability to make informed and timely decisions, supported by a complex enterprise of distributed information and communication systems that provide situational awareness. But, despite the increasingly complex decisions that organizations are called upon to make, decision-making remains human-intensive and anecdotal.

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The Emergence of Industry 4.0

Irving Wladawsky-Berger

In its scale, scope and complexity, what I consider to be the fourth industrial revolution is unlike anything humankind has experienced before…”. which networks a wide range of new technologies to create value.”. The digitization of product and service offerings by adding smart sensors, communication devices, and data analytics tools to create smart digital products and smart services. Data analytics and digital trust are the foundations. “Data fuels Industry 4.0

intangible value

Harold Jarche

It was her work on value network analysis [PDF] that particularly influenced my thinking. Only through the power of value networks can we address our complex issues – together – and create a more hopeful future.” – Verna Allee. “A A value network is a web of relationships that generates economic value and other benefits through complex dynamic exchanges between two or more individuals, groups or organizations. Image: Value Network Analysis by Patti Anklam.

The Internet, Blockchain, and the Evolution of Foundational Innovations

Irving Wladawsky-Berger

Each phase is defined by the degree of novelty, - low or high, - of the applications being supported, and by the complexity required to coordinate the various elements of the application. Its packet switching architecture didn’t require pre-established connections, representing a major innovation over existing circuit switching networks. Internet-based platforms have given rise to ecosystems and network effects.

The Evolution of the Internet of Very Smart Things

Irving Wladawsky-Berger

Cisco defines fog computing as “a highly virtualized platform that provides compute, storage, and networking services between end devices and traditional Cloud Computing Data Centers, typically, but not exclusively located at the edge of network.” In cloud, the data generated by the smart sensors at the edges is transferred and stored in the center, where it’s analyzed, and the appropriate actions then flow back across the network.

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Anticipating the Future of the IT Industry

Irving Wladawsky-Berger

How can we best anticipate the future of a complex, fast changing industry like IT? Over time, sophisticated applications were developed to help manage more complex operations, including enterprise resource planning , customer relationship management and human resources. These early analytics applications dealt mostly with structured information.

The Emerging Data Economy

Irving Wladawsky-Berger

Physicists, astronomers, biologists, and other scientists and engineers were developing methodologies and architectures for dealing with very large volumes of unstructured data, as well as analytical techniques, like data mining , for discovering patterns and extracting insights from all that data. Hardware and software platforms have long raised competitive concerns in the IT industry, due to the economic power of network effects.

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The New Era of Smart, Connected Products

Irving Wladawsky-Berger

We generally think of products as physical entities, - e.g., clothes, light bulbs, appliances, cars, - some quite simple and some much more complex, built using sophisticated mechanical and/or electrical components. The article introduces a whole new class of smart connected products - “complex systems that combine hardware, sensors, data storage, microprocessors, software, and connectivity in myriad ways.”.