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Hi there!
I'm starting to model an urban simulation and I'm having a bit of a dilemma regarding what language to use. Have some experience in Netlogo and I'm starting to make a shift towards Repast, GAMA or MESA (geo-mesa), because it is recommended for large scale simulations.
Have been reading papers about which tool to use, but I need someone working on simulations to help me out.
Still, I have questions because:
1- The user base of MESA is scarce and i feel that dealing with issues will be dificult
2- So far i have only seen and read about limited research done in MESA. Specially, dealing with road network integrations. (move an agent along a network)
3- It seems that Netlogo is good for prototype, will not handle big data projects
Thanks in advance, and any pointers to courses or moocs would be great.
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If you have a little spare time, I'd suggest you try and imllent a *really* rudimentary analogue of your planned model in several of the major frameworks -- focus on the most significant elements -- it might take a week or so for eah, but piing the wrong horse may make for a much slower journey overall.
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I was thinking maybe starting at a pre-determined node on a decision tree and working retroactively to essentially create two models to compare. The models use both qualitative and quantitative data sets. Any ideas or input would be greatly appreciated.
Thanks,
Nick
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This answer can be confusing at the first reading, as it joumps somewhere unexpectedly. Entropy. Entropy could be your guide to navigate into the area when you can substantially increase chances to find an emergent displaying system.
Your local rules will be slightly changed band you will keep the system in the region where emergent behavior is expected. Quite a few authors is using this trick as there is not known theory.
There is an ongoin research on density classification problem, and emergent structures done using cellular automata, and the Game if Life. I recommend to take ideas there.
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complex systems / complex adaptive systems
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After a large study of different complex systems based methods I'm considering to use graph theory and network theory to analyze urban street patterns (based on toplogical indicators).
Among these software tools ArcGIS and NetworkX which do you recommand to use?, what are your suggestions beyond this set ?
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Hi,
I am looking for key literature (mostly significant books) regarding complex systems and complex adaptive systems in social sciences. Just for reference, I use systems thinking and system dynamics and I would like to expand my theoretical knowledge on complex systems.
Thank you in advance
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I would recommend this one particularly if you are looking at environmental aspects of systems thinking in the social sciences:
Capra, F. and Luisi, P.L., 2014. The systems view of life: A unifying vision. Cambridge University Press.
You can find the first chapter online:
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Hi everyone, I might be approaching the field of socio-ecological systems to write the final paper of my PhD. I would like to get an idea of a super MUST-READ literature to get an overview of the approach and ideally a couple of case studies.
To narrow down the search, I am a political and social scientist (somewhere in between ;) ) and I study bioenergy development from a triple bottom line perspective (socio, economic and environmental sustainability). I have been using qualitative system dynamics (i.e. causal mapping) and systems thinking in my previous papers and I am now into the CAS literature. I have been advised to look into socio-ecological systems as a good example of CAS and quite related to my theoretical and methodological framework.
Thanks for any help you can provide
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Can we consider language a complex adaptive system?
If so, how can we model it, and how can we test a model like this?
We tried a model considering it as a system composed of four subsystems/events (Lexicon, Discourse, Semantics and Grammar) in a two-mode network (we proposed eleven hypotheses for this). Speakers are the elements interacting through the events. Comments, critics, and suggestions are welcomed for discussion, in special considering possibilities of data to test the model.
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Dear Michael Lusk, H.Calloway and Sven Beecken.
When we were thinking about a model to simulate language, we were inclined to use only three events (Lexicon, Grammar and Semantics), considering that Discourse would be the signalling process of the Complex Adaptive System – CAS. In this case, we would have three events on one side and the Actors (speakers) on the other. However, we understood that discourse is a fundamental component of the system because, in fact, this is the event responsible for the “adaptive” word at “CAS”, that is, with no discourse, no evolution. For this reason, to have not a frozen language, not a “static language” (does it exist?), discourse is one of the four that affects, all the time, any other event: grammar/lexicon/semantics (anyway, in our model, the four events are in the same level – there is not a superior “event”).
Yet, for this reason, language needs at least two speakers. If there is just one, like Sven described, there´s still a language, but not any more as a CAS. The first three hypotheses of our tentative model presents just this. Another hypothesis (number 9) says that the links (communication) can have different bandwidths, in a way that the influence and intensity from one event to the others can vary all the time but are always present.
I agree it's a difficult computation, so we're trying the simplest rules to deal with and then seeing what the interacting elements could show us. Surely this discussion is helping a lot the advancement of our work - to refine our model - or even discard it. Thanks again for your contributions!
Mauro Faccioni
----the hypotheses cited:
Hypothesis 1. Language is a complex system comprising a two-mode network, where the mode of events consists of four subsystems (Lexicon, Semantics, Discourse and Grammar) and the mode of actors is made up of the speakers of this language.
Hypothesis 2. Language is a two-mode network completely connected and dense.
Hypothesis 3. Language has exactly four distinct events and a minimum of two actors (speakers).
Hypothesis 9. Language is a complex system wherein the links could have variable values, or widths.
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The ability to confidently characterise and quantify links between ecosystem structure, functions and services, and how these respond to perturbation - particularly where multiple ESs interact - seems to still represent a significant challenge for science. Ecological production functions (EPFs) have recently been explored as one way to map and quantify these dynamics - does anyone have any insights into what role EPFs will/might play in resolving these questions in the near future? I'd appreciate any feedback, thanks!
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Well, I think the attached article might be useful to you, in which it tries to introduce a methodology that translates ecosystem services types into user's experienced and recognized ecosystem benefits in the local context. From my point of view, the cascade model suggested by De Groot et al. (2010) did provide a structural way of linking ecosystems to human wellbeing. You can find out more from that article. Thanks.
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I have identified some 50 CAS concepts commonly used by authors in the paper.  They range from those derived thru chaos theory and agent-based modeling, to self-organization of agents as they interact and co-adapt, to emergence, etc. I have created one brief story that weaves together clusters of these concepts and then another brief story that weaves together the clusters.
How important is it to have a coherent story as one applies the concepts to new areas of inquiry?  Is a universal story across domains necessary?  What is it?
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Well done Guibert ~  aha the benefit of discussing these matters freely!
We are in agreement on most all you summarized...  what you have described is that ......
Complex Adaptive Systems  come in two sets ~ closed and open systems
Open systems can be adaptive or evolutionary ~ the second description matches open systems with evolutionary potential ~ eg watershed ecosystems
Closed systems can be adaptive but not evolutionary ~ the first description matches closed systems with adaptive potential... eg engines and motors (diesel and petrol are liquid energy ~modern engines can read and adapt to conditions)
Lets add the other important attributes we discussed earlier ~ how individual agents maybe helpful in analyzing closed systems; however, when dealing with open systems, communities not individuals are the key players....
In future it may be necessary to avoid the term "adaptive" in system science bcos it has become such a buzz word in business and commerce ~ referring to adaptive management within the neo-liberal monetary agenda. This use of "adaptive" does not match what we are discussing... it means a business management strategy for dealing with change... not the performance of the system...
In which case, the 1970's terminology where the primary classification of systems is closed or open ~ is more appropriate ~ Whether the systems are adaptive or evolutionary is a matter to be resolved subsequently ~ if important?
What is most important is to ensure that the appropriate scientific methods are employed for closed systems (alpha numerics) or open systems (intractable mathematics with geospatial imagery/intelligence systems...
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Hi,
I am working on on Complex Adaptive Systems. So far that I have studied the tool suggested for studying the Complex Adaptive Systems is Agent Based Modeling. I have not seen the Agent based Modeling yet but just a query in my mind. Does Agent Based Modeling come in the qualitative domain or quantitative domain?
Also, kindly suggest me some good book or source for learning the agent based modeling.
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Agent based modelling is also used in economics. See for example this article and its list of literature: http://www2.econ.iastate.edu/tesfatsi/acewp1.pdf You can also read this article and find more references: https://en.wikipedia.org/wiki/Agent-based_computational_economics
I think that programming is always quantitative. But it is also true that some quantitative economic variables (like utility, fitness) do not correspond to physically measurable variables, and thus quantities represent virtual variables with their evolution.
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Distributed, Emergent Cultural Cognition {DECC} (Sharifian, 2008) is a proposal that tries to expand standard individual cognition to cultural groups, drawing insights from Complex Adaptive Systems (CAS) Theory. Taking impetus from the Wittgensteinian notion of Language-Games, I believe that DECC can be re-construed and elucidated upon in Game Theoretic terms, with individual cognitions modeled as players of a game. Which of the games in the list below would you choose to achieve that -if any? Why? 
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The word "games" is used in several different ways and confusing them can lead to -- well -- confusion. Gamification is one, common also in nursing training. Wittgenstein's "language games" is, I think, another. "Legitimate" Game theory, to use your phrase, is better referred to as "Interactive decision theory." (Schelling, Aumann). To try to choose from a list of commonly studied "games" one that would address your problem is most probably an unproductive research strategy. There is, however, a literature on game-theoretic semantics. I don't know it but I believe some contributions have come from Brandenburger. If, however, you nevertheless want to begin from a simple, well-studied noncooperative game model, the Battle of the Sexes might be the place to begin. It was originated in Games and Decisions by Luce and Raiffa in the 1950s. 
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If other researchers or practitioners of conservation planning and natural resource management have been using the SenseMaker tools, I'd be interested to hear your experiences and opinions.  I am undertaking SenseMaker training from Cognitive Edge (http://cognitive-edge.com/) as part of my sabbatical work. Very interesting approach to planning and decision making in complex adaptive systems where there are many plausible outcomes and cause-and-effect relationships are only coherent in retrospect because the "effect" is one of many possible outcomes that could have emerged in response to the "cause". These are situations where expert knowledge can mislead if poorly applied... "Relying on expert opinion based on historically stable patterns of meaning will insufficiently prepare us to recognize and act upon unexpected patterns." (Kurtz & Snowdon. 2003. The New Dynamics of Strategy: Sensemaking in a Complex and Complicated World, IBM Systems Journal 42(3), p8). While I've found much benefit from applying Structured Decision Making and Open Standards approaches to conservation problems and natural resource management decisions - there have been many times where I feel assigned to create a cause-and-effect model where such knowledge does not apply. The SenseMaker approach has not yet been broadly applied and tested in conservation settings - but for those curious, a paper was just published looking at applications of SenseMaker to the challenges of climate change and climate change planning (Lynam and Fletcher. 2015. Sensemaking: a complexity perspective. Ecology and Society 20(1):65) (http://www.ecologyandsociety.org/vol20/iss1/art65/).  Looking forward to adding to my elicitation toolbox and thinking about the diverse structures of decision frameworks - simple, complicated, and complex.  Thanks!
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yes
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The 2013 complexity conference hosted by the Nanyang Institute of Technology contained the following excerpts :
"The 21st century," physicist Stephen Hawking has said, "will be the century of complexity." Likewise, the physicist Heinz Pagels has said that "the nations and people who master the new sciences of complexity will become the economic, cultural, and political superpowers of the 21st century."
General systems theory was thought to be the "skeleton of science" (Kenneth E.Boulding)
Is "multidisciplinary" and "interdisciplinary" subsumed under "transdisciplinarity"?
Does "transdisciplinarity" imply "universality"? Is it very different from the notion of "consillience" (coined by Edward O Wilson)
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Would take these words by common sense:
"multidisciplinary" means concerning more than one (=multi) disciplines,
"interdisciplinary" means concerning between disciplines without fixed amounts,
"transdisciplinarity" means going out of borders into an other discipline,
"universality" means valid all over the world and
"consilience" is the desired goal of every new defined terms in science.
Don't  think complex - think simple to solve complexity!
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Murray Gell-Mann attempted something akin to the general theory of complex adaptive systems (CAS) in the linked 1994 writeup.There have been numerous other attempts since, but I like to think of this particular effort as being in the same vein as Bertalanffy's (1968) General Systems Theory.Which sought to unify the various trajectories that systems theory was taking at the time. Since we are touching on a unification of /speculating on converging trajectories of systems science, you might also want to consider a survey by Melanie Mitchell on Complexity & Stephen Wolfram's A New Kind of Science.Together these resources provide a broad and accessible glimpse into the future prospects of CAS. Especially where traditional CAS has overlapped with advances in AI and computational theory.
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Urbanization poses an assemblage of complex issues that are concerned with rapid and seemingly stochastic development, increasing interest in sustainable development as a response to environmental issue, need for long-term thinking, polarizing effects in cities (Spatial & Social) are demanding expansion of the research and policy agenda for spatial planners (Albrechts 2004)⁠. The triggers of urbanization issues may be due to numerous processes like increase in urban population, varying social patterns, motivations of economic growth, swaying political atmosphere, altering landscapes and so on. Hence lately, increased emphasis on integrating non-spatial aspects has been recognized as an important step towards managing change. One such paradigm towards meaningful merger of social and spatial aspects is emphasized through coproduction for reinforcing strategic actions with inclusive decision making process. Such complex issues are not static, but they are dynamic and non-linear in reality, bringing transformation to the cities. As a response various integrative methods, frameworks and strategies have been developed at all scales (Regional, City, Neighborhood or are there others?). A general distinction that has emerged to make spatial planning more instrumental to change is through a reduction approach by developing strategies at regional scale (eg: Land-use Plan, Structure plan) and at local scale (eg: strategic projects, Master plan ).
We will explore the concept of resilience, frequently used in policy documents and project goals , for a framework that can aid in understanding & managing changes. We intend to do this by looking at cities as interdependent systems for their functioning. Systems perspective in spatial planning is not new by any means, but we use it in our research as an underpinning rational to expand its value for spatial planning. This will be further discussed in the methodology section of this chapter. From a systems perspective, a city is a hybrid system comprising of natural (ecological) systems and human (urban) systems (Pickett et al. 2011)⁠. The plurality of systems is emphasized here due to the reason that, even though they are whole system but they comprise of nested systems with numerous parts, which are directly or indirectly related to each other. Implying that any modifications occurring to the parts, either indigenous or endogenous, will have an effect on the system as a whole. With this background we engage with the concept of resilience in cities, and specifically interested in socio-ecological resilience. Here we refer to Holling, who gave the definition of resilience in ecological systems in the 1970's and made a plea to look at ecological and urban systems as interdependent systems which are complex adaptive systems (hereafter referred to as CAS) by themselves. This was established by comparing ecological system with urban system, drawing similarities between the two systems. The similarities of the systems was argued based on three characteristics -i) dependence on the succession of historical events; ii) reliance on spatial linkages; and iii) non-linear structures(Holling & Goldberg 1971)⁠. It explicates cities are not homogeneous entities but a spatial system of mosaics with an interplay of economic, social and ecological variables that sets forth a system with spatial heterogeneity. The complex interplay with and within these variables makes urban systems malleable, thus making them attractors for transformation of both kinds planned and self-organized. Heterogeneity, non-linearity and malleability (Sensitive to changes) in an urban system is compared to CAS which is characterized by numerous parts that interact and self-organize corresponding to collective behavior of the individual parts. We will refrain from establishing urban systems as complex adaptive systems in our research, as it has been done capably elsewhere (Portugali 2011)⁠. However by assuming it, we use it as the for our research. Our interest lies in better understanding of diverse interactions of system variables that enable self-organizing capacities in urban system. Lately in planning theory self-organization is associated with social innovation, discourse on ecosystem services, power relations and coproduction to mention a few (add ref). The causal effect of in the system increases the surprises and risk in urban areas exposing them to disturbances. For us, spatial planning as a field is concerned with tackling these disturbances to provide a productive environment for people. Planning ultimately is for betterment of a society.
In a systemic sense, researchers over the last few decades have focused on the potentials offered by crisis and disturbance as an opportunity for positive development (vale, Lawrence; Campanella 2005)⁠ (Lhomme et al. 2013)⁠. The frame adopted by the researchers is through degree of resilience of urban systems, which is one of the characteristics of a complex adaptive system. Resilience theory discards the assumption of equilibrium state of a system. Instead it emphasizes on the non-linear dynamics of change occurring due to the interdependent parts of an socio-ecological system. He also emphasized on including humans as component of the system as the social processes undertaken by humans altered the processes in natural system. From an ecological point of view, urbanization impacts on ecological components in cities such as urban hydrology, soil substrate, urban climate and urban vegetation to mention a few over time (Pickett et al. 2011)⁠ has been stressed upon by ecologists. These impacts informs us that resilience is concerned with not only sudden changes in a system but also slow processes that can open opportunities like recombining of evolved structures and process, renewal of the system and emergence of new trajectories (Wilkinson 2011; vale, Lawrence; Campanella 2005)⁠. Assuming cities as socio-ecological systems, comprising of both ecological and anthropogenic components whose interactions have an impact on its resilience. A network approach is seen beneficial to gain deeper insight into socio-ecological resilience (Janssen & Bodin 2006)⁠ This will be further discussed in the approach of this dissertation.
However, asking a simple question that can be answered in many ways is – what differentiates cities of 21st century from cities in the past? This question rakes the sheer foundation of our image of a city and makes us think in terms of morphological changes that have occurred. From a systems perspective, analysis of structural elements of complex adaptive systems lies in the nested networks they form(Janssen & Bodin 2006)⁠. For continuity of the same language, we apply network perspective to socio-ecological systems to analyze the structure of interactions in terms of nodes and links. The overarching research question from a network perspective is -
Which roles do (multiple) networks and (diversified) nodes play in making a region/city/city quarter/neighborhood (more) resilient?
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A rather dated but highly technical work in graph theory on nodes and regions- #1 link
A more accessible and recent work on nodes and nested city systems
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Complex systems consist of multiple interacting components. Two components is not enough to make a system complex. But would three or four components be enough for a system to become complex? What would be an example of such a system? 
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I thouht I answered yesterday. My point is that "complexity" is one way the Observer observes the System and is not a property of Systems (to be complex or not...).
For instance a gas watched as a thermodynamical System is a complex System while watched as the ensemble of, say, 1024 molecules can be at most a complicated System and is described with the tools of Statistical Mechanics. From the Thermodynamical point of view a gas (or whatever else) could be thought of even as a continuous it has not to be viewed as composed by descrete particles.
Any object can be complex if described as a whole and described by its overall properties, or be considered composed by n sub-systems if we aim at describing its properties as some result of the properties of the sub-systems.
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The next generation of cancer biology will require that physical scientists learn biology at more than a superficial level, and cancer biologists will appreciate and embrace the power of physics, mathematics, and chemistry to advance our knowledge of cancer. Physical scientists and biologists can use their respective understanding of biology and the physical sciences to create a new generation of teams with the capacity to solve biological problems.
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Dear Mesut, below is ur answer:
M. Itik and S. P. Banks, Chaos in a three dimensional cancer model. IJBC, 20 (2010) 71-79
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Looking for groups/individuals seeking to apply ANN/CAS to forecasting marine ecosystem indicators spanning multiple trophic levels.
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Randall,
That's great, I need to look into in detail. Shame I missed your reply. Had one of our young minds with Beth Fulton all of Feb 2014 (Artur Palacz). Hope you had a chance to meet.
I was publishing the question above to see if we can find colleagues out there that might be having parallel headaches with ANN validation through observations, so that these guys learn from each others headaches.
Ivo