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What is complexity and how does it relate to education?

The assumption made in this Sprint is that you are not necessarily familiar with Complexity Theory or Complexity Thinking. Below you will find quite a number of extracts from the first edition of Davis and Sumara’s book on Complexity and Education. We urge you to read all of these extracts. In the process, you will have to wrestle with the ideas presented and build your understanding of complexity. Some comments and tasks have been inserted between extracts to provide extra information or provide tasks that will enable you to create artefacts. Once you have completed the suggested tasks, please add the artefacts to your eportfolio. The artefacts will help you to complete future sprints, assessments and your Practice-based Project In Part 3 of the programme.

Davis, Brent & Sumara, Dennis. (2006). Complexity and Education. Inquiries into learning teaching and research. London: Lawrence Erlbaum.

Complexity thinking is not a hybrid. It is a new attitude toward studying particular sorts of phenomena that is able to acknowledge the insights of other traditions without trapping itself in absolutes or universals (Davis & Sumara, 2006, p.4).

There is no “complexity scientific method”; there are no “gold standards” for complexity research; indeed, specific studies of complex phenomena might embrace or reject established methods, depending on the particular object of enquiry (Davis & Sumara, 2006, p.5).

…, researchers have identified several necessary qualities that must be manifest for a phenomenon to be classed as complex. The list currently includes:

  • Self-organized – Complex systems / unities spontaneously arise as the actions of autonomous agents come to be interlinked and co-dependent;

  • Bottom-up emergent – complex unities manifest properties that exceed the summed traits and capacities of individual agents , but these transcendent qualities and abilities do not depend on central organizers or overarching governing structures;

  • Short-range relationships – most of the information within a complex system is exchanged among close neighbours, meaning that the system’s coherence depends mostly on agents’ immediate interdependencies, not on centralized control or top-down administration;

  • Nested structure – (or scale-free networks) – complex unities are often composed of and often comprise other unities that might be properly identified as complex - that is, as giving rise to new patterns of activities and new rules of behavior ;

  • Ambiguously bounded – complex forms are open in the sense that they continuously exchange matter and energy with their surroundings (and so judgements about their edges may require certain arbitrary impositions and necessary ignorances);

  • Organizationally-closed – complex forms are closed in the sense that they are inherently stable – that is their behavioural patterns or internal organizations endure, even while they exchange energy and matter with their dynamic contexts ( so judgements about their edges are usually based on perceptible and sufficiently stable coherences);

  • Structure determined – a complex unity can change its own structure as it adapts to maintain its viability within dynamic contexts ; in other words, complex systems embody their histories – they learn – and are thus better described in terms of Darwinian evolution than Newtonian mechanics;

  • Far-from-equilibrium – complex systems do not operate in balance; indeed a stable equilibrium implies death for a complex system (Davis & Sumara, 2006, p.6).


The following table provides some examples of what the qualities of a complex phenomenon or system might look like in education.


Complex qualitiesWhat these look like in education

  • Self-organized

A cross-learning area project comes into being when a number of teachers spontaneously decide to work together to create an integrated learning environment for their students.

  • Bottom-up emergent

The school does not currently focus on on well-being. Some Year 11 learners decide that they would like to organise and implement a workshop day for Year 9 learners that focuses on some key life skills, such as creating a monthly budget, keeping your car on the road, planning for buying a house, etc. The Year 11’s implement their workshop day.

  • Short-range relationships


  • Nested structure

Small groups of students are nested within classrooms - which are nested within schools - which are nested within COLs or regions - which are nested within national ministries of education ….

  • Ambiguously bounded

Perhaps you consider your class a bounded environment? Your students go home after school and they interact with friends, parents, family, community members, avatars in games, etc. In the process they learn things that they bring into the classroom. Where does the learning in your classroom begin and where does it end?

  • Organizationally-closed

Even though schools are constantly changing (e.g. addition of modern learning environments), their essential existence as organisations remain the same. If a school had no buildings at all and only operated online, we would still refer to it as a school.

  • Structure determined

A school believes that teaching groups of students who belong to the same Year level is less effective. So they decide to scrap Year- level groups and create vertical groups of students (across Year levels). They then and continue to teach these students in groups.

  • Far-from-equilibrium

Equilibrium means no change. If a school does not change, it dies.

Complexity thinking does not provide all-encompassing explanations; rather, it is an umbrella notion that draws on and elaborates the irrepressible human tendency to notice similarities among seemingly disparate phenomena. How is an anthill like a human brain? How is a classroom like a stock market? How is a body of knowledge like a species? These are questions that invoke a poetic sensibility and than rely on analogy, metaphor, and other associative (that is, non-representational) functions of language) (Davis & Sumara, 2006, p.7).

Complexity thinking does not rise over, but arises among other discourses. Like most attitudes toward inquiry, complexity thinking is oriented by the realization that the act of comparing diverse and seemingly unconnected phenomena is both profoundly human and, at times, tremendously fecund (Davis & Sumara, 2006, p.8).

Within these sorts of complex systems, interactions of components are not fixed and clearly defined, but are subject to ongoing co-adaptations. The behaviours of simple and complicated systems are mechanical. They can be thoroughly described and reasonably predicted on the basis of precise rules, whereas the rules that govern complex systems can vary dramatically from one system to the next. Moreover, these rules can be volatile, subject to change if the system changes. Such precariousness arises in part from the fact that the “components” of the complex system - at least for all of the systems that are considered in this text – are themselves dynamic and adaptive (Davis & Sumara, 2006, p.11).

With regard to pedagogy, then, complexivists tend to follow Freud’s assertion that teaching, understood in terms of determining learning, is one of the impossible professions (Davis & Sumara, 2006, p.12).

It is thus that we arrive at one of our central assumptions and assertions: in this discussion of educational and research implications of complexity thinking, we are concerned with learning systems. Moreover, following Kauffman, we seek our principles of learning in complex systems anew (Davis & Sumara, 2006, p.12).

Returning the issue at hand, then, a learner in this text is understood to be a structuring, structured structure, to borrow from Dyke. A learner is a complex unity that is capable of adapting itself to the sorts of new and diverse circumstances that an active agent is likely to encounter in a dynamic world (Davis & Sumara, 2006, p.14).

The term environment must be used carefully. In complexity terms, it is not meant to imply the presence of a clear, unambiguous physical boundary between an agent and its context. For complex systems, agents are necessarily parts of their environments (Davis & Sumara, 2006, p.15).

Thus, for the purposes of studying a complex form, the physical or conceptual boundaries of a complex / open system are always contingent on the criteria used to define or distinguish the system from its backdrop (Davis & Sumara, 206, p.15).

Richardson and Cilliers sum up some of the variations in sensibilities that are represented across complexity research by identifying three broad schools of thought that have been represented, these being:

a) Hard (or reductionist) complexity science – an approach dominated by physicists that, in effect, maintains the same desire as analytic science to uncover and understand the nature of reality, oriented by the assumption that such a reality is determined and hence determinable.

b) Soft complexity science – an approach more common in the biological and social sciences that draws on the metaphors and principles developed within hard complexity science to describe living and social systems. In this case, complexity is more a way of seeing the world, an interpretive system rather than a route to or representation of reality.

c) Complexity thinking – an attitude that lies somewhere between the hard and soft approach. It is concerned with the philosophical and pragmatic implications of assuming a complex universe, and might thus be described as representing a way of thinking and acting (Davis & Sumara, 2006, p.18).

As it turns out, hard complexity science seems to be positioned within this contradiction, in its desire to assemble an objective account of the universe even while acknowledging the possibility of emergent forms that simply could not be anticipated on the basis of the current state of affairs. A preferred means of investigation within the hard approach is computer simulation, in which complex systems are modeled in attempts to uncover the conditions that underlie their emergence and to make sense of the transcendent capacities that arise once emergent (Davis & Sumara, 2006, p.22).

To be clear, we are arguing here that a hard approach to complexity science, while relevant, powerful, and appropriate for certain emergent phenomena, is of limited value to educators and educational researchers. Further, a major premise of the hard approach – namely the assumed stability of the phenomenon studied – is inherently problematic for educationists (Davis & Sumara, 2006, p.23).

Soft complexity science, then, refers to an increasingly popular movement within the social sciences toward an embrace of images and metaphors to highlight the intricate intertwinings of complex phenomena. For example, personal memories might be characterised in terms of a fractal structure in which virtually any recollection, when closely inspected, can explode into a vast web of associations. In a similar vein, neurologists and sociologists have drawn on a sub-discourse of complexity science – namely network theory – to redescribe interneuronal structures and interpersonal relationships in terms of “scale-free networks” (see chapter 3) (Davis & Sumara, 2006, p.24).

Much of our own research within education might be characterized as fitted to a soft complexity science sensibility, having characterised learning, classrooms, schools, curricula, and administrative structures in terms of nested, open, self-organized systems that operate far from equilibrium (Davis & Sumara, 2006, p.25).

To reiterate, complexity thinking might be described as a way of thinking and acting. Linking it to a term introduced in chapter 1, complexity thinking might be understood as an acknowledgement of one’s complicity – not just complicity with / in one’s research interests, but with / in the grander systems that contribute to the shape of and that are shaped by those research interests (Davis & Sumara, 2006, p.25).

Significantly, complexity thinking in no way represents an abandonment of science. However, it does reject an uncritical - and, at times, unjustified – faith in the analytic method, its mechanical and statistical tools, and other features of much of educational research through the 20th century (Davis & Sumara, 2006, p.25).

A point to underscore here is that, with specific regard to educational research, complexity thinking does not permit a simplistic separation of established knowledge and how knowledge is established. Both obey similar, evolutionary dynamics. As such, in complexity terms, the key distinction is not between product and process, but between relatively stable aspects of collective knowledge and the somewhat more volatile dynamics that underpin that stability (Davis & Sumara, 2006, p.29-30).

Complexity thinking understands knowledge to refer to systems’ stabilized but mutable patterns of acting – and thus supports the commonsense usages of the word knowledge to refer to what humans, non-humans and human collectives know (Davis & Sumara, 2006, p.30).

This means that, in a complex network, no part of the system has any meaning in isolation from the rest of the system (an assertion shared by coherence theorists), and so one must take into account the structure of the whole system. In other words – and it is here that complexity theories splits from coherence theories – complexity is incompressible and ever-expanding (Davis & Sumara, 2006, p.34).

The notion of distributed representation, taken seriously, compels a realization that there are no universal truths. The representations that are used to make sense of the world are products of the frames that have been chosen in order to generate meaning; they are not a pure characteristic of things in themselves, but neither are they completely dissociated from those things. Rather, they are evolving and ever-expanding conversations between sense-makers, the sense made, and sensorial encounters with the universe (Davis & Sumara, 2006, p.34).

Such assertions bring us very close to the postmodern mistrust of overarching theoretical paradigms, a suspicion encapsulated in Lyotard’s emphatic statement, “ I define postmodernism as incredulity toward metanarratives” (Davis & Sumara, 2006, p.35).

One of the important conclusions about languages by the 20th century thinkers – shared by structuralists, post-structuralists, psychoanalytic theorists, and pragmatists – is that meanings tend to be caught up in complex webs of association, tangled metaphors, and forgotten referents (Davis & Sumara, 2006, p.38).

In schools, Euclid is present in the grids used to lay out curricula, order the school day, organize students rooms, frame their learning experiences, mark their progress and so on. Within educational research, it is revealed in the prominence of normal curves and linear regressions. So dominant is this geometry that the unruly and organic are often surprising and even unwelcome. What tend to be preferred are narratives of control, predictability, efficiency, and correlation – such as is demanded by Plato’s logic and embodied in Euclid’s images (Davis & Sumara, 2006, p.42).

It is only recently, with the study of complex dynamics, that scientists have realized that many commonplace phenomena simply do not follow a normal distribution (Davis & Sumara, 2006, p.47).

In current parlance, earthquakes and net worth – along with a host of other scale-free phenomena – follow power law distributions, not normal distributions … In brief, for phenomena that follow a power law distribution, there is no such thing as a “norm” – that is, a “typical” event, instance, member, or fragment (Davis & Sumara, 2006 p.49).

One feature that is well understood, however, is that power law distributions only arise in scale-free phenomena – that is, in phenomena whose organizations might be described in terms of the nested, scale independent qualities of fractal forms. More recent investigations of this category of phenomena have highlighted that they all have a particular sort of strict architecture: They can all be characterized in terms of a specific sort of network structure … More bluntly, and perhaps even more surprisingly, the very same organizing principles seem to be at work in both the physical-biological world and the socio-cultural world (Davis & Sumara, 2006, p.49).

In brief then, scale-free networks have two main advantages: first, they are able to move information efficiently because nodes are never too distant from one another. Second, they are usually able to withstand shocks to the system because there are no nodes that are too critical to the global functioning (although failure or destruction of certain nodes can lead to a fragmentation of the network) (Davis & Sumara, 2006, p.52).

Applied to education, this discussion of scale-free networks points to several possible sites of interpretation and study. For example, administrative units – including jurisdictions, schools, and classrooms – tend to be highly centralized. Might a decentralized structure better serve some educational ends (Davis & Sumara, 2006, p. 53)?

In turn, the structure of a curriculum would have to be transformed from a directed movement through topics to a study of neighbourhoods of concepts – … (Davis & sumara, 2006, p.53).

In other words, the notion of an edifice of knowledge – of unambiguous foundations and logical hierarchies of associations – is likely a fiction, and a damaging one when imposed on schools and curricula. Evidence points to the likelihood that collective knowledge, like the individual brain, has an organic, networked structure. The implications for education and for educational research, it would seem, are profound (Davis & Sumara, 2006, p.55).

For complexivists, the emergence of new interpretive possibility is framed more in terms of expansiveness and outward movement. The associated image is something more toward the ever-branching possibilities that appear as water flows outward over a surface. In other words, the development of insight seems to be more a matter of expanding the space of the possible by exploring the current space of possibility. As such, the creation of knowledge is “progressive” not because it is moving in a given direction, but because it is constantly elaborating what has already been established. It is expansive, but not directional (Davis & Sumara, 2006, p.57).

To perhaps oversimplify, knowledge tends to be commonsensically cast in terms of something “out there”, whereas understanding tends to be described in terms of more tentative, ever-shifting, fallible personal interpretations that reside inside one’s head. Within this frame, learning is a process of internally representing what is out there – or, less critically, as ingesting knowledge (Davis & Sumara, 2006, p.60-61).

…, Saussure framed language as a living, organic form composed of ever-evolving and intertwining parts. For him, languages are the products of circular (recursive) interactions between two or more brains. Linguistic symbols are the go-betweens (in the sense of mutual triggers, not in the sense of physical objects) (Davis & Sumara, 2006, p.64).

In terms of the relationship of his work to structuralist (and complexivist) sensibilities, the critical point to remember is that Piaget’s theory of cognition revolves around the assumption that the sense a person makes of an event is less a function of the qualities of that event and more about the complex history of the agent’s linguistically affected, biologically enabled, and culturally infused structure … In the language of complexity – noting that such vocabulary was not available at the time – Piaget described personal cognition as a self-organizing, adaptive phenomenon (Davis & Sumara, 2006, p.65).

But perhaps the most important contribution of post-structuralist discourses is around their explications of the role of social and cultural norms and conventions (Davis & Sumara, 2006, p.68).

In effect, interobjectivity is a restatement of the notion of complicity, as developed in previous chapters. The terms point to the emergent realization that the cultural project of knowledge production, whether identified as scientific or otherwise, must be understood in terms of the complicity of the observer in knitting the fabric of relations through which observations are rendered sensible (Davis & Sumara, 2006, p.70).

An important principle here is that descriptions of the universe are actually part of the universe. Hence, the universe changes as descriptions of the universe change – again foregrounding the evolutionary assumption that the universe is not a fixed or finished form (Davis & Sumara, 2006, p.70).

Deliberate participation in the development and maintenance of knowledge, then, always and already entails a contribution to the unfolding universe (Davis & umara, 2006, p.70).

An important contribution of psychoanalysis to contemporary thought is its refusal to separate the individual’s constitution of the world and the world’s constitution of the individual, which could be read as a variant of the notion of interobjectivity (Davis & Sumara, 2006, p.72).

With regard to discussions in education, this emphasis of complexity research has contributed to an important elaboration of 20th century discussions of the relationship between collective knowledge and individual understanding, specifically that there are other levels of dynamic and adaptive coherence that must be taken into account in discussions of education. Complexity thinking points to the inadequacy of nesting personal understanding within collective knowledge … as it posits the presence of several intermediary layers of nested coherence that are of vital relevance to educators… (Davis & Sumara, 2006, p.75).

Put differently, contra Descartes’ assumption and assertion that humans are logical, it appears that members of the species are not principally rational creatures . The capacity for logico-rational thought rides on the surface of connection-making systems like the associative structure of the brain or the weave of language (Davis & Sumara, 2006, p.76).

Certainly the most commonly cited quality of a complex system is the manner in which it bootstraps itself into existence. Somehow, these sorts of collectives develop capacities that can exceed the possibilities of the same group of agents if they were made to work independently … Self-organization is also known as emergence and, of the many insights of complexity science, it is simultaneously the most important and the most difficult to appreciate. Somehow, agents that need not have much in common – much less be oriented by a common goal – can join into collectives that seem to have clear purposes (Davis & Sumara, 2006, p.81).

Davis and Simmt raise the provocative suggestion that the often-mentioned phenomenon of “the teachable moment” may in fact be a case of emergence on the classroom level – that is, a moment in which a unity of action and purpose arises. They further point out, however, that many of the features of the contemporary classroom, including a fragmented curriculum and radically individualized assessment practices, militate against instances of self-organization (Davis & Sumara, 2006, p.82).

A surprising aspect of the phenomenon of self-organization is that it can happen without the assistance of a central organizer … Among the evidence-based assertions presented by Surowiecki [Wisdom of crowds], the following have particular relevance to educators and educational researchers:

  • Non-polarized groups can consistently make better decisions and come up with better answers than most of their members and … often the group outperforms the best member.

  • You do not need a consensus in order … to tap into the wisdom of a crowd, and the search for consensus encourages tepid, lowest-common-denominator solutions which offend no one rather than exciting everyone.

  • [The] rigidly hierarchical, multilayered corporation … discourage[s] the free flow of information.

  • Decisions about local problems should be made, as much as possible, by people close to the problem…People with local knowledge are often best positioned to come up with a workable and efficient solution.

  • The evidence in favour of decentralization is overwhelming … The more responsibility people have with their own environments, the more engaged they will be.

  • Individual irrationality can add up to collective rationality.

  • Paradoxically, the best way for a group to be smart is for each person to act as independently ads possible (Davis & Sumara, 2006, p.85).

A key – and, a paradox – here is that intelligent group action is dependent on the independent actions of diverse individuals. This point is reflexive of a core tenet of complexity thinking and a consistent finding across studies of complex unities: Intelligent collective action arises out of the bottom-up, independent (but co-specified) actions of individual agents who act out of self-interest and who may even be motivated by profound selfishness (Davis & Sumara, 2006, p.85).

To make sense of this assertion, it is important to be clear on what is meant by “intelligence” and “intelligent action”. Breaking from technocratic and psychologistic definitions developed through the 20th century, complexity thinkers define intelligence in terms of exploring a range of possible actions and selecting ones that are well-suited to the immediate situation (Davis & Sumara, 2006, p.85).

Given the nested character of complex systems, this conception means that intelligent action must occur simultaneously across several layers of the organization (Davis & Sumara, 2006, p.86).

Phrased somewhat differently, and as suggested in previous chapters, what is normally called “evolution” is, in complexity thinking, an instance of cognition on a much grander scale and over a much longer time frame than is typically considered (Davis & Sumara, 2006, p.86).

To this end, the branch of complexity research known as network theory, as introduced in chapters 3 and 4, offers some useful principles for recognizing complex structures and describing complex dynamics … As Barabasi develops, a decentralized network will decay into a more vulnerable (but informationally efficient) centralized network if stressed. Working from this suggestion, Fuite has hypothesized that the tendency of educators to perceive of time as a scarce resource may be one of the main reasons that the most common organizational strategy in the contemporary classroom is the centralized network, … Of course, this sort of organizational structure militates against an intelligent collective , as it prevents agents from pursuing their own self-interests and obsessions, which in turn prevents the representation and juxtaposition of diverse interpretations and actions … In brief, and to repeat, the decentralized network is the architecture necessary for intelligent systems (Davis & Sumara, 2006, p.88-89).

Complex unities can be (and usually are) simultaneously autonomous unities, collectives of autonomous unities, and subsystems within grander unities. They are nested (Davis & Sumara, 2006, p.90).

Complexity thinking occasions a different manner of interpretation. It prompts attention to, for example, the role of classroom knowledge. What sorts of local conventions, interests, and so on contribute to the shapes (and are shaped by) individual understandings (Davis & Sumara, 2006, p.91-92).

Significantly, the point is not that all levels must be taken into consideration for each and every event of teaching or educational research. Rather, the issue is that any attempt to understand an educational phenomenon must be understood as partial – in the dual sense of incomplete and biased (Davis & Sumara, 2006, p.92).

One of the problems with specifying a complex system – for both educational and educational research purposes – is that its boundaries tend to shift. There are many reasons for this fluidity, three of which are of particular relevance here: First, complex systems are “open”; that is, they are constantly exchanging matter and / or information with their contexts. Second, and as noted in the previous section, complex systems usually arise from and are part of other complex systems, even while being coherent and discernible unities. Hence it is not always clear which level (s) should be the focus of one’s immediate attentions. Third, and perhaps most confounding, distinguishable but intimately intertwined networks can and do exist in the same “spaces” (Davis & Sumara, 2006, p.94).

Whereas the first issue related to efforts to discern the boundaries of complex unities has to do with inter-system exchanges, the second issue has to do with a haziness of definition as one endeavours to distinguish among different levels of organization within a given system. Where, for instance, does an agent stop and a collective begin (Davis & Sumara, 2006, p.96)?

In some ways, this point has been recognized in education through prominent debates on the relative importance of nature and nurture on individuals’ characters, actions and abilities … Unfortunately, as the popular debate tends to be articulated, it is generally assumed that biological structure is separable from cultural influence (usually through some manner of statistical manipulation). The former tends to be cast as fixed and limiting, the latter as dynamic and freeing. Brains, for example, are popularly understood as some sort of pre-given, biologically-determined architecture, whereas the “contents” of brains (e.g. memories and preferences) are seen in terms of contextual influence (Davis & Sumara, 2006, p.97).

Not only can it be difficult to distinguish one system from another, or one level from another, systems can unfold within one another (Davis & Sumara, 2006, p.97).

It is one thing to make sense of where a complex unity begins and ends. But even if that issue could somehow be unambiguously settled, another-at-least-as-difficult issue is the fact that it is the system – and not the system’s context – that determines how it will respond to emergent conditions (Davis & Sumara, 2006, p.99).

First, a complex system learns, that is it is constantly altering its own structure in response to emergent experiences … Second, systems that are virtually identical will respond differently to the same perturbation. Hence, one cannot generalize the results from one system to another … In brief, the notion of structure determinism stands as a critique of virtually all educational research that is based on a linear cause-effect mentality … Clearly, such assertions render the project of schooling a difficult if not impossible one, at least insofar as formal education is understood in terms of compelling learners to learn what they have been mandated to learn (Davis & Sumara, 2006, p.100).

With regard to pedagogy, for example, a prominent conclusion is that the act of teaching must be understood in terms of a sort of emergent choreography in which the teacher’s and students’ actions are able to specify one another (Davis & Sumara, 2006, p.100).

In other words, it has been our experience that most experienced teachers have a deep appreciation for the structure-determined nature of their students and their classes. What they have lacked are schooling contexts and curricula that enable them to act responsibly to these embodied understandings (Davis & Sumara, 2006, p.100).

The popular but uninterrogated assumption that dynamic systems tend toward equilibrium could be concisely restated as: Negative feedback is good, positive feedback is bad. As it turns out, however, mechanisms to amplify small perturbations are essential to the viability of living and learning systems (Davis & Sumara, 2006, p.102).

The experienced teacher, of course, is intimately familiar with striking the balance between classroom and lesson structures that are too rigid to allow for innovative responses and structures that are too loose to enable coherent activity. Unfortunately, in the contemporary rhetoric, such structures are most often described in terms of “behaviour management” reflecting deeply seated behaviourist (i.e., mechanistic and individualistic) assumptions about human cognition (Davis & Sumara, 2006, p.103).

The importance of positive feedback mechanisms in learning / research systems is commonly overlooked in current discussions of educational research, where the emphasis is often on careful top-down organization rather than nurturing healthy local interactions. In complexity terms, the latter is vastly more important as most of the information within a complex system is exchanged among near neighbors rather than being distributed from a central hub (Davis & Sumara, 2006, p.104).

Positive and negative feedback mechanisms do not have anything to do with concepts of ‘good’ or ‘bad’. An example from current debates around climate change illustrates the difference between positive and negative feedback mechanisms very well. We know that the current rate of carbon dioxide release into the atmosphere is causing the planet to warm. This warming is causing the melting of ice caps (Arctic and Antarctic) - which means that less sunlight is reflected off the earth’s surface. The absorption of sunlight by the oceans means that the oceans are becoming warmer - thus warming the planet even further. In effect, the melting ice-caps serve to amplify the effects of warming caused by carbon dioxide in the atmosphere. So, the melting of the ice-caps is a positive feedback mechanism since it amplifies planetary warming caused by carbon dioxide. The planting of millions of trees would mean that more carbon dioxide would be absorbed by their leaves. Potentially, this could remove carbon dioxide from the atmosphere - which could (for the sake of this argument) halt planetary warming. In this sense, the planting of trees could diminish the warming of the planet and could be considered a negative feedback mechanism.

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