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Posts Tagged ‘complex adaptive systems’

complexity

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dana boyd has written a post in which she discusses why America is self-segregating and she comes up with a few suggestions such as the role of social media in segregating people into filter bubbles and echo chambers. But a key point she makes is that diversity, which is ‘often touted as highly desirable’ is hard – ‘uncomfortable, emotionally exhausting and downright frustrating’. So instead of using the many online tools we now have at our disposal to become diversely connected, we use them instead to find like-minded people who, as Kirschner wrote in 2015, ‘discuss, confirm, validate and strengthen the group’s position’ (p.622). In doing this we reduce diversity.

(This tendency to try to reduce diversity is not only evident in online networks. It can also be seen in ‘The Big Sort’ and geographical clustering that I mentioned in my last post, i.e. people physically move geographical location to live near those more like themselves.)

More than ten years ago in 2005 in his ‘Introduction to Connective Knowledge’ (revised in 2007) Stephen Downes wrote of diversity as a key principle of ‘knowing’ networks. Downes sees the fostering of diversity as the means to

 ‘counterbalance the tendency toward a cascade phenomenon in the realm of public knowledge’.  

(Information cascades occur when external information obtained from previous participants in an event overrides one’s own private signal, irrespective of the correctness of the former over the latter’ (Wikipedia ). Cascade phenomena can sweep through densely connected networks very rapidly).

Downes writes

the excesses made possible by an unrestrained scale-free network need to be counterbalanced through either one of two mechanisms: either a reduction in the number of connections afforded by the very few, or an increase in the density of the local network for individual entities’.

According to Downes, the only way to avoid information cascades is to ensure multiple viewpoints and alternative perspectives from observers with different sets of prior experiences, world views and interpretations.

Related to this, a couple of years later Downes wrote of the different affordances of groups and networks – Groups vs. Networks: The Class Struggle Begins – saying that a group is about what members have in common, whereas ‘a network is like an ecosystem where there is no requirement that all the entities be the same.’ If we accept this it follows that a group tends towards homogeneity, but a network to heterogeneity (see also my post on the hazards of group work). Diversity is therefore essential to a healthy network.

But what is diversity?  Dictionaries, e.g. Cambridge dictionary, define diversity as being many different types of things or people, ideas or opinions, being included in something. I would add that in addition many different resources are needed to inform these ideas or opinions. In a paper that Carmen Tschofen and I published in 2012, Connectivism and Dimensions of Individual Experience, we also suggested that there is a need to recognise the importance of psychological diversity of online learners, the complexity of their human needs and connections, i.e. that diversity is not just an external manifestation of difference, but also internal to individuals. Each individual is unique. We argued that connectivity needs to be viewed not only in terms of the network but also in terms of individual characteristics and biases, further complicating an understanding of diversity.

But why is diversity ‘desirable’? dana boyd points to more diverse teams outperforming homogeneous teams and claims that diversity increases cognitive development. In my own field of research into learning in open online environments, this point of view is endorsed by the call for more interdisciplinary, multidisciplinary and cross global, international working (see for example Haywood, 2016 and Eynon et al., 2016).

However, Cilliers (2010) suggests that there are deeper reasons. These are related to viewing the world in which we live as a complex adaptive system. Complex systems are heterogeneous, asymmetrical and full of non-linear, unpredictable interactions, which means we cannot fully know or control them. Complex environments exhibit the following characteristics (and more!):

  • Distributed knowledge
  • Disequilibrium
  • Adaptive
  • Self-organisation
  • Unpredictable
  • Emergence
  • Connectedness
  • Diversity
  • Openness
  • Co-evolution
  • Interaction
  • Retrospective coherence

Cilliers tells us that diversity is a key characteristic of complex systems and is essential to the richness of the system, because it is difference not sameness that generates meaning.

An abundance of difference is not a convenience, it is a necessity. Complex systems cannot be what they are without it, and we cannot understand them without the making of profuse distinctions. Since the interactions in such systems are non-linear, their complexity cannot be reduced. The removal of relationships, i.e. the reduction of difference in the system, will distort our understanding of such systems. (Cilliers, 2010, p.58)

But this does not mean that ‘anything goes’. To get the most out of diversity and difference, complex systems require boundaries and constraints, negative, enabling constraints, ‘which determine what is not allowed to happen, rather than specifying what does have to happen’ (Williams, Karousou & Mackness, 2011, p.46). There needs to be an effective balance between openness and constraint, structure and agency.

And difference does not mean opposition. Meaningful relationships develop through difference (Cilliers, 2010), but achieving the right amount of difference to support this development, depends on ethical judgement and choice.

To make a responsible judgement—whether it be in law, science or art—would therefore involve at least the following components:

  • Respecting otherness and difference as values in themselves.
  • Gathering as much information on the issue as possible, notwithstanding the fact that it is impossible to gather all the information.
  • Considering as many of the possible consequences of the judgement, notwithstanding the fact that it is impossible to consider all the consequences.
  • Making sure that it is possible to revise the judgement as soon as it becomes clear that it has flaws, whether it be under specific circumstances, or in general. (Cilliers, 1998, p.139)

These points seem as relevant today, if not more so, than when they were written in 1998. Respect for differences and an understanding of diversity is a key ethical rule for complex systems and no amount of retreating into homogeneous groups will help us cope with living in an increasingly complex world.

As Stephen Downes wrote in 2005 when proposing connectivism as a new learning theory appropriate for living and learning in a digitally connected world:

‘Connective knowledge is no magic pill, no simple route to reliability and perhaps even more liable to error because it is so much more clearly dependent on interpretation.’

but

‘Freedom begins with living free, in sharing freely, in celebrating each other, and in letting others, too, to live free. Freedom begins when we understand of our own biases and our own prejudices; by embracing autonomy and diversity, interaction and openness….’

I agree with dana boyd – diversity is hard, but if as Cilliers (2010, p.56) says, ‘Difference is a necessary condition for meaning’ in a complex world, in order to learn we will need to embrace diversity and maintain, sustain and increase our global networks and connections.

References

Cilliers, P. (1998). Complexity and postmodernism. Understanding complex systems. London and New York, Routledge

Cilliers, P. (2010). Difference, Identity, and Complexity. Philosophy Today, 54(1), 55–65.

Downes, S. (2007). An Introduction to Connective Knowledge in Hug, Theo (Ed.) (2007): Media, Knowledge & Education – Exploring New Spaces, Relations and Dynamics in Digital Media Ecologies. Proceedings of the International Conference held on June 25-26, 2007. – http://www.downes.ca/post/33034

Eynon, R., Hjoth, I., Yasseri, T., & Gillani, N. (2016). Understanding Communication Patterns in MOOCs: Combining Data Mining and qualitative methods. In S. ElAtia, D. Ipperciel, and O. Zaïane (Eds.), Data Mining and Learning Analytics: Applications in Educational Research, Wiley.

Haywood, J. (2016). Learning from MOOCs: lessons for the future. In E. de Corte, L. Engwall, & U. Teichler (Eds.), From Books to MOOCs? Emerging Models of Learning and Teaching in Higher Education, p. 69-80. Oregon: Portland Press Limited.

Kirschner, P. A. (2015) ‘Facebook as learning platform: Argumentation superhighway or dead-end street?’ Computers in Human Behavior, vol. 53, December, pp. 621–625. Elsevier Ltd. [Online] Available at http://dx.doi.org/10.1016/j.chb.2015.03.011

Tschofen, C., & Mackness, J. (2012). Connectivism and Dimensions of Individual Experience. The International Review of Research in Open and Distance Learning, 13(1). http://www.irrodl.org/index.php/irrodl/article/view/1143

Williams, R., Karousou, R., & Mackness, J. (2011). Emergent Learning and Learning Ecologies in Web 2.0. The International Review of Research in Open and Distance Learning, 12(3). http://www.irrodl.org/index.php/irrodl/article/view/883

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Notes from Dave Snowden’s Presentation to Week 17 of ChangeMooc, 18-01-12

Recording of his presentation

Learning and the Human Brain

The assumption that the human brain is a computational device has led to an information focus in learning. There is a dominance of machine-type metaphors. We have been seduced by machines, leading to an education system dominated by input/output models (promoted by some types of systems thinking) and a view of the brain as an information-processing device (see All Watched Over by Machines of Loving Grace ).

The human brain is a pattern processing intelligence not an information-processing machine. We need to avoid the pattern entrainment  that results from group think. The human brain evolved to handle messy coherence, not structure and order. It allows us to innovate, have insights and see things in a different way

Knowledge Acquisition

We need more generalists, people with a mix of generalised and deep knowledge, for working in a complex world – people who can work quickly across subjects and in contexts of high levels of uncertainty.

There are whole tracts of knowledge that can only be understood through interaction, e.g. through an apprenticeship model of education, which allows for imitation and failure, such as for London taxi drivers. Failure is key to human knowledge acquisition and the two-year apprenticeship of London taxi drivers has been shown to change the hippocampus area of the brain

The minute a measure becomes a target it ceases to be a measure, e.g. as when academics are measured on the number of papers they produce  rather than the originality of their ideas. PhDs destroy intelligence rather than build it. In universities we are training recipe book users and assessing whether they can reproduce the recipe. We are not training chefs who can achieve a huge amount without a recipe. Chefs have a mix of practical and theoretical wisdom and willingness to engage conceptually and theoretically with real world problems.

We need to avoid the anti-intellectualism that is endemic in Europe and N. America i.e. don’t use big words or read books, keep things simple and become simplistic as a consequence.

Complex Adaptive Systems

Complex adaptive systems are not causal but dispositional, i.e. they are pre-disposed to evolve in random ways which cannot be predicted. So models based on cause and effect which do not have a predictive capacity are of no use in complex spaces.

A way of handling uncertainty is to make use of collective or distributed cognition. Complex spaces need experts to compete/disagree with each other to increase diversity, rather than a consensus based approach. For emergence we need to force conflict by bringing in different people with different backgrounds. In complex systems we should also bring in safe-to-fail experiments and prevent premature convergence by moving people around into different groups.

By contrast, complicated spaces need experts whose judgment we trust.

Innovation and Creativity

Deception is the heart of innovation in any system. Play the game and innovate (under the radar).

Creativity is a symptom of innovation not a cause. Innovative people are creative. Pressure, starvation and perspective shift produce innovation which produces creativity.

Failure, consensus and facilitation

Negative stories carry more learning than positive stories. Appreciative Inquiry is often unethical and used in inappropriate contexts; it tells people what stories they are allowed to tell.  Open space is also like this in that it rewards consensus and punishes dissent. Anyone who survives in an open space does so because the only people there are those who listen – everyone else votes with their feet. Knowledge Management which focuses on best practice also entrains past practice and fosters consensus. In a complex system we have to increase diversity and conflict so that emergent possibilities become visible and can be consolidated.

If you haven’t failed, you have failed.

Any technique which relies on really good facilitation isn’t going to work on a consistent basis. We need processes that don’t require facilitation. It’s not about creating spaces to enable thing, but about creating processes (e.g. Ritual Dissent).

Final Message from Dave Snowden

Don’t give up on formal education, but interact with the ‘real world’ and read outside your subject.

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