Complex systems and projects
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Complex systems and projects
Inspiring news (engl-fr- de) that help to develop a systemic, mindful, complex adaptive thinking and leadership
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ICTAM: Bringing mental models to numerical models

ICTAM: Bringing mental models to numerical models | Complex systems and projects | Scoop.it

How can we capture the highly qualitative, subjective and rich nature of people's thinking – their mental models - and translate it into formal quantitative data to be used in numerical models?

Philippe Vallat's insight:

Interesting approach, merging systems thinking and psychology (and keeping in mind that, according to Meadows , the mindset is the most effective leverage place in a system http://donellameadows.org/archives/leverage-points-places-to-intervene-in-a-system/)

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Defining and Modeling Complex Adaptive Systems

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.
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Agents of influence

Models of complex systems have become a staple of business strategy, and now they are showing early promise for improving economic forecasts.

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luiy's curator insight, March 6, 2013 4:36 PM

Self-Aware Agents

 

Busemeyer is helping to develop the theories needed to create “quantum agents” in future models. These would need to contain additional feedback loops, in which some agents’ actions are informed by the existence and output of other agent-based models.

 

This approach may be particularly suited to the world of high finance. As investors learn more about complexity theory, they become aware of their status as agents in predictive models, and they also run agent-based models to inform their own decision making—just like Busemeyer’s quantum agents.

Ultimately, though, none of these models will offer iron-clad predictions, because they have to make simplifying assumptions about human behavior. The true test will be whether those assumptions, and the resulting outputs of the models, convince policymakers to act on their advice.

 

“The way that our computational approach will eventually outrun conventional analytical and numerical methods in economics and finance is by having much more supple and succinct representations of human behavior,” says Axtell. But even then, “we don’t want policymakers to simply take the results of the model completely at face value without any use of their own judgment.”