The past several days I have been searching the web for articles or books that explore the connections between Foucault's work and complexity science. I am happy to report that I have found a few very interesting things.
First, Kurt Richardson and Paul Cilliers (who have written some incredible stuff on complexity and management and complexity and philosophy) have a book Explorations in Complexity Thinking. It is an edited book comprised of the pre-proceedings submitted for the two-day Complexity and Philosophy workshop held 22nd-23rd February 2007, in Stellenbosch, South Africa.
One of the pre-postings is by Ken Baskin, who is affliated with The Instititute for the Study of Coherence and Emergence (ISCE), which originally grew out of the New England Complex Systems Institute's Organizational-Related Programmes department in mid-1999.
Baskin's paper is Foucault, Complexity, and Myth: Toward a Complexity-based Approach to Social Evolution (a.k.a. History). (You can preview the paper by opening the cover in Amazon and going to it--it is the first chapter in the book)
Second is Mark Olssen's Foucault as Complexity Theorist: Overcoming the problems of classical philosophical analysis. Published in Educational Philosophy and Theory. Olssen is at the University of Surrey.
As I come across more articles and books I will post them.
23/06/2009
17/06/2009
The Built Environment: Communities as Complex Systems
One of the fastest growing areas in the study of community health in complex systems terms is the built environment literature.
The built environment refers to the human-made surroundings that provide the setting for human activity, ranging from large-scale civic surroundings to smaller settings such as work and home.
The term is also now widely used to describe the interdisciplinary field of study which addresses the design, management and use of these human-made surroundings and their relationship to the human activities which take place within them. The field draws upon a wealth of disciplines and areas of study including geography, urban planning, epidemiology, computational and spatial economics, law, medicine, health care, medical sociology, management, architecture, and design and technology.
An excellent website that has devoted considerable attention to this topic is The Prevention Institute. Check it out for more information, in particular their PDF on eleven communities that have implemented programs to improve the built environment.
Other places to explore include:
1. The Center for the Built Environment at Berkeley.
2. The Built Environment Blog.
3. The Built Environment and Health at Columbia University.
The built environment refers to the human-made surroundings that provide the setting for human activity, ranging from large-scale civic surroundings to smaller settings such as work and home.
The term is also now widely used to describe the interdisciplinary field of study which addresses the design, management and use of these human-made surroundings and their relationship to the human activities which take place within them. The field draws upon a wealth of disciplines and areas of study including geography, urban planning, epidemiology, computational and spatial economics, law, medicine, health care, medical sociology, management, architecture, and design and technology.
An excellent website that has devoted considerable attention to this topic is The Prevention Institute. Check it out for more information, in particular their PDF on eleven communities that have implemented programs to improve the built environment.
Other places to explore include:
1. The Center for the Built Environment at Berkeley.
2. The Built Environment Blog.
3. The Built Environment and Health at Columbia University.
30/05/2009
Social Science & Medicine: Special Edition on Health & Complexity
In 2007, the periodical, Social Science & Medicine (one of the leading journals in community health science) published a special edition on the complexities of studying community health--65, Nov 2007, starting page 1281.
The theme of the special edition was PLACING HEALTH IN CONTEXT. As the editors of this edition, James Dunn and Steve Cummins state, "While there is a long history of interest in place and health in the geography of health, in the past decade or more a number of disciplines have witnessed an increasing interest in the ‘effect’ that attributes of collective social organization and the local built environment at neighbourhood scale have on a variety of social outcomes, including health, health behaviours, early child development, youth delinquency, crime and deviance, political behaviour, employment outcomes and other economic opportunities" (p. 1821).
While Dunn and Cummins agree that significant advances resulted from the research surrounding the community-as-context model (see earlier post), there is much still to be done. Put simply by me (and I do mean simply), the community-as-context model needs to be replaced by the community-as-complex-system model. That is not quite what they say, but it works for a general sense of the articles. The community-as-context needs to get sophisticated; as it stands currently, it lacks the theoretical and methodological rigor to get the job done.
As data for my statement, here, for example, is a quote from Dunn and Cummins toward the end of their editorial overview: "The collection of papers presented here that sow the seeds of debate, for example, on the role of neighbourhood preference in understanding associations between context and health, is a potential lightning rod. Similarly, the use of complexity theory, given its novelty and its dissimilarity to the conventional ‘black box’ approach of investigating the effects of interventions should also spark responses in the literature. All of the papers in this Special Issue point us in compelling new directions for research that places health in context. We hope that this special issue sparks debate and new lines of inquiry and look forward to its future repercussions" (p. 1821).
The list of authors that Dunn and Cummins draw upon is impressive. The arguements made by these authors is even more incredible. Agree with them or not, you need to read this special edition and consider the arguments its authors make!
The theme of the special edition was PLACING HEALTH IN CONTEXT. As the editors of this edition, James Dunn and Steve Cummins state, "While there is a long history of interest in place and health in the geography of health, in the past decade or more a number of disciplines have witnessed an increasing interest in the ‘effect’ that attributes of collective social organization and the local built environment at neighbourhood scale have on a variety of social outcomes, including health, health behaviours, early child development, youth delinquency, crime and deviance, political behaviour, employment outcomes and other economic opportunities" (p. 1821).
While Dunn and Cummins agree that significant advances resulted from the research surrounding the community-as-context model (see earlier post), there is much still to be done. Put simply by me (and I do mean simply), the community-as-context model needs to be replaced by the community-as-complex-system model. That is not quite what they say, but it works for a general sense of the articles. The community-as-context needs to get sophisticated; as it stands currently, it lacks the theoretical and methodological rigor to get the job done.
As data for my statement, here, for example, is a quote from Dunn and Cummins toward the end of their editorial overview: "The collection of papers presented here that sow the seeds of debate, for example, on the role of neighbourhood preference in understanding associations between context and health, is a potential lightning rod. Similarly, the use of complexity theory, given its novelty and its dissimilarity to the conventional ‘black box’ approach of investigating the effects of interventions should also spark responses in the literature. All of the papers in this Special Issue point us in compelling new directions for research that places health in context. We hope that this special issue sparks debate and new lines of inquiry and look forward to its future repercussions" (p. 1821).
The list of authors that Dunn and Cummins draw upon is impressive. The arguements made by these authors is even more incredible. Agree with them or not, you need to read this special edition and consider the arguments its authors make!
22/05/2009
Interview with David Byrne
The following is a brief interview I conducted with British Sociologist and Complexity Scientist, David Byrne.
Dr. Byrne is Professor in the School of Applied Social Sciences at Durham University, England, where he is also Director of Postgraduate Studies. Dr. Byrne is the author of several books and a long list of articles, including his 1998 book, Complexity Theory and the Social Sciences--the first book to critically review and explore the application of complexity science to sociological inquiry. His most recent book, edited with noted sociologist and methodologist, Charles Ragin is The SAGE Handbook of Case-Based Methods
Dr. Byrne is an expert in methods, urban planning, community health, social policy, social exclusion and complexity science.
-----------------------------------------------
INTERVIEW WITH DR. BYRNE
CASTELLANI: Dr. Byrne, thanks so much for taking the time to do this interview. Your research agenda is rather vast in its scope—ranging from the philosophy of complexity science to method to urban planning to health care inequality. If you do not mind, I am going to narrow in on method first, given its wider implications for those reading this blog—most of whom are students and researchers new to the field of complexity science and its practice within sociology.
A. Case-Based Research
CASTELLANI: For the last several years, you have been a major advocate of a case-based approach to research. You specifically endorse what you and Charles Ragin refer to as Qualitative Comparative Analysis (QCA). First, how do you define QCA?
BYRNE: It is a method which is ‘set theoretic’ i.e. it understands causal relations in the social world in terms of relationships in combination – sets, rather than the unique contribution of single variables. It is based on systematic comparison – essentially an extension of John Stuart Mills’ method of differences. It requires careful qualitative engagement with specific cases as the foundation of that comparison.
CASTELLANI: Of the three major types of QCA (crisp-set, multi-value and fuzzy set), which do you find most useful and why? Or, do you approach the distinctions within QCA a different way?
BYRNE: I generally work with crisp set techniques and actually almost never go beyond the truth table. So I use QCA as a kind of mix of exploratory / explanatory – often focusing on ‘contradictory configurations’ in which the assemblage of elements in the line of the truth table – the configuration – generates different outcomes. That makes me look at those cases for what else is different about them. I see multi-value QCA as an extension of crisp set but it is much more complex to use. I frequently use Cluster Analysis as a data reduction technique and binarize membership of a cluster. Fuzzy set is very interesting and I have thought about how we might use distance from a cluster centre as a fuzzying principle but I have never managed to bring it off.
CASTELLANI: For researchers and graduate students new to case-based research, what is your best argument (apologetic) for including QCA in their toolbox of techniques?
BYRNE: For me the crucial things about QCA are the following:
•It allows for complex causation – lots of things acting together to generate an outcome. Conventional statistical modelling can do this in a limited sort of way through interaction.
•It allows for multiple causation – different combinations – in QCA terms configurations – can generate the same outcome. More than one way to skin a cat.
•It really makes us think about ‘what is a case’ – what Charles Ragin calls the processes of casing – just as important to specify the character and boundaries of cases as to be careful about operationalizing in measurement of what I prefer to call attributes or variate traces rather than variables.
•It really does have qualitative phases – conventionally at the beginning because the researcher really does have to engage closely with cases using qualitative techniques in order to establish attribute values. If you start, as I have often, with a data set of pre-given measures, you often have to move on to qualitative investigation to explore further differences.
•That word – differences – QCA is founded on distinctions.
B. Epistemology
CASTELLANI: Your research agenda is grounded in what you refer to as a complex/critical realist approach. What is complex/critical realism?
BYRNE: The term comes from David L. Harvey and his collaborator Reed. It involves a synthesis of the critical realist perspective of Roy Bhaskar (but the early Bhaskar) and complexity theory. So it says most of the world is made up of complex systems – although see Paul Cilliers’ important work on how such systems are both real and the products of scientific construction – the complexity part. Then it endorses critical realism’s deep ontology of the real as generative mechanisms, the actual as the contingently and contextually expressed outcome of those mechanisms (I wish we had another word than mechanisms), and the empirical as what we as scientists make from those mechanisms in action in the actual. Note ‘make’. This is a constructionist position but one which says that the real also has a say.
CASTELLANI: Why should researchers consider your epistemological approach important enough to adopt?
BYRNE: I would say it is David L. Harvey’s and I adopted his approach because it enabled me to make sense of social causality and allows agency, including conscious and informed agency, into play with the potential for knowledge to actually be applied in a meaningful and useful fashion. Does that for me and I recommend the treatment to others for the same reason.
C. The Complexity of Place, Space and Health.
CASTELLANI: Our Q&A is situated within the larger theme that I have been blogging on for the past couple weeks: how to improve the community health science literature by adopting a complexity science perspective.
You may disagree, but a major theme that I see in your work over the last decade is your rigorous and nuanced attempt to develop a methodological-epistemological framework researchers can use to develop better models of the complexities surrounding place, space and health. This includes the complexities of social exclusion, urban planning, spatial inequality, and the challenges surrounding the relationship between individuals and the communities in which they live. For example in your chapter, Complex and Contingent Causation—the Implications of Complex Realism for Quantitative Modeling (found in Carter and New’s Making Realism Work, 2004) you address one of the biggest challenges facing the community health science literature today: the inability of researchers to create a satisfactory way to address the relationship between micro-level health outcomes and aggregate level phenomena such as the neighborhood effect.
You state: “Multi-level modeling has been proposed as a way of resolving the difficulties of cross-level relationships among individually expressed health and social conditions. This interesting approach does represent a genuine effort to confront problems which are central to the relationship between the collective and the individual. However, this chapter will argue that the approach remains unsatisfactory, precisely because it ‘disembodies’ both aspects of the complex individual and aspects of the complex social systems through which individuals lead their lives” (p. 51).
CASTELLANI: What do you mean that researchers tend to “disembody” complexity?
BYRNE: Disembody is a specific kind of abstraction. Abstraction is necessary – I think Katherine Hayles is great on this in her How we became post-human but we also have to be very careful. I was using Chris Allen’s arguments – which I found interesting, well put and provocative – to frame my own argument. Chris was saying: don’t lets regard agentic human beings as physiological dopes ‘determined’ by the external and their own attributes in interaction. He pointed out that there is real variation in outcome – the reality of any probabilistic form of explanation of cause e.g. in a randomized controlled trial (RCT). I agree up to a point but think that we can move towards a better account if we think really hard about complex and contingent causation. I have written elsewhere about how I don’t have TB despite being exposed to cases in adolescence and having a very strong Heaf test reaction at that point. Too well fed, too well housed, and with parents who didn’t get the disease or die of it whilst they both had siblings who did and did so bred for resistance. But if I get AIDS or am starved in conditions like a WWII Japanese prisoner of war camp, then I will get TB. That is complexity expressed in my individual body and I want a modelling process which moves towards allowing for that.
CASTELLANI: As a solution, how do you think the methodological-epistemological framework you have developed helps researchers to preserve the complexity of their models?
BYRNE: First by making us think about it. Second, by looking for and using methods, quantitative and qualitative, which respect the complexity of the real as opposed to artificial (I owe this distinction to Elias Khalil) world. So always be skeptical about simplicity. It might be there but mostly it isn’t.
CASTELLANI: Related, you and others (such as Paul Cilliers and Charles Ragin) have criticized complexity scientists for making the same reductionistic mistake as multi-level researchers: complexity scientists still seem to reduce to an unnecessary level the complexity of systems. Why do you think complexity scientists fall prey to this reductionistic tendency? How do they get out of this trap?
BYRNE: See Morin’s excellent essay on this very point at: http://cogprints.org/5217/1/Morin.pdf
My take is that the kind of complexity which says we can always generate complexity from simple interactions following for example rules – note always, I have no quarrel with sometimes here – ends up with specifications which ‘look like’ the laws of Newtonian science although of course they are nothing of the kind. However, they are reductionist – you can do this if not in a white coat then in a techy sort of way which makes you look like a proper scientistic scientist. There is a real battle to be fought here although interestingly there are physicists – Peter Allen’s excellent work for example – and lots of eco centred biologists – as well as medics – who are beginning to recognize that they cannot deal with problems of explanation and action without dealing in what Morin calls general complexity.
D. The Future of Sociology
CASTELLANI: Without creating a straw-person, I think it is fair to say that sociologists, particularly those in the main-street of the profession have been slow to embrace or involve themselves in a critical dialogue with complexity science. What is your best argument for why sociologists should involve themselves in the new science(s) of complexity?
BYRNE: Because it allows us to deal with systems without falling into the Parsonian trap (although note that Parsons did have a sense of the complex from time to time). It also is a way towards agentic intervention. My first degree was in Sociology and Social Administration – we would usually but not necessarily correctly talk about Social Policy instead of administration today – and my Master’s was in that field rather than mainstream Sociology. I am an applied social scientist and complexity pushes towards action. It also is a way of getting past what frankly I see as the dead hand of much of contemporary sociological theory. Post modernism is a dead end but I am thinking here as much of Giddens and even of Bourdieu (and I have a deal of respect for Bourdieu). We need to engage empirically and get beyond the absolutely necessary preliminary task of empirical description into a serious and non-positivist engagement with social causality. That is what complexity lets me do.
---------------------
CASTELLANI: Dr. Byrne, thank you so much for your time. For more information on Dr. Byrne's work, visit his website by clicking here.
Dr. Byrne is Professor in the School of Applied Social Sciences at Durham University, England, where he is also Director of Postgraduate Studies. Dr. Byrne is the author of several books and a long list of articles, including his 1998 book, Complexity Theory and the Social Sciences--the first book to critically review and explore the application of complexity science to sociological inquiry. His most recent book, edited with noted sociologist and methodologist, Charles Ragin is The SAGE Handbook of Case-Based Methods
Dr. Byrne is an expert in methods, urban planning, community health, social policy, social exclusion and complexity science.
-----------------------------------------------
INTERVIEW WITH DR. BYRNE
CASTELLANI: Dr. Byrne, thanks so much for taking the time to do this interview. Your research agenda is rather vast in its scope—ranging from the philosophy of complexity science to method to urban planning to health care inequality. If you do not mind, I am going to narrow in on method first, given its wider implications for those reading this blog—most of whom are students and researchers new to the field of complexity science and its practice within sociology.
A. Case-Based Research
CASTELLANI: For the last several years, you have been a major advocate of a case-based approach to research. You specifically endorse what you and Charles Ragin refer to as Qualitative Comparative Analysis (QCA). First, how do you define QCA?
BYRNE: It is a method which is ‘set theoretic’ i.e. it understands causal relations in the social world in terms of relationships in combination – sets, rather than the unique contribution of single variables. It is based on systematic comparison – essentially an extension of John Stuart Mills’ method of differences. It requires careful qualitative engagement with specific cases as the foundation of that comparison.
CASTELLANI: Of the three major types of QCA (crisp-set, multi-value and fuzzy set), which do you find most useful and why? Or, do you approach the distinctions within QCA a different way?
BYRNE: I generally work with crisp set techniques and actually almost never go beyond the truth table. So I use QCA as a kind of mix of exploratory / explanatory – often focusing on ‘contradictory configurations’ in which the assemblage of elements in the line of the truth table – the configuration – generates different outcomes. That makes me look at those cases for what else is different about them. I see multi-value QCA as an extension of crisp set but it is much more complex to use. I frequently use Cluster Analysis as a data reduction technique and binarize membership of a cluster. Fuzzy set is very interesting and I have thought about how we might use distance from a cluster centre as a fuzzying principle but I have never managed to bring it off.
CASTELLANI: For researchers and graduate students new to case-based research, what is your best argument (apologetic) for including QCA in their toolbox of techniques?
BYRNE: For me the crucial things about QCA are the following:
•It allows for complex causation – lots of things acting together to generate an outcome. Conventional statistical modelling can do this in a limited sort of way through interaction.
•It allows for multiple causation – different combinations – in QCA terms configurations – can generate the same outcome. More than one way to skin a cat.
•It really makes us think about ‘what is a case’ – what Charles Ragin calls the processes of casing – just as important to specify the character and boundaries of cases as to be careful about operationalizing in measurement of what I prefer to call attributes or variate traces rather than variables.
•It really does have qualitative phases – conventionally at the beginning because the researcher really does have to engage closely with cases using qualitative techniques in order to establish attribute values. If you start, as I have often, with a data set of pre-given measures, you often have to move on to qualitative investigation to explore further differences.
•That word – differences – QCA is founded on distinctions.
B. Epistemology
CASTELLANI: Your research agenda is grounded in what you refer to as a complex/critical realist approach. What is complex/critical realism?
BYRNE: The term comes from David L. Harvey and his collaborator Reed. It involves a synthesis of the critical realist perspective of Roy Bhaskar (but the early Bhaskar) and complexity theory. So it says most of the world is made up of complex systems – although see Paul Cilliers’ important work on how such systems are both real and the products of scientific construction – the complexity part. Then it endorses critical realism’s deep ontology of the real as generative mechanisms, the actual as the contingently and contextually expressed outcome of those mechanisms (I wish we had another word than mechanisms), and the empirical as what we as scientists make from those mechanisms in action in the actual. Note ‘make’. This is a constructionist position but one which says that the real also has a say.
CASTELLANI: Why should researchers consider your epistemological approach important enough to adopt?
BYRNE: I would say it is David L. Harvey’s and I adopted his approach because it enabled me to make sense of social causality and allows agency, including conscious and informed agency, into play with the potential for knowledge to actually be applied in a meaningful and useful fashion. Does that for me and I recommend the treatment to others for the same reason.
C. The Complexity of Place, Space and Health.
CASTELLANI: Our Q&A is situated within the larger theme that I have been blogging on for the past couple weeks: how to improve the community health science literature by adopting a complexity science perspective.
You may disagree, but a major theme that I see in your work over the last decade is your rigorous and nuanced attempt to develop a methodological-epistemological framework researchers can use to develop better models of the complexities surrounding place, space and health. This includes the complexities of social exclusion, urban planning, spatial inequality, and the challenges surrounding the relationship between individuals and the communities in which they live. For example in your chapter, Complex and Contingent Causation—the Implications of Complex Realism for Quantitative Modeling (found in Carter and New’s Making Realism Work, 2004) you address one of the biggest challenges facing the community health science literature today: the inability of researchers to create a satisfactory way to address the relationship between micro-level health outcomes and aggregate level phenomena such as the neighborhood effect.
You state: “Multi-level modeling has been proposed as a way of resolving the difficulties of cross-level relationships among individually expressed health and social conditions. This interesting approach does represent a genuine effort to confront problems which are central to the relationship between the collective and the individual. However, this chapter will argue that the approach remains unsatisfactory, precisely because it ‘disembodies’ both aspects of the complex individual and aspects of the complex social systems through which individuals lead their lives” (p. 51).
CASTELLANI: What do you mean that researchers tend to “disembody” complexity?
BYRNE: Disembody is a specific kind of abstraction. Abstraction is necessary – I think Katherine Hayles is great on this in her How we became post-human but we also have to be very careful. I was using Chris Allen’s arguments – which I found interesting, well put and provocative – to frame my own argument. Chris was saying: don’t lets regard agentic human beings as physiological dopes ‘determined’ by the external and their own attributes in interaction. He pointed out that there is real variation in outcome – the reality of any probabilistic form of explanation of cause e.g. in a randomized controlled trial (RCT). I agree up to a point but think that we can move towards a better account if we think really hard about complex and contingent causation. I have written elsewhere about how I don’t have TB despite being exposed to cases in adolescence and having a very strong Heaf test reaction at that point. Too well fed, too well housed, and with parents who didn’t get the disease or die of it whilst they both had siblings who did and did so bred for resistance. But if I get AIDS or am starved in conditions like a WWII Japanese prisoner of war camp, then I will get TB. That is complexity expressed in my individual body and I want a modelling process which moves towards allowing for that.
CASTELLANI: As a solution, how do you think the methodological-epistemological framework you have developed helps researchers to preserve the complexity of their models?
BYRNE: First by making us think about it. Second, by looking for and using methods, quantitative and qualitative, which respect the complexity of the real as opposed to artificial (I owe this distinction to Elias Khalil) world. So always be skeptical about simplicity. It might be there but mostly it isn’t.
CASTELLANI: Related, you and others (such as Paul Cilliers and Charles Ragin) have criticized complexity scientists for making the same reductionistic mistake as multi-level researchers: complexity scientists still seem to reduce to an unnecessary level the complexity of systems. Why do you think complexity scientists fall prey to this reductionistic tendency? How do they get out of this trap?
BYRNE: See Morin’s excellent essay on this very point at: http://cogprints.org/5217/1/Morin.pdf
My take is that the kind of complexity which says we can always generate complexity from simple interactions following for example rules – note always, I have no quarrel with sometimes here – ends up with specifications which ‘look like’ the laws of Newtonian science although of course they are nothing of the kind. However, they are reductionist – you can do this if not in a white coat then in a techy sort of way which makes you look like a proper scientistic scientist. There is a real battle to be fought here although interestingly there are physicists – Peter Allen’s excellent work for example – and lots of eco centred biologists – as well as medics – who are beginning to recognize that they cannot deal with problems of explanation and action without dealing in what Morin calls general complexity.
D. The Future of Sociology
CASTELLANI: Without creating a straw-person, I think it is fair to say that sociologists, particularly those in the main-street of the profession have been slow to embrace or involve themselves in a critical dialogue with complexity science. What is your best argument for why sociologists should involve themselves in the new science(s) of complexity?
BYRNE: Because it allows us to deal with systems without falling into the Parsonian trap (although note that Parsons did have a sense of the complex from time to time). It also is a way towards agentic intervention. My first degree was in Sociology and Social Administration – we would usually but not necessarily correctly talk about Social Policy instead of administration today – and my Master’s was in that field rather than mainstream Sociology. I am an applied social scientist and complexity pushes towards action. It also is a way of getting past what frankly I see as the dead hand of much of contemporary sociological theory. Post modernism is a dead end but I am thinking here as much of Giddens and even of Bourdieu (and I have a deal of respect for Bourdieu). We need to engage empirically and get beyond the absolutely necessary preliminary task of empirical description into a serious and non-positivist engagement with social causality. That is what complexity lets me do.
---------------------
CASTELLANI: Dr. Byrne, thank you so much for your time. For more information on Dr. Byrne's work, visit his website by clicking here.
20/05/2009
Health & Place: An International Journal
While the community-as-complex-system model is relatively new, it already has a major journal outlet, called Health & Place: An International Journal.
Edited by Graham Moon, University of Southampton, School of Geography, Highfield, Southampton, the journal is dedicated to the study of all aspects of health and health care in which place or location matters.
As stated on its website, "Recent years have seen closer links evolving between medical geography, medical sociology, health policy, public health and epidemiology. The journal reflects these convergences, which emphasise differences in health and health care between places, the experience of health and care in specific places, the development of health care for places, and the methodologies and theories underpinning the study of these issues.
The journal brings together international contributors from geography, sociology, social policy and public health. It offers readers comparative perspectives on the difference that place makes to the incidence of ill-health, the structuring of health-related behaviour, the provision and use of health services, and the development of health policy.
At a time when health matters are the subject of ever-increasing attention, Health & Place provides accessible and readable papers summarizing developments and reporting the latest research findings."
It is important to note that the journal is a combination of both the community-as-context model and the community-as-complex-system model. So, it is important to identify the model being used in a particular paper. Overall, it is an excellent resource for the lastest developments in the field.
Edited by Graham Moon, University of Southampton, School of Geography, Highfield, Southampton, the journal is dedicated to the study of all aspects of health and health care in which place or location matters.
As stated on its website, "Recent years have seen closer links evolving between medical geography, medical sociology, health policy, public health and epidemiology. The journal reflects these convergences, which emphasise differences in health and health care between places, the experience of health and care in specific places, the development of health care for places, and the methodologies and theories underpinning the study of these issues.
The journal brings together international contributors from geography, sociology, social policy and public health. It offers readers comparative perspectives on the difference that place makes to the incidence of ill-health, the structuring of health-related behaviour, the provision and use of health services, and the development of health policy.
At a time when health matters are the subject of ever-increasing attention, Health & Place provides accessible and readable papers summarizing developments and reporting the latest research findings."
It is important to note that the journal is a combination of both the community-as-context model and the community-as-complex-system model. So, it is important to identify the model being used in a particular paper. Overall, it is an excellent resource for the lastest developments in the field.
19/05/2009
Placing Health by Tim Blackman
In yesterday's post, I discussed the three models currently used to do community health science. Of the three models, I am obviously a champion of the third--the community-as-complex-system model.
While I provided a basic overview of this model, I did not provide much in the way of references. I did, however, mention a book at the end.
The book is Placing Health by Tim Blackman. A link to the Google Books peek into the book is here.
Blackman's basic goal is to explain and demonstrate (through empirical inquiry) how complexity science improves our understanding of the role communities play in the health of people. Specifically, it explores how communities function as complex systems and the role these complex systems play in the lives of people, particularly in terms of spatial inequality.
Rather than review the book here, I will list several reviews for you to read. I will, however, make one point. Toward the end of the book, Blackman points to one of the explicit ways that a complex systems viewpoint changes how one approaches improving community health. While the community-as-context model is a major step forward, it is, nonetheless, a top-down model. This means that is treats the citizens of a community as objects of treatment. This leads to top-heavy, public health--the kind that does NOT involve people in their own health improvement. The community-as-complex-system model, however, is entirely different. Because it takes a bottom-up approach, it begins, by definition, with an interactive (relational) view of people and their communities, looking at how both effect the other. As such, it follows an action research protocol--people need to be involved in the improvement of their health care and their communities, which in turn, impacts of health of these people.
Okay, I will stop there. I think the book is fantastic and needs to be read by anyone serious about community or public health.
Here are some reviews to read
Review 1. International Journal of Integrated Care
2. Journal of Epidemiology and Community Health
While I provided a basic overview of this model, I did not provide much in the way of references. I did, however, mention a book at the end.
The book is Placing Health by Tim Blackman. A link to the Google Books peek into the book is here.
Blackman's basic goal is to explain and demonstrate (through empirical inquiry) how complexity science improves our understanding of the role communities play in the health of people. Specifically, it explores how communities function as complex systems and the role these complex systems play in the lives of people, particularly in terms of spatial inequality.
Rather than review the book here, I will list several reviews for you to read. I will, however, make one point. Toward the end of the book, Blackman points to one of the explicit ways that a complex systems viewpoint changes how one approaches improving community health. While the community-as-context model is a major step forward, it is, nonetheless, a top-down model. This means that is treats the citizens of a community as objects of treatment. This leads to top-heavy, public health--the kind that does NOT involve people in their own health improvement. The community-as-complex-system model, however, is entirely different. Because it takes a bottom-up approach, it begins, by definition, with an interactive (relational) view of people and their communities, looking at how both effect the other. As such, it follows an action research protocol--people need to be involved in the improvement of their health care and their communities, which in turn, impacts of health of these people.
Okay, I will stop there. I think the book is fantastic and needs to be read by anyone serious about community or public health.
Here are some reviews to read
Review 1. International Journal of Integrated Care
2. Journal of Epidemiology and Community Health
18/05/2009
Three Different Approaches to Community Health
At present, one can organize the community health science literature into three dominant approaches.
1. Social Pathways Model: The oldest and most widely practiced approach is the social pathways model. This model takes a nomothetic position, seeking to determine how a small set of social factors impacts the health of a community. In this model, community is also treated as a dependent (or grouping) variable.
2. Community as Context Model: This more recent approach emerged during the 1990s and has remained very hot! In this model, community context is treated as an independent variable, separate from the contribution of various other social factors--income, educational level, family health behaviors, etc. This approach to studying communities is a top-down model.
3. Community as a Complex System: The last model is the newest and least practiced. It views communities as complex systems; and takes a bottom-up approach to modeling.
The strength of the third approach is its ability to overcome the limitations of the other two models.
The other two models suffer from a reductionistic approach to community health--community is either an independent or dependent variable, with little research done to explore the "system-level" effects of a community; or, for that matter, the link within a community between micro-level (agent-based) and macro-level (emergent) behaviors. There is also no sense of environmental forces or the dynamics of a community over time--as a system--in the other two models.
Obviously, the limitations of the first two models are challenges that a complexity science approach to communities can handle. It can handle these challenges because this third approach has a complex view of communities as systems--that is, it sees the link between the micro and macro; has the tools to study system-level, emergent behavior; and has the ability to frame how environmental forces and the larger systems within which communities are situated impacts their respective health. Its bottom-up approach also allows it to see communities as both independent and dependent variables (via the concept of feedback loop). And, its bottom-up approach allows it to see communities as both context and composite--in other words, it does not construct a false dichotomy between community and other social (individual-level) factors such as income, education, etc.
For a basic introduction to the community-as-complex-system model, see Tim Blackman's new book, Placing Health.
1. Social Pathways Model: The oldest and most widely practiced approach is the social pathways model. This model takes a nomothetic position, seeking to determine how a small set of social factors impacts the health of a community. In this model, community is also treated as a dependent (or grouping) variable.
2. Community as Context Model: This more recent approach emerged during the 1990s and has remained very hot! In this model, community context is treated as an independent variable, separate from the contribution of various other social factors--income, educational level, family health behaviors, etc. This approach to studying communities is a top-down model.
3. Community as a Complex System: The last model is the newest and least practiced. It views communities as complex systems; and takes a bottom-up approach to modeling.
The strength of the third approach is its ability to overcome the limitations of the other two models.
The other two models suffer from a reductionistic approach to community health--community is either an independent or dependent variable, with little research done to explore the "system-level" effects of a community; or, for that matter, the link within a community between micro-level (agent-based) and macro-level (emergent) behaviors. There is also no sense of environmental forces or the dynamics of a community over time--as a system--in the other two models.
Obviously, the limitations of the first two models are challenges that a complexity science approach to communities can handle. It can handle these challenges because this third approach has a complex view of communities as systems--that is, it sees the link between the micro and macro; has the tools to study system-level, emergent behavior; and has the ability to frame how environmental forces and the larger systems within which communities are situated impacts their respective health. Its bottom-up approach also allows it to see communities as both independent and dependent variables (via the concept of feedback loop). And, its bottom-up approach allows it to see communities as both context and composite--in other words, it does not construct a false dichotomy between community and other social (individual-level) factors such as income, education, etc.
For a basic introduction to the community-as-complex-system model, see Tim Blackman's new book, Placing Health.
14/05/2009
Complexity Science & Community Health--Univ of Michigan Style
As I have discussed in previous posts, my two main substative foci are medical professionalism and community health--both from a complexity science perspective.
Over the next week I will be posting on the topic of community health, from a complexity science perspective, highlighting key ideas, scholars, periodicals, books, videos, and institutes.
I will begin with one of the leading institutes involved in the study of community health from a complexity science perspective, the Center for Social Epidemiology and Population Health (CSEPH), at the University of Michigan.
Working in conjunction with the world-renowned Center for the Study of Complex Systems at the Univ of Michigan, the CSEPH sits at the forefront of a complexity science approach to community health.
In 2007, the CSEPH held a symposium on complexity and community health. Here is an excellent video introducing the CSEPH symposium, housed at the National Institutes of Health, titled Symposium on a Complex Systems Approach to Population Health.
Over the next week I will be posting on the topic of community health, from a complexity science perspective, highlighting key ideas, scholars, periodicals, books, videos, and institutes.
I will begin with one of the leading institutes involved in the study of community health from a complexity science perspective, the Center for Social Epidemiology and Population Health (CSEPH), at the University of Michigan.
Working in conjunction with the world-renowned Center for the Study of Complex Systems at the Univ of Michigan, the CSEPH sits at the forefront of a complexity science approach to community health.
In 2007, the CSEPH held a symposium on complexity and community health. Here is an excellent video introducing the CSEPH symposium, housed at the National Institutes of Health, titled Symposium on a Complex Systems Approach to Population Health.
08/05/2009
SACS Toolkit: E-Social Science from a Systems Perspective
I am presenting the following paper at the upcoming sociocybernetics conference this June in Urbino Italy.
This year's conference is all about e-science and web science. The title is 'MODERNITY 2.0': EMERGING SOCIAL MEDIA TECHNOLOGIES AND THEIR IMPACTS.
For those following this blog, you know that I include e-science and web science on my map of complexity, situating them as the two newest areas of complexity science research.
My paper explores how the new toolkit my colleague, Fred Hafferty, and I have developed for modeling complex social systems (called the SACS Toolkit) can be used to manage and analyze web-based data. In fact, one of the reasons we created our toolkit was to find ways to address the growing complexity of digital data.
Here is the abstract of our paper. I will post the paper later in June, 2009.
----------------------------------------
The SACS Toolkit provides researchers a new informatics-based ontology and methodology for managing and analyzing the massive, multi-dimensional databases regularly encountered on the web today. The SACS Toolkit does this by functioning as an intermediary between the web and researcher. Its intermediary function provides researchers several advantages. In terms of ontology, the SACS Toolkit: 1) provides a user-based filing system (social complexity theory) that help researchers organize and link multidimensional databases in a theoretically meaningful manner; 2) the filing system is also designed to form a complex system—to match the complexity of most web-based data. In terms of method, the SACS Toolkit: 1) provides a novel algorithm (assemblage) researchers can use to model complex systems with web data; 2) this algorithm works with any type of data; and 3) can be used with most methodological techniques (e.g., field research, statistics, etc), including the latest advances in agent-based modeling, network analysis, e-science and web science. In the current paper, we demonstrate the utility of the SACS Toolkit by applying it to a web-based community health science database we are currently studying. We begin with a review of the SACS Toolkit. Next, we explore the ontological and methodological challenges our database presented us—focusing on how the SACS Toolkit solved them. Fourth, we examine the model of community health we built, showing how the SACS Toolkit allowed us to make important advances in the current health sciences literature. We end inductively, suggesting how others may likewise use the SACS Toolkit.
This year's conference is all about e-science and web science. The title is 'MODERNITY 2.0': EMERGING SOCIAL MEDIA TECHNOLOGIES AND THEIR IMPACTS.
For those following this blog, you know that I include e-science and web science on my map of complexity, situating them as the two newest areas of complexity science research.
My paper explores how the new toolkit my colleague, Fred Hafferty, and I have developed for modeling complex social systems (called the SACS Toolkit) can be used to manage and analyze web-based data. In fact, one of the reasons we created our toolkit was to find ways to address the growing complexity of digital data.
Here is the abstract of our paper. I will post the paper later in June, 2009.
----------------------------------------
The SACS Toolkit provides researchers a new informatics-based ontology and methodology for managing and analyzing the massive, multi-dimensional databases regularly encountered on the web today. The SACS Toolkit does this by functioning as an intermediary between the web and researcher. Its intermediary function provides researchers several advantages. In terms of ontology, the SACS Toolkit: 1) provides a user-based filing system (social complexity theory) that help researchers organize and link multidimensional databases in a theoretically meaningful manner; 2) the filing system is also designed to form a complex system—to match the complexity of most web-based data. In terms of method, the SACS Toolkit: 1) provides a novel algorithm (assemblage) researchers can use to model complex systems with web data; 2) this algorithm works with any type of data; and 3) can be used with most methodological techniques (e.g., field research, statistics, etc), including the latest advances in agent-based modeling, network analysis, e-science and web science. In the current paper, we demonstrate the utility of the SACS Toolkit by applying it to a web-based community health science database we are currently studying. We begin with a review of the SACS Toolkit. Next, we explore the ontological and methodological challenges our database presented us—focusing on how the SACS Toolkit solved them. Fourth, we examine the model of community health we built, showing how the SACS Toolkit allowed us to make important advances in the current health sciences literature. We end inductively, suggesting how others may likewise use the SACS Toolkit.
26/04/2009
Complexity Art
The above picture is another example of what I call complexity art. The technique is called assemblage (or, alternatively, assembled cubism). For more info, see my post from 25 April 2009.
The goal of assemblage is not to redefine the role of space or time in a picture. Instead, the goal is to pictorally represent complex systems--be these systems a single individual, two people in relationship, groups, humans and nature, humans and machines, etc.
Given this goal, the completed picture, while highly representational, is primarily symbolic. It is an iconic representations of a complex system--by icon I mean here a visual (semiotic) sign that stands in place of or acts as a simulacra of something else.
The above picture is an inconic representation of a mother and daughter. One can see the structural similarities between mother and daughter in terms of their eyes, neck muscles, etc. And yet, one loses a clear sense of who's part is which. Instead, the parts blend together to create a face that is neither the mother's or daughter's. This face is an emergent system entirely dependent upon the nuanced parts of which it is made. The result is a multi-singularity: multiplicity and difference within union and integration.
25/04/2009
ASSEMBLAGE: Complexity Science Art
Sociology is not the only trajectory along which I have pursued the study of complexity. In fact, long before I figured out how to apply complexity science to sociology I was working on it in my art. As my geek t-shirt stuff suggests, art is part of my complexity agenda.
The above picture is an example of the type of complexity art I have been doing, which I call assemblage--partially in homage to the cubists and, more specifically, Robert Rauschenberg, the famous American painter.
In terms of technique, assemblage extends the work of Picasso and Braque by going beyond analytic and synthetic cubism into a new area, assembled cubism. Following Raushenberg, assembled cubism takes a complex systems approach to paintings, attempting to examine the inter-dependence and inter-connectedness of humans and the world in which they live. It also treats this inter-dependence and inter-connectedness as a system, where the whole is more than the sum of its parts. How, for example, can one paint two people, showing the entanglement of their relationship, to arrive at a whole; and yet, at the same time, allow the individuals to shine through?
The spirit of assembled cubism is found in the following quote from William Johnston: "When people meet at the level of personal love achieved through radical non-attachment, they do not merge, nor are they absorbed in one another.... There is at once a total unity and a total alterity" (Silent Music, 1976, p. 147, Perennial Library).
Complexity Art--Geek T-shirts
I have been working on a few new geek t-shirts shirts, which people have seemed to like. The whole idea behind these shirts is to promote complexity science specifically and science and math more generally, particularly amongst young people and kids.
CHECK EM OUT.
SHIRTS RANGE FROM 11.99 AND UP. THERE ARE ORGANIC SHIRTS AS WELL, AMERICAN APPAREL, ETC.
TO BUT THESE SHIRTS AND OTHERS CLICK HERE
CHECK EM OUT.
SHIRTS RANGE FROM 11.99 AND UP. THERE ARE ORGANIC SHIRTS AS WELL, AMERICAN APPAREL, ETC.
TO BUT THESE SHIRTS AND OTHERS CLICK HERE
09/04/2009
Qualitative Comparative Analysis
For those interested in learning more about qualitative comparative analysis (QCA), here is the link to Ragin's overview, which provides lots of information.
CLICK HERE
As I stated in my post 7 April 2009 post, QCA provides the best option for integrating qualitative and quantitative method into a new toolkit for the study of complex social systems. Check it out.
CLICK HERE
As I stated in my post 7 April 2009 post, QCA provides the best option for integrating qualitative and quantitative method into a new toolkit for the study of complex social systems. Check it out.
08/04/2009
The SAGE Handbook of Case-Based Methods
For the last several posts, I have been discussing the need for complexity science to truly overcome the qualitative/quantitative divide by doing more work to develop qualitative method. The next question, then, is how?
Of the various options available to complexity scientists, I think the best is case-based method. Actually, the better term is cross-case analysis. Cross-case analysis is an inductive approach to scientific inquiry that begins with a set of cases in order to explore what makes them similar to and yet different from one another. Cross-case analysis is very iterative and data-driven: the researcher develops ideas about the non-obvious patterns of relationship amongst a database by exploring its cases.
Perhaps the most well-known cross-case method is grounded theory, which was developed by Glaser and Strauss in the middle 1960s. While their method is referred to in the popular literature as grounded theory, they actually called it (at least initially) the constant comparative method, which they argued could be used to generate grounded theory. In other words, their famous book title, The Discovery of Grounded Theory was meant to imply that, through the constant comparative method one could generate grounded theory. Instead, the name Grounded Theory stuck.
In the sticking of this name, however, a major feat in the history of social science method was lost. In a paper I published in 2003, my colleagues and I made it clear that Glaser and Strauss never meant their method to be limited to narrative data. The constant comparative method could be equally applied to numerical or narrativel data. Grounded theory was not only a breakthrough in the popularization of cross-case analysis, it was a major breakthrough in the blurring of qualitative and quantitative method.
Here is a blurb from their book:
"Our position in this book is as follows: there is no fundamental clash between the purposes and capacities of qualitative and quantitative methods or data. What clash there is concerns the primacy of emphasis on verification or generation of theory—to which heated discussions on qualitative versus quantitative data have been linked historically. We believe that each form of data is useful for both verification and generation of theory, whatever the primacy of emphasis. Primacy depends only on the circumstances of research, on the interests and training of the researcher, and on the kinds of material he needs for his theory (1967:17–18)."
Grounded theory is not the only cross-case method. Others do exist. The problem, however, is these methods have not made it into the mainstream of sociological or social scientific inquiry.
What is fascinating to me is that, while case-based method remained on the margins of sociological inquiry throughout the 1980s and 1990s, over on the other side of the scientific fence, in the natural and computational sciences, cross-case method was being rediscovered. This time, however, it emerged in the form of distributed artificial intelligence, cluster analysis, data mining, decision-tree analysis, artificial neural networking, the self-organizing map algorithm, machine intelligence, genetic algorithms, fuzzy-set theory, fuzzy-set logic, and the host of robots and algorithms running our washing machines, cars, industrial machinery, traffic lights, the internet and, the soon to come, Web 2.0.
And still sociologists sit idle, believing case-based method is something wishy washy that qualitative type people do. Just like sociologists and many social scientists have sat idle and watched complexity science emerge.
We are out of the loop--big time! Trust me, I am not being dramatic. If you approached the average sociology professor or graduate student and asked them if they could implement any of the above methods I just listed from the natural and computational sciences, and could they do so while integrating these methods with qualitative methods to conduct qualitative, cross-case analysis of large, complex databases, they would probably say no.
Hence the need for David Byrne and Charles Ragin's forthcoming book, The SAGE Handbook of Case-Based Methods. Actually, the sub-title of the book should be qualitative, comparative analysis (QCA), because that is the method they have been advocating for several years.
It is great to see this book published. It is also great that it is a handbook, because that means other scholars are working with these ideas; and the fact that SAGE has published it means that QCA has, in some small way, gained the authority it deserves.
A quick review of the chapters in the book demonstrates the broad utility of cross-case analysis and, more specifically, QCA (click here to see the complete index). There are chapters integrating cluster analysis with case-based method, as well as chapters applying QCA to the analysis of large, complex, digital databases.
The book also goes a long way to integrating cross-case analysis with complexity science. Byrne and Ragin are major social science scholars in complexity science. In my book on Sociology and Complexity Science (SACS), for example, I identify them as two of the leading scholars in SACS--see my map of SACS. For example, Byrne wrote a very important book in 1998 titled, Complexity Theory and the Social Sciences. Ragin's related book is Fuzz-Set Social Science (2000).
For those interested in developing a method for studying complex social systems, Byrne and Ragin's book provides the necessary foundation. In the name of QCA, they bring together the best of qualitative and quantitative method in order to overcome both.
Of the various options available to complexity scientists, I think the best is case-based method. Actually, the better term is cross-case analysis. Cross-case analysis is an inductive approach to scientific inquiry that begins with a set of cases in order to explore what makes them similar to and yet different from one another. Cross-case analysis is very iterative and data-driven: the researcher develops ideas about the non-obvious patterns of relationship amongst a database by exploring its cases.
Perhaps the most well-known cross-case method is grounded theory, which was developed by Glaser and Strauss in the middle 1960s. While their method is referred to in the popular literature as grounded theory, they actually called it (at least initially) the constant comparative method, which they argued could be used to generate grounded theory. In other words, their famous book title, The Discovery of Grounded Theory was meant to imply that, through the constant comparative method one could generate grounded theory. Instead, the name Grounded Theory stuck.
In the sticking of this name, however, a major feat in the history of social science method was lost. In a paper I published in 2003, my colleagues and I made it clear that Glaser and Strauss never meant their method to be limited to narrative data. The constant comparative method could be equally applied to numerical or narrativel data. Grounded theory was not only a breakthrough in the popularization of cross-case analysis, it was a major breakthrough in the blurring of qualitative and quantitative method.
Here is a blurb from their book:
"Our position in this book is as follows: there is no fundamental clash between the purposes and capacities of qualitative and quantitative methods or data. What clash there is concerns the primacy of emphasis on verification or generation of theory—to which heated discussions on qualitative versus quantitative data have been linked historically. We believe that each form of data is useful for both verification and generation of theory, whatever the primacy of emphasis. Primacy depends only on the circumstances of research, on the interests and training of the researcher, and on the kinds of material he needs for his theory (1967:17–18)."
Grounded theory is not the only cross-case method. Others do exist. The problem, however, is these methods have not made it into the mainstream of sociological or social scientific inquiry.
What is fascinating to me is that, while case-based method remained on the margins of sociological inquiry throughout the 1980s and 1990s, over on the other side of the scientific fence, in the natural and computational sciences, cross-case method was being rediscovered. This time, however, it emerged in the form of distributed artificial intelligence, cluster analysis, data mining, decision-tree analysis, artificial neural networking, the self-organizing map algorithm, machine intelligence, genetic algorithms, fuzzy-set theory, fuzzy-set logic, and the host of robots and algorithms running our washing machines, cars, industrial machinery, traffic lights, the internet and, the soon to come, Web 2.0.
And still sociologists sit idle, believing case-based method is something wishy washy that qualitative type people do. Just like sociologists and many social scientists have sat idle and watched complexity science emerge.
We are out of the loop--big time! Trust me, I am not being dramatic. If you approached the average sociology professor or graduate student and asked them if they could implement any of the above methods I just listed from the natural and computational sciences, and could they do so while integrating these methods with qualitative methods to conduct qualitative, cross-case analysis of large, complex databases, they would probably say no.
Hence the need for David Byrne and Charles Ragin's forthcoming book, The SAGE Handbook of Case-Based Methods. Actually, the sub-title of the book should be qualitative, comparative analysis (QCA), because that is the method they have been advocating for several years.
It is great to see this book published. It is also great that it is a handbook, because that means other scholars are working with these ideas; and the fact that SAGE has published it means that QCA has, in some small way, gained the authority it deserves.
A quick review of the chapters in the book demonstrates the broad utility of cross-case analysis and, more specifically, QCA (click here to see the complete index). There are chapters integrating cluster analysis with case-based method, as well as chapters applying QCA to the analysis of large, complex, digital databases.
The book also goes a long way to integrating cross-case analysis with complexity science. Byrne and Ragin are major social science scholars in complexity science. In my book on Sociology and Complexity Science (SACS), for example, I identify them as two of the leading scholars in SACS--see my map of SACS. For example, Byrne wrote a very important book in 1998 titled, Complexity Theory and the Social Sciences. Ragin's related book is Fuzz-Set Social Science (2000).
For those interested in developing a method for studying complex social systems, Byrne and Ragin's book provides the necessary foundation. In the name of QCA, they bring together the best of qualitative and quantitative method in order to overcome both.
01/04/2009
Grounded Neural Networking
The above publication is the type of work I am referring to as an example of developing qualitative method for studying complex systems. It is an article I wrote in 2003 integrating grounded theory method (a hallmark in qualitative methodology) with the artificial intelligence technique known as the Kohonen Self-Organizing Map. The result is a qualitative method for analyzing large, complex databases that draws upon the strength of traditional qualitative method and the latest advances in numerical analysis and, more specifically, data mining.
Santa Fe and qualitative numerical analysis
This post builds on yesterday's Qualitative/Narrative Complexity Science.
Part of my argument in the above post was that, in terms of qualitative method, the major advance complexity science makes is the qualitative study of numerical data. To demonstrate this point, click on the following link to the Santa Fe Institute (the leading world institute for the study of complexity) and, in the search box, type in "qualitative method." You will get roughly 700 hits. Almost all of them contain the terms qualitative and numerical.
You will find, however, almost no mention of qualitative method, as it is understood in the social science sense of the term. This is not to say there is no such work being done. But, it by no means has a dominant voice.
Part of my argument in the above post was that, in terms of qualitative method, the major advance complexity science makes is the qualitative study of numerical data. To demonstrate this point, click on the following link to the Santa Fe Institute (the leading world institute for the study of complexity) and, in the search box, type in "qualitative method." You will get roughly 700 hits. Almost all of them contain the terms qualitative and numerical.
You will find, however, almost no mention of qualitative method, as it is understood in the social science sense of the term. This is not to say there is no such work being done. But, it by no means has a dominant voice.
Qualitative/Narrative Complexity Science
For all of its advances (and they are many) complexity science has yet to bridge fully the rift between qualitative and quantitative method.
Before I explain myself, however, some quick definitions are in order. First, by qualitative method, I mean the non-numerical analysis of narrative and verbal data, as typically studied in historical inquiry, ethnography, qualitative interviews, and grounded theory. By quantitative method, I mean the study of numerical data, primarily through the application of statistics and top-down equation-based modeling.)
To its credit, complexity science has significantly progressed the qualitative analysis of numerical data. By "qualitative analysis" I mean the study of the complex, emergent, relational, dynamic, evolving, idiographic dimensions of numerical data. In fact, one could claim that complexity science method is really a major advance in the qualitative study of complex numerical data.
What complexity science has not advanced, however, is the non-numerical study complexity. To date, only a handful of articles have applied qualitative method to the study of complexity. And even fewer articles have examined how to advance the usage of qualitative method for studying complex systems.
The earliest examples I know of that apply qualitative method to the study of complexity were written by Crabtree and colleagues (most of whom are in medicine, nursing or health finance) and their study of medical practices:
1. Crabtree, B. F. (1997). Individual attitudes are no match for complex systems. Journal of Family Practice, 44(5), 447-448.
2. Crabtree, B. F. (2003). Primary care practices are full of surprises! Health Care Management Review, 28(3), 279-283.
3. Crabtree, B. F., Miller,W. L., Aita,V. A., Flocke, S. A.,&Stange, K. C. (1998). Primary care practice organization and preventive services delivery: Aqualitative analysis. Journal of Family Medicine, 46(5), 403-409.
4. Crabtree, B. F., Miller,W. L.,&Stange, K. C. (2001). Understanding practice from the ground up. Journal of Family Practice, 50(10), 881-887.
The earliest (and most widely popular) example of the development of qualitative method for the study of complex systems is Charles Ragin's Fuzzy Set Social Science (2000). Ragin also has a new book with David Byrne (a prominent British sociologist and leading scholar in the social science application of complexity science--I will blog more about this book later). The title of the book is The SAGE Handbook of Case-Based Methods (2009).
Despite being a small literature within complexity science, these scholars make some very compelling arguments for developing the qualitative (non-numerical) study of complexity. Perhaps the best argument is that a significant amount of data goes unexplored when qualitative method is not used.
What, for example, are the phenomenological dimensions of complex networks? What does it mean for people to be connected to one another by six or fewer links? What are the emotional dimensions of being part of a massive online social network? What role do power, conflict, hate, greed, anger, and love play in the complex global system? How does one study "confidence" in a system? What does a state of domination within a complex social system look like? Is altruism within a system more than a prisoner dilemna? I could go on and on and on.
Okay, just one more example: Think about the current global financial collapse in which most (if not all) the world is struggling? How do people make meaning of this experience? And, to consider second-order cybernetics and sociocybernetics, what consquence does the meaning people make have for the way in which our global economic system will evolve? And so on and so forth.
There is a lot qualitative method can offer complexity science. And, there is a lot complexity science can offer qualitative method. If complexity scientists turned their attention to this dimension of method, they could create some very incredible tools.
Before I explain myself, however, some quick definitions are in order. First, by qualitative method, I mean the non-numerical analysis of narrative and verbal data, as typically studied in historical inquiry, ethnography, qualitative interviews, and grounded theory. By quantitative method, I mean the study of numerical data, primarily through the application of statistics and top-down equation-based modeling.)
To its credit, complexity science has significantly progressed the qualitative analysis of numerical data. By "qualitative analysis" I mean the study of the complex, emergent, relational, dynamic, evolving, idiographic dimensions of numerical data. In fact, one could claim that complexity science method is really a major advance in the qualitative study of complex numerical data.
What complexity science has not advanced, however, is the non-numerical study complexity. To date, only a handful of articles have applied qualitative method to the study of complexity. And even fewer articles have examined how to advance the usage of qualitative method for studying complex systems.
The earliest examples I know of that apply qualitative method to the study of complexity were written by Crabtree and colleagues (most of whom are in medicine, nursing or health finance) and their study of medical practices:
1. Crabtree, B. F. (1997). Individual attitudes are no match for complex systems. Journal of Family Practice, 44(5), 447-448.
2. Crabtree, B. F. (2003). Primary care practices are full of surprises! Health Care Management Review, 28(3), 279-283.
3. Crabtree, B. F., Miller,W. L., Aita,V. A., Flocke, S. A.,&Stange, K. C. (1998). Primary care practice organization and preventive services delivery: Aqualitative analysis. Journal of Family Medicine, 46(5), 403-409.
4. Crabtree, B. F., Miller,W. L.,&Stange, K. C. (2001). Understanding practice from the ground up. Journal of Family Practice, 50(10), 881-887.
The earliest (and most widely popular) example of the development of qualitative method for the study of complex systems is Charles Ragin's Fuzzy Set Social Science (2000). Ragin also has a new book with David Byrne (a prominent British sociologist and leading scholar in the social science application of complexity science--I will blog more about this book later). The title of the book is The SAGE Handbook of Case-Based Methods (2009).
Despite being a small literature within complexity science, these scholars make some very compelling arguments for developing the qualitative (non-numerical) study of complexity. Perhaps the best argument is that a significant amount of data goes unexplored when qualitative method is not used.
What, for example, are the phenomenological dimensions of complex networks? What does it mean for people to be connected to one another by six or fewer links? What are the emotional dimensions of being part of a massive online social network? What role do power, conflict, hate, greed, anger, and love play in the complex global system? How does one study "confidence" in a system? What does a state of domination within a complex social system look like? Is altruism within a system more than a prisoner dilemna? I could go on and on and on.
Okay, just one more example: Think about the current global financial collapse in which most (if not all) the world is struggling? How do people make meaning of this experience? And, to consider second-order cybernetics and sociocybernetics, what consquence does the meaning people make have for the way in which our global economic system will evolve? And so on and so forth.
There is a lot qualitative method can offer complexity science. And, there is a lot complexity science can offer qualitative method. If complexity scientists turned their attention to this dimension of method, they could create some very incredible tools.
28/03/2009
Rockin' Mandelbrot Song
CLICK HERE TO SEE THIS ABSOLUTELY ROCKIN' MANDELBROT SONG
This is absolutely the coolest math song ever written. I gave a MATH DAY presentation about two weeks ago for 300 math geeks and they went crazy! It is fanstastic. Play it for yourself, friends, profs, and students--especially students in the social sciences and humanities.
The song is by Jonathan Coulton. The video was made by Pisut Wisessing in Film 324: Cornell Summer Animation Workshop, taught by animator Lynn Tomlinson.
This is absolutely the coolest math song ever written. I gave a MATH DAY presentation about two weeks ago for 300 math geeks and they went crazy! It is fanstastic. Play it for yourself, friends, profs, and students--especially students in the social sciences and humanities.
The song is by Jonathan Coulton. The video was made by Pisut Wisessing in Film 324: Cornell Summer Animation Workshop, taught by animator Lynn Tomlinson.
24/03/2009
Dungeons & Dragons--the Geek Stereotype
Okay, so most of us geeks fit the stereotype--instead of going on dates in highschool with humans, we were dating elves (male or female) or any other assorted group of medieval characters. D&D anyone?
We geeks eventually grew out of this phase. Actually, no we didn't--which brings me to the point of this post. One of my geek buddies (Michael Ball) has gone and done the worst thing a medieval geek can do. He wrote a book about it.
Mike's first fiction book is titled The Stone Men. It is an excellent short story with fantastic illustrations drawn by Christopher Bort. Check it out. And, bewaare, the stone men are coming...
19/03/2009
Map of Science
This is a great graphic overview of the increasing complexity and interdiscplinary nature of scientific inquiry. (As a side note, it also shows that the social sciences play a much larger role in science than typically acknowledged.) This graph was part of a recent article published in PLoS ONE on 11 March 2009.
Title: Clickstream Data Yields High-Resolution Maps of Science Johan Bollen1*, Herbert Van de Sompel1, Aric Hagberg2#, Luis Bettencourt2,3#, Ryan Chute1#, Marko A. Rodriguez2, Lyudmila Balakireva1
Great Blog: Social Media Today
When people take time to post a comment on this blog, I always take the time to read about their work. Recently, Tom Mandel posted a comment on "Is Foucault a Complexity Scientist?"
One of the blogs on his site is Social Media Today. This is a great site because it is part of the latest trends in internet life. But, it is also an observer of these trends. In short, it is part of the latest movement known as e-science.
As much as I enjoy the web, I find myself in that endless double-bind of participant and researcher. I am fascinated with the web, and yet my researcher side is always asking: What is going on here? Why am I participating in all this? What is this all about? But, no sooner do I ask such questions when I make another click and go: Wow, this is really cool and I've got to tell someone about this new technology or social network, or blog, etc, etc, etc, ugh!
It is because of my double-bind that I really like the blog, Social Media Today. It is a participant in and researcher of the latest trends in information and the forthcoming Web 2.0. Very good stuff for those complexity scientists and sociologists interested in life on the web and where things are going.
One of the blogs on his site is Social Media Today. This is a great site because it is part of the latest trends in internet life. But, it is also an observer of these trends. In short, it is part of the latest movement known as e-science.
As much as I enjoy the web, I find myself in that endless double-bind of participant and researcher. I am fascinated with the web, and yet my researcher side is always asking: What is going on here? Why am I participating in all this? What is this all about? But, no sooner do I ask such questions when I make another click and go: Wow, this is really cool and I've got to tell someone about this new technology or social network, or blog, etc, etc, etc, ugh!
It is because of my double-bind that I really like the blog, Social Media Today. It is a participant in and researcher of the latest trends in information and the forthcoming Web 2.0. Very good stuff for those complexity scientists and sociologists interested in life on the web and where things are going.
17/03/2009
Complexity 1001: One More Question
Another overwhelming aspect to sticking my toe into the complexity rapids is the number of new concepts and terms I have encountered (from agent-based modeling to neural networking to fractal geometry, etc.). So -- in addition to a key/core reference(s) -- what would be the half dozen or so key concepts or terms I would need to master so I can build a foundation in understanding complexity science? I'm not sure why, but I imagine myself standing on a beach with dozens upon dozens of interesting looking shells -- and while I can picture myself picking up any one of them here and another one or two of them there -- and eventually working my way across all of the shells -- I suspect there would be some shells that are "basic" and thus fundamental to understanding all shells -- and I would appreciate your suggestions here as well.
Dr. Castellani's Reponse
Dear Complexity Challenged, I would start with my complexity science map. Here is why.
The map is conceptual.
Like you, I struggled early on to get a grasp of this field. It is so amazingly interdisciplinary and scattered that it is hard for the beginner (and even expert) to have a true appreciation for what is going on with the field as a whole. After years of struggling to obtain some type of synthesis, I realized that some degree of closure could be obtained if I looked for similarities across the wealth of research taking place. I asked myself, what concepts (be they theoretical or methodological) do all complexity scientists use? And, how do these concepts relate? Also, could I identify the leading scholars associated with these concepts? And, could I highlight one particular sub-concept or area of study with which each of these scholars could be identified? The result was the map.
So, long story short, I would work on mastering the concepts on the map. That will give you an excellent working knowledge and vocabulary sufficient to communicate with any complexity scientist, regardless of their otherwise intractable or incomprehensible research--hee haw!
Dr. Castellani's Reponse
Dear Complexity Challenged, I would start with my complexity science map. Here is why.
The map is conceptual.
Like you, I struggled early on to get a grasp of this field. It is so amazingly interdisciplinary and scattered that it is hard for the beginner (and even expert) to have a true appreciation for what is going on with the field as a whole. After years of struggling to obtain some type of synthesis, I realized that some degree of closure could be obtained if I looked for similarities across the wealth of research taking place. I asked myself, what concepts (be they theoretical or methodological) do all complexity scientists use? And, how do these concepts relate? Also, could I identify the leading scholars associated with these concepts? And, could I highlight one particular sub-concept or area of study with which each of these scholars could be identified? The result was the map.
So, long story short, I would work on mastering the concepts on the map. That will give you an excellent working knowledge and vocabulary sufficient to communicate with any complexity scientist, regardless of their otherwise intractable or incomprehensible research--hee haw!
Complexity 1001: Getting Started
Professor Castellani: I want to begin a study of complexity -- as it applies to sociology and to issues of healthcare, but I am not sure where to begin. I've done a bit of googling, read through some of the materials on your site (loved your Complexity Science Map BTW), visited amazon.com -- and at the end of it all, feel a little overwhelmed.
DR. C'S RESPONSE
Dear Complexity Challenged, thanks for becoming part of this blog. I think the best way to "jump in and get your feet wet" is to take a historical macro-level approach and begin with two of the best known reviews of the field.
1. The first is Capra's The Web of Life. While written in 1997, this book still provides the best introductory review of complexity science and its historical roots--in particular, systems science, cybernetics and artificial intelligence and their links to the major themes in complexity science.
2. The second book is Waldrop's Complexity. This is another excellent book because it covers what Capra misses--the historical development of the Santa Fe Institute, the first and most important institute involved in the creation of complexity science and its most cutting-edge research. Almost every major figure in complexity science during the 1980s and 1990s had something to do with Santa Fe. Complexity is a bit journalistic and sensationalist (even gossipy) in style, but it really does give a good historical account of the early years of complexity science.
Most important about The Web of life and Complexity, they introduce you to all the major concepts of complexity science: emergence, self-organization, tipping-points, autopoiesis, self-organizing criticality, computational economics, cellular automata, agent-based modeling, fractals, chaos theory, networks, and so on.
These two books also introduce you to the major players during the 1980s and 1990s: from Holland and Kauffman to Prigogine and Bak to Matarana and Varela.
Once you have a basic sense of the field, you can move to a review of the methods of complexity science. Here is where things become more technical and less macro. You start to move down to the meso and even micro level, exploring specific topics like neural networks, agent-based modeling, the new science of networks, fractals, modeling complex systems, power laws, etc.
But, let's not get into the deep section of the pool too quick. I would get those two books and read them first.
I saw the link for Complexity 1001 and thought I might use it to jump start my learning.
Where would be a good place to start? What article (book chapter etc.) could you suggest -- something to get my feet wet. Perhaps from here I could raise a question or two for subsequent discussion, pick up another yet another suggesting resource or two, and go from there?
Thanks
DR. C'S RESPONSE
Dear Complexity Challenged, thanks for becoming part of this blog. I think the best way to "jump in and get your feet wet" is to take a historical macro-level approach and begin with two of the best known reviews of the field.
1. The first is Capra's The Web of Life. While written in 1997, this book still provides the best introductory review of complexity science and its historical roots--in particular, systems science, cybernetics and artificial intelligence and their links to the major themes in complexity science.
2. The second book is Waldrop's Complexity. This is another excellent book because it covers what Capra misses--the historical development of the Santa Fe Institute, the first and most important institute involved in the creation of complexity science and its most cutting-edge research. Almost every major figure in complexity science during the 1980s and 1990s had something to do with Santa Fe. Complexity is a bit journalistic and sensationalist (even gossipy) in style, but it really does give a good historical account of the early years of complexity science.
Most important about The Web of life and Complexity, they introduce you to all the major concepts of complexity science: emergence, self-organization, tipping-points, autopoiesis, self-organizing criticality, computational economics, cellular automata, agent-based modeling, fractals, chaos theory, networks, and so on.
These two books also introduce you to the major players during the 1980s and 1990s: from Holland and Kauffman to Prigogine and Bak to Matarana and Varela.
Once you have a basic sense of the field, you can move to a review of the methods of complexity science. Here is where things become more technical and less macro. You start to move down to the meso and even micro level, exploring specific topics like neural networks, agent-based modeling, the new science of networks, fractals, modeling complex systems, power laws, etc.
But, let's not get into the deep section of the pool too quick. I would get those two books and read them first.
Is Michel Foucault a Complexity Scientist?
In 1999 I wrote an article for Studies in Symbolic Interaction titled, Michel Foucault and Symbolic Interactionism: The Making of a New Theory of Interaction. The article sits at the heart of the theoretical framework (social complexity theory) that Hafferty and I outline in our new book, Sociology and Complexity Science: A New Field of Inquiry. Our theoretical framework, in turn, is part of the SACS Toolkit, which is our new method for modeling complex social systems.
While it may seem odd to some, my journey into complexity science is through the work of Michel Foucault, particularly his later theory of social practice. For me, Foucault’s work has always been about complex social systems and their impact on individuals.
From Madness and Civilization to The Archeology of Knowledge to Discipline and Punish, what are Foucault’s books about? Think about it. At least theoretically and methodologically speaking, they are about complex social systems! Foucault is trying to understand, in post-structural terms, how systems go from one state to another—from one set of self-organizing relations to another. How, for example, does the care of mental disorders, prisoners, deviants, or the self in the west go from a medieval apparatus of care to a modern apparatus of care?
Given this orientation, could we not call Foucault’s work the study of tipping points? Is not Foucault studying how complex social systems evolve over time to become something new, where they suddenly shift from one self-organizing form to another as a function of some type of punctuated equilibrium, some type of major phase shift? Is that not what Foucault’s whole discourse is about, along with the impact these shifting systems have on individuals and their care of self?
Also, could we not call his early work (up to Archeology of Knowledge) a top-down approach to system modeling? Something similar to Luhmann’s view of systems? I mean, is not Foucault, at least early on, trying to understand how systems change without having to call upon some micro-level theory of agency? Something Luhmann and Parsons and others tried to do? Is Foucault not also trying to understand the system within the confines of the system itself?
Then, beginning with Discipline and Punish and his interviews in Power and Knowledge, is not Foucault suddenly grounding his complex systems view in social practice? Suddenly shifting to a bottom-up perspective? Is that not what his methodological shift from archaeology to genealogy is all about? Top-down to bottom-up? A macro to a micro level shift in orientation?
Think about it? How would Foucault sound if he talked about dispositifs and apparatus as complex systems? What if he talked about apparatus which obey their own internal logic as emergent self-organizing systems? What if Foucault talked about his post-structuralism as a way of talking about history as changing dynamic systems that do more than just follow the dialectic? What if he talked about complex social systems that evolve over time along multiple trajectories? Suddenly his idea of systems containing their own resistance (his Nietzschian theory of power) makes more sense: we are talking about the multiplicity of systems, differentiation and feedback loops. And, suddenly his ideas would not seem so unique—at least by today’s knowledge of complexity science. Suddenly his ideas sound less structural and more systems-oriented.
Because this is a blog, I will not blag on too much. So, just consider one of Foucault’s key concepts, the dispositif. For Foucault, this concept forms the field of relations in which his work, up to the end, is situated within.
Foucault states: "What I’m trying to pick out with this term is, firstly, a thoroughly heterogeneous ensemble consisting of discourses, institutions, architectural forms, regulatory decisions, laws, administrative measures, scientific statements, philosophical propositions, moral and philanthropic propositions--in short, the said as much as the unsaid. Such are the
elements of the apparatus [dispositif]. The apparatus [the grid of intelligibility] itself is the system of relations that can be established between these elements. Secondly, what I am trying to identify in this apparatus is precisely the nature of the connections that can exist between these heterogeneous elements (Language, Counter-Memory, Practice, 1980, p. 194)."
As this quote shows, Foucault's work is always about mapping the grid of intelligibility (the dispositif) for some complex system in historical time-—be the system medicine, mental health, the social sciences, criminal justice, psychoanalysis, religion, or government. For Foucault, the dispositif is a system’s self-organizing order of things, its field of organizing practices. But this dispositif is not a totalizing system of relations as in the dialectic. Nor is it something the historian simply uncovers. It is both the interpretive framework that the historian imposes upon the discourses of the past (which is why Foucault often refers to his works as fictions, 1991, p. 33) and the relations that exist between the various discursive and nondiscursive heterogeneous elements making up the field of organizing practices—I mean, does that not sound like 2nd order cybernetics or sociocybernetics? The dispositif is a system of strategies that exist as practice, both on the part of the historian and on the part of the period in question. The dispositif isn’t found within some external structure or within the heads of particular controlling agents. It is within the practice of practice itself. It is fragmented, disjointed and broken, and yet inter-related, unified and organized. It is not a Parsionian system that exists as homeostasis, which then requires us to explain how change happens. It is a changing system where we question how order itself is possible.
Again, this is just a thought. But, it does open up the possibilities for some incredible connections between the last twenty years of sociological inquiry and the new science of complexity. To see a more thorough argument of my point of how Foucault can be used to build a theory of social complexity, see our new book, Sociology and Complexity Science.
While it may seem odd to some, my journey into complexity science is through the work of Michel Foucault, particularly his later theory of social practice. For me, Foucault’s work has always been about complex social systems and their impact on individuals.
From Madness and Civilization to The Archeology of Knowledge to Discipline and Punish, what are Foucault’s books about? Think about it. At least theoretically and methodologically speaking, they are about complex social systems! Foucault is trying to understand, in post-structural terms, how systems go from one state to another—from one set of self-organizing relations to another. How, for example, does the care of mental disorders, prisoners, deviants, or the self in the west go from a medieval apparatus of care to a modern apparatus of care?
Given this orientation, could we not call Foucault’s work the study of tipping points? Is not Foucault studying how complex social systems evolve over time to become something new, where they suddenly shift from one self-organizing form to another as a function of some type of punctuated equilibrium, some type of major phase shift? Is that not what Foucault’s whole discourse is about, along with the impact these shifting systems have on individuals and their care of self?
Also, could we not call his early work (up to Archeology of Knowledge) a top-down approach to system modeling? Something similar to Luhmann’s view of systems? I mean, is not Foucault, at least early on, trying to understand how systems change without having to call upon some micro-level theory of agency? Something Luhmann and Parsons and others tried to do? Is Foucault not also trying to understand the system within the confines of the system itself?
Then, beginning with Discipline and Punish and his interviews in Power and Knowledge, is not Foucault suddenly grounding his complex systems view in social practice? Suddenly shifting to a bottom-up perspective? Is that not what his methodological shift from archaeology to genealogy is all about? Top-down to bottom-up? A macro to a micro level shift in orientation?
Think about it? How would Foucault sound if he talked about dispositifs and apparatus as complex systems? What if he talked about apparatus which obey their own internal logic as emergent self-organizing systems? What if Foucault talked about his post-structuralism as a way of talking about history as changing dynamic systems that do more than just follow the dialectic? What if he talked about complex social systems that evolve over time along multiple trajectories? Suddenly his idea of systems containing their own resistance (his Nietzschian theory of power) makes more sense: we are talking about the multiplicity of systems, differentiation and feedback loops. And, suddenly his ideas would not seem so unique—at least by today’s knowledge of complexity science. Suddenly his ideas sound less structural and more systems-oriented.
Because this is a blog, I will not blag on too much. So, just consider one of Foucault’s key concepts, the dispositif. For Foucault, this concept forms the field of relations in which his work, up to the end, is situated within.
Foucault states: "What I’m trying to pick out with this term is, firstly, a thoroughly heterogeneous ensemble consisting of discourses, institutions, architectural forms, regulatory decisions, laws, administrative measures, scientific statements, philosophical propositions, moral and philanthropic propositions--in short, the said as much as the unsaid. Such are the
elements of the apparatus [dispositif]. The apparatus [the grid of intelligibility] itself is the system of relations that can be established between these elements. Secondly, what I am trying to identify in this apparatus is precisely the nature of the connections that can exist between these heterogeneous elements (Language, Counter-Memory, Practice, 1980, p. 194)."
As this quote shows, Foucault's work is always about mapping the grid of intelligibility (the dispositif) for some complex system in historical time-—be the system medicine, mental health, the social sciences, criminal justice, psychoanalysis, religion, or government. For Foucault, the dispositif is a system’s self-organizing order of things, its field of organizing practices. But this dispositif is not a totalizing system of relations as in the dialectic. Nor is it something the historian simply uncovers. It is both the interpretive framework that the historian imposes upon the discourses of the past (which is why Foucault often refers to his works as fictions, 1991, p. 33) and the relations that exist between the various discursive and nondiscursive heterogeneous elements making up the field of organizing practices—I mean, does that not sound like 2nd order cybernetics or sociocybernetics? The dispositif is a system of strategies that exist as practice, both on the part of the historian and on the part of the period in question. The dispositif isn’t found within some external structure or within the heads of particular controlling agents. It is within the practice of practice itself. It is fragmented, disjointed and broken, and yet inter-related, unified and organized. It is not a Parsionian system that exists as homeostasis, which then requires us to explain how change happens. It is a changing system where we question how order itself is possible.
Again, this is just a thought. But, it does open up the possibilities for some incredible connections between the last twenty years of sociological inquiry and the new science of complexity. To see a more thorough argument of my point of how Foucault can be used to build a theory of social complexity, see our new book, Sociology and Complexity Science.
14/03/2009
I've got a bunch of new geek t-shirts at my CAFE PRESS STORE. Check em out. I particularly like this one and the irony of it.
Factory Wiz
13/03/2009
Complexity 1001
Starting today, I will be featuring a new segment on this blog, called Complexity 1001. Like the name sounds, Complexity 1001 will provide an undergraduate (college) level introduction to complexity science and, related, the intersection of complexity science with the social sciences, specifically sociology.
I have asked a few friends who are new to complexity science (a couple profs and a couple students) to post any questions, concerns, or issues they have as they learn about and apply the tools of complexity science.
I also welcome anyone else to post questions they would like answered. You can email me at factory.as@gmail.com or you can post a question in any of the recent Complexity 1001 postings.
Any time I respond to a post, the heading of my post will always be Complexity 1001. This way you can find older postings as the months go by.
Finally, make sure you sign up for a posting feed or all comments feed so you get Complexity 1001 sent directly to your email or whatever place you daily go to see what's happening on the web!
So, let the online course and the postings begin.
10/03/2009
Reprise: Intersecting the Study of Social and Complex Networks
Several blogs ago I posted on the need for researchers to do more work intersecting the new science of networks (complexity science) with the sociological literature on social networks, in particular the global network literature. Some sociologists do not see much to be gained from such a merger. For those resistant to the idea or unclear as to what such a merger is about, you need to read Vega-Redondo's Complex Social Networks.
The purpose of this book is to outline, in detail, the avenues of study that emerge from the intersection of the new science of networks and social network analysis.
Rather than going on, I recommend you go to Josep Pujol's excellent review of the book, published at JASSS (Journal of Artificial Societies and Social Simulation)
One note is, however, necessary. Given that my blog caters to social science students and researchers new to complexity science, it is worth mentioning that Vega-Redondo's book primarily makes its case through mathematics. Do not let that scare you away. It is something social scientists have to get used to: complexity science makes extensive use of mathematics to make its arguments. Social scientists are often poorly trained to deal with equation-based modeling. They receive little training outside the study of statistics. We need to get past this hurdle to adopt a much broader and stronger toolset. Having said that, here is one such opportunity to learn something new. Your hard work moving through such a book is worth the effort!
04/03/2009
Complexity 5
Okay, so I just posted on Gershenson's blog, COMPLEXES and now I am posting on his recent book, Complexity 5.
I have to admit that this is the exact book I have wanted to write myself. It is a series of overviews (interview style) of leading thinkers in the field of complexity science.
What I particularly like about the book is that it interviews people who other complexity scientists view as TOP NOTCH--rather than the same list of popular people who often get far too much attention. I am particularly excited to see Nigel Gilbert, Paul Cilliers, and Bar-Yam in the list, as well as Melanie Mitchell. There are lots of women in complexity science who have yet to get their dues, and so this is great! (I cannot help making the last point, I am a sociologist.)
Here is the complete list of contributors: Peter M. Allen, Philip W. Anderson, W. Brian Arthur, Yaneer Bar-Yam, Eric Bonabeau, Paul Cilliers, Jim Crutchfield, Bruce Edmonds, Nigel Gilbert, Hermann Haken, Francis Heylighen, Bernardo A. Huberman, Stuart A. Kauffman, Seth Lloyd, Gottfried Mayer-Kress, Melanie Mitchell, Edgar Morin, Mark Newman, Grégoire Nicolis, Jordan B. Pollack, Peter Schuster, Ricard V. Solé, Tamás Vicsek, Stephen Wolfram.
I have to admit that this is the exact book I have wanted to write myself. It is a series of overviews (interview style) of leading thinkers in the field of complexity science.
What I particularly like about the book is that it interviews people who other complexity scientists view as TOP NOTCH--rather than the same list of popular people who often get far too much attention. I am particularly excited to see Nigel Gilbert, Paul Cilliers, and Bar-Yam in the list, as well as Melanie Mitchell. There are lots of women in complexity science who have yet to get their dues, and so this is great! (I cannot help making the last point, I am a sociologist.)
Here is the complete list of contributors: Peter M. Allen, Philip W. Anderson, W. Brian Arthur, Yaneer Bar-Yam, Eric Bonabeau, Paul Cilliers, Jim Crutchfield, Bruce Edmonds, Nigel Gilbert, Hermann Haken, Francis Heylighen, Bernardo A. Huberman, Stuart A. Kauffman, Seth Lloyd, Gottfried Mayer-Kress, Melanie Mitchell, Edgar Morin, Mark Newman, Grégoire Nicolis, Jordan B. Pollack, Peter Schuster, Ricard V. Solé, Tamás Vicsek, Stephen Wolfram.
Great Blog: Complexes
I have been following another excellent blog: COMPLEXES.
Complexes is run by Carlos Gershenson, who is a bit of an iconoclast. He has very broad interests in complexity, computer engineering, artificial life, and complexity-based art. He is also the book review editor for Artificial Life and the editor-in-chief of Complexity Digest--the leading compendium of all things complexity on the web.
(As a side note, if you do not have Complexity Digest bookmarked, please do so now. Also, Gottfried Mayer, the founding editor of Complexity Digest and leading systems/complexity scientist, passed away last month.)
Complexes is a fantastic blog because of the range of topics it addresses; and because Gershenson writes in a fresh way, with an insider's insights into various concepts, tools and techniques.
Also, check out his art.
I highly recommend the blog!
Complexes is run by Carlos Gershenson, who is a bit of an iconoclast. He has very broad interests in complexity, computer engineering, artificial life, and complexity-based art. He is also the book review editor for Artificial Life and the editor-in-chief of Complexity Digest--the leading compendium of all things complexity on the web.
(As a side note, if you do not have Complexity Digest bookmarked, please do so now. Also, Gottfried Mayer, the founding editor of Complexity Digest and leading systems/complexity scientist, passed away last month.)
Complexes is a fantastic blog because of the range of topics it addresses; and because Gershenson writes in a fresh way, with an insider's insights into various concepts, tools and techniques.
Also, check out his art.
I highly recommend the blog!
03/03/2009
Widening the study of global networks
It is time to widen the complexity science vocabulary on global networks. Two rather disparate literature currently exist:
First, there is the new science of networks, and its very specific focus, web science. This literature is dominated by the work of Watts, Newman, Barabasi and scholars in the natural sciences. Web science is a specific focus, examining the world wide web and internet.
Second, there is the globalization literature, and its very specific focus on network society. This literature is dominated by the work of Wallerstein (world systems theory), Manuel Castells (global network society) and John Urry (mobile society and global complexity).
While these two literature are outstanding, not much has been done to bridge them. The closest example from the globalization side is Urry's work in Global Complexity. Related is Wellman's work on web science.
Again, these two literature differ in scholarly background--the first comes from physics and the natural sciences, while the second comes from sociology and political science.
They have lots to say to one another. The global network society literature has a lot to say on the social factors within which global networks are currently situated. The new science of networks has a lot to say about how the structure and dynamics of global networks work--for example, see Barabasi's recent article in nature on mobility.
Disserations, masters theses and funded research await those willing to integrate these two viewpoints in empirically grounded ways.
First, there is the new science of networks, and its very specific focus, web science. This literature is dominated by the work of Watts, Newman, Barabasi and scholars in the natural sciences. Web science is a specific focus, examining the world wide web and internet.
Second, there is the globalization literature, and its very specific focus on network society. This literature is dominated by the work of Wallerstein (world systems theory), Manuel Castells (global network society) and John Urry (mobile society and global complexity).
While these two literature are outstanding, not much has been done to bridge them. The closest example from the globalization side is Urry's work in Global Complexity. Related is Wellman's work on web science.
Again, these two literature differ in scholarly background--the first comes from physics and the natural sciences, while the second comes from sociology and political science.
They have lots to say to one another. The global network society literature has a lot to say on the social factors within which global networks are currently situated. The new science of networks has a lot to say about how the structure and dynamics of global networks work--for example, see Barabasi's recent article in nature on mobility.
Disserations, masters theses and funded research await those willing to integrate these two viewpoints in empirically grounded ways.
01/03/2009
What are we? Complexity science, complexity theory, complex systems, complex self-organizing systems, etc????
Complexity science has been around long enough for the field to finally settle on a name. Systems science has a clear name, as does cybernetics and agent-based modeling. Complexity science needs similar clarity.
To demonstrate the current lack of clarity, do a basic search on Wikipedia, or examine the various web trackers. It becomes quickly clear that fuzziness and chaos abound--and I do not use these terms in a positive way.
Not having a name is a problem for a new science. It makes it hard for people to know what they are doing.
I strongly recommend complexity science as a name. Here is why
Not Complexity theory: Complexity is more than a theory. In fact, i would like someone to show what complexity theory is? I have yet to see a theory of complexity. I have seen complexity theories about evolution (Kauffman); social systems (Luhmann); organizations (Cilliers). But, I have not seen a complexity theory. No such thing exists.
Not complex self-organizing systems: Of all the possible terms, this one comes close, but it is too cumbersome. Complexity science is definitely the study of complex, self-organizing systems. However, complexity science is broader than just self-organizing systems. It deals with a variety of complex systems. Also, complexity science is cleaner and terse.
Not Chaos theory: While complexity science is indebted to chaos theory, it is something else. It is interested in organized chaos.
Not Agent-based modeling: While complexity science makes use of agent-based modeling, complexity science is more than just method.
Not e-science or web-science: This isn't going to work because the former is too substantively focused and the second is all method and often not systems oriented.
Not Post-systems science or Post-cybernetics: Complexity science is indebted to systems science and cybernetics--these fields are the historical lineage upon which complexity science is based. But, complexity science makes a break with these two fields, turning to a much larger literature to define its theories, methods and substantive problems.
Not Complexity: Complexity science is not just complexity. This term is too wide and ambiguous--we have always had complexity.
Not Computational Complexity: Computational complexity is too focused: it has to do with computational problem solving, not the study of complex systems.
And still more:The other reason complexity science is preferrable is because it separates the field from metaphorical, political and spiritual uses of this new science. A major criticism of complexity science today, particularly in the management literature, is a lack of rigor. Can a car company really be autopoietic? I doubt it. Is emergence some kind of quasi-spiritual mysterious force? If it is, then science might as well stop studying crowd behavior and the standing ovation problem.
Complexity science is a science. Let's call it that.
27/02/2009
What Does 6-Degrees of Separation Mean? Or, why sociology is important to complexity science
In the 2002 January/February issue of Society, Judith Kleinfeld published an interesting article titled "THE SMALL WORLD PROBLEM." Kleinfeld's article is an excellent review and critique of the small-world problem--the idea that, in very large social networks everyone is connected to everyone else in the world by 6 or fewer links. The reason: networks are not random; instead, they contain weak-ties sufficient to link up everyone.
More important, Kleinfeld's article demonstrates the importance of sociology to complexity science. While physics can be used to study society, it needs sociology. Social systems are not physical systems--for the record, Watts agrees with this point (See Watts 2004).
For example, while a poor, female living in Mexico may be separated by less than six-degrees from a rich male living in Germany, it is very unlikely this poor female can make use of her links the same way someone of a higher socioeconomic status could. Sociology (and social network analysis, specifically, along with the study of kinship networks and health) has a lot to say about the quality of the connections in large social networks--above and beyond such terms as weak and strong ties, triangles, centroids, clusters, etc.
A whole language (all of it sociological, and much of it within social network analysis) awaits to be intersected with the new science of networks. This language includes community health, inequality, social stratification, medical sociology, gender, occupations, etc.
To learn more about the sociological approach to the new science of networks, see Barry Wellman's website and the INTERNATIONAL NETWORK FOR SOCIAL NETWORK ANALYSIS, See also Kleinfeld's COULD IT BE A BIG WORLD?
More important, Kleinfeld's article demonstrates the importance of sociology to complexity science. While physics can be used to study society, it needs sociology. Social systems are not physical systems--for the record, Watts agrees with this point (See Watts 2004).
For example, while a poor, female living in Mexico may be separated by less than six-degrees from a rich male living in Germany, it is very unlikely this poor female can make use of her links the same way someone of a higher socioeconomic status could. Sociology (and social network analysis, specifically, along with the study of kinship networks and health) has a lot to say about the quality of the connections in large social networks--above and beyond such terms as weak and strong ties, triangles, centroids, clusters, etc.
A whole language (all of it sociological, and much of it within social network analysis) awaits to be intersected with the new science of networks. This language includes community health, inequality, social stratification, medical sociology, gender, occupations, etc.
To learn more about the sociological approach to the new science of networks, see Barry Wellman's website and the INTERNATIONAL NETWORK FOR SOCIAL NETWORK ANALYSIS, See also Kleinfeld's COULD IT BE A BIG WORLD?
23/02/2009
What Happened to Neural Networking?
NOTE: This post is one of my rants. It is not based on serious data analysis. Instead, it is an impression I have had for a while. If you think I am wrong, let me know. If you think I am right, let me know--that would make my day! ;)
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Back in the 1990s artificial neural networking was everywhere. The number of conferences, journal articles and grants devoted to its exploration was phenomenal. Then, suddenly, it seems, everyone moved on. Now, the rage is social networks.
I am not saying neural networks has been completely dropped. There is still some exciting stuff going on. But, it just never got integrated into the mainstay of complexity science method the way one would think.
And yet, neural networking is a major line of thinking in complexity science. As shown in my map of complexity, cybernetics and artificial intelligence (specifically distributed artificial intelligence)
One particular area that has yet to be fully appreciated by complexity scientists is Kohonen's self-organizing map--known as the SOM.
The SOM represents the latest advance in what can be called "qualitative computing."
By this term I mean that, the SOM is ready-made for finding nonobvious patterns in very large, complex digital, numerical databases. However, unlike statistics, the SOM is not driven by traditional hypotheses; it is not governed by the linear model; it searches for patterns of difference rather than aggregate norms and trends; it focuses on the relationships between conceptual indicators rather than the most powerful single variables; and, most important, while “intelligent,” it is actually dumb: the SOM does not tell you why it arrived at the results it gives you. There are no t-tests of significance to tell you what you found.
Instead, the SOM's output is open-ended, visual, and intuitive. To make sense of the nonobvious patterns and trends found, the researcher must apply traditional qualitative techniques--including coding, memo writing, and theoretical sampling. The qualitative orientation of the SOM does not mean one does not use statistics or formal mathematical modeling. I use these techniques all the time with it.
But, it does mean that the SOM is both computational and qualitative--a rare thing in method.
The SOM can do all of this because it is essentially a data reduction technique--while preserving the complexity of a system, it reduces its complexity to a 2-dimensional grid, onto which it projects the nuanced relationships between a set of factors. One combs this grid and the underlying factor structure to determine the dominant ways a data set clusters and the set of factors responsible for this clustering.
Familiar examples of the SOM are facial pattern recognition, analysis of disease trends, tumor detection, and primitive learning in robots and smart machines (See Kohonen 2001).
So, why aren't complexity scientists, particularly those in the social sciences, using the SOM? I do not know. Perhaps there is just so much going on that we have not reached an integration point. A method is explored, applied, developed and then everyone moves on to the next big method. Complexity science has not reached the point where multiple methods are combined to create a toolkit.
The other reason I think the SOM is not widely used, particularly amongst social scientists, is because of the geek factor involved. For example, I run Kohonen's free-ware program--the SOM Toolkit--in Matlab. If you cannot program your own neural net or you are not comfortable with Matlab or other programs with a high geek factor, it can be a bit overwhelming making use of this method. That, more than anything, is probably the unspoken reason neural nets and the SOM have not made a major splash in the social sciences. They are not overly easy to use.
They also do not fit the traditional paradigm of being numerical and quantative. Social scientists have an emotional breakdown when a method cannot be classified as qualitative or quantitative. Worse, if a numerical method does not have a t-test or some exact statistical way of determining the significance of its results, they just lose it! :)
Anyway, it just seems the SOM can be used to advance complexity science. For example, it can be used to explore how people cluster in a social network; it can be used to create conceptual maps of a complex systems; it can be used with agent-based modeling to improve the intelligence of agents, etc.
Again, I am not saying that the above types of work are not being done. I'm just saying that it seems more could be done.
What do you think?
-------------------------
Back in the 1990s artificial neural networking was everywhere. The number of conferences, journal articles and grants devoted to its exploration was phenomenal. Then, suddenly, it seems, everyone moved on. Now, the rage is social networks.
I am not saying neural networks has been completely dropped. There is still some exciting stuff going on. But, it just never got integrated into the mainstay of complexity science method the way one would think.
And yet, neural networking is a major line of thinking in complexity science. As shown in my map of complexity, cybernetics and artificial intelligence (specifically distributed artificial intelligence)
One particular area that has yet to be fully appreciated by complexity scientists is Kohonen's self-organizing map--known as the SOM.
The SOM represents the latest advance in what can be called "qualitative computing."
By this term I mean that, the SOM is ready-made for finding nonobvious patterns in very large, complex digital, numerical databases. However, unlike statistics, the SOM is not driven by traditional hypotheses; it is not governed by the linear model; it searches for patterns of difference rather than aggregate norms and trends; it focuses on the relationships between conceptual indicators rather than the most powerful single variables; and, most important, while “intelligent,” it is actually dumb: the SOM does not tell you why it arrived at the results it gives you. There are no t-tests of significance to tell you what you found.
Instead, the SOM's output is open-ended, visual, and intuitive. To make sense of the nonobvious patterns and trends found, the researcher must apply traditional qualitative techniques--including coding, memo writing, and theoretical sampling. The qualitative orientation of the SOM does not mean one does not use statistics or formal mathematical modeling. I use these techniques all the time with it.
But, it does mean that the SOM is both computational and qualitative--a rare thing in method.
The SOM can do all of this because it is essentially a data reduction technique--while preserving the complexity of a system, it reduces its complexity to a 2-dimensional grid, onto which it projects the nuanced relationships between a set of factors. One combs this grid and the underlying factor structure to determine the dominant ways a data set clusters and the set of factors responsible for this clustering.
Familiar examples of the SOM are facial pattern recognition, analysis of disease trends, tumor detection, and primitive learning in robots and smart machines (See Kohonen 2001).
So, why aren't complexity scientists, particularly those in the social sciences, using the SOM? I do not know. Perhaps there is just so much going on that we have not reached an integration point. A method is explored, applied, developed and then everyone moves on to the next big method. Complexity science has not reached the point where multiple methods are combined to create a toolkit.
The other reason I think the SOM is not widely used, particularly amongst social scientists, is because of the geek factor involved. For example, I run Kohonen's free-ware program--the SOM Toolkit--in Matlab. If you cannot program your own neural net or you are not comfortable with Matlab or other programs with a high geek factor, it can be a bit overwhelming making use of this method. That, more than anything, is probably the unspoken reason neural nets and the SOM have not made a major splash in the social sciences. They are not overly easy to use.
They also do not fit the traditional paradigm of being numerical and quantative. Social scientists have an emotional breakdown when a method cannot be classified as qualitative or quantitative. Worse, if a numerical method does not have a t-test or some exact statistical way of determining the significance of its results, they just lose it! :)
Anyway, it just seems the SOM can be used to advance complexity science. For example, it can be used to explore how people cluster in a social network; it can be used to create conceptual maps of a complex systems; it can be used with agent-based modeling to improve the intelligence of agents, etc.
Again, I am not saying that the above types of work are not being done. I'm just saying that it seems more could be done.
What do you think?
20/02/2009
The Complexity Blog: By Graduate Students for Graduate Students
I have been following an excellent complexity science blog for the past couple of weeks. It is called COMPLEXITY BLOG and it is run by two graduate students at the University of Michigan, who are affiliated with the university's international and renown Center for the Study of Complex Systems.
The two students are Kenneth Zick and Aaron Bramson--although the majority of recent posts are all by Bramson.
What is excellent about the site (even for undergraduates) is its cross-disciplinary viewpoint and breadth! For example, in addition to the blog, they provide a host of additional resources, which includes just about everything a student needs--from maps of the schools with complexity science programs to an excellent glossary to a good overview of all the major centers in complexity. I looked over their stuff and it is authoritative. You will not be led in the wrong direction.
I strongly recommend including this blog in your favorites! I put it in mine.
The two students are Kenneth Zick and Aaron Bramson--although the majority of recent posts are all by Bramson.
What is excellent about the site (even for undergraduates) is its cross-disciplinary viewpoint and breadth! For example, in addition to the blog, they provide a host of additional resources, which includes just about everything a student needs--from maps of the schools with complexity science programs to an excellent glossary to a good overview of all the major centers in complexity. I looked over their stuff and it is authoritative. You will not be led in the wrong direction.
I strongly recommend including this blog in your favorites! I put it in mine.
19/02/2009
CALRESCO: A Good Educational Website on Complexity
Several years ago I came across an excellent educational website on complexity--at a time when few such websites existed. In the late 1990s you basically had the Santa Fe Institute Website and a few other things.
Then came along CALRESCO, which stands for the Complexity & Artificial Life Research Concept for Self-Organizing Systems. If you typed on Google or any other web browser words like "complexity" or "complex systems" the top hit was from CALRESCO.
Now, Wikipedia dominates, which is a good thing. Survivial of the fittest! However, the drawback is that an excellent educational website is getting less attention.
What makes CALRESCO so good is the level of detail. When I was first learning the numerous concepts of complexity science, CALRESCO not only provided excellent information, but it had lots of papers I could download to read, and they were at a basic level without being basic--does that make sense?
Anyway, if you want to get a good grasp on the basic cocnepts of complexity science, I recommend making this part of your favorites list.
Then came along CALRESCO, which stands for the Complexity & Artificial Life Research Concept for Self-Organizing Systems. If you typed on Google or any other web browser words like "complexity" or "complex systems" the top hit was from CALRESCO.
Now, Wikipedia dominates, which is a good thing. Survivial of the fittest! However, the drawback is that an excellent educational website is getting less attention.
What makes CALRESCO so good is the level of detail. When I was first learning the numerous concepts of complexity science, CALRESCO not only provided excellent information, but it had lots of papers I could download to read, and they were at a basic level without being basic--does that make sense?
Anyway, if you want to get a good grasp on the basic cocnepts of complexity science, I recommend making this part of your favorites list.
18/02/2009
Excellence: Springer Complexity Series
If you are looking for excellent books and journals on complexity, you have to check out the Springer Complexity Series
I took the following paragraph directly from their website:
About this series
Future scientific and technological developments in many fields will necessarily depend upon coming to grips with complex systems. Such systems are complex in both their composition (typically many different kinds of components interacting with each other and their environments on multiple levels) and in the rich diversity of behavior of which they are capable. The Understanding Complex Systems series (UCS) promotes new strategies and paradigms for understanding and realizing applications of complex systems research in a wide variety of fields and endeavors. UCS is explicitly transdisciplinary. It has three main goals: First, to elaborate the concepts, methods and tools of self-organizing dynamical systems at all levels of description and in all scientific fields, especially newly emerging areas within the Life, Social, Behavioral, Economic, Neuro- and the Cognitive Sciences (and derivatives thereof): Second, to encourage novel applications of these ideas in various fields of Engineering and Computation such as Robotics, Nanotechnology and Informatics: Third, to provide a single forum within which commonalities and differences in the workings of complex systems may be discerned, hence leading to deeper insight and understanding. UCS will publish monographs and selected edited contributions from specialized conferences and workshops aimed at communicating new findings to a large transdisciplinary audience.
I took the following paragraph directly from their website:
About this series
Future scientific and technological developments in many fields will necessarily depend upon coming to grips with complex systems. Such systems are complex in both their composition (typically many different kinds of components interacting with each other and their environments on multiple levels) and in the rich diversity of behavior of which they are capable. The Understanding Complex Systems series (UCS) promotes new strategies and paradigms for understanding and realizing applications of complex systems research in a wide variety of fields and endeavors. UCS is explicitly transdisciplinary. It has three main goals: First, to elaborate the concepts, methods and tools of self-organizing dynamical systems at all levels of description and in all scientific fields, especially newly emerging areas within the Life, Social, Behavioral, Economic, Neuro- and the Cognitive Sciences (and derivatives thereof): Second, to encourage novel applications of these ideas in various fields of Engineering and Computation such as Robotics, Nanotechnology and Informatics: Third, to provide a single forum within which commonalities and differences in the workings of complex systems may be discerned, hence leading to deeper insight and understanding. UCS will publish monographs and selected edited contributions from specialized conferences and workshops aimed at communicating new findings to a large transdisciplinary audience.
Springer Book on Sociology and Complexity Science
My new book, Sociology and Complexity Science: A New Field of Inquiry was released by Springer on 4 Feb 2009. The book is part of Springer's complexity series.
To go to the book at Springer, CLICK HERE
Wikipedia Article on Sociology and Complexity Science
If you want a quick overview of sociology and complexity science and its major areas of study, see the Wikipedia article I recently posted on Wikipedia.
Complexity Map posted on Wikipedia
If you type in COMPLEXITY at Wikipedia, you will find my new map of complexity science there along with the article.
CLICK HERE TO GO DIRECTLY TO THE WIKIPEDIA ARTICLE ON COMPLEXITY
The map seems to be of use to people. The article is getting about 500 hits a day and the electronic map is getting about 50 hits a day. I hope it is of use to people. If you have questions about the map, post them here.
CLICK HERE TO GO DIRECTLY TO THE WIKIPEDIA ARTICLE ON COMPLEXITY
The map seems to be of use to people. The article is getting about 500 hits a day and the electronic map is getting about 50 hits a day. I hope it is of use to people. If you have questions about the map, post them here.
New map of complexity science
I just put together a new internet-based map of the field of complexity science, including all its major areas of study, leading scholars and intellectual lineage. It is an excellent resource for learning about complexity. In fact, I will make use of this map throughout my postings. CLICK HERE TO VISIT THE MAP
23/01/2009
Complexity Science Geek T-Shirts
Hello fellow complexity science geeks.
You have got to check out the complexity science t-shirts I put on Cafe Press. In the vernacular of the 1970s, "they totally rock!" Take a look. Also, if you have a t-shirt idea you would like me to make, post a comment. Or, just post a comment on what you think of the t-shirts.
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