text:
Identi.ca

Identi.ca (launched 2008) is an open source social networking and micro-blogging service. It uses the software StatusNet (licence: Affero GPLv3+).

Some advantages to use identi.ca are:
 * all notices are published with Creative Commons Attribution 3.0 license
 * better view of replies (example: identi.ca, click hell at twitter: )
 * one can forward messages also to twitter
 * FOAF (Friend of a friend) support: a machine-readable ontology describing persons, their activities and their relations to other people and objects. example:
 * when you install your own StatusNet instance you have control over your data (e.g. you decide with whom to share or not, ...) + can connect with other network systems (e.g. twitter, ...) via federation

Wikiversity related info on identi.ca

 * tag #wikiversity: you can generate these when you add in your message #wikiversity
 * group !wikiversity: you can post here when you add in your message !wikiversity

some Wikiversity users who use identi.ca
(alphabetical)
 * Cary Bass
 * Leigh Blackall
 * Laura Hale
 * Pieter Jansegers
 * Samuel Klein
 * Cormac Lawler
 * James Neill
 * Nils Simon
 * Erkan Yilmaz

 * add yourself

title: Identi.ca


text: Category:Microblogging

license: Creative Commons - Attribution Share-Alike - https://creativecommons.org/licenses/by-sa/4.0/

title: Category:Microblogging


text:
User:JøMa

[ˈjøːˌmaˑ]

de:Benutzer:JøMa fr:Utilisateur:JøMa

title: User:JøMa


text:
User talk:JøMa

 Hello JøMa, and welcome to Wikiversity! If you need help, feel free to visit my talk page, or contact us and ask questions. After you leave a comment on a talk page, remember to sign and date; it helps everyone follow the threads of the discussion. The signature icon in the edit window makes it simple. All users are expected to abide by our Privacy policy, Civility policy, and the Terms of Use while at Wikiversity.

To get started, you may

 * Take a guided tour and learn to edit.
 * Visit a (kind of) random project.
 * Browse Wikiversity, or visit a portal corresponding to your educational level: pre-school, primary, secondary, tertiary, non-formal education.
 * Find out about research activities on Wikiversity.

 * Read an introduction for teachers and find out how to write an educational resource for Wikiversity.
 * Give feedback about your initial observations
 * Discuss Wikiversity issues or ask questions at the colloquium.
 * Chat with other Wikiversitans on #wikiversity-en.

And don't forget to explore Wikiversity with the links to your left. Be bold to contribute and to experiment with the sandbox or your userpage, and see you around Wikiversity! If you're a twitter user, please follow http://twitter.com/Wikiversity. --Ottava Rima (talk) 14:41, 4 January 2011 (UTC)

title: User talk:JøMa


text:
User talk:AdamBMorgan

 Hello AdamBMorgan, and welcome to Wikiversity! If you need help, feel free to visit my talk page, or contact us and ask questions. After you leave a comment on a talk page, remember to sign and date; it helps everyone follow the threads of the discussion. The signature icon in the edit window makes it simple. All users are expected to abide by our Privacy policy, Civility policy, and the Terms of Use while at Wikiversity.

To get started, you may

 * Take a guided tour and learn to edit.
 * Visit a (kind of) random project.
 * Browse Wikiversity, or visit a portal corresponding to your educational level: pre-school, primary, secondary, tertiary, non-formal education.
 * Find out about research activities on Wikiversity.

 * Read an introduction for teachers and find out how to write an educational resource for Wikiversity.
 * Give feedback about your initial observations
 * Discuss Wikiversity issues or ask questions at the colloquium.
 * Chat with other Wikiversitans on #wikiversity-en.

And don't forget to explore Wikiversity with the links to your left. Be bold to contribute and to experiment with the sandbox or your userpage, and see you around Wikiversity! If you're a twitter user, please follow http://twitter.com/Wikiversity. --Ottava Rima (talk) 14:41, 4 January 2011 (UTC)

title: User talk:AdamBMorgan


text:
User talk:Darth Kule

 Hello Darth Kule, and welcome to Wikiversity! If you need help, feel free to visit my talk page, or contact us and ask questions. After you leave a comment on a talk page, remember to sign and date; it helps everyone follow the threads of the discussion. The signature icon in the edit window makes it simple. All users are expected to abide by our Privacy policy, Civility policy, and the Terms of Use while at Wikiversity.

To get started, you may

 * Take a guided tour and learn to edit.
 * Visit a (kind of) random project.
 * Browse Wikiversity, or visit a portal corresponding to your educational level: pre-school, primary, secondary, tertiary, non-formal education.
 * Find out about research activities on Wikiversity.

 * Read an introduction for teachers and find out how to write an educational resource for Wikiversity.
 * Give feedback about your initial observations
 * Discuss Wikiversity issues or ask questions at the colloquium.
 * Chat with other Wikiversitans on #wikiversity-en.

And don't forget to explore Wikiversity with the links to your left. Be bold to contribute and to experiment with the sandbox or your userpage, and see you around Wikiversity! If you're a twitter user, please follow http://twitter.com/Wikiversity. --Ottava Rima (talk) 14:41, 4 January 2011 (UTC)

title: User talk:Darth Kule


text:
User talk:MaxHarmony

 Hello MaxHarmony, and welcome to Wikiversity! If you need help, feel free to visit my talk page, or contact us and ask questions. After you leave a comment on a talk page, remember to sign and date; it helps everyone follow the threads of the discussion. The signature icon in the edit window makes it simple. All users are expected to abide by our Privacy policy, Civility policy, and the Terms of Use while at Wikiversity.

To get started, you may

 * Take a guided tour and learn to edit.
 * Visit a (kind of) random project.
 * Browse Wikiversity, or visit a portal corresponding to your educational level: pre-school, primary, secondary, tertiary, non-formal education.
 * Find out about research activities on Wikiversity.

 * Read an introduction for teachers and find out how to write an educational resource for Wikiversity.
 * Give feedback about your initial observations
 * Discuss Wikiversity issues or ask questions at the colloquium.
 * Chat with other Wikiversitans on #wikiversity-en.

And don't forget to explore Wikiversity with the links to your left. Be bold to contribute and to experiment with the sandbox or your userpage, and see you around Wikiversity! If you're a twitter user, please follow http://twitter.com/Wikiversity. --Ottava Rima (talk) 14:41, 4 January 2011 (UTC)

title: User talk:MaxHarmony


text:
User talk:Micheals/Bosonless particle physics

Some good basic concepts. Have you studied the similarities with Haisch & Rueda's very well developed GED? diversity 15:23, 4 January 2011 (UTC)


text:
Ada/Introduction

Overview
We will be writing the famous Hello World program in Ada in this lesson. We will talk about the history of the language, the language structure, and what each line of our program does.

Hello, World!
Here we go! This is the program you've been waiting to see. If you want to write this yourself here are the steps on Linux:
 * 1) Open the terminal
 * 2) Type :
 * 3) Type the following code
 * 4) ;Hello.adb:

Before we compile this we need to save the file. To do this using the keyboard shortcuts in Emacs, hit Ctrl-x Ctrl-s. Now to compile this program. You may have noticed at the top of the Emacs toolbar that there is an option called "Ada". Under that option you will see the option to compile the program and also run it. First compile the program to make sure no errors are present in the code. Then you can run the program and see the output in that bottom Emacs buffer that appears when you compile.

Analyzing Hello, World!
Now what exactly does each line in that program do?

This line tells the compiler that we need to be able to use the Text_IO package contained in the larger Ada package. You always need to tell compiler what packages you will be using with your Ada program using similar with statements. Note the semicolon at the end of the statement. Most languages based on Algol, such as C++ and Ada, use a semicolon to indicate that a particular statement has ended and that the next one should be executed. Leaving out the semicolon will generate an error at compile time in most cases.

This line is how functions in Ada, called procedures (hence the procedure keyword), are defined. This is only the very first part of defining a procedure. We then need these next critical lines:

The code contained between these begin and end keywords determine how the procedure will work. The code length for procedures, and functions in general, can be very short, very long, or anywhere in between. It just depends on what needs to happen when that procedure is called. Again, note the semicolon. In other languages we use the curly braces {} to indicate that we are defining a function. In the case of Ada, the begin and end keywords mean essentially the same thing and the semicolon right after end means that that particular procedure is finished.

In the procedure definition we have this line: Wait a minute? Did we not tell the compiler that we were using Ada.Text_IO? Why do we have to say it again? Well, Ada has a pretty cool feature that allows you to rename packages temporarily and we will be talking a bit more about it later. But for now, since Ada.Text_IO was not renamed, we need to have it as shown. That next part Put_Line is a function defined under Text_IO. This allows us to output text to the console. Notice how we have parentheses with quotation marks to tell that function what to output. This is true of most Algol-based languages as well. A string, or text in general being output to the console, needs to be enclosed in quotation marks so the compiler knows how to handle it appropriately. This statement also ends with a semicolon, just like every other statement we have looked at so far.

That's pretty much a basic analysis of this program. This follows a pretty basic structure of almost every program we will be writing and looking at:
 * 1) Bring in needed packages
 * 2) Define procedures, the main procedure being the first one defined with subsequent ones following

title: Ada/Introduction


text:
User talk:Richmarkey

 Hello Richmarkey, and welcome to Wikiversity! If you need help, feel free to visit my talk page, or contact us and ask questions. After you leave a comment on a talk page, remember to sign and date; it helps everyone follow the threads of the discussion. The signature icon in the edit window makes it simple. All users are expected to abide by our Privacy policy, Civility policy, and the Terms of Use while at Wikiversity.

To get started, you may

 * Take a guided tour and learn to edit.
 * Visit a (kind of) random project.
 * Browse Wikiversity, or visit a portal corresponding to your educational level: pre-school, primary, secondary, tertiary, non-formal education.
 * Find out about research activities on Wikiversity.

 * Read an introduction for teachers and find out how to write an educational resource for Wikiversity.
 * Give feedback about your initial observations
 * Discuss Wikiversity issues or ask questions at the colloquium.
 * Chat with other Wikiversitans on #wikiversity-en.

And don't forget to explore Wikiversity with the links to your left. Be bold to contribute and to experiment with the sandbox or your userpage, and see you around Wikiversity! If you're a twitter user, please follow http://twitter.com/Wikiversity. --Ottava Rima (talk) 15:49, 4 January 2011 (UTC)

title: User talk:Richmarkey


text:
User talk:Peterajackson

 Hello Peterajackson, and welcome to Wikiversity! If you need help, feel free to visit my talk page, or contact us and ask questions. After you leave a comment on a talk page, remember to sign and date; it helps everyone follow the threads of the discussion. The signature icon in the edit window makes it simple. All users are expected to abide by our Privacy policy, Civility policy, and the Terms of Use while at Wikiversity.

To get started, you may

 * Take a guided tour and learn to edit.
 * Visit a (kind of) random project.
 * Browse Wikiversity, or visit a portal corresponding to your educational level: pre-school, primary, secondary, tertiary, non-formal education.
 * Find out about research activities on Wikiversity.

 * Read an introduction for teachers and find out how to write an educational resource for Wikiversity.
 * Give feedback about your initial observations
 * Discuss Wikiversity issues or ask questions at the colloquium.
 * Chat with other Wikiversitans on #wikiversity-en.

And don't forget to explore Wikiversity with the links to your left. Be bold to contribute and to experiment with the sandbox or your userpage, and see you around Wikiversity! If you're a twitter user, please follow http://twitter.com/Wikiversity. --Ottava Rima (talk) 15:49, 4 January 2011 (UTC)

title: User talk:Peterajackson


text:
Talk:Complex socio-ecological systems

HOW TO POST READING COMMENTS:

In order to post, please follow these steps:

1. Click on the "DISCUSS" tab on the top left of the "Week 1/Background" page-- you will be able to read previous posts now.

2. Click on the "EDIT" tab on the top right of this page.

3. Enter your text.

4. Sign your post. There are instructions at the top of the page, but basically all you need to do is end your text with 3 tildas.

5. Scroll down and click on "SAVE PAGE".

title: Talk:Complex socio-ecological systems


text:
Complex socio-ecological systems/Background

'''Folke, C. 2006. Resilience: The emergence of a perspective for social-ecological systems analyses. Global Environmental Change 16: 253-267.'''

In this article, Carl Folke reviews the origin and evolution of the resilience approach as applied to socio-ecological systems. Research on discovery of multiple "stable states" or basins of attraction in ecosystems during the 1960-1970s led scientists to review and challenge the view of a dominant stable equilibrium in ecosystems. Crawford Stanley Holling, a prominent ecologist, was one of the forerunners of the concept with his seminal work on ecology of predation (1973). He proposed that resilience determines the persistence of relationships within a system and is a measure of the ability of these systems to absorb change of state variables, driving variables, and parameters, and still persist. Since then, the resilience perspective has grown to influence other disciplines in social, economic and political fields. The author presents different possible interpretations developed for the concept, from more narrow ones related to engineering resilience to the social-ecological resilience approach, which focuses on adaptive capacity, transformability, learning and innovation. Folke conducts an extensive literature review on research done in resilience by diverse scholars, and concludes pointing out some implications of applying the resilience approach in policy making and sustainable development.

'''Scheffer, M,, S. Carpenter, J. Foley, C. Folke and B. Walker. 2001. Catastrophic shifts in ecosystems. Nature 413: 591-596.'''

This paper's main contribution to the resilience of socio-ecological systems is presenting empirical evidence of alternative stable states related to the dynamics or shift in ecosystems. The authors summarize results from research carried out in lakes, coral reefs, woodlands, deserts and oceans, in which for certain environmental conditions, a given ecosystem will switch between two alternative stable states, separated by an unstable equilibrium that marks the "border of attraction" of the states. According to the authors, this phenomenon has implications for ecosystem's response to environmental change. The authors state that catastrophic shifts from one state to the other occur typically unannounced, and early signals of these changes are sometimes difficult to note. Resilience in this case would refer to the size of the valley or basin of attraction around a state, corresponding to the maximum perturbation that can be taken without causing a shift to an alternative stable state. Due to the dynamic character of these states, they might also be named "dynamic regimes". They recognize that disturbance is a natural component of ecosystems that promotes diversity and renewal processes. They conclude that efforts directed to ecosystem management should address the gradual changes that affect resilience rather than merely controling disturbance.

SUMMARY OF CLASS DISCUSSION AND MAIN FOLLOW-UP QUESTIONS

A key point from the Folke article (Table 1) is that the resilience concept has evolved, from engineering resilience (i.e. return time to a relatively constant and stable equilibrium), to ecological resilience (i.e. buffer capacity and persistence -- how much shock the system can absorb without losing function), to social-ecological resilience. This latter, most recent concept (since 2004) incorporates the ability to re-organize and undergo change while still retaining essentially the same function, structure, identity and feedbacks. In this sense, resilience is about adaptive capacity, learning and self-organization.

This evolution of the concept highlights that research on resilience is still in the exploratory phase. Frontier areas include social learning, visioning and scenario building, social networks, leadership, organizational change, adaptive capacity and transformability.

 * Reading comments posted by participants point to earlier developments of the ideas of adaptive landscapes and regime shifts, mentioned by Jeremy (as early as 1930), for instance. This is a good critique of Folke's paper, since he did not mention early roots of concepts that led to the evolution of the resilience approach to socio-ecological systems. It seems that in science, as well as in systems, there exists a disconnect between schools of thought analysing the same principles, in which one ignores the other. Folke does not mention the application of systems thinking and resilience in institutions and business management, which has become a huge part of corporate management nowadays (explored by Peter Senge in next week's readings). Flavia also commented on earlier development of the concept of socio-ecological systems developed by scholars in Sociology in the end of the 1970s.

 * Adam presents a compelling question that will be explored by our group throughout the semester, relating to "Resilience of What to What?" In other words, what is the desirable regime that we might want to foster resilience, and for whom? This is a question that also takes us to reflect on the anthropocentric approach that theories on socio-ecological systems and the resilience concept carry. Resilience relates more to human continuity in the planet, since you apply a filter of desirable regime to ecosystem dynamics? Or is it our difficulty (mental models) to view socio-ecologycal systems as one, removing the conceptual and artificial divide between humans and nature?

 * Another interesting topic brought up by Claudia was the case for seeing everything as cyclic and multi-phenomenon and abandoning linearity approach to ecological studies. Is that the case? To which extent complexifying a system might also lead to an undesirable and stagnant simplification?

 * Different people have different interpretations, likes and dislikes about ideas and ways to represent phenomena in science, for instance, regarding the usefulness of the adaptive cycle. Some believe it is unattractive, perhaps because it implies a deterministic sequence and pathway. Others see it as a very useful heuristic, and have applied it to every aspect of daily life, including personal relationships.
 * We also discussed the concept of climax in secondary succession and its relationship with complex systems and resilience concepts, which challenge the predictability and the view of linear progression of an ecosystems towards a stable climaxic state.

 * Claudia Monzon poses a question of the limitation of science to model complex socio-ecological systems, given its unpredictable nature. We will be reviewing some attempts to modeling SES in future sessions.


text:
User talk:MartinA~enwikiversity

 Hello MartinA, and welcome to Wikiversity! If you need help, feel free to visit my talk page, or contact us and ask questions. After you leave a comment on a talk page, remember to sign and date; it helps everyone follow the threads of the discussion. The signature icon in the edit window makes it simple. All users are expected to abide by our Privacy policy, Civility policy, and the Terms of Use while at Wikiversity.

To get started, you may

 * Take a guided tour and learn to edit.
 * Visit a (kind of) random project.
 * Browse Wikiversity, or visit a portal corresponding to your educational level: pre-school, primary, secondary, tertiary, non-formal education.
 * Find out about research activities on Wikiversity.

 * Read an introduction for teachers and find out how to write an educational resource for Wikiversity.
 * Give feedback about your initial observations
 * Discuss Wikiversity issues or ask questions at the colloquium.
 * Chat with other Wikiversitans on #wikiversity-en.

And don't forget to explore Wikiversity with the links to your left. Be bold to contribute and to experiment with the sandbox or your userpage, and see you around Wikiversity! If you're a twitter user, please follow http://twitter.com/Wikiversity. --Ottava Rima (talk) 19:43, 4 January 2011 (UTC)

Your account will be renamed
Hello,

The developer team at Wikimedia is making some changes to how accounts work, as part of our on-going efforts to provide new and better tools for our users like cross-wiki notifications. These changes will mean you have the same account name everywhere. This will let us give you new features that will help you edit and discuss better, and allow more flexible user permissions for tools. One of the side-effects of this is that user accounts will now have to be unique across all 900 Wikimedia wikis. See the announcement for more information.

Unfortunately, your account clashes with another account also called MartinA. To make sure that both of you can use all Wikimedia projects in future, we have reserved the name MartinA~enwikiversity that only you will have. If you like it, you don't have to do anything. If you do not like it, you can pick out a different name.

Your account will still work as before, and you will be credited for all your edits made so far, but you will have to use the new account name when you log in.

Sorry for the inconvenience.

Yours, Keegan Peterzell Community Liaison, Wikimedia Foundation 23:42, 17 March 2015 (UTC)

Renamed
 This account has been renamed as part of single-user login finalisation. If you own this account you can |log in using your previous username and password for more information. If you do not like this account's new name, you can choose your own using this form after logging in: . -- Keegan (WMF) (talk) 06:15, 19 April 2015 (UTC)

title: User talk:MartinA~enwikiversity


text:
Complex socio-ecological systems/System dynamics

Required readings:

'''* Sterman, John D. 2002. All models are wrong: Reflections on becoming a systems scientist. System Dynamics Review Vol. 18, No. 4, (Winter 2002): 501–531'''

John Sterman was director of the Systems Dynamic group at MIT, author of a definitive textbook called Business Dynamics: Systems Thinking and Modeling for a Complex World. In this Jay Forrester Prize lecture he gives an overview and retrospective of the Systems Dynamics approach developed at MIT, initially be Jay Forrester. Systems dynamics has roots in control theory and nonlinear dynamics. Much of the paper elucidates how normal human thinking is at odds with the way systems actually work, because our worldview tends to be narrow, event-oriented, and reductionist. Examples: looking at certain effects of interest, and ignoring others; in reality, he states, "there are no side effects - only effects;" expecting cause and effect to be close in time and space, and thus ignoring larger scale and indirect effects; expecting cause and effect to be linear, thus ignoring tipping points and other nonlinearities. Sterman describes some empirical testing with MIT students and others of simple stock and flow models, demonstrating that erroneous thinking dominates, even in very simple systems. He then goes on to look at some actual models and their assumptions. For example, mineral resources are often modeled as if the stock is unlimited and harvest rate is only a function of investment; the same models typically ignore waste and pollution implication of the resource exploitation. Similarly, early climate models assumed that the ocean is a constant carbon sink that would never be saturated, no matter how much carbon input it received. Sterman's main point is that we need to be explicit about the models we use (whether we are aware that we are using models or not) so that we an examine the assumptions and limitations. He also argues for the use of mathematical simulations as a tool to overcome our inability to mentally simulate the dynamics of complex nonlinear systems.

'''* Forrester, Jay. 1971. Counterintuitive behavior of social systems. Technology Review 73(3): 52–68'''

Forrester's paper is an early presentation of the application of systems dynamics to social systems. Similar to Sterman, he presents models not as "correct" but as a way to be explicit about assumptions and how they interact; computer simulation "can reliably determine the future dynamic consequences of how the assumptions within the model interact with one another." One set of models dealing with urban poverty, a powerful issue of this period, indicate that an "equilibrium between all areas in total attactiveness" means that improving one region (i.e. improving the housing in a certain city) will not have the desired result because it creates a dis-equilibrium compared to other regions of the country, causing a net inflow of poor people, and reestablishing the equilibrium level of poverty and crowding. This result is counter-intuitive, but in actual fact southern cities would often treat their poverty problem by handing out bus tickets to New York, where welfare payments were much higher, thus driving down the attractiveness of New York towards an equilibrium level of misery. The second half of the paper takes a global perspective, presenting the "World Dynamics" model -- the model that was later refined to form the basis for the famous Meadows et. al. Club of Rome report, "Limits to Growth." The model looks at interrelationships of population, capital investment, natural resources, pollution and agriculture. The model is obviously very coarse and abstract, and contains gross assumptions, e.g. "death rate will double if pollution becomes 20 times as severe as in 1970." These assumptions result in interesting dynamics and allow the testing of multiple scenarios. In retrospect, none of these scenarios look very realistic, yet they support some interesting findings about the impacts of industrialization, the likelihood that we are currently at a peak of "quality of life" that is unlikely to be maintained, and the unlikeliness of present underdeveloped countries to reach the standard of living of the present industrialized nations.

Additional readings:

'''Senge, P.M., 1990a. Prisoners of the system, or prisoners of our own thinking? Chapter 3 In The fifth discipline: the art and practice of the learning organization. New York: Doubleday/Currency, pp. 27-54. ''' Peter Senge is another member of the MIT school of Systems Dynamics thinking, with a focus on application in business. Senge uses numerous years of experience and practice in a management setting to illustrate how sustainable competitive advantage is exemplified by an organization’s ability to learn faster than its competition. This third chapter in the text discusses how individuals in a system become prisoners to structure, how structure in a system is often subtle, and how new forms of “leverage” come from innovative ways of thinking. The author uses the extended metaphor of a brewery, distributor and retailer to demonstrate how learning disabilities are unhelpful to resolving complex systems and situations. He contends that the most effective way for managers to contend with complexity is by recognizing that structure is systemic and generative for new forms of learning, that behavior is generally responsive, and that in almost all cases actual events are merely reactive, as is usually the case in discrete management decisions within firms.

'''Senge, P.M., 1990b. Mental models. Chapter 10 In The fifth discipline: the art and practice of the learning organization. New York: Doubleday/Currency, pp. 174-204.''' In the tenth chapter of the Fifth Discipline, Senge describes how humans and human organizations are often constricted by mental models, which are counterproductive because they force assumptions upon their carriers. The author uses the example of Shell in the ‘70s to describe how a firm was able to resist the temptation to fall prey to a mental model that beset larger petroleum companies: conservative notions of supply and demand. As a result, Shell’s adaptiveness allowed the firm to overcome the oil crisis later in the decade. Similarly, Senge explores the Hanover Insurance Company approach to mental models, which he points to being effective at various levels in a system, from organizational, to interpersonal to intrapersonal. He concludes the chapter by offering a prescription for complex systems management based on the “Hanover Credo,” including how one should avoid leaps of abstraction, utilize a left-hand column of thinking, balance one’s sense of inquiry with one’s sense of advocacy, and decide when to accept theories or test theories in action.

'''Meadows, Donella H. 2008. Thinking in Systems: A Primer. Chelsea Green Publishing, White River Junction, Vermont.''' Donella Meadows was lead author of the famous and influential 1972 book, Limits|to Growth], which is an extension of the World Dynamics model presented by Forrester in the 1971 article cited above. The current work presents the basic elements of Systems Dynamics thinking. In Part I, systems are defined as a set of parts or elements, their interactions, and a set of effects of this "whole" that goes beyond the effects of the individual parts. Basic system concepts are stocks, flows, and feedbacks, which can be balancing (negative) or reinforcing (positive). Meadows uses these simple concepts to construct models of a population or resource in ways that effectively illustrate, for example, how extraction rates affect a resource stock over time, in sometimes surprising ways due to different kinds of feedback effects. Part II turns to applications, beginning with how systems can surprise us (nonlinear relationships, ignoring key variables by considering them "exogenous" or outside the boundaries of the system, and delays in feedbacks that can lead to oscillations rather than stabilization). She concludes with an evaluation of the best places to intervene in social systems: the apparently powerful intervention of changing numerical constants and parameters (e.g. taxes or subsidies) is actually one of the least powerful interventions because it leaves relationships and feedbacks unchanged; whereas changing feedback loops, information flows, rules, goals and paradigms are successively more powerful. This book is indeed a primer to systems thinking, didactic yet powerful.

An important critique that questions the validity of combining many different variables to achieve a global prediction:

'''Nordhaus, W.D. 1992. Lethal Model 2: The Limits to Growth Revisited. Brookings Papers on Economic Activity 2: 1-59'''

Thomas, K. 1983. Man and the natural world. Changing attitudes in England 1500-1800. London: Allen Lane.

Summary of Class Discussion:

This week we added four new participants to our discussion group. After reviewing last week’s discussion and the wikiversity site, we dove into the world of systems dynamics.

The Beer Game:

To illustrate some of the key concepts that emerged from the assigned and supplemental readings, we simulated the “beer game” mentioned in chapter 3 of Peter Senge’s Fifth Discipline. A guide to the online simulation is available at http://www.masystem.com/o.o.i.s/1366, and the game itself can be found at http://www.masystem.com/beergame. Our experience with the beer game brought to light several interesting points:

•	There is no one to blame in this game – except perhaps the system itself. Often, and throughout this game, the system and its complexity causes the problems observed; it is not the individuals or external factors. Only once did the amount of beer ordered from the end consumer change in the game, yet other numbers fluctuated greatly due to the relationships among various actors. The structure of the system caused great challenges.

•	Though at times the behavior of a particular individual in the supply chain may have appeared erratic or “crazy,” each individual was acting on incomplete information while attempting to make the “right” decision within the system.

•	Time lags in ordering and delivery proved extremely confusing and difficult to contend with throughout the game, illustrating the challenges of dealing with systems that have components operating at different time and spatial scales.

So what can models do – and not do?

After reflecting on the game itself, we turned to a broader discussion of the role of models in decisions and policy making. Some of the points/questions were as follows:

•	There was some disagreement about the usefulness and applicability of models. Are models useful, even if they are not comprehensive and are, as Sterman asserts “always wrong”? Yes, in the sense that models may help us to understand the dynamics of a system, unwrap our assumptions about that system, and uncover our unknowns. But they are not a panacea, though there is sometimes an assumption that models can (and/or should) influence actors to behave in certain ways.

•	Some policy-makers want the answer, reinforcing the positivist approach to modeling that Sterman states is problematic. Instead, perhaps model building needs to be participatory, though even then, there will always be some person or position who doesn’t win within the process and/or the resulting model. Plus, models can’t capture power dynamics and some other human relations that may be essential to our understanding of systems.

•	In order for models to be useful, they must be extremely dynamic and avoid complacency.

•	There is no way to make a decision without a model, given we are constantly acting on our mental models. Mental models are our interpretations of “reality, ”guiding how we think and make decisions. But our mental capacity to understand complexity is limited. Folks noted, however, that our mental models are quite different from the large science-based models that are often used to drive policy decisions.

•	While it’s probably not true that "if everyone would just think in systems, we’d be able to solve the world’s problems" (something heard by a class member from a member of the systems thinking community), it seems true that seeing the world through a systems perspective may expand and enhance the way we understand complexity in socio-ecological systems.Deb.wojcik 22:59, 14 January 2011 (UTC)


text:
Complex socio-ecological systems/Complex Adaptive Systems

The concept of social-ecological resilience from week 1 includes adaptation, learning and self-organization. This week's reading is an entrée to fundamental system concepts, and shed light on the concept of self-organization. Next week we will look at how Complex Adaptive Systems adapt and learn.

Although it is a difficult concept to grasp, self organization was presented by Folke's resilience paper (Week 1) as a contrast between lack of organization, and organization forced by external factors. Lack of organization = chaos, and organization forced by external factors may be akin to order that can not adopt. In this sense, self-organization is at an intermediate place between order and chaos. In Lansing's Figure 2, he labels this intermediate place between chaotic and ordered (which can be periodic or fixed) as "complex." This place has been popularly called "the edge of chaos," but perhaps more literally could be called "an intermediate state between totally ordered and chaotic."

It may be worth noting that the "edge of chaos" concept comes from physical science, specifically a "phase transition." For example, if you gradually cool liquid water, it slowly cools and the molecules slow down, but they maintain their chaotic motion. As water nears the freezing point and cooling continues, there is a sudden change, when all of the molecules rapidly switch from chaotic (liquid) to ordered (frozen). This phase transition is non-linear, it is a tipping point or sudden change from chaotic to ordered.

The key assertion is that complex adaptive systems populate this intermediate area between order and chaos, or perhaps (more specifically, but hypothetically) they are in the ordered state but close to the edge or boundary of this phase transition. And that being in this state allows systems to self-organize, adapt and evolve ever-increasing complexity.

Required readings:

'''Lansing, J. S. 2003. Complex Adaptive Systems. Annual Review of Anthropology 32 (2003), pp. 183-204.'''

Lansing starts with a question (from Holland, the pioneer in the field of artificial intelligence) that is the opposite of the Beer Game: why is a supply chain (in this case, for pickled herring) not chaotic?

The first 10 pages of Lansing are a tough read. Nevertheless, it is about as brief and coherent an explanation of the emerging thinking about complex adaptive systems that you will find. To ease your burden, I offer 2 points. (1) It is true. Well, not "true" in the positivist sense. But I've made a hobby of surveying this literature for the past year (e.g. the Kauffman book described below) and I can tell you there is a deep and very wide-ranging foundation underlying the overview that Lansing offers. (2) You don't need to understand this to enjoy the remaining reading, which is much easier and familiar and applied. But I think it's good to get a flavor of the underlying science, so that when you read that "Both E. coli and IBM coevolve in their respective worlds," you'll have a sense of where that's coming from.

'''Levin, Simon A. 1998. ''' Ecosystems and the Biosphere as Complex Adaptive Systems. Ecosystems 1: 431-436 Simon Levin is a famous mathematical ecologist. His article starts with the fundamentals of Complex Adaptive Systems, probably more understandable than that of Lansing -- so you could go back and forth between Lansing's 184-192 and Levin's 432-433.

By focusing on ecosystems and the loss of biodiversity, the author introduces the concept of Complex Adaptive System (CAS) as applied to ecosystems, and represents the existence of processes that link elements of the ecosystem and beyond it partially determining its future state. Levin builds up on  Arthur’s work on economics (1994) and Holland (1995) and proposes a simpler framework to look at CAS, with these traits: sustained diversity and individuality of components; localized interactions among those components that have implications at higher scales (hierarchical organization within the CAS); an autonomous process that selects from among those components based on the results of local interactions (selection); and a subset for replication or enhancement (learning). In the process of adaptation CAS acquire hierarchical and aggregation properties that emerge from within the structural linkages and feedbacks amongst elements, and between the system and the environment. CAS cannot follow completely random trajectories throughout its history, but rather is constrained by history (resilience interpreted as resistance to change). At the same time, this history provides windows for opportunity for novel configurations. As environmental conditions change they influence the surrounding matrix on which CAS are embedded: the extent to which CAS will retain its nature will depend on the environmental regimes (e.g. disturbance frequency and intensity), as well as on the functional diversity of the system. At the end of the paper, Levin enunciates 6 questions that are the foundation to understand ecosystems as CAS, and which are basic to understand ecosystem sustainability issues. Some of those have been partially addressed by evolutionary ecologists in the following years (e.g. patterns in the distribution of organisms are determined by local conditions as well as historical and spatial factors). For him human interventions are not an integral component of ecosystem resilience: he rather considers those factors exogenous. Claudia 14:39, 27 January 2011 (UTC)

Additional reading:

'''Kauffman, Stuart. 1996. At Home in the Universe: The Search for Laws of Self-Organization and Complexity. Oxford University Press, 321 pp'''

This is a less mathematical version of Kauffman's 1993 "The Origins of Order: Self-Organization and Selection in Evolution." Kauffman believes that selection in insufficient to explain the degree of development of complex adaptive systems, and that there is some innate tendency for such systems to "self-organize". He looks at systems such as the development of life from a set of ever more diverse organic chemicals; ontology of complex organisms from a single, uniform genome; and the co-evolution of ecological communities. The analysis also applies to technology and economic systems.

Much of his analysis focuses on how such systems explore a "fitness landscape" (applying the concept originally developed for biological evolution by Sewall Wright in 1937). He shows that such fitness landscapes are exceedingly large (2 to the nth power, where n is the number of possible "alleles" or different "bits" of information, which quickly becomes larger than the number of atoms in the universe). His basic argument is that the "genetic algorithm" based on mutation and selection is not sufficiently powerful to explore this entire fitness landscape, and that there must be other processes leading to the complex systems that are at near global optima -- self-organization, which he calls "order for free" because it is achieved quicker than simple selection would permit.

Kauffman does not provide the mechanism; he argues that it probably exists and finding the mechanism will be the next great breakthrough in our understanding of nature. He does, however, analyze how several such systems seem to find this "order for free" by working at the "edge of chaos", i.e. systems that are neither chaotic (which would never converge on an optimum in the fitness landscape) nor extremely ordered (which would be frozen at a local optimum and unable to navigate the fitness landscape in search of a near global optimum). For example, figure 3.4 on page 57 shows that connected webs undergo a "phase transition" when the ratio of links to nodes is at 0.5; with fewer connections, the systems consists of a multiple of unconnected parts; with more connections the system gets locked into one large cluster, but there is a non-linear transition at the link:node ratio of 0.5 which is just when one giant interconnected component of the system crystallizes.

Kauffman offers a model of how large, constrained systems can effectively explore the fitness landscape, by breaking it down into "patches" of interconnected components (pp 252-271). Such a model could serve as a representation of federalism, profit centers, restructuring, or checks and balances.

'''Mitchell, M., 2009. Complexity: a guided tour, New York: Oxford University Press.''' Mitchell addresses what is meant by a complex system: “a system in which large networks of components with no central control and simple rules of operation give rise to complex collective behavior, sophisticated information processing, and adaptation via learning or evolution.” She recounts the history of many important scientific discoveries, from information theory, to quantum phenomena, to dynamics. The author explains non-intuitive phenomena (Maxwell's demon) and more intuitive (entropy, information theory, and computation) with equal clarity. Like many in the field the author struggles with adaptive systems, and how information theory often does not have direct application to biological and social systems. She notes that no one quantitative measure of complexity has surfaced, which complicates its study. While innovations in Turing machines and cellular automata can result in sophisticated patterns of activity, Mitchell cautions that the necessary starting conditions and computational time are limiting factors in the application of these ideas to the real world.Tegeticula


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Complex socio-ecological systems/Genetic algorithms

Discussants: Ricardo and Bob

Holland, John H. 1992. Genetic Algorithms. Scientific American, July 1992, pp 66-72.

Holland's classical paper on Genetic Algorithms is a description of the application of natural selection and biological evolution in computer science to solve complex problems in fields as design of jet turbines or to anticipate effects of choices in agent-based models to explain human behavior. In the 1960's, building on previous research on the applications of genetic evolution to devise computer programs, Holland developed a program technique which he called genetic algorithm that emulates both natural selection and mating or sexual reproduction evolutionary processes existent in living organisms. The program consists of strings with binary combination of zeros and ones, forming classifier systems that anticipates outcomes encoded as conditions and actions, encoded in the strings. To evolve classifier rules that solve a particular problem, one simply starts with a population of random strings of 1's and 0's and rates each string according to the quality of its result. According to the desired outcome or question, high-quality strings mate, whereas low-quality ones perish. The strength of simple genetic algorithms is that they are at their best when exploring complex landscapes to locate regions of enhanced opportunity. However, according to the author, if a partial solution can be improved further by making small changes in a few variables, it is better to augment the genetic algorithm with other standard methods.

Axelrod, Robert. 1997. '''Evolving New Strategies. Chapter 1 in Axelrod, Robert, 1997, The Complexity of Cooperation: Agent-based models of competition and collaboration. Princeton University Press.''' Axelrod, the known dad of “Prisoner Dilemma” and one of the exponents on the theory of cooperation, search in this Book collect a sequence of studies that, using computational agent based models, explain how people interact and make decisions in competitive and collaborative environments. The nonlinear non rational decision models outputs are used to explain emergent properties of the system. The first chapter, used for discussion in this section, presents the use of Holland “Genetic Algorithm” to simulate multiple rounds of the Prisoner Dilemma Game, impossible to be simulated in tournaments, like tested by Axelrod in his previous works. The chapter presents Genetic Algorithm and the adequacy of use this technique to multiple rounds of the game. The model results show the emergence of a rule behavior of tit for tat after several generations, evolving from a less collaborative strategy at the beginning. As conclusion he express the efficiency of genetic algorithms to simulate strategies in a complex environment

Gell-mann, Murray. 1994. The Jaguar and the Quark. pp: 16-29.

'''Gell-Mann, M., 1994. The quark and the jaguar : adventures in the simple and the complex, New York: Owl Books.''' Gell-Mann explores the notion that underlying complexity are fundamentally simple principles. His metaphor woven throughout the book explores the interconnectivity (logical, conceptual, and practical) between the simplicity of the quark and the mystery of the jaguar. The volume is a collection of essays in which Gell-Mann surveys current (as of 1994) scientific thought and the philosophy of science. Much of the text is autobiographical in nature. The content touches upon quantum physics, sociobiology, ecology and computer engineering. The chapters are written in a sweeping fashion and reflect a quasi-omniscience on the part of the author. "Scientific Enterprise" is surveys science at large. The largest section "Quantum Universe," explores physic and many of the developments throughout the 40s and 70s in which Gell-Mann was a part. "Diversity" explores the suite of theories using a complex systems (plectics) framework. The author spends substantial time on complex adaptive systems, in contrast to discussions of both chaos and algorithmic complexity, and attempts to identify how emergent properties might be understood.Tegeticula (Sam)


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Complex socio-ecological systems/Networks

Facilitators: Sam and Renato

Required readings:

'''Watts Duncan J. and Steven H. Strogatz. 1997. Collective dynamics of ‘small-world’ networks. Nature. 393: 440-442.''' Illustrates "six degrees of separation," the popular notion that all humans are separated by a maximum of 5 chains in a network. While this has become kind of an "urban legend," it is based on an actual experiment that is described and analyzed in the Linked book, below.

'''Lijeros, et al. 2001. The Web of Human Sexual Contacts. Nature 407-408.''' This brief communication describes the scale-free nature of networks of human sexual partners, and the implication that the way to "attack" the disease transmission properties of this network is by attacking the hubs of the network.

'''Bascompte, J and Pedro Jordano. 2007. Plant-animal mutualistic networks: The architecture of biodiversity. Annual Reviews of Ecology, Evolution and Systematics. 38:567-593.''' Reviews network structures in ecosystems. Reviews complex network concepts, describes the structure of mutualisms in plant-animal communities, looks at ecological and evolutionary processes that could create these networks, and the implications of network structure for co-evolution, robustness and conservation.

'''Palla, Barabasi and Vicsek. 2007. Quantifying Social Group Evolution. Nature 446: 664-667.''' Compares the dynamics of small versus large social groups over time, using 2 network cases: scientific co-authorship and mobile phone calling. Has cool graphs.

Supplementary Readings:

'''Barabasi, Albert-Laszlo and Reka Albert. 1999. Emergence of Scaling in Random Networks. Science 286: 509-512''' This technical paper provides a mathematical (or logical) explanation of how scale-free networks can emerge by following a simple rule. Remember that "scale-free" networks are those that have the same structure at any scale. Mathematically, such networks follow a power function of the probability of "k", where "k" is the number of connections for a given node. The power function expresses mathematically the distribution of k, such that many nodes have low k or few connections, and few nodes have high k or many connections. [Incidentally, fractals are also "scale-free", i.e. the structure repeats at multiple scales, from the minute to the grandiose -- fractals can also be described by the same mathematical power law function.]

In this paper, Barabasi demonstrates mathematically a simple but elegant rule which results in such a distribution. The rule is that if you start with a small network and gradually add nodes/connections, such that the probability of an existing node receiving a new connection is proportional to the number of existing connections, the power law distribution naturally arises. In other words, "the rich get richer." This creates the hub and spoke structure of scale-free networks, because new nodes are more likely to connect to existing hubs than to a random node.

'''Barabasi, Albert-Laszlo. 2003. Linked: How Everything Is Connected to Everything Else and What It Means''' This is a popular overview of network theory and it's application to daily life. It discusses the implications of "scale-free networks" and "small-world" networks.

Hahn, T.; L. Schultz; C. Folke; and P. Olsson. 2008. Social Networks as Sources of Resilience in Social-Ecological Systems. In: J. Norberg and G. S. Cumming (eds.). Complexity theory for a sustainable future. New York: Columbia University Press. 119-148 p. This chapter of the book "Complexity theory for a sustainable future", the authors relate the study and management of social networks in processes of adaptive co-management of SES, and in building resilience at both the ecosystem and the social system dimensions. Using a case study from southern Sweden (Kristiantads Vattenrike), the authors show the role of multileveled social networks for generating visions and ecological knowledge and connecting this to management and governance of SES. They focus on three main topics that connect stakeholders in different scales: motivations, values and vision; investing in local context adaptive co-management of ecosystems; and navigating the larger environment. Extreme reliance on one or few key persons (nodes) may indicate vulnerability in a process or project for ecosystem management. Therefore, there is need to invest in social capital in the weak ties to bridge the interests of diverse stakeholder groups. Also, the formalization and institutionalization of some actions in the co-management process may reduce vulnerability in case key persons leave the network. The authors conclude that social network studies for resilience of SES are important tools in knowledge generation, mobilizing people, identifying common interests, staring projects and providing feedback.

Christakis NA, Fowler JH (2009) Connected: The Surprising Power of Our Social Networks and How They Shape Our Lives (Little Brown, New York). Christakis, a physician, and Fowler, a political scientist, are well known for their academic articles showing that obesity, smoking, and happiness can spread through social networks like contagions or epidemics. In Connected, the authors describe the powerful ways in which social networks shape society and individual human livelihoods. The authors discuss a wide assortment of phenomena, including the physical properties of human waves at sporting events, why emotions and suicides are contagious, and how social groups influence the spread of financial scams, voting behavior, and romantic relationships. This book makes use of a large set of longitudinal data to establish its claims that Millgram’s six degrees of separation can actually be understood in terms of three degrees of influence, an idea that is very controversial in the scholarly world.

Summary of discussion Seminar was opened with a reading from Christakis and Fowler (2009) on bucket brigades, and how the metaphor of a superogranism might help us to understand the concept of the emergent properties and nested hierarchies. UCINET, perhaps the most common software used for analyzing whole social networks, was then introduced to the class to best explain some of the concepts in the readings and in previous discussions. There were many questions from the group relating to what exactly the visualizations in NetDraw, a offshoot of UCINET, depict. We then dove into the readings, and there were a number of questions relating to how useful SNA is to understanding ecological networks and vice-versa. The Palla et al. (2007) article was pointed to as perhaps the best bridge for the two, but much of the seminar was devoted to deciphering Bascompte, and indeed how ecological networks operate differently. A back and forth ensued over how resilience thinking might be used to bridge the social and ecological networks, and the Hahn et al. (2008) chapter was cited as an example, though not without its own problems. Many questions abounded about the scale, weight, and initial energy of networks, and how best to bound these. Segue into next week’s dicussion: adaptive management.


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Complex socio-ecological systems/Coupled Human and Natural Systems

Discussants: Sam and Simone

Liu, Jianguo et al. 2007. Coupled Human and Natural Systems. Ambio 36(8): 639-648. In this paper, Liu and collaborators describe aspects of the complexity of organizational, spatial, and temporal couplings of Coupled Human and Natural Systems (CHANS). The authors argue that reciprocal effects and emergent properties of those coupled systems can lead to novel scientific discoveries, being essential for the development of effective policies for ecological and socioeconomic sustainability. The authors describe:1) organizational couplings properties, which include reciprocal effects and feedbacks, indirect effects, emergent properties, tresholds and resilience; 2) Spatial couplings, including couplings accross spatial scales, beyond boundaries and heterogeneity; and 3) temporal couplings, including massive increases in human impacts on natural systems, rising natural impacts on humans, legacy effects, time lags, increased scales and pace and escalating indirect effects. The authors claim that understanding CHANS requires effective nurturing of interdisciplinary research, comparative studies, integrated tools and effective ways of commmunicating science "beyond the ivory tower."

Liu, Jianguo et al. 2007. Complexity of Coupled Human and Natural Systems. Science 317: 1513-1516. This paper, by Liu and many collaborators from various universities and reserach centers accros the globe describe six study-cases to illustrate emergent properties and characteristics of coupled human and natural systems. They highlight properties of CHANS that are commomn accross the cases which exhibit nonlinear dynamics with tresholds, reciprocal feedback loops, time lags, resilience, heterogeneity and surprises.

Westley, F.; S. R. Carpenter, W. A. Brock; C. S. Holling and L. H. Gunderson. 2002. Why Systems of People and Nature are Not Just Social and Ecological Systems. In: L. H. Gunderson and C. S. Holling. eds. Panarchy: Understanding Transformations in Human and Natural Systems. Washington, Island Press. This chapter of Panarchy lays the foundation for the concept of socio-ecological systems (SES), and seeks to bridge the divide in holistic scientific conceptualization between the extreme systems ecologists who posit that humans are components in an ecosystem, and social scientists who posit that human-environmental interactions are merely another type of social system. Both ecosystems and social systems are described separately, but the section on social systems discusses two features peculiar to them: abstraction and reflexivity. Abstraction is the use of communication, language, and symbols to invent and reinvent a meaningful order, which then acts in accordance with the invented world. Reflexivity is the human tendency to create a social or virtual reality that is externalized, objectified, and then experienced. The authors also discuss the role of modeling and technology as further features to differentiate the social and ecological. The chapter concludes by saying that only a clearer understanding of these systems in conjunction with each other can lead to a fuller understanding of these relationships.

Additional reading:

Redman, Charles L., J. Morgan Grove and Lauren H. Kuby. 2004. Integrating Social Science into the Long-Term Ecological Research (LTER) Network: Social Dimensions of Ecological Change and Ecological Dimensions of Social Change. - '''Redman, C.L., Grove, J.M. & Kuby, L.H., 2004. Integrating social science into the Long-Term Ecological Research (LTER) network: social dimensions of ecological change and ecological dimensions of social change. Ecosystems, 7(2), pp.161-171.''' The authors call for the immediate integration of the social sciences into a pre-existing research program known as the Long-Term Ecological Research (LTER) Network. Redman et al articulate a conceptual framework for understanding the human dimensions of ecological changed, as prescribed in the LTER Network. They endorse the use of complex social-ecological systems (SES) as the means to accomplish this, although they do not discriminate between complex ecological systems and complex systems more generally in their formulation. The authors list a set of areas for the identification of key social science research areas, which are termed multiscalar investigatory frameworks necessary for implementing integrated research. The paper acts as a manifesto for how cross-site research projects can be carried out when investigators agree that bringing together social, biological, and earth scientists can lead to synthetic approaches and a unified understanding of the mechanisms regulating SES.


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Complex socio-ecological systems/Exploring Resilience

Discussants: Simone and Bob

Holling, C.S. 2001. Understanding the complexity of economic, ecological and social systems. Ecosystems 4: 390-405. [Note: this paper is essentially a short version of the Panarchy book, and thus is perhaps the critical reference of the semester.]

Cumming, G.S. et al. 2005. An exploratory framework for the empirical measurement of resilience. Ecosystems 8: 975-987.

Walker, Brian et al. 2006. A handful of heuristics and some propositions for understanding resilience in social-ecological systems. Ecology and Society 11(1): 13.

Additional reading:

Jannsen, M. A., M. L. Schoon, W. M. Ke, and K. Borner. 2006. Scholarly networks on resilience, vulnerability and adaptation within the human dimensions of global environmental change. Global Environmental Change 16: 240-252.

Folke, C. S.R. Carpenter, B. Walker, M. Scheffer, T.Chapin, and J.Rockström. 2010. Resilience Thinking: Integrating Resilience, Adaptability and Transformability. Ecology and Society 15(4): 20. [online] URL: http://www.ecologyandsociety.org/vol15/iss4/art20/


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Complex socio-ecological systems/Social systems

Discussants: Thaissa, Ricardo and Simone

Required readings:

Armitage, D. 2008. Governance and the commons in a multi-level world. International Journal of the Commons 2 (1): 7-32. In this paper Armitage based on a review of case studies around the world try to connect three complementary bodies of scholarship with insights for commons governance in a multi-level world: common property theory, resilience thinking and political ecology. He argues that governance is more complex than normative principles. It is important to understand the contexts, stakeholders’ relationships and positions, knowledge valuation and how social problems are constructed. He concludes that to understand the governance dynamic, intersections and hybrid analysis will help to overcome disciplinary boundaries while also linking social and natural scientists, policy makers, managers and communities.

Crane, T. A. 2010. Of Models and Meanings: Cultural Resilience in Social–Ecological Systems. Ecology and Society 15(4): 19. [online] URL: http://www.ecologyandsociety.org/vol15/iss4/art19/. In this paper, Todd Crane addresses the place of culture in social-ecological systems. He argues that much of the literature on resilience of SES implicitly privileges the material, both in terms of ecosystem functions and huma-livelihood outcomes. The paper presents a critique on the use of models in the analysis of social-ecological systems, proposing that the concept of cultural resilience should be understood from the point of view of those peoples which hold the knowledge and the experience about a given ecological system. He contraposes emic and ethic interpretations of resilience, presenting results of qualitative research done with two groups of agropastoralists from Mali, the Marka and the Fulani. Both groups define causes of soil fertility in central Mali in different ways, reflecting their views and adaptive knowledge. He defines cultural resilience as "the ability to maintain livelihoods that satisfy both material and moral (normative) needs in the face of major stresses and shocks; environmental, political, economic, or otherwise." He concludes that determining the resilience of a system depends on the analytical frame through which a social-ecological system is viewed, which is at least partly a function of the social position of the analyzer. Harrison, N. 2003. Good governance, complexity, institutions and resilience. Online: http://sedac.ciesin.columbia.edu/openmtg/docs/Harrison.pdf. In this paper Harrison argues that resilience science fail in integrate social and ecological system as one complex system. His critique is centered in the argument in which resilience science arose from the study of ecological system and become a problem applying the same modeling framework for explain societal characteristics. He highlights four main points: Most of the studies of socio-ecological systems are based on analyses of simple ruled communities, not in complex political system; the social systems have people desires and institutions, which not appear in ecological systems; hierarchy represent a fundamental rule in human decisions and is not clearly stated in SES, in social systems the self- organization property is less intense when the system complexify. The last section of the paper is dedicated to introduce the importance of study the role of “Institutions” in socio-ecological systems analyses as the way of understand the integration between social and ecological systems. Ricmel 18:13, 22 March 2011 (UTC)

Additional readings:

Axelrod, Robert. 1997. Promoting Norms. Chapter 3 in Axelrod, Robert, 1997, The Complexity of Cooperation: Agent-based models of competition and collaboration. Princeton University Press.

Berkes, F., J. Colding, and C. Folke. 2000. Rediscovery of Traditional Ecological Knowledge as Adaptive Management. Ecological Applications 10 (5): 1251-1262.

Innes, Judith E. and Devid E. Boher. 1999. Consensus building and complex adaptive systems: A framework for evaluating collaborative planning. APA Journal 65(4): 412-421.

Pretty, J.; B. Adams; F. Berkes; S. F. Athayde; N. Dudley; E. Hunn; L. Maffi; K. Milton; D. Rapport; P. Robbins; E. Sterling; S. Stolton; A. Tsing;  E. Vintinner;  and S. Pilgrim. The Intersections of Biological Diversity and Cultural Diversity: Towards Integration. Conservation & Society: 7 (2):100-112. Stringer, Lindsay C, A.J. Dougill, E. Fraser, K. Hubacek, C. Prell and Mark Reed. 2006. Unpacking "Participation" in the adaptive management of social-ecological systems: a critical review. Ecology and Society 11(2): 39.

Summary of Discussion:

We started the session on social systems watching a trailler of the series on the Kalahari family by Anthropologist and Film-maker John Marshall. We had a discussion on how could we define social and ecological resilience for peoples that ara continuouslly and dynamically changing. Is it possible to quantify or measure cultural resilience? Another big question we still have in our minds relates to who should have supremacy in terms of resilience in a situation in which the coexistence between humans and nature is not possible in a sustainable way: humans or environment? We then presented a case-study carried out among the Kaiabi people in the Brazilian Amazon, in which the researcher attempted to measure the robustness of indigenous knolwedge systems using qualitative and quantitative analysis, defending that cultural resilience should be defined according to the view and self-determination of the peoples with whom we work with. Questions were how can we quantify change? It would be having a measure of robustness of the cultural system in one point in time and another one at another point. You need a reference point to do that, it was argued. In the study-case, Athayde (2010) used consensus analysis, measures ok knolwedhe distribution and knowledge transmission among men and women in three areas after 40 years of a displacement situation. She found that innovation, leadership and access to social, technical and financial capital contributed to the perpetuation of weaving knowledge among one of the groups, in detriment of the other two. Therefore, that group is more cultural resilient in comparison to the others. She concluded that the resilience concept, when applied to indigenous peoples, might be an interesting lens to understand continuty and change. Some critiques were that the environmental part was not included (or not presented) for the study, and that there was a confusion between capacity to resist change and amonut of change undergone in the system. People think that social resilience is hard to quantify. We then discussed a bit the paper by Harrison, which presents a critique of the social-ecological approach to study social AND ecological systems. According to him, one framework cannot be applied to study of SES as one system, because they have different properties. He argues that most of the studies on SES were done among "simple" societies, therefore they don't apply to more complex societies, such as those living in urban areas. Many of us disagree with him that those simple societies are not that simple and cannot be put under a common black box. Furthermore, there are general principles of societies that can be applied to what he called "simple"as well as more complex societies such as urban ones.


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Complex socio-ecological systems/Applying resilience and systems analysis

Discussants: Adam and Danielle

Carpenter, Steve, Brian Walker, J. Mary Anderies and Nick Abel. 2001. From metaphor to measurement: resilience of what to what? Ecosystems 4: 765-781.

Levin, Simon A. and Jane Lubchenco. Resilience, robustness and marine ecosystem-based management. BioScience 58(1): 27(6).

Ostrom, Elinor. 2009. A General Framework for Analyzing Sustainability of Social-Ecological Systems. Science 319: 422-425.

Wilson, J., L. Yan, and C. Wilson. 2007. The precursors of governance in the Maine lobster fishery. PNAS: September 25, 2007 vol. 104 no. 39. http://www.pnas.org/cgi/doi/10.1073/pnas.0702241104


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Complex socio-ecological systems/Resilience-based management

Discussants: Flavia and Xanic

Scheffer, M, Willliam Brock and Frances Westley. 2000 Socioeconomic Mechanisms Preventing Optimum Use of Ecosystem services. Ecosystems 3: 451-471


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Complex socio-ecological systems/Systems modeling

Discussants: Andre and Ricardo

Janssen, Marco A. and Elinor Ostrom. 2006. Empirically Based, Agent-Based Models. Ecology and Society 11(2): 37.

Expore modeling tools such as NetLogo.


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Complex socio-ecological systems/Ecosystem-Based Management

Discussants:

Frost, Peter, Bruce Campbell, Gabriel Medina and Lonard Usongo. 2006. Landscape-scale approaches for integrated natural resource management in tropical forest landscapes. Ecology and Society 11(2): 30

Ruckelshaus, Mary, Terrie Klinger, Nancy Knowlton and Douglas P. DeMaster. 2008. Marine ecosystem-based management in practice: scientific and governance challenges. BioScience: 58(1): 53.


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Complex socio-ecological systems/Other Annotated Bibliography entries

'''March, James G. 1991. Exploration and Exploitation in Organizational Learning. Organization Science 2(1): 71-87'''

This paper explores a formal model of group learning. It models knowledge as a series of correct or incorrect beliefs towards m dimensions of reality. It models an "organizational norm" set of beliefs, and a series of individuals who each have their own set of beliefs. In the model, individual belief sets converge towards the organizational norm, and the organizational norm is influenced by the "more correct" group of individuals. This seems like a very artificial situation, but it is representative of the methodology of formal models and simulation. Despite its artificiality, exploration of the model reveals some interesting characteristics. In all simulations, the individual knowledge sets and the organizational norms converge over time. However, if learning by individuals is slow, the model yields a higher equilibrium level of knowledge than if individual learning is fast. This is because there is more time for the "more correct" individuals to influence the organizational norm, before all individuals converge to that norm. A related result is that having a mix of slow and fast learners yields a better final equilibrium level of knowledge than a uniform, intermediate rate of learning. And, having turnover in the group of individuals (e.g. as people leave an organization and are replaced by newcomers) gives a better final result, especially if the rate at which newcomers converge towards the organizational norm is slow.

March then extrapolates from the model of organizational learning to the broader issue of "exploration" and "exploitation." Exploitation would be converging on current best practice; specialization on what you know to be the best current solution. Exploration means investing resources to look for other possible solutions; the cost of this is that you know that over the short-term you are moving away from what you know works best now. In terms of a fitness landscape, exploitation is moving towards or staying at the local optimum; exploration is moving away from the local optimum to find a higher fitness peak elsewhere in the landscape. Organizations (and all agents in systems) must make tradeoffs between "investments in learning" (exploration) and "consumption of the fruits of current capabilities" (exploitation).

'''Schelling, Thomas C. 1978. Micromotives and macrobehavior. Norton and Company. 252 pp.'''

Schelling is a winner of the Nobel Prize in Economics for his game-theory analyses of the cold war. In this volume, he uses simple models to help understand "social dilemmas" -- the often unexpected group outcomes that result from the sum of individual choice. A significant part of his research looked at racial sorting between neighborhoods. He shows that a small preference by individuals to avoid being in an extreme minority can result in a process that inexorably leads to complete racial separation, even though that result is far from the actual preference of any individuals. The result comes from individual decisions to optimize with very minor constraints, which replicate throughout the system to produce an "emergent property" of the system that is far removed from any individual's desired outcomes. There are many examples, some of them apparently trivial: if no one wants to be in the least skillful decile of a tennis club, then those who are in this decile will leave; as they do, the next decile becomes the lowest, and they too leave, until there is no one left in the club.


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Talk:Complex socio-ecological systems/Background

Please post your pre-class comments here. We request that you log in to Wikiversity, and then end your posts with 3 tildas Rbusch

comments
Every time I read about complex adaptive systems, everything in life and death makes sense.

I think Sewall Wright invented this stuff in the 1930's with his heuristic of "adaptive landscapes" - where the fitness of different genotypes were mapped onto a landscape. As individuals or populations adapt with recombination and selection pressure, they climb their local fitness mountains. The hypothetical landscape stretches into the distance, but populations usually just climb up the fitness mountain they begin on, unable to jump across the valleys to higher peaks... until... they are disrupted a sudden change in the landscape or a mutation and allowed to explore other peaks. Then things settle down, and they again climb their new peak, although it may be a very different one.

Life is a series of "stable states" (I think a 3-dimensional peak is a better analogy), where things seems static and quiet. Then everything explodes, opening up opportunity for recombination and eventually a new state. It is this way in the stock market, relationships, social-ecological systems... some call it evolutionary "optimization", but that makes it seem that we know what we want to optimize. Resilience = how stuck you are on your mountain.

Two challenges to managing complex systems: 1. Knowing what you really want, and 2. Recognizing and capitalizing upon opportunities. There are ways to cross valleys to reach higher peaks, but you must have vision and guts. Jradachowsky

Folke (2006) is a review about the development of the concept of Resilience, and how its influenced other sciences besides Systems Ecology, where resilience came from. The concept emerged from empirical observation of ecosystem dynamics that challenged the notion of equilibrium in Ecology. Later, social dimensions were included in the discussion of resilience linking social and ecological systems. He argues for the use of broader concept of resilience to manage complex socio-ecological systems.

Scheffer et al (2001) discussed the concept of alternative stable state giving examples of different ecosystems reactions to disturbance, like lakes, oceans, deserts and so forth. They argue that is important to understand this process in order to avoid unwanted state shifts that can affect resilience of the system and the implications of that for management of resources. I am not much familiar with some ecological concepts presented but I am interested in understand how they can be used to understand social systems.Tha sob

These two papers are great choices for introducing the concept of ecological resilience sensu Holling. The Folke paper does a great job tracing the recent history of the concept (I say so to give the nod to Wright's work mentioned above) and the review hits the major authors and high points. The famous cosmic-lemniscate model to describe capital and connectivity is introduced, although to those unfamiliar with its use it could use more attention--likely during our first class, I'd imagine. Moving from first theoretical principles to empirical evidence, the Scheffer et al paper also represents a great first read. It makes a compelling case and goes into enough depth to satisfy a range of levels of understanding of ecological principles. The question of describing resilience in more specific and quantitative terms, usually countered by the question "resilience of what to what?", is certainly helpful to keep in mind. As the system being considered moves from the realm of natural ecology toward coupled human-natural systems, keeping one's head wrapped around exactly what one means by "resilience" can become difficult. I suspect that we will probably spend much of the first meeting discussing definitions--particularly to guide thinking in this latter area--but that seems helpful and even necessary. 24.136.33.118Adam

Folke examines how the ideas about resilience evolved. He traces the term back to the field of ecology and its evolution to understanding ecosystems and linked social-ecological ecosystems. The idea of resilience is based on the empirical observation that ecosystems are not stable -- they change over time at different speeds. There are critical points in which ecosystems change fast followed by periods of slow change. The study of linked social-ecological systems is based on the more recent understanding that the environment and society are inseparable.

Early environmental sociologists proposed this inseparability during the 1970s. Two famous articles within the field of environmental sociology that argues that society should be understood together with the environment are the articles by Catton and Dunlap (1978, 1979). However, they did not talk about environmental change, the foundation of the concept of resilience. Their perspective focused on society - they argued that one needs to pay attention to the physical environment that surrounds people to understand society and social behavior.

It would be interesting to contrast the perspective of "ecological modernization" that is supported by many Dutch scholars to the ideas about resilience and adaptation that is popular among American, Canadian, and Swedish scholars.

Is the Brazilian "jeitinho" a form of social resilience? Flaleite12345 23:14, 6 January 2011 (UTC) Flavia

I appreciated Folke’s narrative about how resilience thinking came to be. It made me reflect about the challenges of resilience theory and its applicability. Something that has been bothering me about resilience literature is how to move from the description of the system to the explanation of the interrelationships. Folke mentions “the resilience approach is concerned with how to persist through continuous development in the face of change and how to innovate and transform into new more desirable configurations” (p.260). Although those how questions are explanatory, the unpredictable nature of the complex adaptive systems limits our potential research to a description of a process that has already taken place. Has anyone read something about attempts to model socio-ecological / complex adaptive systems?

The idea of talking about renewal, regeneration and re-organization as oppose to recovery as well as talking about regimes or attractors as oppose to stable states or equilibria in resilience framework got my attention.--Cmonzon 01:14, 7 January 2011 (UTC)

Rondonxj 16:51, 12 January 2011 (UTC) I found an interesting article that may be of interest to some of you. It is on 'Resilience and the arts' Here is the link: http://www.resalliance.org/index.php/resilience_and_the_arts

mentioned terms/persons from above

 * Sewall Wright (1889-1988)
 * William R. Catton, Jr. (born 1926)
 * Jeitinho


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User:Nushensia

User:/Spanish

title: User:Nushensia


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User talk:Nushensia

 Hello Nushensia, and welcome to Wikiversity! If you need help, feel free to visit my talk page, or contact us and ask questions. After you leave a comment on a talk page, remember to sign and date; it helps everyone follow the threads of the discussion. The signature icon in the edit window makes it simple. All users are expected to abide by our Privacy policy, Civility policy, and the Terms of Use while at Wikiversity.

To get started, you may

 * Take a guided tour and learn to edit.
 * Visit a (kind of) random project.
 * Browse Wikiversity, or visit a portal corresponding to your educational level: pre-school, primary, secondary, tertiary, non-formal education.
 * Find out about research activities on Wikiversity.

 * Read an introduction for teachers and find out how to write an educational resource for Wikiversity.
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And don't forget to explore Wikiversity with the links to your left. Be bold to contribute and to experiment with the sandbox or your userpage, and see you around Wikiversity! If you're a twitter user, please follow http://twitter.com/Wikiversity. Erkan Yilmaz uses the Chat 22:00, 4 January 2011 (UTC)

Perhaps this helps (click here to see source):
 * when you want to create a userbox telling what languages you can speak:
 * User:Nushensia/Spanish: when you want to create a sub page, see also Red Link City, Erkan Yilmaz uses the Chat 22:17, 4 January 2011 (UTC)

title: User talk:Nushensia


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User talk:Cmontero

 Hello Cmontero, and welcome to Wikiversity! If you need help, feel free to visit my talk page, or contact us and ask questions. After you leave a comment on a talk page, remember to sign and date; it helps everyone follow the threads of the discussion. The signature icon in the edit window makes it simple. All users are expected to abide by our Privacy policy, Civility policy, and the Terms of Use while at Wikiversity.

To get started, you may

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 * Visit a (kind of) random project.
 * Browse Wikiversity, or visit a portal corresponding to your educational level: pre-school, primary, secondary, tertiary, non-formal education.
 * Find out about research activities on Wikiversity.

 * Read an introduction for teachers and find out how to write an educational resource for Wikiversity.
 * Give feedback about your initial observations
 * Discuss Wikiversity issues or ask questions at the colloquium.
 * Chat with other Wikiversitans on #wikiversity-en.

And don't forget to explore Wikiversity with the links to your left. Be bold to contribute and to experiment with the sandbox or your userpage, and see you around Wikiversity! If you're a twitter user, please follow http://twitter.com/Wikiversity. --Ottava Rima (talk) 22:05, 4 January 2011 (UTC)

title: User talk:Cmontero


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User talk:Anthonyhcole

It's best to contact me at en.Wikipedia. Anthony (talk).

be bold
Please Be bold and start The effect of stress on oligodendrocyte myelin production. --JWSchmidt 01:25, 5 January 2011 (UTC)

title: User talk:Anthonyhcole


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User talk:Laurifindil

 Hello Laurifindil, and welcome to Wikiversity! If you need help, feel free to visit my talk page, or contact us and ask questions. After you leave a comment on a talk page, remember to sign and date; it helps everyone follow the threads of the discussion. The signature icon in the edit window makes it simple. All users are expected to abide by our Privacy policy, Civility policy, and the Terms of Use while at Wikiversity.

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 * Find out about research activities on Wikiversity.

 * Read an introduction for teachers and find out how to write an educational resource for Wikiversity.
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 * Discuss Wikiversity issues or ask questions at the colloquium.
 * Chat with other Wikiversitans on #wikiversity-en.

And don't forget to explore Wikiversity with the links to your left. Be bold to contribute and to experiment with the sandbox or your userpage, and see you around Wikiversity! If you're a twitter user, please follow http://twitter.com/Wikiversity. --Ottava Rima (talk) 00:50, 5 January 2011 (UTC)

title: User talk:Laurifindil


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User:Anthonyhcole

I mostly edit at en.Wikipedia. Anthony (talk).

title: User:Anthonyhcole


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User talk:Soap

 Hello Soap, and welcome to Wikiversity! If you need help, feel free to visit my talk page, or contact us and ask questions. After you leave a comment on a talk page, remember to sign and date; it helps everyone follow the threads of the discussion. The signature icon in the edit window makes it simple. All users are expected to abide by our Privacy policy, Civility policy, and the Terms of Use while at Wikiversity.

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And don't forget to explore Wikiversity with the links to your left. Be bold to contribute and to experiment with the sandbox or your userpage, and see you around Wikiversity! If you're a twitter user, please follow http://twitter.com/Wikiversity. --Ottava Rima (talk) 00:59, 5 January 2011 (UTC)

title: User talk:Soap


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User:AaronJNewman

Aaron J. Newman, PhD
Associate Professor, Psychology, Psychiatry, Surgery, and Pediatrics (Division of Neurology), Neuroscience Institute, and Brain Repair Centre

Dalhousie University, Halifax, Nova Scotia, Canada

Affiliate Scientist, National Research Council Institute for Biodiagnostics (Atlantic)

http://neuroimaging.psychology.dal.ca/

The affiliations above will give you a feel for who I am professionally. I hold a PhD in Cognitive Psychology from the University of Oregon, and did postdoctoral training at the University of Rochester. As a university faculty member, my work time is divided between three primary duties: research, teaching, and administration. My research is generally in the areas of Cognitive and Clinical Neuroscience, primarily using non-invasive neuroimaging methods such as MRI, EEG, and MEG. I currently teach two third-year undergraduate courses in Psychology & Neuroscience: Psycholinguistics and Research Methods in Cognitive Neuroscience, as well as mentoring people at the undergraduate, graduate, postdoctoral, and faculty levels in cognitive neuroscience research. I also am Canadian Director of the Trans-Atlantic Neuroscience Teaching Network (TANTEN), a graduate-level Canada-EU exchange program. Administratively I focus my efforts on tasks that I see as performing important functions (e.g., undergraduate and graduate education) and/or being vehicles to create positive change, create community, and make scientific knowledge accessible and useful.

My involvement in Wikiversity was initiated by a strong belief in Open Education and the realization that with little additional effort, and in fact a lot more fun, I could empower my students, educate them better, and make my teaching efforts accessible to the global community rather than to a miniscule, highly-privileged fraction of that. My current (and first) Learning Project is Psycholinguistics. This is being developed with my students in the context of Dalhousie University's PSYO3190 and NESC3190 Psycholinguistics class. My personal page will evolve over the course of this project.

title: User:AaronJNewman


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User talk:AaronJNewman

 Hello AaronJNewman, and welcome to Wikiversity! If you need help, feel free to visit my talk page, or contact us and ask questions. After you leave a comment on a talk page, remember to sign and date; it helps everyone follow the threads of the discussion. The signature icon in the edit window makes it simple. All users are expected to abide by our Privacy policy, Civility policy, and the Terms of Use while at Wikiversity.

To get started, you may

 * Take a guided tour and learn to edit.
 * Visit a (kind of) random project.
 * Browse Wikiversity, or visit a portal corresponding to your educational level: pre-school, primary, secondary, tertiary, non-formal education.
 * Find out about research activities on Wikiversity.

 * Read an introduction for teachers and find out how to write an educational resource for Wikiversity.
 * Give feedback about your initial observations
 * Discuss Wikiversity issues or ask questions at the colloquium.
 * Chat with other Wikiversitans on #wikiversity-en.

And don't forget to explore Wikiversity with the links to your left. Be bold to contribute and to experiment with the sandbox or your userpage, and see you around Wikiversity! If you're a twitter user, please follow http://twitter.com/Wikiversity. --Ottava Rima (talk) 02:57, 5 January 2011 (UTC)

What I have learned
Hello Aaron, it's been about 6 months since your last edit. I thought this is a good time to reflect on your experiences with WV. Would be great if you could share some feedback on What I have learned, e.g. Brick and mortar collaboration shows Wikiversity efforts more globally, Erkan Yilmaz uses the Chat + Identi.ca 14:41, 24 October 2011 (UTC)
 * could I achieve the goals I had from begin with Wikiversity (WV)?
 * If not, perhaps some suggestions (so people here could initiate appropriate steps).
 * perhaps experiences by the students/participants?
 * will I use Wikiversity again?

title: User talk:AaronJNewman


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User talk:Ariston Boston

 Hello Ariston Boston, and welcome to Wikiversity! If you need help, feel free to visit my talk page, or contact us and ask questions. After you leave a comment on a talk page, remember to sign and date; it helps everyone follow the threads of the discussion. The signature icon in the edit window makes it simple. All users are expected to abide by our Privacy policy, Civility policy, and the Terms of Use while at Wikiversity.

To get started, you may

 * Take a guided tour and learn to edit.
 * Visit a (kind of) random project.
 * Browse Wikiversity, or visit a portal corresponding to your educational level: pre-school, primary, secondary, tertiary, non-formal education.
 * Find out about research activities on Wikiversity.

 * Read an introduction for teachers and find out how to write an educational resource for Wikiversity.
 * Give feedback about your initial observations
 * Discuss Wikiversity issues or ask questions at the colloquium.
 * Chat with other Wikiversitans on #wikiversity-en.

And don't forget to explore Wikiversity with the links to your left. Be bold to contribute and to experiment with the sandbox or your userpage, and see you around Wikiversity! If you're a twitter user, please follow http://twitter.com/Wikiversity. --Ottava Rima (talk) 02:58, 5 January 2011 (UTC)

title: User talk:Ariston Boston


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User:Lonewolf123

Main page is wikisource.

title: User:Lonewolf123


text:
Classical Chinese

This course will gradually introduce you to Classical Chinese, also known as Literary Chinese, and eventually help you master this extremely poetic and contradictorily simple and complex language.

Before beginning, research the the Chinese language itself, and the history and development of the Chinese writing system for better understanding the basics of Classical Chinese.

Reading Assignments

 * Chinese Language
 * Written Chinese
 * Classical Chinese
 * Chinese Character Classification
 * Mandarin Chinese Tones

1) Vocabulary 1

2) Structure 1

3) Particles 1

4) /Exercise 1: Vocabulary/

5 /Exercise 1: Structure/

title: Classical Chinese


text:
User talk:Lonewolf123

 Hello Lonewolf123, and welcome to Wikiversity! If you need help, feel free to visit my talk page, or contact us and ask questions. After you leave a comment on a talk page, remember to sign and date; it helps everyone follow the threads of the discussion. The signature icon in the edit window makes it simple. All users are expected to abide by our Privacy policy, Civility policy, and the Terms of Use while at Wikiversity.

To get started, you may

 * Take a guided tour and learn to edit.
 * Visit a (kind of) random project.
 * Browse Wikiversity, or visit a portal corresponding to your educational level: pre-school, primary, secondary, tertiary, non-formal education.
 * Find out about research activities on Wikiversity.

 * Read an introduction for teachers and find out how to write an educational resource for Wikiversity.
 * Give feedback about your initial observations
 * Discuss Wikiversity issues or ask questions at the colloquium.
 * Chat with other Wikiversitans on #wikiversity-en.

And don't forget to explore Wikiversity with the links to your left. Be bold to contribute and to experiment with the sandbox or your userpage, and see you around Wikiversity! If you're a twitter user, please follow http://twitter.com/Wikiversity. --Ottava Rima (talk) 03:19, 5 January 2011 (UTC)

title: User talk:Lonewolf123


text:
Classical Chinese/Vocabulary

This is a list of basic verbs, nouns, pronouns, adjectives, and grammatical particles. Pronunciations used are from Mandarin Chinese (see Mandarin Chinese Tones).

Return to Lesson Page

title: Classical Chinese/Vocabulary


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Talk:Classical Chinese

Hello,

I hope a few people that also have knowledge of Classical Chinese can help make this a great resource! I'll be continuously adding to this, probably daily. O.J.S. 04:38, 5 January 2011 (UTC)

title: Talk:Classical Chinese


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Talk:Genetics/Human Leukocyte Antigen/A*11:04

 * Is it really present in America? PCano 07:57, 5 January 2011 (UTC)


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Talk:Genetics/Human Leukocyte Antigen/A*24:25

 * We need to confirm ethnic origin. PCano 08:04, 5 January 2011 (UTC)


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