Archive for the ‘communities’ Category

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thesis: Learning and knowledge building practices for teachers’ professional development in an extended professional community

July 7, 2013

Kairit Tammets, my first doctoral student will defend her thesis at 21st of August.

Now her dissertation is available from here http://e-ait.tlulib.ee/330/1/tammets_kairit.pdf

Tammets, Kairit (2013) Learning and knowledge building practices for teachers’ professional development in an extended professional community.

The purpose of her PhD research project is to investigate the process of the learning and knowledge building (LKB) in the extended professional community that is supported with the socio-technical system.
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Learning and knowledge-building in extended organizations

May 20, 2012

The IntelLEO project is going to its end, and we are looking back at what we achieved.

In the project we assumed that to support organizational responsiveness, cross-organizational learning and knowledge-building should be supported. Our assumption was that learning and knowledge-building (LKB) activities across organizational borders as well as within organizations would create conditions for organizational responsiveness to appear.

We adopted the knowledge conversion model by Nonaka & Takeuchi (1995) into cross-organizational settings,

identified learning and knowledge-building enablers and inhibitors,

and developed technological services that support those learning and knowledge-building activities that support responsive organization to emerge:

  • the competence-based reflections in the format of the construction of learning paths and monitoring personal development in socially and organizationally embedded context (externalization and internalization activities) (see Siadty et al. 2011)
  • the competence-annotated sharing and searching of knowledge (externalization, internalization)
  • the competence-annotated searching for other learners or working partners and team-building (socialization and combination activities)
  • the construction, accumulation and provision of organizational knowledge to its employees using the semantic web technologies and ontology framework (externalization and internalization activities)

We assumed that extended organizations are connected with temporal learning and knowledge building activities, and we may conceptualize such an extended organization (an IntelLEO – intelligent learning extended organization) as a distributed cognitive system.

Socially distributed cognition, where cognitive processes are distributed across members of a social group by knowledge exchanges also contains mutual awareness, communicating and socially provided support as an external locus of control for cognition. The forms of socially distributed cognition are:

- monitoring peers’ activities for mutual awareness, social surveyillance (such as friend-feeds, wall, mashups)

- peer-scaffolding (commenting, rating, favouriting)

 

Distributed cognition involves coordination between internal and external (material and environmental) structures through causal coupling (an embodied cognition) that enables adapting one’s actions to fit to environmental conditions.

These also associate with the distributed intelligence and dispersed learning processes carried out in a loosely coupled way. Such distributed intelligence creates a distributed cognitive system that also contains a feedback loop to community/organizational culture – cognitive processes can be distributed through time in such a way that products of earlier events (of the same person, of other community/organization members or members from different community) can transform the nature of later events.

This may take different formats:

- creating and using personal knowledge aggregations

- using the external knowledge organization of peer’s (tags, annotations to the resources they have used)

- using bottom-up or top-down aggregated organizational knowledge (tagclouds, semantic search)

- creating and organizing personal reflections (blog posts)

- using externalized peer’s knowledge (blog posts)

- creating personal networks (mashing feeds to monitor)

- benefitting from community browsing (from shortcuts the personal networks create in the community).

Here are some results from the interviews with workplace learners about using the IntelLEO framework for learning and knowledge-building (LKB):

The temporal LKB activities that have been identified as the prerequisites of organizational responsiveness These acts create distributed cognition possibilities across EO in IntelLEO Examples of temporal LKB acts perceived by workplace learners

1. The presence of knowledge exchanges among employees

Cognitive processes are distributed across the members of a social group (a socially distributed cognition).

Better communication

Becoming open

Exchanging knowledge and experiences

Acknowledging that someone might read and learn from my reflections.

Sharing, asking and commenting to support the development of learning partners

Helping my colleagues to discover interesting online resources

Cross-organizational  collaboration on research projects

Starting and sharing new learning areas in the company

Sharing relevant information with a group

Shared goal or experience supports LKB

Sharing information complements each other’s knowledge and increases group synergy

2. The opportunity for employees within an organisation to use knowledge to adapt their actions to appropriately fit environmental conditions

Cognitive processes involve coordination between internal and external (material or environmental) structure through causal coupling (an embodied cognition)

The continuously changing and evolving job requirements impose the need for constant learning of new things

Autonomy  for deciding when and how to learn

Performing LKB primarily for oneself

Organising learner’s current/planned knowledge is increasing the willingness to get involved in LKB

Giving the big picture – what have you done, how have you done it and what else you should learn

Reflection makes to analyse development and think thoroughly about the activities

Showing the learning progress motivates others’ learning

Documenting one’s knowledge increases the others’ motivation to learn within the organization

Reading colleagues’ entries  help to realize that my contribution can also be useful for my colleagues

Seeking external solutions for internal challenges

Seeing what and how others have learnt  – that makes to think should I learn it as well, how could I learn it

Reusing the „lessons learned“ of my colleagues for planning learning

Peers’ contributions influence to see own things from different viewpoint

Providing the organisational goals on what to learn

Can take a look at the example-learning paths, created by organization

Benefiting and learning from the crowd-sourced knowledge and annotations gathered by the entire organization

Organizational goals may be harmonized with input from personal goals and work-practces

3. Distributed intelligence and dispersed learning processes carried out within loosely coupled different organisations

Processes are distributed through time in such a way that the products of earlier events can transform the nature of later events (feedback loop to organizational culture).

The sufficient mass of initial content in the system increases motivation to add

Looking back/finding at own entries and annotated resources

Identifying potential learning and/or research partners

Getting an insight into others’ interests and goals

Following resources or persons

Associating the discovered resources with the task

Letting others to know of new contributions

Seeing the activities in interesting topics and of colleagues

Better structuring and organizing of the collective knowledge

The collaborators can easily access task-relevant resources

Collaboration between organizations influences positively the development of individuals

It influences the growth of the organizational and individual knowledge

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connectivity through (digital)ecosystem engineering that influences niche construction of communities

October 7, 2010

I found a nice paper in which Kevin Laland, the author of influential book Niche Construction: The Neglected Process in Evolution (2003) has co-written the paper of human niche construction from the archeological perspective. Thanks to Emanuele Bardone from Pavia Computational Philosophy Lab i got the file in the morning!

It is interesting from the point of view of explaining the niche construction effects of humans using the long-lasting and cultural “traits” that humans transfer to the next generations as mediators or carriers which have the indirect accumulative modification pressure on environments and thereby on the other organisms that can affect human life and human gene evolution.
He highlights the indirect interactions between species and the organism connectivity by the engineering web and not by the food web:

The ecosystem engineers can regulate energy flows, mass flows, and trophic patterns in ecosystems to generate an “engineering web”—a mosaic of connectivity comprising the engineering interactions of diverse species.

On my opinion, this is exactly what happens in human-artifact networks that represent this kind of connectivity in engineering web.

Basically the process is:
Human cultural traits = human behaviour as ecosystem engineering for increasing their fitness to the niche
-> changing the niche for other organisms associated with humans
-> evolutionary response of other organisms to the changing niche
-> evolutionary changes in humans in response to other organisms
-> human behaviour changes or consistency = modifying or strenghtening certain cultural traits

A meme (Dawkins, 1976) is a unit of cultural ideas, symbols or practices, which can be transmitted from one mind to another through writing, speech, gestures, rituals or other imitable phenomena.
Memes evolve by natural selection (in a manner analogous to that of biological evolution) through the processes of variation, mutation, competition, and inheritance influencing an individual meme’s reproductive success. Memes spread through the behaviors that they generate in their hosts.

And possibly by niche generation as well!
Dawkins noted it as a condition which must exist for evolution to occur: differential “fitness”, or the opportunity for one element to be more or less suited to the environment than another

In order to use ecology principles for explaining interactions of humans and human communities with social software systems the following analogy may be used:

specimen of one species = human

species with certain gene frequency = specimen with similar range of identity perception, a community (note that identity is based on shared meanings or actions / /memes??/cultural traits??/)

niche as a range of environmental factors that allow fitness to the species = niche as a range of affordances perceived and frequently used in actions by certain community in their interactions with each other and with their environments (virtual or real) that allow their semiotic or cultural fitness

The semiotic fitness, should ideally measure the semiotic competence or success of natural systems in managing the genotype-envirotype translation processes (Hoffmeyer, 1998).
Semetic interactions refer to interactions in which regularities (habits) developed by one species (or individual) successively become used (interpreted) as signs by the individuals of the same or another species, thereby eliciting new habits in this species eventually to become – sooner or later – signs for other individuals, and so on in a branching and unending web integrating the ecosystems of the planet into a global semiosphere (Hoffmeyer, 1993).
The semiotic adaptability is a process, in the course of which the subject
correlates self-related and environment-related information, thereby localising
itself in the environment (Maran, 2005).

community (a number of species) in certain environmental locations = several human communities who coexist in certain virtual social software or hybrid environments

co-adaptive niches apply for such communities which consist of a number of species who may be connected by food-webs or by engineering webs= several human communities connected mainly by engineering webs create co-adaptive niches for each other and may influence each other

Ecosystem ecology that studies how matter and energy circulates in ecosystems,should also consider how ecosystem (entropy, succession, networks, communities, interactions) is influenced by the ecosystem engineering done by co-existing species in this ecosystem as part of connectivity comprising the engineering interactions of diverse species. The same applies for communities in this digital or hybrid ecosystem.

Can such co-existing human communities in social software environments or hybrid environments engineer their niches so that this niche starts to constrain or facilitate other community?
Can such pressure influence some ways the individuals in each community to perceive certain affordances as useful for their cultural or semiotic fitness in the niche and influence the community identity?

For example if we take the long tail phenomenon, which reveals little niche artifacts, meanings, conceptions of certain communities. Can interaction in the same social software ecosystem (eg. shelfari for choosing books; delicious for choosing resources by tags) influence some communities to become more fit to their environment by broadening or narrowing their activity choices as a result of other community’s actions and niche construction (eg. choosing particular books or resources introduced by other community)

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Niche Construction Theory and Archaeology
Kevin N. Laland & Michael J. O’Brien
J Archaeol Method Theory
DOI 10.1007/s10816-010-9096-6

Their basic idea is:
Niche construction is “the process whereby organisms, through their metabolism, their activities and their choices, modify their own and/or each other’s niches” (Odling-Smee et al. 2003, p. 419). The conceptual leap that niche construction theorists embrace is to regard niche construction as an evolutionary process in its own right. Some organism-driven changes in environments persist as a legacy to modify selection on subsequent generations, which Odling-Smee (1988) called an “ecological inheritance.”
Niche Construction Theory is sometimes referred to as “triple-inheritance theory” (genetic, cultural, and ecological inheritance; e.g., Odling-Smee et al. 1996, 2003; Laland et al. 1999, 2000, 2001; Day et al. 2003; Shennan 2006).
Rather than slipping into the assumption that the external environment (e.g., climate change) triggers an evolutionary or cultural response, NCT enthusiasts are from the outset inclined to consider those additional hypotheses stressing self-constructed (and other organism-constructed) conditions that instigate change.

Jones et al. (1994, 1997) uses concept of “ecosystem engineering,” as a relevant synonym for niche construction to describe the focus on organisms’ modification of environments.

Jones and his collaborators point out that many species of ecosystem engineers can regulate energy flows, mass flows, and trophic patterns in ecosystems to generate an “engineering web”—a mosaic of connectivity comprising the engineering interactions of diverse species, which regulates ecosystem functioning in conjunction with the well-studied webs of trophic interactions (Wilby 2002).

Organisms do considerably more in ecosystems than compete with each other, eat, and be eaten (trophic interactions). Organisms also produce, modify, and destroy habitat and resources for other living creatures, in the process driving co-evolutionary dynamics.

From the niche construction perspective, the connectivity in ecosystems is massively increased.

Hardesty (1972) stated that culture is the human ecological niche.
There are several examples of culturally induced genetic responses to human agriculture (Odling- Smee et al. 2003),
The best known being the co-evolution of the gene for lactose absorption and dairy farming (Durham 1991);
The Kwa-speaking yam cultivators in Africa who modified the environment and increased the amount of standing water which provided better feeding grounds for mosquitoes and increased the prevalence
of malaria and induced the increase in the frequency of the sickle-cell (HbS) in Kwa-speakers population that provides protection against malaria (Durham 1991).
The evolution of the human amylase gene which is responsible for starch consumption is a feature of agricultural societies and hunter–gatherers in arid environments, whereas other hunter–gatherers and some pastoralists consume much less starch (Perry et al. 2007).

Odling-Smee et al. (2003) describe as inceptive niche construction all cases in which organisms initiate changes in any factor, through either perturbation or relocation. Organisms express inceptive niche construction when by their activities they generate a change in the environment to which they are exposed. Conversely, if an environmental factor is already changing, or has changed, organisms may oppose or cancel out that change, a process labeled counteractive niche construction. They thereby restore a match between their previously evolved features and their environment’s factors. Counteractive niche construction is therefore conservative or stabilizing, and it generally functions to protect organisms from shifts in factors away from states to which they have been adapted.

Niche construction provides a non-Lamarckian route by which acquired characteristics can influence the selection on genes. Whereas the information acquired by individuals through ontogenetic processes clearly cannot be directly (genetically) inherited, processes such as learning can nonetheless still be of considerable importance to subsequent generations because learned knowledge can guide niche construction in ways that
modify natural selection acting on future generations.
This route is considerably enhanced by social learning, which allows animals to learn from each other.
There should be a significant relationship between the pertinent environmental state and the recipient character only when the niche-constructing activity is also present.
The same logic applies at the cultural level, and the same methods can be applied to hominins or to contemporary human populations, where they may shed light on the relationship between different kinds of cultural niche construction and their different consequences.

Laland et al. (2001) concluded that, because cultural processes typically operate faster than natural selection, cultural niche construction probably has more profound consequences than gene-based niche construction.
It also has driven coevolutionary interactions with other species, including domesticated animals and plants, commensal species adapted to human-constructed environments (e.g., rats, mice, and insects), and microbes (Boni and Feldman 2005; Smith 2007a, b).
There are no genes for domesticating dogs, manufacturing cheese, or cultivating rice (using “genes for” in the sense of Williams (1966) and Dawkins (1976) to mean alleles specifically selected for that function), and these activities, while frequently adaptive (increasing fitness in the present), are not adaptations (traits directly fashioned by natural selection).
If human activities have imposed selection on mice, houseflies, or mosquitoes is it because we are their competitors or predators, or even because we are linked in an elaborate food chain. Such co-evolutionary episodes are probably driven by nontrophic and indirect interactions between species—that is, by the engineering web (Jones et al. 1994) and not by the food web.

Cultural niche-constructing processes that contribute to plant domestication include selective collecting of reproductive propagules; transporting and storing of propagules; firing of grasslands, either intentionally or accidentally; cutting of trees; incidental tilling; and creating organically rich dump heaps, all of which are
potent forms of niche construction. Plants that are involved may undergo a series of phenotypic changes such as a general increase in size, an increase in the size of propagules, loss of delayed seed germination, simultaneous ripening of the seed crop, and so on. These changes occur as interaction with human agents increases the fitness of the plant community, which, in turn, increases the yield of the plant community. Increasing yield in turn generates selection favoring those cultural traits that maintain or increase productivity of the plants. This reinforcing mutualistic relation between plant and human populations is one process by which plant domestication, and human coadaptation, evolves.

Because of our habitat degradation as part of our niche construction we destroy the (engineering) control webs that underlie ecosystems.

Wilby, A. (2002). Ecosystem engineering: A trivialized concept? Trends in Ecology & Evolution, 17, 307.
Jones, C. G., Lawton, G. H., & Shachak, M. (1994). Organisms as ecosystem engineers. Oikos, 69, 373–386.
Jones, C. G., Lawton, G. H., & Shachak, M. (1997). Ecosystem engineering by organisms: Why semantics matters. Trends in Ecology & Evolution, 12, 275.

Maran, T. (2005?) ECOSEMIOTIC BASIS OF LOCALITY

Hoffmeyer, J. (1998). The Unfolding Semiosphere. In Gertrudis Van de Vijver, Stanley Salthe and Manuela Delpos (eds.), Evolutionary Systems. Biological and Epistemological Perspectives on Selection and Self-Organization. Dordrecht: Kluwer 1998, pp. 281-293.

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An Ontospatial Representation of Writing Narratives in Hybrid Ecosystem

August 29, 2010

Tomorrow i will be at 3rd International Workshop on Social and Personal Computing for
Web‐Supported Learning Communites, DEXA 2010, Bilbao

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Rules for (digital)ecosystem growth and development?

April 22, 2010

I have been reading a good paper of ecosystem growth and development and thinking to the interrelations with digital ecosystems.
I predict that digital ecosystem thermodynamics would be a way to predict and measure the quality of systems in operation.

Digital ecosystem is an open, self-organizing agent environment containing human individuals, information services as well as network interaction and knowledge sharing tools along with resources that help maintain synergy among human beings or organizations, where each agent of each species is proactive and responsive regarding its own benefit/profit but is also responsible to its system (Boley & Chang, 2007).

This paper gives useful guidelines to explain the development and changes in digital ecosystems.

Ecosystem growth and development
Brian D. Fath, Sven E. Jørgensen, Bernard C. Patten, Milan Straškraba
BioSystems 77 (2004) 213–228

The development pattern of ecosystem structure and function has two aspects. First, at the broadest scale of time, in millennia, ecosystems have changed in form and distribution over the earth’s surface.
Second, within shorter time periods, ecosystems undergo ecological succession Golley (1994).

Figures of community succession:

Thermodynamics is one of the main laws of ecology.

Ecosystems are complex adaptive systems (Levin, 1998), and as such one would expect the thermodynamic properties of the ecosystem to change during development.

Biological systems are special – they possess relatively little entropy (measure of the disorder) compared with the space around them. In other words, biological systems are able to build complex structures (order) within their boundaries by diverting low-entropy input and exporting high-entropy output (Lawson, 1999).

Entropy is essentially a measure of how organized or disorganized a system is, of the number of ways in which a system may be arranged, often taken to be a measure of “disorder” (the higher the entropy, the higher the disorder).

The digital ecosystems (if we consider for example one certain social software system) as part of the whole web seem to be possessing similar low entropy, they are more ordered areas in comparison of the surrounding web.

Schrödinger (1944) wrote, “thus the device by which an organism maintains itself stationary at a fairly high level of orderliness (= fairly low level of entropy) really consists of continually sucking orderliness from its environment”.

Any general law of biology should take into account these entropy-diminishing properties of open system processes.

Chorley and Kennedy (1971) described four types of change for open systems:
a) first, change due to changing input–output relationships of both mass and energy;
b) second, changes in the energy content and distribution within the system;
c) third, change associated with shifts in the internal organization of the system
itself; and
d) finally, change associated with the development of energy and mass stores which introduce time lags in the operation of system processes.

New social software systems are open similarly to the input of system components (or ideas) from the surrounding systems in web (basically functional ideas that seem to work are sucked in to the systems.

Moreover, some of it is and open source software that generally allows anyone to create modifications of the software, port it to new operating systems and processor architectures, share it with others or market it. This increases openness to the out-of system elements, that result in bigger orderliness of systems.

One of the most important features of biosystems is how they are able to maintain local order (low entropy) within their system boundaries.

This organization can be observed in the thermodynamic parameters that describe it.

These thermodynamic parameters can be used to track ecosystem growth and development during succession.

Ecosystems grow and develop in four progressive growth forms reflected in boundary, structure, network, and information relationships.

1. Boundary growth brings the input of low-entropy material into the system.

We can see it at software development level, but also looking at individuals who do not have allocation to any communities yet, or artifacts that have not been systematized under any categories yet?

2. Structural growth occurs when the physical quantity of biomass in the system increases, often as a result of the increase in the amount, number, and size of components in the ecosystem.

The components of the ecosystem are individuals, system components and artifacts. In digital ecosystems we may see kind of species: community is a species because it has certain identity, certain group of tools may be seen as a species as well, because they share functional similarity (???), even artifacts may be viewed as species depending of what type of information or what information content they carry. However, if in nature the individual can belong only to one species, in digital ecosystems any of the described agents may be classified belonging to several “species”.

A community as we understand biological communities consists of several species, thus in our case the community of communities is inhabiting a certain ecosystem.

3. Network growth is growth in connectivity of the system through additional energy–matter transactions, which results in pathway proliferation and more cycling of matter and energy. Network growth deals specifically with the internal organization of the system.

In each social software system we can see the increase on connections appearing between individuals, individals and system components, and artifacts, also the synthesis of certain system services and the composition and combination of artifacts appears. The communities of individuals is also a way how system obtains higher connectivity.

4. Information growth is qualitative growth in system behavior from exploitative patterns to more conservative patterns, which are more energetically efficient.
Information growth deals with the development of ecosystem compartments themselves, as they tend to increase their own performance within the system.

At this level we can see the behavior changes how the individuals/communities operate with energy. For example there appear new community browsing strategies by tags or aggregating and mashing information and similar ecologically more effective ways of sharing information, finding people and using tools.

This four-stage model provides a general conceptual model for ecosystem growth and development.

The typical sequence that occurs during primary and secondary succession corresponds roughly to these four forms, however expression of the four forms is not strictly sequential because they can operate in parallel (e.g., boundary input initiates growth, but continues on as long as the system organization remains; network and informational growth both can occur simultaneously, etc.).

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Some important terms to understand theromodynamics in ecosystem:

Exergy is the maximum amount of work the system can perform relative to an environmental reference state; therefore, a system with greater exergy is moved further from its reference state and further from thermodynamic equilibrium. An eco-exergy is how far an ecosystem has moved away from thermodynamic equilibrium.

In ecosystems an increase in exergy corresponds to an increase in the overall biomass and an increase in the species’ internal organization.

Exergy dissipation refers to the energy given off by breaking down the high quality, low-entropy energy (orderliness) for both growth and maintenance of the system.

This point is interesting – it refers that organised ecosystem elements (communities, agglomerated-annotated arifacts, superorganised software components) are used (and reused by breaking them down) to get exergy that is needed to get to the next ecosystem equilibrium level. Basically any community of communities gives exergy to get to the next level of ecosystem, but this means breaking down the very same communities (or at least it should happen all the time).

As an ecosystem develops, it will import and capture more exergy.
Exergy storage increases during ecosystem development (Jørgensen et al., 2000;
Jørgensen, 2002; Fath et al., 2001).

Exergy dissipation is dependent on the exergy capture or the ability of the ecosystem to divert a greater amount of low-entropy energy across its border.

Minimizing specific entropy means minimizing the entropy production (reflecting maintenance cost) for a given biomass (structure). In other words, an ecosystem tends to become more energetically efficient.

It seems then that for keeping the exergy from dissipation it is necessary to let always some communities, ideas, artifacts, software ideas out of the ecosystem.

Lotka–Odum’s hypothesis states that an ecosystem develops towards maximizing
power (Lotka, 1922; Odum and Pinkerton, 1955), interpreted as the highest possible throughflow of energy.

Both the exergy storage and the energy throughflow of an ecosystem increase over time.

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Thermodynamically, ecosystem growth is the increase of energy throughflow and stored biomass.

Ecosystem development is the internal reorganization of these energy mass stores, which affect transfers, transformations, and time lags within the system.

Growth is the quantitative increase of some measure such as biomass or throughflow.

For example individuals, communities, artifacts

Development is the qualitative change that occurs such as the organization
or information of existing quantities.

For example new web 2.0 information management and navigation behaviors.

Odum (1969) proposed several energy-related trends to be expected in the growth and development of ecosystems from early to mature stages.
(1) ecosystem biomass increases (physical structure);
(2) feedback increases (including recycling of energy and matter);
(5) larger animals and plants become more dominant;
(7) total entropy production is relatively high in an ecosystem at an early stage, but relatively low at mature stages;
(8) information increases

All these we can observe at any social software system, if we think of Twitter or Delicious for example. The communities with strong identity developed and dominate, the ways how the communities use software and artifacts to create knowledge have changed and it allows the community members also better feedback between the community niches and themselves.

Five thermodynamic hypotheses for ecosystem growth and development:
1) At a steady state, specific entropy production is minimized.
2) Ecosystem development tends to maximize energy throughflow through the system, the ecosystem develops biosystem components and configurations that
increase the amount of internal energy flow.
3) Exergy that enters the system is either stored as biomass or used to maintain the biological components far from thermodynamic equilibrium after which time it is dissipated in degraded form back to the environment (throughflow)
4) Exergy input directly stored as biomass (the limiting form of cycling) as well as the increase of biological information due to a shift to a more complex species composition.
5) Biological components develop mechanisms to increase time lags in order to maintain the energy stores longer.

The stages of ecosystem development are the following:

The initial requirement of any system’s growth and development is the input of low-entropy energy or matter across its system boundary.

During the first growth and development form exergy captured increases rapidly, thus increasing the structure of the system.

During the next growth and development form, system connectivity and cycling increase through additional network transactions. This retains the energy–matter within the system boundaries for a longer time and further increases the throughflow and structure (exergy storage) in the system. As a result, specific
entropy production decreases.

In the final growth and development form, throughflow, exergy storage, and retention time (based on mechanisms of biological components to increase time lags in order to maintain the energy stores longer) continue to increase and specific entropy production continues to decrease as the system efficiency and information content increase.

Why i put so much effort in looking the thermodynamic laws in digital ecosystems is mainly to understand how far our existing digital ecosystems have developed. I think it may be possible to predict using these laws the ecosystem development.

For example, we can take the study of Vuorikari & Koper – Ecology of social search for learning resources about MELT portal as an ecosystem. They have measured the usage of old and new search behaviors in the system, finding that community browsing etc. appears merely at 21 %.
Now, what this would indicate about the state of the system, i would say that the connectivity level is yet quite low.
Can we predict that it will increase and at which conditions?

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