Design thinking & complexity pt 1

Dave Snowden

I promised to address this yesterday following a presentation on Design Thinking at the conference here in Ohio. I started talking about the differences that complexity theory makes to design thinking some time ago - In Malmo at the XP conference as I remember it - and have now introduced that material in modified form onto day four of our accreditation programme. Designers going into the field to observe people in action.

Cognitive Task Analysis

Clark Quinn

A technique for doing that is Cognitive Task Analysis (CTA). While useful for system design, CTA is also valuable for designing performance support, and training. When I talk about the performance ecosystem , particularly for complex tasks, you want just this sort of support to determine what should be distributed across formal learning and performance support. design mobile strategy

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Complexity and Design in Warfare

Irving Wladawsky-Berger

On March 11 I participated in a workshop in Washington, - Design Unbound , - organized by The Highlands Forum. The workshop explored how design-oriented approaches can help us better understand and deal with the very complex problems we are increasingly encountering in all aspects of business, economies, government, public policy, military operations and society in general. Analysis involves a relatively linear set of steps. Designing.

Complexity and Public Administration

Dave Snowden

Some 10 years ago now when I first became interested in how complexity science might be used in public administration there was a body of thinking but relatively few practical examples of applications. This has brought attention to approaches informed by complexity science for working with intractable problems as well as a way to square the circle of making localization workable and relevant. So what is applying complexity thinking to public policy really all about?

Managing in Complexity

Harold Jarche

As our markets and technologies get more complex, we need new models to get work done. Simple systems are easily knowable, whereas complicated systems, while not simple, are still knowable through analysis. However, complex systems are not fully knowable, though they can be partially understood through interaction with them. If companies want to remain competitive in the global market, they need to focus on complex and creative work. complexity Wirearchy Work

Increased complexity needs simplified design

Harold Jarche

Now that many of us live in messy democracies and work in loose networks, learning has become complex with more connections to influence us. According to the authors of Getting to Maybe , in complex environments : Rigid protocols are counter-productive. As Jay has said, informal learning is a better approach for more complex environments. Analysis for Informal Learning.

Scale and Complex Systemic Innovation

Irving Wladawsky-Berger

They are highly motivated to bring to market new products which offer significantly improved designs and/or lower costs, and thus gain competitive advantage and market share from the established leaders in their segment of the market. We generally think of large companies as being good at operations and incremental innovations, which require them to excel at management discipline, detailed information analysis and project management.

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Intervention Design: Overcoming Stiff Resistance to a New, Disruptive Innovation

Irving Wladawsky-Berger

The September issue of the Harvard Business Review features a spotlight on The Evolution of Design Thinking. With four articles on the subject, HBR ’s overriding message is that design is no longer just for physical products, being increasingly applied to customer experiences, innovation, business strategy, and complex problem solving. . Last week I discussed this expanded view of design thinking based on one of the articles, - Design Thinking Comes of Age.

Strategy and Execution in an Increasingly Complex World

Irving Wladawsky-Berger

Often, this is because they associate strategy with analysis and execution with getting things done, and they attribute more value to doing than to analyzing.” . Twenty-thirty years ago, the business world was arguably less complex, slower moving and more predictable. Once upon a time, design and manufacturing were quite distinct steps in the development of products. Complex Systems Innovation Management and Leadership Services Innovation Technology and Strategy

How Can We Ensure that Our Complex AI Systems Do What We Want Them to Do?

Irving Wladawsky-Berger

Things are even tougher with people-centric sociotechnical systems, which not only have to deal with potential software problems, but with the even more complex issues involved in human behaviors and interactions. And, at the bleeding-edges of complexity are data-driven AI systems , which are generally based on statistical analysis and machine learning methods. . What is it that makes these systems so intrinsically complex? What purpose does this complexity serve?

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7 principles of intervention in complex systems

Dave Snowden

Now one of the basic Cynefin mantras is that of safe-fail interventions, and lots of them, if the problem is complex. One can only really understand a complex system by interacting with it (the probe of probe-sense-respond ) and while people get that, getting them to take on board the full implications is another matter. It is important to realise that a lot of conflict happens in the complex arena. I may buy this T-Shirt as its a great slogan.

Social Learning, Complexity and the Enterprise

Harold Jarche

Our relationship with knowledge is changing as our work becomes more intangible and complex. PDL – Personal Directed Learning: from Clockwork & Predictable to Complexity & Surprising. Complexity, or maybe our appreciation of it, has rendered the world unpredictable, so the orientation of learning is shifting from past (efficiency, best practice) to future (creative response, innovation). In complex environments it no longer works to sit back and see what will happen.

learning in complexity and chaos

Harold Jarche

Most of our current work structures are designed to address complicated situations, such as constructing a building, launching a campaign, or designing a piece of equipment. But more of our challenges are complex and cannot be solved in a standard way — inequality, refugees, populism, racism. Whenever people are involved, within a global context of climate change, the situation is likely complex. We have to learn constantly in complexity. Complexity PKMastery

Complex Organizational Systems: Some Common Principles

Irving Wladawsky-Berger

Following Vest’s keynote, I participated in a panel on Critical Issues and Grand Challenges , in which we discussed the kinds of complex and exciting societal challenges Chuck told us are needed to inject excitement into engineering. I was the last speaker in the panel, and talked about complex organizational systems. After listening intently to what they said, I came up with four key such common principles for complex systems. It is very complicated, but not quite complex.

Training Design

Tony Karrer

I've been struggling a bit to capture a concept that I believe represents a fairly fundamental shift in how we need to think about Training Design. Back in 2005, 2006 and 2007, I would regularly show the following slides to help explain the heart of what Training Design is all about and how it has changed over the years. Oh, and I called it Learning Design in the diagrams, but I'm afraid that it's really more about Training Design.

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How Design Can Tackle Real World Problems: SF Public Design Jam Recap

Adaptive Path

And while it may sound like a toy store was opening shop in the middle of our space, this was the scene of the first SF Public Design Jam. Filled with designers, students, city government staff, citizens, programmers, researchers, and non profit organizations, the Adaptive Path studio was buzzing for two days as teams were rapidly applying design methods and their skills or expertise to explore opportunities for how design and the public sector can work together to have real impact.

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In Defense of Cognitive Psychology

Clark Quinn

I’m reminded of Todd Rose’s End of Average , which did a nice job of pointing out the problems of trying to compress complex phenomena into single measures. This might be more easily derivable from neural net models, but still provided a useful basis for design.

Educational Game Design Q&A

Clark Quinn

How many years have you been designing educational games? Started with my first job out of college, designing and programming educational computer games. Using a design framework of Analysis, Specification, Implementation, and Evaluation: Analysis. For any educational task, you have to start by looking at what your design objective is: you need to document what folks should be able to do that they can’t do now. Game design is a team sport.

The Simple Test and Complex Phenomena

Stephen Downes: Half an Hour

Either he thinks very poorly of certification programs, or he did not read Thalheimer's analysis. And it can be expressed generally as the following: The test author believes (based on some research, which is never cited) that "Learning is better if F" where 'F' is some principle, such as "Performance objectives that most clearly specify the desired learner behavior will produce the best instructional design." Test the page after design b.

Different Ways Nonprofits Are Using Design Thinking to Solve Problems and Achieve Impact

Beth Kanter

Note from Beth: Several years ago, I was got trained in design thinking facilitation methods using Luma and have incorporated these techniques into my consulting and training practice. Different Ways Nonprofits Are Using Design Thinking to Solve Problems and Achieve Impact. This is exactly why the practices of Design Thinking and Systems Theory has enjoyed such welcome from the social sector. Design Thinking moves fast, and relies on “failure” to learn and iterate.

Blockchain - the Networked Ecosystem is the Business

Irving Wladawsky-Berger

Based on the analysis of over 25 blockchain networks in various stages of production across multiple industries and geographies, the report recommends that a company’s blockchain journey should evolve along three distinct stages: in search of value, - establishing a minimum value ecosystem; getting to scale - creating value for an overall industry; and designing for new markets, - creating entirely new markets and business models.

“the future of work will be based on hacking uncertainty”

Harold Jarche

The unit of analysis is now communication and emergence, not entities. Esko said that in order to develop the necessary emergent practices to deal with complexity you need to first cultivate diversity and by this I would say the autonomy of each learner. “The network design principles successful organizations follow are: ( 1 ) shortening the distance between two randomly picked files/nodes/people. ( Communities Complexity Learning

The Business Value of Augmented Reality

Irving Wladawsky-Berger

We generally think of products as physical entities built with a combination of mechanical and electrical components, - e.g., appliances, cars, agricultural machines, industrial equipment, - some quite simple and some highly complex. D igital technologies are designed right into the products. We need smart connected humans to help us better deal with our complex SCPs.

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The Economic Value of Digital Identity

Irving Wladawsky-Berger

McKinsey’s analysis was informed by nearly 100 concrete uses of digital IDs in seven countries: Brazil, China, Ethiopia, India, Nigeria, the UK and the US. Designed carefully and scaled to high levels of adoption in multiple application areas, it can also create significant economic value, particularly in emerging economies, with benefits for both individuals and institutions. Identity plays a major role in our everyday life.

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solving problems together

Harold Jarche

The challenge for organizational design is to make it easy to move new problems into the knowable space. Exception handling is complex work, which requires sensemaking, curiosity, and initiative. Today’s complex work may become tomorrow’s merely complicated or even simple work. In addition, with complex work, failure has to be tolerated, as there are no best practices for exceptions. In complex situations there is no time to commission a detailed analysis.

AI and the Evolution of History

Irving Wladawsky-Berger

AI, in contrast, is able to prescribe its own objectives. “AI systems, through their very operations, are in constant flux as they acquire and instantly analyze new data, then seek to improve themselves on the basis of that analysis. How can we ensure that our increasingly complex AI systems do what we want them to do ? We’re all familiar with software bugs, especially bugs in highly complex software, which is the case with AI systems.

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Blockchain Can Reshape Financial Services… But it Will Take Significant Time and Investment

Irving Wladawsky-Berger

Not surprisingly given their complexity, change comes much slower to global financial infrastructures. The majority of the 130-page report consists of a deep-dive analysis of how DLT might apply to several different use cases. Transforming this highly complex global ecosystem is very difficult. Overcoming these challenges will add complexity and delay large-scale, multi-party DLT implementations.

Social Physics - Reinventing Analytics to Better Predict Human Behaviors

Irving Wladawsky-Berger

They’ve enabled the construction of AI algorithms that can be trained with lots and lots of sample inputs, which are subsequently applied to complex problems like language translation, natural language processing, and playing championship-level Go. Machine learning has been most successful when used for complex computational problems like image and voice recognition, where a huge body of data is available and the data is fairly static.

Can AI Help Translate Technological Advances into Strategic Advantage?

Irving Wladawsky-Berger

Go is a very complex game, for which there are more possible board positions than there are particles in the universe. A recent Harvard Business Review article, - Designing the Machines that Will Design Strategy , - takes these questions a few steps further. The authors concluded that innovation involves two fundamental processes: analysis and interpretation. Analysis is essentially rational, quantitative, data-driven decision making and problem solving.

Artificial Intelligence is Ready for Business; Are Businesses Ready for AI?

Irving Wladawsky-Berger

Demand forecasting, supply chain optimization, and improved product design and productivity. “ Organizations need to constantly anticipate the future to gain competitive advantage. AI allows businesses to provide better forecasts for their supply chain and design better offerings. In health care, analysis of public health data can help predict and prevent the spread of disease, including major epidemics. AI is now seemingly everywhere.

A Framework for Building AI Capabilities

Irving Wladawsky-Berger

Based on a study of over 150 AI-based projects, the authors found that AI can play a major role in three important business needs: advanced process automation, cognitive insight through data analysis, and cognitive engagement with customers and employees. In the 1960s and 1970s, IT brought automation to a number of discrete business processes, including transaction processing, financial planning, engineering design, inventory management, payroll and personnel records.

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Data-Driven Decision Making: Promises and Limits

Irving Wladawsky-Berger

In a recently published article, Data Science and its Relationship to Big Data and Data-Driven Decision Making , Foster Provost and Tom Fawcett succinctly define data-driven decision making as “the practice of basing decisions on the analysis of data rather than purely on intuition.” They tend to be complex, and unstructured because of the uncertainty and risks that generally accompany longer term decisions. A complex context is quite different.

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As Big Data and AI Take Hold, What Will It Take to Be an Effective Executive?

Irving Wladawsky-Berger

In principle, such jobs deal with non-routine, cognitive tasks requiring high human skills, including expert problem solving, complex decision-making and sophisticated communications for which there are no rule-based solutions. “As Garbage in, garbage out applies as much to data analysis today as it has to computing in general since its early years. The authors concluded that innovation involves two fundamental processes: analysis and interpretation.

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Corporate Survival: Lessons from Biology

Irving Wladawsky-Berger

Based on their extensive analysis of data about publicly traded US companies, they were surprised to discover that a typical firm lasts about ten years before it gets merged, acquired or liquidated, and that a firm’s mortality rate is independent of its age, how well established it is or what it does. Let’s take a closer look at the nature of complex adaptive systems. Why are these systems so complex?

Developing Decisions

Clark Quinn

What will make the difference between ordinary and extraordinary organizations is the ability to make decisions in this new VUCA environment (volatile, uncertain, complex, and ambiguous). We can use semantic analysis to read documents and make a system that can answer questions about them. So, for instance, for instructional design, we should have an awareness of interface design, graphic design, media production, etc. design meta-learning

Skills and Jobs in the Digital Economy

Irving Wladawsky-Berger

Non-routine, cognitive tasks tend to be high-skill human activities that involve expert problem solving and complex communications for which there are no rule-based solutions. Equipment : Equipment Maintenance, Installation, Operation Monitoring, Repairing, Systems analysis, Troubleshooting, …. The analysis was repeated for the 2014 occupations, yielding 5 distinct skill factors: .

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It's All About the Business Model, - Not the Technology

Irving Wladawsky-Berger

The power of this deceptively simple framework lies in the complex interdependencies of its parts. They include B-to-C models like Warby Parker ’s, which offers designer eyeglasses to online consumers, and B-to-B models like Dow Corning Xiameter 's, which sells silicone-based products to manufacturers. Complex Systems Economic Issues Innovation Services Innovation Technology and Strategy

The Evolution of the Internet of Very Smart Things

Irving Wladawsky-Berger

It’s impractical to move all that data to a central cloud for analysis and actions. With fog on the other hand, the analysis is done in real time at or near the edge devices, from which actions then flow across the network, with only the data to be stored being transferred to the central cloud. These factors eventually led to significantly increased management complexities and costs.

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On the Governance of Blockchain: Lessons from the Internet

Irving Wladawsky-Berger

All are generally based on the original design released in 2009 by Satoshi Nakamoto , but with wide variations and limited interoperability. Their analysis helped me better appreciate the design objectives and organizational cultures of the various blockchain camps, as well as the key governance challenges each faces going forward. T he Internet continues to have serious limitations for business and economic activity, - including security, complexity and trust.

“Soft” AI Is Suddenly Everywhere

Irving Wladawsky-Berger

These are generally statistically oriented, computational intelligence methods for addressing complex problems based on the analysis of vast amounts of information using powerful computers and sophisticated algorithms, whose results exhibit qualities we tend to associate with human intelligence. AI-based tools are enhancing our own cognitive powers, helping us process vast amounts of information and make ever more complex decisions.

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Shaping the Future of the Internet

Irving Wladawsky-Berger

What design choices have led to the Internet as we know it? As is the case with any complex system, the Internet’s design has been the result of many different choices. Being general purposes is a major design choice, best appreciated when considering the alternatives, such as the telephone network, which was designed specifically to carry telephone calls. But the design characteristics that underwrote these gains also supported cybercrime, spam, and malice.”.

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A Framework for Building AI Capabilities

Irving Wladawsky-Berger

Based on a study of over 150 AI-based projects, the authors found that AI can play a major role in three important business needs: advanced process automation, cognitive insight through data analysis, and cognitive engagement with customers and employees. In the 1960s and 1970s, IT brought automation to a number of discrete business processes, including transaction processing, financial planning, engineering design, inventory management, payroll and personnel records.

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adapting to perpetual beta

Harold Jarche

To mark the anniversary, this excerpt from finding perpetual beta is a summary of what I believe are some of the most important issues facing organizational design today. Chaos, Complexity, Complication. Complex environments are not chaotic but they cannot be completely understood in advance. Weather systems are complex. Emerging practices need to be developed while staying engaged with complex systems. Pretty well all human systems are complex.