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Our Self-Inflicted #Complexity | #economy

Our Self-Inflicted #Complexity | #economy | e-Xploration | Scoop.it

Our ability to make progress against large-scale problems requires that we figure out how to tackle inter-domain complexity writes Roger Martin. The HBR blog post is part of a series of perspectives on complexity leading up to Global Drucker Forum in Vienna 14 + 15 November 2013. 


Via Kenneth Mikkelsen
luiy's insight:

My own clan — the economists — is particularly inclined in this direction. There are a thousand economists working on partial equilibrium problems for every one working on a general equilibrium problem. This is despite the fact that no one would contest that general equilibrium clarity is the most valuable knowledge by far. Why? Because it is really difficult to specify any general equilibrium cause-and-effect relationships.

 

Instead, most of the guns deployed in modern knowledge advancement are aimed at narrow problems for which the cause-and-effect relationship is specified with the famous “all other things being equal” proviso. Each narrow knowledge domain develops analytical tool-sets that deepen the narrow knowledge domain. Each narrow domain develops ever more algorithmic knowledge, and those developing the knowledge are extremely confident that they are right because they are so specialized within their own domain. The liver expert is completely confident that he or she is correct even if it is the interaction with another condition that threatens your health most.

This approach has created another kind of complexity: inter-domain complexity. Every field is segmented into multiple domains, each with deep algorithmic knowledge, specialized tools, and experts in the domain who think they are absolutely right. And they are indeed right, as long as we ignore the reality of detail complexity.

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Defining and Modeling Complex Adaptive Systems

Defining and Modeling Complex Adaptive Systems | e-Xploration | Scoop.it
Almost all the critical problems of our time are problems of control and almost all of them concern complex adaptive systems. If we want to know more about our bodies, it is not just to increase knowledge but so that we can control our health.

 

CAS are  “systems that don’t yield compact forms of representation”1. In other words a complex system cannot be described by a simple set of equations. Why would this be the case? It is the “adaptive” nature of these systems that leads to this intractability. Agents within the system respond to each set of environmental conditions within a complex adaptive system with a different set of responses and the number of such environments and their corresponding agent responses that need to be accounted for to construct an accurate model of the system is simply too large. But is this simply a problem of impracticality? Could we, at least in theory, construct a model that takes into account all possible environmental conditions and all possible agent behaviours? Although some scientists may argue that such an approach is theoretically possible, there is ample evidence that the critical “adaptive” component of some complex adaptive systems may in fact be unmodelable


Via Anne Caspari, Alejandro J. Alvarez S.
luiy's insight:
What Is A Complex Adaptive System?

The first question that then needs to be answered is: What is a complex adaptive system? David Krakauer defines complex systems as “systems that don’t yield compact forms of representation”1. In other words a complex system cannot be described by a simple set of equations. Why would this be the case? As Krakauer notes, it is the “adaptive” nature of these systems that leads to this intractability. Agents within the system respond to each set of environmental conditions within a complex adaptive system with a different set of responses and the number of such environments and their corresponding agent responses that need to be accounted for to construct an accurate model of the system is simply too large. But is this simply a problem of impracticality? Could we, at least in theory, construct a model that takes into account all possible environmental conditions and all possible agent behaviours? Although some scientists may argue that such an approach is theoretically possible, there is ample evidence that the critical “adaptive” component of some complex adaptive systems may in fact be unmodelable. There is no better example of this than the problems faced by the economist Hyman Minsky in formalising many of his most important ideas.

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Anne Caspari's comment, March 14, 2013 2:56 PM
complex adaptive systems can often be identified by observing the critical role that disorderly processes play in maintaining system resilience. For example, the disorderly and often unpredictable nature of flooding is a vital factor in maintaining the productivity and resilience of many complex river-floodplain systems. But the same rarely holds for simpler systems such as small temperate streams... Interesting the examples on trying to manage CAS with the opposite effect of reducing resilience and robbing them of antifragility. Forests, rivers, ecnomic systems..
Luciano Lampi's curator insight, March 19, 2013 9:02 AM

Interesting to read!

Léonne Willems's curator insight, March 25, 2013 4:27 PM

Another way to think about environmental influences at the source of tensions at work (as opposed to individual lack of employee performance or motivational problems). Are these 'tensions' actually symptoms of a system out of balance? Are these tensions the real gems for organisational steering? Check out how Holacracy capitalises just on that! 

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Asset Management Tools for #Change: Social Network Analysis | #SNA #KM

Asset Management Tools for #Change: Social Network Analysis | #SNA #KM | e-Xploration | Scoop.it
Asset Management Tools for Change: Social Network Analysis
luiy's insight:

SOCIAL NETWORK ANALYSIS (SNA)


SNA is a methodology for determining and analyzing relationships between people in order to show how information flows and decisions are made, ultimately investigating how work gets done. This enables managers and teams to understand:

 

Who the prominent players are and whom others depend on to solve problems and provide technical information. Who do people turn to for advice? The actual nature of the communication network in reality, demonstrating how communications actually occur regarding work related issues and who is central to these communications. This illustrates both informal collaborative relationships and holes within the structures. Whether subgroups emerged that are disconnected or partially connected to the core. Which individuals are isolated and limited in their roles or, conversely, who faces a situation of overload.  

SNA is a means to analyze the informal organization beyond the organizational chart. The analysis allows managers and teams to visualize and understand the myriad of relationships that can either facilitate or impede information flow, decision processes and knowledge creation. Thus, mapping opportunities and constraints in invoking change within the organization.

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