This reminds me of the parable of beer and diapers. I guess moms need more than just a rest break.
Friday, May 29, 2009
Wednesday, May 20, 2009
Reading list on complex networks
Apparently there's a reading group in Santa Fe on complex networks. The list provided is excellent.
This one looked interesting:
Aguirre, B. E., Quarantelli, E. L. and Mendoza, J. L. The collective behavior of fads: the characteristics, effects, and career of streaking. American Sociological Review 53, 569-584 (1988).
This one looked interesting:
Aguirre, B. E., Quarantelli, E. L. and Mendoza, J. L. The collective behavior of fads: the characteristics, effects, and career of streaking. American Sociological Review 53, 569-584 (1988).
Tuesday, May 19, 2009
Evolution of the bio-tech industry
Herein are slides on the development of the bio-tech industry. I honestly can't recall where I found the link to this, but I like topic. The best part is it uses bipartite networks and doesn't collapse the different classes of nodes and studies them in the raw.
The interesting part about the dynamics of this network is that it grows and grows until it hits a capacity some time in 1992. The authors find a power-law distribution in the number of links, which is pretty typical for such networks. And then the vogue thing to do with dynamic networks with power-law distributions is to look at the change in 'alpha' over time. There's not a lot to gain from the slides though, I'm interested in the paper this might go along with.
So I found Powell's website and found this paper on Network Dynamics and Field Evolution. It seems similar, but not exactly. Anywho - it's a good example of anlysis of a dynamic two-mode network.
Trends in cognitive science: sociology
Computational Models of Collective Behavior
From the conclusion:
Cognitive scientists often act as though individuals are the sole loci of organized thought, but ABMs remind us that organized behavior can be described at multiple levels, and that our thoughts both depend upon and determine the social structures that contain us as elements within those structures.
From the conclusion:
Cognitive scientists often act as though individuals are the sole loci of organized thought, but ABMs remind us that organized behavior can be described at multiple levels, and that our thoughts both depend upon and determine the social structures that contain us as elements within those structures.
Monday, May 18, 2009
A nexus of political network lit.
I found a really big list of political network literature sorted by topic. For anyone working on networks of political networks (qualitative or quantitatively) this could be a good resource.
Sunday, May 17, 2009
Taste tripping parties
Wild.
Berries that contain a substance that binds to taste buds. The result is sour taste is attenuated and sweetness is amplified.
“You kept hearing ‘oh, oh, oh,’ ” he said, and then the guests became “literally like wild animals, tearing apart everything on the table.”
“It was like no holds barred in terms of what people would try to eat, so they opened my fridge and started downing Tabasco and maple syrup,” he said.
Berries that contain a substance that binds to taste buds. The result is sour taste is attenuated and sweetness is amplified.
“You kept hearing ‘oh, oh, oh,’ ” he said, and then the guests became “literally like wild animals, tearing apart everything on the table.”
“It was like no holds barred in terms of what people would try to eat, so they opened my fridge and started downing Tabasco and maple syrup,” he said.
The Necessity of Complexity: Taking environmental-conflict research beyond mechanism
The Necessity of Complexity: Taking environmental-conflict research beyond mechanism
Tom Deligiannis, Thomas Homer-Dixon, and Dirk Druet
The authors argue that there is a deep divide between qualitative and quantitative (as I took it) researchers regarding violence and environmental scarcity. The quants argue that there is little correlation between scarcity and violence, while the quals think case studies show clear causal relationships. Enter complexity science: "A mechanistic ontology is entirely inappropriate for investigation of causal processes within socio-ecological systems, because these systems are fundamentally "complex." Recent advances in complexity theory show that such systems are characterized by causal openness, emergent properties, disproportionality of cause and effect (i.e., nonlinear behavior), fractal scaling, and causal interaction (synergy)." They argue that a complexity approach reveals a number of the missing nonlinear causal connections between violence and scarcity.
The paper makes it incredibly obvious that there is a lot of controversy over the topic. Much of it is laid at the creation of variables for abstract concepts (How the heck do you quantify environmental scarcity? asks one research. Like this! says another. That's stupid. says the first researcher. So it goes...). Of course the debate is far more nuanced than this paper could even elaborate, but that's not the point. What we want to know is what a complexity perspective can give to this debate.
The most powerful element of complexity theory, according to the authors, is its ability to reframe causality. The authors take issue with the idea of 'if X occurs then Y occurs' and suggest that multiple causes can act together to produce an effect. The idea is that environmental scarcity can cause conflict, but it is not necessary and sufficient to cause conflict - I think that is obvious, but it appear the authors are criticizing other researchers who pose a strawman of cause-and-effect ideas about environmental scarcity.
They use a case study of community unrest in Peru to illustrate this idea. I feel that I couldn't quite summarize the list of causal relationships that lead to the violence in the region. There was a mixture of migration, lack of government legitimacy, environmental scarcity, over-population, and heterogeneity. One thing causes another which amplifies something else, which aggravates the first thing, and *pow* avalanche! For instance migration from one area to another might mean that the new area is not equipped to handle the new population resulting in lack of water, land, and food. Scarcity is a problem, but overpopulation is a problem in and of itself - and both together is a bigger problem than either in isolation.
I feel this paper dressed up pre-existing ideas about causality with a complexity veneer. While complexity science really does bring a lot of explanatory power to complex social situations, the author's use of it in this context seemed superficial. Well, at least in places. I think the application to a review of causality, using complexity science for legitimation, was effective. But there probably was little need of injecting unnecessary complexity jargon words into the discussion. Plus there was not very much illustration of what exactly complexity science and it's corresponding theories have done and truly can do. I agree, complexity is a necessary consideration, but I don't think someone who wasn't already convinced of that would buy into it from this paper.
Tom Deligiannis, Thomas Homer-Dixon, and Dirk Druet
The authors argue that there is a deep divide between qualitative and quantitative (as I took it) researchers regarding violence and environmental scarcity. The quants argue that there is little correlation between scarcity and violence, while the quals think case studies show clear causal relationships. Enter complexity science: "A mechanistic ontology is entirely inappropriate for investigation of causal processes within socio-ecological systems, because these systems are fundamentally "complex." Recent advances in complexity theory show that such systems are characterized by causal openness, emergent properties, disproportionality of cause and effect (i.e., nonlinear behavior), fractal scaling, and causal interaction (synergy)." They argue that a complexity approach reveals a number of the missing nonlinear causal connections between violence and scarcity.
The paper makes it incredibly obvious that there is a lot of controversy over the topic. Much of it is laid at the creation of variables for abstract concepts (How the heck do you quantify environmental scarcity? asks one research. Like this! says another. That's stupid. says the first researcher. So it goes...). Of course the debate is far more nuanced than this paper could even elaborate, but that's not the point. What we want to know is what a complexity perspective can give to this debate.
The most powerful element of complexity theory, according to the authors, is its ability to reframe causality. The authors take issue with the idea of 'if X occurs then Y occurs' and suggest that multiple causes can act together to produce an effect. The idea is that environmental scarcity can cause conflict, but it is not necessary and sufficient to cause conflict - I think that is obvious, but it appear the authors are criticizing other researchers who pose a strawman of cause-and-effect ideas about environmental scarcity.
They use a case study of community unrest in Peru to illustrate this idea. I feel that I couldn't quite summarize the list of causal relationships that lead to the violence in the region. There was a mixture of migration, lack of government legitimacy, environmental scarcity, over-population, and heterogeneity. One thing causes another which amplifies something else, which aggravates the first thing, and *pow* avalanche! For instance migration from one area to another might mean that the new area is not equipped to handle the new population resulting in lack of water, land, and food. Scarcity is a problem, but overpopulation is a problem in and of itself - and both together is a bigger problem than either in isolation.
I feel this paper dressed up pre-existing ideas about causality with a complexity veneer. While complexity science really does bring a lot of explanatory power to complex social situations, the author's use of it in this context seemed superficial. Well, at least in places. I think the application to a review of causality, using complexity science for legitimation, was effective. But there probably was little need of injecting unnecessary complexity jargon words into the discussion. Plus there was not very much illustration of what exactly complexity science and it's corresponding theories have done and truly can do. I agree, complexity is a necessary consideration, but I don't think someone who wasn't already convinced of that would buy into it from this paper.
Saturday, May 16, 2009
Financial Contagion on the International Trade Network
Financial Contagion on the International Trade Network
Raja Kali & Javier A. Reyes
I've posted about this paper before when the financial collapse was on fire. As before I quoted that the international trade system is affected more if a well-connected country is in crisis than if a less-connected country is. Conversely more well connected countries can absorb the shock better, probably because they transfer all that 'crisis' to others.
I found this paper incredibly interesting because it is one of the few papers that integrates network statistics, like centrality, into the statistical models of an outcome. I'm surprised that this is not done more often. The downside is that they don't do their analysis on a weighted network as they should. Instead they generate the network using various thresholds for link weight. This makes the analysis easier, but it also means they have like 28 statistical models testing for the significance of various thresholds. I found it interesting that trade with the 'epicenter country' (the country where a particular crisis occurred) wasn't terribly significant in predicting outcomes.
Overall network measures are very significant in predicting outcomes, even in the presence of other well-known control variables. It happens to explain even if a country's economy is small a crisis within can have large and lasting effects if it is well-connected. The end by suggesting that a network approach to financial shocks has important policy implications. They suggest further 'what-if' simulation using network models in the future since they are more important than previously considered.
Raja Kali & Javier A. Reyes
I've posted about this paper before when the financial collapse was on fire. As before I quoted that the international trade system is affected more if a well-connected country is in crisis than if a less-connected country is. Conversely more well connected countries can absorb the shock better, probably because they transfer all that 'crisis' to others.
I found this paper incredibly interesting because it is one of the few papers that integrates network statistics, like centrality, into the statistical models of an outcome. I'm surprised that this is not done more often. The downside is that they don't do their analysis on a weighted network as they should. Instead they generate the network using various thresholds for link weight. This makes the analysis easier, but it also means they have like 28 statistical models testing for the significance of various thresholds. I found it interesting that trade with the 'epicenter country' (the country where a particular crisis occurred) wasn't terribly significant in predicting outcomes.
Overall network measures are very significant in predicting outcomes, even in the presence of other well-known control variables. It happens to explain even if a country's economy is small a crisis within can have large and lasting effects if it is well-connected. The end by suggesting that a network approach to financial shocks has important policy implications. They suggest further 'what-if' simulation using network models in the future since they are more important than previously considered.
Micromotives and Macrobehavior
Micromotives and Macrobehavior
Thomas Schelling
How can we theorize the origins of segregation? How sensitive is segregation to the whims of actors? Thomas Schelling conceived a model of segregation that helps illuminate a number of tricky questions and produces some stunning insights. The model was first run on a checker board. The idea is that agents may desire at least some degree of similarity of people they move in next door to. If they do not find a desired level of similarity they move randomly to an open spot. The process continues.
Checker boards are slow though, so if you want to play with this idea with the NetLogo model and thousands of actors, please do so. You will find that even a desire for 30% similarity among the agents will globally produce 70% similarity. I played with the model and found a nonlinear relationship - at about 76% desired similarity you get about 99.9% global similarity. Over 76% desired (with 2000 agents) you get chaos, the system never crystallizes.
What the heck is the invisible hand of the economy? I think that's what this book seeks to explain. And it's a fantastic book. Really, I can't play it up enough. It's going to be assigned in classes I teach in the future, I spent half the time reading it thinking 'ooohhhh, right!'
Schelling is really looking for true principles of organization. For instance the Inescapable Mathematics of Musical chairs - someone is always left without a chair when a stable state is achieved. There are some inescapable facts of some social systems. For instance one person's raise is everyone else's inflation.
One thing this book really counters, like Sawyer did (but more concisely and effectively, I think), is methodological individualism. Individual experience only marginally explains global effects. I think case-studies and interviews are useful for understanding, but they tend to ignore how individual behavior interacts with the rest of the system and creates structure that can in turn affect behavior. Like how Schelling opens his book about a lecture he gave where everyone sat in the back of the auditorium and the first 15 rows were completely empty. Agent decision was important in this, but not as important as path-dependency. When the first few people sat down it set a norm that newcomers conformed to. Assuming humans are predictable behavior systems, initial seating conditions are more responsible for the seating arrangement than the individuals who sat down.
The book is rife with examples. Schelling often starts listing off cases where his principles are displayed. "I walk across the lawn if that seems to be what others are doing; I sometimes double-park if it looks as though everybody is double-parked. I stay in line if everybody is standing politely yin line, but if people begin to surge toward the ticket window I am alert to be - though never among the first - not among the last" (93).
Schelling comments on a number of phenomena common to the social science and complexity science. He mentions 'tipping points' for instance. This is a result of feedback. If we invite a certain number of people to a party there is an expectation among those invited that not everyone will attend. Since no one wants to be the few awkward people at a party people will hesitate. If there is knowledge that plenty of people are invited, so much so that the liklihood of being an awkward few is small, then there is a critical change and a higher proportion of people invited will actually attend. That's the idea at least, it's because of positive feedback, but the term isn't mentioned here.
The emphasis of the book is on modeling phenomena as a way of understanding it. Schelling is particularly interested in modeling sorting and aggregation of income, race, and cultural preference. His modeling assumes that the read has the mathematical prowess of an average economist (which, I think he is an economist, actually, which would explain the chapter on intersecting curves). Like Miller and Page's book, Schelling emphasizes the science over the technical bits of modeling. Good science always comes first and should provide insights and understanding to complex social situations. This book predates much of the vernacular of complexity science, but it belongs right smack in the middle of it. *recommended*
Thomas Schelling
How can we theorize the origins of segregation? How sensitive is segregation to the whims of actors? Thomas Schelling conceived a model of segregation that helps illuminate a number of tricky questions and produces some stunning insights. The model was first run on a checker board. The idea is that agents may desire at least some degree of similarity of people they move in next door to. If they do not find a desired level of similarity they move randomly to an open spot. The process continues.
Checker boards are slow though, so if you want to play with this idea with the NetLogo model and thousands of actors, please do so. You will find that even a desire for 30% similarity among the agents will globally produce 70% similarity. I played with the model and found a nonlinear relationship - at about 76% desired similarity you get about 99.9% global similarity. Over 76% desired (with 2000 agents) you get chaos, the system never crystallizes.
What the heck is the invisible hand of the economy? I think that's what this book seeks to explain. And it's a fantastic book. Really, I can't play it up enough. It's going to be assigned in classes I teach in the future, I spent half the time reading it thinking 'ooohhhh, right!'
Schelling is really looking for true principles of organization. For instance the Inescapable Mathematics of Musical chairs - someone is always left without a chair when a stable state is achieved. There are some inescapable facts of some social systems. For instance one person's raise is everyone else's inflation.
One thing this book really counters, like Sawyer did (but more concisely and effectively, I think), is methodological individualism. Individual experience only marginally explains global effects. I think case-studies and interviews are useful for understanding, but they tend to ignore how individual behavior interacts with the rest of the system and creates structure that can in turn affect behavior. Like how Schelling opens his book about a lecture he gave where everyone sat in the back of the auditorium and the first 15 rows were completely empty. Agent decision was important in this, but not as important as path-dependency. When the first few people sat down it set a norm that newcomers conformed to. Assuming humans are predictable behavior systems, initial seating conditions are more responsible for the seating arrangement than the individuals who sat down.
The book is rife with examples. Schelling often starts listing off cases where his principles are displayed. "I walk across the lawn if that seems to be what others are doing; I sometimes double-park if it looks as though everybody is double-parked. I stay in line if everybody is standing politely yin line, but if people begin to surge toward the ticket window I am alert to be - though never among the first - not among the last" (93).
Schelling comments on a number of phenomena common to the social science and complexity science. He mentions 'tipping points' for instance. This is a result of feedback. If we invite a certain number of people to a party there is an expectation among those invited that not everyone will attend. Since no one wants to be the few awkward people at a party people will hesitate. If there is knowledge that plenty of people are invited, so much so that the liklihood of being an awkward few is small, then there is a critical change and a higher proportion of people invited will actually attend. That's the idea at least, it's because of positive feedback, but the term isn't mentioned here.
The emphasis of the book is on modeling phenomena as a way of understanding it. Schelling is particularly interested in modeling sorting and aggregation of income, race, and cultural preference. His modeling assumes that the read has the mathematical prowess of an average economist (which, I think he is an economist, actually, which would explain the chapter on intersecting curves). Like Miller and Page's book, Schelling emphasizes the science over the technical bits of modeling. Good science always comes first and should provide insights and understanding to complex social situations. This book predates much of the vernacular of complexity science, but it belongs right smack in the middle of it. *recommended*
Friday, May 15, 2009
Coping with Complexity: The adaptive value of changing utility
Cohen, Michael D and Axelrod, Robert (1984). Coping with Complexity: The Adaptive Value of Changing Utility American Economic Review, 74 (1), 30-42
I took this paper on particularly because of the authors and because I thought it might be a short version of Harnessing Complexity, their book on using complexity science ideas in organization management. The paper is about management and managing complexity, actually, as per the title. It moves from econometric modeling and implies that even the best models are incomplete because complex organizations always have elements that screw up your model.
They create a model based on an AI model of playing checkers- yes, checkers, I know, it's pointless because we've solved checkers. The AI model for checkers based learning on 'surprise.' If ever a move on the other player's part was unexpected and the algorithm is surprised then there is a change in preferences. The example they start with is how a manager is trying to maximize productivity by choosing the right amount of labor. The manager is assumed to use an inverted parabola as the utility function, after each time period the basic algorithm would adjust a coefficient as it searches for the optimal distribution of labor for productivity.
But there could be things like thievery! The manager did not account for this. So Cohen and Axelrod quantify a measure of surprise, which to me, just feels like a residual from a regression model. They then suggest a dynamic model of adjusting preferences to account for the hidden complexity. The model adjusts preferences, but not too much since it dampens the changes.
I didn't see a terribly large amount of application here. This paper is from 1984 and it is reasonable to expect that improved econometric models have come into vogue since then. The one thing I did like was that "the simple principles underlying the success of the Samuel Checker Player can be transfered with powerful effect to other task domains." The other importance here is that hidden complexity can be something that can be 'coped' with. Perhaps their book, mentioned above, goes much further than that.
I took this paper on particularly because of the authors and because I thought it might be a short version of Harnessing Complexity, their book on using complexity science ideas in organization management. The paper is about management and managing complexity, actually, as per the title. It moves from econometric modeling and implies that even the best models are incomplete because complex organizations always have elements that screw up your model.
They create a model based on an AI model of playing checkers- yes, checkers, I know, it's pointless because we've solved checkers. The AI model for checkers based learning on 'surprise.' If ever a move on the other player's part was unexpected and the algorithm is surprised then there is a change in preferences. The example they start with is how a manager is trying to maximize productivity by choosing the right amount of labor. The manager is assumed to use an inverted parabola as the utility function, after each time period the basic algorithm would adjust a coefficient as it searches for the optimal distribution of labor for productivity.
But there could be things like thievery! The manager did not account for this. So Cohen and Axelrod quantify a measure of surprise, which to me, just feels like a residual from a regression model. They then suggest a dynamic model of adjusting preferences to account for the hidden complexity. The model adjusts preferences, but not too much since it dampens the changes.
I didn't see a terribly large amount of application here. This paper is from 1984 and it is reasonable to expect that improved econometric models have come into vogue since then. The one thing I did like was that "the simple principles underlying the success of the Samuel Checker Player can be transfered with powerful effect to other task domains." The other importance here is that hidden complexity can be something that can be 'coped' with. Perhaps their book, mentioned above, goes much further than that.
Emergence: The Dynamics of Network Formation
Emergence: The Dynamics of Network Formation
Brian Uzzi, Roger Guimera, Jarrett Spiro, Luis Amaral
This paper could almost be considered the culmination of everything I've read on my list. It has a lot of elements, network analysis, network modeling, empirical data, and empirical verification of a model. And it's all wrapped up by a veneer of Broadway musicals. I'm a fan of Brian Uzzi and Roger Guimera, they both do some very interesting work. In this case, they and their coauthors hope to explain the mechanisms of growth for bipartite networks. They examine empirical data of the growth of co-workers on broadway musical playbills and suggest a model where the type of link is important. They even analytically solve the model and make predictions which they successfully test on their data.
When researchers construct networks from existing data we have to ask questions such as how they define actors and how they define links. In this case the network is originally bipartite. The actors are directors, costume designers, actors (the acting kind) who appear on a playbill for a musical. They form links by being on the same playbill together. The network when it is expressed and modeled is not bipartite though. Bipartite networks are two-colorable, meaning you can assign every node one of two colors and no adjacent nodes will have the same color. A true representation of the network would have actors and playbills as two kinds of nodes in the network. Instead they treat the playbill as creating 'cliques' where every node in the playbill is connected to every other node in the playbill. As the network evolves there aren't so much as links being created so much as cliques.
When the network starts out there are only newcomers. Newcomers have collaborated on their first work and appear on a playbill for the first time. Eventually we see incumbents who have worked before and they can form links with other incumbents. There are several levels of links N-N (newcomer, newcomer), N-I (newcomer, incumbent), I-I (incumbent, incumbent, but the actors have not collaborated before, they're just incumbent), and I-R (same as incumbent incumbent except that both actors had collaborated on something before. These types of links, it is hypothesized, drive the emergence of the fully connected network.
There are a number of parameters: p, the probability of linking to an incumbent; q, the probability of linking to a past collaborator; m, the "team-size" which means the number of people on the playbill; and f, the amount of time you keep an inactive actor on the field before removing him.
The model displays some interesting dynamics. The image above is from a NetLogo simulation of the model you can play with. I liked setting all the variables constant but varying, up and down, the q variable. This made the network very modular, while otherwise it would have just been a single densely connected community.
The best part, which is something terribly lacking from most models, is the empirical validation. They analytically solve for the predicted fraction of of N-N, N-I, and I-I and find that all the parameters but p drop out (which is interesting). Then they show that the curves match the empirical data very well.
I'm left wondering what else could be done with this model. If it is a valid model then what sort of perturbations can we make that produce interesting predictions that we can go out and test for? Like varying the probability of working with a past collaborator, does that really produce more modular networks, like I was finding? Are there social systems where that value actually does oscillate where we can test the modularity? Conference papers can be pretty cool sometimes.
Brian Uzzi, Roger Guimera, Jarrett Spiro, Luis Amaral
This paper could almost be considered the culmination of everything I've read on my list. It has a lot of elements, network analysis, network modeling, empirical data, and empirical verification of a model. And it's all wrapped up by a veneer of Broadway musicals. I'm a fan of Brian Uzzi and Roger Guimera, they both do some very interesting work. In this case, they and their coauthors hope to explain the mechanisms of growth for bipartite networks. They examine empirical data of the growth of co-workers on broadway musical playbills and suggest a model where the type of link is important. They even analytically solve the model and make predictions which they successfully test on their data.
When researchers construct networks from existing data we have to ask questions such as how they define actors and how they define links. In this case the network is originally bipartite. The actors are directors, costume designers, actors (the acting kind) who appear on a playbill for a musical. They form links by being on the same playbill together. The network when it is expressed and modeled is not bipartite though. Bipartite networks are two-colorable, meaning you can assign every node one of two colors and no adjacent nodes will have the same color. A true representation of the network would have actors and playbills as two kinds of nodes in the network. Instead they treat the playbill as creating 'cliques' where every node in the playbill is connected to every other node in the playbill. As the network evolves there aren't so much as links being created so much as cliques.
When the network starts out there are only newcomers. Newcomers have collaborated on their first work and appear on a playbill for the first time. Eventually we see incumbents who have worked before and they can form links with other incumbents. There are several levels of links N-N (newcomer, newcomer), N-I (newcomer, incumbent), I-I (incumbent, incumbent, but the actors have not collaborated before, they're just incumbent), and I-R (same as incumbent incumbent except that both actors had collaborated on something before. These types of links, it is hypothesized, drive the emergence of the fully connected network.
There are a number of parameters: p, the probability of linking to an incumbent; q, the probability of linking to a past collaborator; m, the "team-size" which means the number of people on the playbill; and f, the amount of time you keep an inactive actor on the field before removing him.
The model displays some interesting dynamics. The image above is from a NetLogo simulation of the model you can play with. I liked setting all the variables constant but varying, up and down, the q variable. This made the network very modular, while otherwise it would have just been a single densely connected community.
The best part, which is something terribly lacking from most models, is the empirical validation. They analytically solve for the predicted fraction of of N-N, N-I, and I-I and find that all the parameters but p drop out (which is interesting). Then they show that the curves match the empirical data very well.
I'm left wondering what else could be done with this model. If it is a valid model then what sort of perturbations can we make that produce interesting predictions that we can go out and test for? Like varying the probability of working with a past collaborator, does that really produce more modular networks, like I was finding? Are there social systems where that value actually does oscillate where we can test the modularity? Conference papers can be pretty cool sometimes.
A systems approach to unravel complex water management institutions
A systems approach to unravel complex water management institutions
This paper was not what I was looking for at all. The methods were interesting as I'd never seen an implementation of Bayesian network analysis before. This paper is a good reminder that 'systems' approaches and 'complexity' approaches are different in some subtle but important ways. The goal is to understand the way water management rights are decided by analyzing how much surveyed individuals felt one thing led to a feeling about something else. The idea was that there were 'rules' governing the behavior of agents in an institutional water management environment in a hamlet in the Indian Himalayas and that decisions made did not account for the adaptive behavior of agent beliefs in their arena.
There were a number of rules the author defined in the system. The first, and which I thought most important, were the boundary rules. Control of boundary rules were critical since they defined who had some control and who did not. For instance, India's Land Reform Act defined who had the right to act in the water management based on land ownership. On top of that court rulings had an effect on the definition of land ownership as well, so two different acts defined actors in the system.
There were a number of things I didn't understand. For instance this sentence: "About 67% of the households perceived the probability of inadequate leadership affecting water distribution." What does that mean? They perceived it to be inadequate? They perceived the probability to be what? Did they just perceive it to be probable? There were something things I understood, for instance the periphery of the belief network were the boundary rules, which fed into position rules. In essence we could map out how boundary rules (defined who the actors were) interacted with position rules (the political position the actors take) with lead to aggregation rules (perception of the outcomes).
The goal the author had was to do away of the linear approach to outcomes and consider a nonlinear approach where relationships between actors and rules were important. I think the author accomplished this. But I don't feel any more enlightened by the approach. I think there was better information in the narrative than there was in the Bayesian network - the Bayesian network seems completely superfluous. I think this is a clear case where the methodology has outstripped the ability to fruitfully apply it.
This paper was not what I was looking for at all. The methods were interesting as I'd never seen an implementation of Bayesian network analysis before. This paper is a good reminder that 'systems' approaches and 'complexity' approaches are different in some subtle but important ways. The goal is to understand the way water management rights are decided by analyzing how much surveyed individuals felt one thing led to a feeling about something else. The idea was that there were 'rules' governing the behavior of agents in an institutional water management environment in a hamlet in the Indian Himalayas and that decisions made did not account for the adaptive behavior of agent beliefs in their arena.
There were a number of rules the author defined in the system. The first, and which I thought most important, were the boundary rules. Control of boundary rules were critical since they defined who had some control and who did not. For instance, India's Land Reform Act defined who had the right to act in the water management based on land ownership. On top of that court rulings had an effect on the definition of land ownership as well, so two different acts defined actors in the system.
There were a number of things I didn't understand. For instance this sentence: "About 67% of the households perceived the probability of inadequate leadership affecting water distribution." What does that mean? They perceived it to be inadequate? They perceived the probability to be what? Did they just perceive it to be probable? There were something things I understood, for instance the periphery of the belief network were the boundary rules, which fed into position rules. In essence we could map out how boundary rules (defined who the actors were) interacted with position rules (the political position the actors take) with lead to aggregation rules (perception of the outcomes).
The goal the author had was to do away of the linear approach to outcomes and consider a nonlinear approach where relationships between actors and rules were important. I think the author accomplished this. But I don't feel any more enlightened by the approach. I think there was better information in the narrative than there was in the Bayesian network - the Bayesian network seems completely superfluous. I think this is a clear case where the methodology has outstripped the ability to fruitfully apply it.
Thursday, May 14, 2009
Illegal and saves lives?
Per a CNN story:
Cheney has said he wants the documents released so there can be a more "honest debate" on the Bush administration's approval of "alternative" interrogation techniques against suspected terrorists. He argued that those techniques provided valuable intelligence that saved American lives, but critics say they amounted to the illegal torture of prisoners in U.S. custody.
Emphasis is mine. Why are these two things exclusive? Maybe there could be some honest dialogue if people dumped their tribal idealism and confronted the issue in the raw.
Anywho - I'm wholly against torture. People should experience consequences for breaking the law. I listened to a really interesting story on NPR about contracted interrogators who were mostly responsible. The best quote, I thought (and I'm probably butchering it), was when a senator said that 'torture persists because it works.' And Ali Soufan replied, 'No, torture persists because it's easier to hit someone than outsmart them.' So true.
Cheney has said he wants the documents released so there can be a more "honest debate" on the Bush administration's approval of "alternative" interrogation techniques against suspected terrorists. He argued that those techniques provided valuable intelligence that saved American lives, but critics say they amounted to the illegal torture of prisoners in U.S. custody.
Emphasis is mine. Why are these two things exclusive? Maybe there could be some honest dialogue if people dumped their tribal idealism and confronted the issue in the raw.
Anywho - I'm wholly against torture. People should experience consequences for breaking the law. I listened to a really interesting story on NPR about contracted interrogators who were mostly responsible. The best quote, I thought (and I'm probably butchering it), was when a senator said that 'torture persists because it works.' And Ali Soufan replied, 'No, torture persists because it's easier to hit someone than outsmart them.' So true.
Science stands mute
It is possible to search twitter then set up a feed for the search to put in your reader. So I have one set up for "complexity science". A single quote has been hitting that feed rampantly over the past few days:
There is a complexity to human affairs before which science and analysis simply stands mute.
It refers to an op-ed article by David Brooks in the NYT. The article refers to a longitudinal study of 268 men called the Grant Study. It seems the purpose was the see what makes people happy and how to get there.
The quote bothers me since it implies social science is a fruitless endeavor. I think the quote has spread through Twitter because it says something about the ethereal qualities of human relationships. People revel when they have access to something that science does not, like a soul maybe. When an exhaustive longitudinal study reveals "Happiness is love" there is a common relief that their intuitions are correct and the science is useless. Their connection to an realm outside the bounds of this physical dreary world is secure.
Brooks though is commenting a single study and a single essay. There has been gobs of research*, from psychology, sociology, economics, etc, recently studying happinness from a number of angles. Science doesn't stand mute on the topic... just that one ill-designed study of a single convenience sampling of white males.
* - ask me later if you really want me to start citing stuff. I need a collection of happiness paper and cites for my endnote anyhow.
P.S. Let's extend that 'happiness is love' into science, no?
There is a complexity to human affairs before which science and analysis simply stands mute.
It refers to an op-ed article by David Brooks in the NYT. The article refers to a longitudinal study of 268 men called the Grant Study. It seems the purpose was the see what makes people happy and how to get there.
The quote bothers me since it implies social science is a fruitless endeavor. I think the quote has spread through Twitter because it says something about the ethereal qualities of human relationships. People revel when they have access to something that science does not, like a soul maybe. When an exhaustive longitudinal study reveals "Happiness is love" there is a common relief that their intuitions are correct and the science is useless. Their connection to an realm outside the bounds of this physical dreary world is secure.
Brooks though is commenting a single study and a single essay. There has been gobs of research*, from psychology, sociology, economics, etc, recently studying happinness from a number of angles. Science doesn't stand mute on the topic... just that one ill-designed study of a single convenience sampling of white males.
* - ask me later if you really want me to start citing stuff. I need a collection of happiness paper and cites for my endnote anyhow.
P.S. Let's extend that 'happiness is love' into science, no?
Inequality and network structure
Inequality and network structure
Garud Iyengar, Willemien Kets, Rajiv Sethi, and Samuel Bowles
One of the more powerful implications of network analysis is that structure is an explanation. How things are connected to each other can effect the the distribution of resources or cooperative behavior. Here we have a paper that explores this idea in strictly mathematical terms - wow, it takes me back to advanced analysis. I haven't had to consider the behavior convex functions* in quite awhile.
The authors build off work by Myerson (1977) on cooperation on graphs. So, the way their model works is that each player in the network generates "value" if they are connected to other players. In addition the players can break off and define their own network, in which case their value is distributed only with their own network and they may or may not cut off some players' connections to other players. A player will only break off if they gain from it. The gains are relative to some function that determines value based on the number of individuals in the network 'clique'**.
The main implications of this model is that network structure is important, but the traditional measures of centrality, like betweenness and degree, are not. For instance the authors show examples of networks where the player with the highest betweenness and degree is the least valued. They also show an example of two networks with the same number of players, but the one with the more unequal degree distribution is actually more egalitarian in distributing value. They extend and continue having fun with their new model showing that all bipartite networks have a unique extremal distribution and extend the model allowing groups of players within k-distance of each other to break off.
This was just a discussion paper, but it does bring up some interesting points. For instance, if value is determined by those you interact with then it is certainly possible that someone several steps disconnected from me can disconnect me from value creation. Because each player has the choice of defecting to their own network if it benefits them we find the most central player, contrary to much network theory, is not the wealthiest. However, as the authors point out, this model is far too abstract to be empirically verified. It's value is in the theory and method, methinks.
* - in mathese that means you can draw a straight line and intersect the function at two points.
** - clique is defined totally differently than what I'm used to... sigh.
Garud Iyengar, Willemien Kets, Rajiv Sethi, and Samuel Bowles
One of the more powerful implications of network analysis is that structure is an explanation. How things are connected to each other can effect the the distribution of resources or cooperative behavior. Here we have a paper that explores this idea in strictly mathematical terms - wow, it takes me back to advanced analysis. I haven't had to consider the behavior convex functions* in quite awhile.
The authors build off work by Myerson (1977) on cooperation on graphs. So, the way their model works is that each player in the network generates "value" if they are connected to other players. In addition the players can break off and define their own network, in which case their value is distributed only with their own network and they may or may not cut off some players' connections to other players. A player will only break off if they gain from it. The gains are relative to some function that determines value based on the number of individuals in the network 'clique'**.
The main implications of this model is that network structure is important, but the traditional measures of centrality, like betweenness and degree, are not. For instance the authors show examples of networks where the player with the highest betweenness and degree is the least valued. They also show an example of two networks with the same number of players, but the one with the more unequal degree distribution is actually more egalitarian in distributing value. They extend and continue having fun with their new model showing that all bipartite networks have a unique extremal distribution and extend the model allowing groups of players within k-distance of each other to break off.
This was just a discussion paper, but it does bring up some interesting points. For instance, if value is determined by those you interact with then it is certainly possible that someone several steps disconnected from me can disconnect me from value creation. Because each player has the choice of defecting to their own network if it benefits them we find the most central player, contrary to much network theory, is not the wealthiest. However, as the authors point out, this model is far too abstract to be empirically verified. It's value is in the theory and method, methinks.
* - in mathese that means you can draw a straight line and intersect the function at two points.
** - clique is defined totally differently than what I'm used to... sigh.
Swarm Intelligence
Swarm Intelligence
James Kennedy and Russell C Eberhart
This was a really cool book. I will have to get the last few chapters later on, since I focused on the first half of the book. The first half is about human society as an information processor. Swarm optimization is essentially a model of social learning that has optimization applications, but was originally conceived as a method of exploring theories of social cognition. The book starts by discussing social theory, brains, language, information, culture, etc, and eventually moves in to the more technical application of coding and implementing swarm optimization. The emphasis of this book is not on the individual but on the social. The interactions between actors is what creates social intelligence and culture and that is what they hope to model and (since one of them is an engineer) exploit.
This book reminded me of the reasons I first went into social science. I wanted to explore the evolution of culture as a thing. The book starts by discussing modeling intelligence and what intelligence really is. They integrate ideas such as emergence and evolutionary computation. Then they move into culture suggesting that 'truth evolves' specifically since it relies on fuzzy systems like the brain to interpret symbols and produce meaning. The next chapters give an introduction to evolution and optimization, we even get some brief commentary on the creation / evolution debate*. Eventually we get to a topic I tend to find very interesting: flocks, herds, and swarms, and treating social behavior as optimization.
The idea is that even random (sorry, stochastic) movement can induce learning. A snail moving about a space is essentially searching for food. I often feel social scientists (well, sociologists in particular) are hesitant to draw parallels between other biological phenomena of organization and social learning and human methods of social learning. Kennedy and Eberhart don't even hesitate, and contrary to other points in the book where they justify evolution or justify modeling, we get little justification for the use of sociobiology. Ants are such an amazing evolved system for problem solving and decision making. The chapter on herds and swarms illustrates how ants problem solve, how this system can be modeled, and what it can tell us about how humans process and act on information.
On a very local level humans appear chaotic and unpredictable, but at the right scale human groups tend to behave in a very fluid manner. I've always taken the view that organisms respond and adapt to their own environment, but what if the environment is made of other organisms like them? Maybe all that complexity in human behavior is not so much that humans individually are unpredictable, but that we can't control the multitude of influences on behavior. Herbert Simon suggests, "A man, viewed as a behaving system, is quite simple. The apparent complexity of his behavior over time is largely a reflection of the complexity of the environment in which he finds himself" (100). Especially if that environment is other humans reacting to other humans. The book then moves into models and science behind social influence (and we see Axelrod come up again, damn he has made SO many influential models of human behavior... so many).
At the end we get our math and our code and our engineering. The particle swarm optmization model uses most of the ideas from the preceding chapters. The particles are networked together to each other and seek consistency with those they are connected to. They also try to optimize their own fitness while still maintaining that consistency of those they are connected to. In this way, if you put a particle swarm in a space the particles tend towards optimal solutions, and since they are stochastic like gnats they also explore the space around optimal solutions which leads to even better solutions.
They end the book by saying, "It is almost unbelievable, even to us, that the computer program that started out as a social-psychology simulation is now used to optimize power grids in Asia; develop high-tech products in the United States; and to solve high-dimensional, nonlinear, noisy mathematical problems" (427). The suggest that study of this 'paradigm' is in its infancy and is far from exhausted. This book gives a fantastic and incredibly cool look at social science, modeling, evolution, information, culture, and computation. I definitely could not spend enough time digging into it, as I would desperately like to code my own particle swarm optimization and experiment with it. Eventually... I have to keep focused on what I have, I guess.
* - "The disagreement between religious advocates and evolutionary scientists comes down to this: the creationists know how life began on earth, and the evolutionists don't." (82)
James Kennedy and Russell C Eberhart
This was a really cool book. I will have to get the last few chapters later on, since I focused on the first half of the book. The first half is about human society as an information processor. Swarm optimization is essentially a model of social learning that has optimization applications, but was originally conceived as a method of exploring theories of social cognition. The book starts by discussing social theory, brains, language, information, culture, etc, and eventually moves in to the more technical application of coding and implementing swarm optimization. The emphasis of this book is not on the individual but on the social. The interactions between actors is what creates social intelligence and culture and that is what they hope to model and (since one of them is an engineer) exploit.
This book reminded me of the reasons I first went into social science. I wanted to explore the evolution of culture as a thing. The book starts by discussing modeling intelligence and what intelligence really is. They integrate ideas such as emergence and evolutionary computation. Then they move into culture suggesting that 'truth evolves' specifically since it relies on fuzzy systems like the brain to interpret symbols and produce meaning. The next chapters give an introduction to evolution and optimization, we even get some brief commentary on the creation / evolution debate*. Eventually we get to a topic I tend to find very interesting: flocks, herds, and swarms, and treating social behavior as optimization.
The idea is that even random (sorry, stochastic) movement can induce learning. A snail moving about a space is essentially searching for food. I often feel social scientists (well, sociologists in particular) are hesitant to draw parallels between other biological phenomena of organization and social learning and human methods of social learning. Kennedy and Eberhart don't even hesitate, and contrary to other points in the book where they justify evolution or justify modeling, we get little justification for the use of sociobiology. Ants are such an amazing evolved system for problem solving and decision making. The chapter on herds and swarms illustrates how ants problem solve, how this system can be modeled, and what it can tell us about how humans process and act on information.
On a very local level humans appear chaotic and unpredictable, but at the right scale human groups tend to behave in a very fluid manner. I've always taken the view that organisms respond and adapt to their own environment, but what if the environment is made of other organisms like them? Maybe all that complexity in human behavior is not so much that humans individually are unpredictable, but that we can't control the multitude of influences on behavior. Herbert Simon suggests, "A man, viewed as a behaving system, is quite simple. The apparent complexity of his behavior over time is largely a reflection of the complexity of the environment in which he finds himself" (100). Especially if that environment is other humans reacting to other humans. The book then moves into models and science behind social influence (and we see Axelrod come up again, damn he has made SO many influential models of human behavior... so many).
At the end we get our math and our code and our engineering. The particle swarm optmization model uses most of the ideas from the preceding chapters. The particles are networked together to each other and seek consistency with those they are connected to. They also try to optimize their own fitness while still maintaining that consistency of those they are connected to. In this way, if you put a particle swarm in a space the particles tend towards optimal solutions, and since they are stochastic like gnats they also explore the space around optimal solutions which leads to even better solutions.
They end the book by saying, "It is almost unbelievable, even to us, that the computer program that started out as a social-psychology simulation is now used to optimize power grids in Asia; develop high-tech products in the United States; and to solve high-dimensional, nonlinear, noisy mathematical problems" (427). The suggest that study of this 'paradigm' is in its infancy and is far from exhausted. This book gives a fantastic and incredibly cool look at social science, modeling, evolution, information, culture, and computation. I definitely could not spend enough time digging into it, as I would desperately like to code my own particle swarm optimization and experiment with it. Eventually... I have to keep focused on what I have, I guess.
* - "The disagreement between religious advocates and evolutionary scientists comes down to this: the creationists know how life began on earth, and the evolutionists don't." (82)
Wednesday, May 13, 2009
Complexity in World Politics: Concepts and Methods of a New Paradigm
Complexity in World Politics: Concepts and Methods of a New Paradigm
Edited by Neil E. Harrison
The feeling I got from this book was similar to much of the work I found on complexity and the social sciences, especially those that emphasized a place for agent based models. Complexity, emergence, and agent-based modeling offers an opportunity for greater scientific legitimacy, in this case, for the study of world politics. There is already a long history of modeling in political science, but I think the complexity banner has managed to illustrate that the models aren't just numeric abstracts but have power to explain the strange dynamic world of global politics. I'm going to focus on two chapters in particular.
I was disappointed with this book. While there was a good amount of discussion of the implications of complexity on the study of world politics and the usefulness of agent based modeling, there wasn't any actual modeling! That might be a big plus for the computational modeler - lots of interesting open questions have been posed, get to work! Regardless of the lack of actual models there is still a lot of value in this volume.
One of my favorite chapters was by Matthew J Hoffman. I have read papers by him before and I wasn't surprised that I enjoyed his take on environmental politics. In environmental politics there has long been something called 'regime theory'. Regime theory is supposed to explain how states can reach compromises without some hegemonic central authority to guarantee or enforce the agreements. He focuses on the Montreal Protocol and abstracts 'rules' to the 'agents' of the international system. There are rules like 'CFC control is a North-only problem.' Changes to the environment due to evaluations of the Montreal Protocol by the United States causes the rule to change to 'universal participation' precipitating a change. This is a nice theory, methinks, but it needs a model! Hoffman ends the chapter saying that this explanation will gain great credibility if a model can be produced using theoretical assumptions that produces universal participation as an emergent property. Hm... anyone game? The theory needs to be cleaned up a bit so that a model can be reliably detailed, but it's possible.
Another interesting chapter was by Ravi Bhavnani on ethic norms and violence. I am reminded of one of the open questions Miller and Page posed of complexity science, When does heterogeneity matter? Well, Bhavnani has identified one case: ethnic violence. There seems to be, on the one hand, a normalization of deviance argument. Killing the other group became the norm and a game. As Hoffman, Bhavnani poses a model that isn't actually created. In this case he suggests that the model has no business being empirically validated as such validation is ridiculously impossible. He suggests that agents have varying levels of out-group extremism and in-group favoritism and that agents need a mechanism of punishing slackers - these assumptions are informed by careful analysis of the Rwandan genocide. There, of course, should be mechanisms for learning such as adopting the behavior of those the agent encounters. In addition the agents should have a pre-existing network or an endogenously-grown network of relationships between each other. A number of things could be explored, such as what the structure of the network does, the effect of varied compositions of pacifists and extremists, and what sort of effect the absence of punishment has. This model would give social scientists a laboratory to experiment with the emergence of a cascade of ethnic violence - since experimenting with the real thing is not very feasible. The author ends the chapter by championing agent-based models in contrast to game-theoretic or equation-based models - and I generally agree.
This book poses a lot of great questions, but provides very few answers. I feel that anyone who is curious about agent-based models or complexity in world politics would see a lot of value here. Although, this book is probably best for the agent-based modeler who is out of ideas and wants to publish some low-hanging fruit.
Edited by Neil E. Harrison
The feeling I got from this book was similar to much of the work I found on complexity and the social sciences, especially those that emphasized a place for agent based models. Complexity, emergence, and agent-based modeling offers an opportunity for greater scientific legitimacy, in this case, for the study of world politics. There is already a long history of modeling in political science, but I think the complexity banner has managed to illustrate that the models aren't just numeric abstracts but have power to explain the strange dynamic world of global politics. I'm going to focus on two chapters in particular.
I was disappointed with this book. While there was a good amount of discussion of the implications of complexity on the study of world politics and the usefulness of agent based modeling, there wasn't any actual modeling! That might be a big plus for the computational modeler - lots of interesting open questions have been posed, get to work! Regardless of the lack of actual models there is still a lot of value in this volume.
One of my favorite chapters was by Matthew J Hoffman. I have read papers by him before and I wasn't surprised that I enjoyed his take on environmental politics. In environmental politics there has long been something called 'regime theory'. Regime theory is supposed to explain how states can reach compromises without some hegemonic central authority to guarantee or enforce the agreements. He focuses on the Montreal Protocol and abstracts 'rules' to the 'agents' of the international system. There are rules like 'CFC control is a North-only problem.' Changes to the environment due to evaluations of the Montreal Protocol by the United States causes the rule to change to 'universal participation' precipitating a change. This is a nice theory, methinks, but it needs a model! Hoffman ends the chapter saying that this explanation will gain great credibility if a model can be produced using theoretical assumptions that produces universal participation as an emergent property. Hm... anyone game? The theory needs to be cleaned up a bit so that a model can be reliably detailed, but it's possible.
Another interesting chapter was by Ravi Bhavnani on ethic norms and violence. I am reminded of one of the open questions Miller and Page posed of complexity science, When does heterogeneity matter? Well, Bhavnani has identified one case: ethnic violence. There seems to be, on the one hand, a normalization of deviance argument. Killing the other group became the norm and a game. As Hoffman, Bhavnani poses a model that isn't actually created. In this case he suggests that the model has no business being empirically validated as such validation is ridiculously impossible. He suggests that agents have varying levels of out-group extremism and in-group favoritism and that agents need a mechanism of punishing slackers - these assumptions are informed by careful analysis of the Rwandan genocide. There, of course, should be mechanisms for learning such as adopting the behavior of those the agent encounters. In addition the agents should have a pre-existing network or an endogenously-grown network of relationships between each other. A number of things could be explored, such as what the structure of the network does, the effect of varied compositions of pacifists and extremists, and what sort of effect the absence of punishment has. This model would give social scientists a laboratory to experiment with the emergence of a cascade of ethnic violence - since experimenting with the real thing is not very feasible. The author ends the chapter by championing agent-based models in contrast to game-theoretic or equation-based models - and I generally agree.
This book poses a lot of great questions, but provides very few answers. I feel that anyone who is curious about agent-based models or complexity in world politics would see a lot of value here. Although, this book is probably best for the agent-based modeler who is out of ideas and wants to publish some low-hanging fruit.
Tuesday, May 12, 2009
Wherein the tags 'comics' and 'statistics' have a common post
What is cool? When a book review in the Journal of Statistical Software cites Scott McCloud's book, Understanding Comics. Of any comic book to be cited in a statistics journal, that's the one.
I guess that it makes sense in that the book review is about using dynamic data visualization software, GGobi (which I effing love!), and R.
I guess that it makes sense in that the book review is about using dynamic data visualization software, GGobi (which I effing love!), and R.
Complexity science and management fads
From Complexity Theory: Fact-free Science or Business Tool
"There's no doubt this is a fad, and the fad will pass," says Robert R. MacDonald over lunch in downtown Santa Fe. But Mr. MacDonald, a venture capitalist and executive who has been president of four start-up companies, thinks there will be much of value left when the fad passes. He is counting on it, in fact. He moved from Boston to Santa Fe a week before this conference began, to become president of the Bios Group L.P., a new venture that he started with Dr. Kauffman. The new company is aiming to market practical applications to businesses and consumers, Mr. MacDonald says.
"Some of them are concrete, like plant scheduling and dynamic feedback control of chaotic systems, and others are more abstract, such as sizing an organization, structuring for innovation and interactions of competitors, suppliers and customers in economic webs," he e-mails after the conference.
Science can go through fads, especially sciences on the border of the humanities (critical theory, for instance, which is almost like fashion for knowledge). Major CEOs and presidents of companies rarely connect with the academic world. Instead the hire consultants who briefly foray into the sciences or were formally part of them. They push a consulting product and so are always trying to hoc the latest and greatest product. The fear is that complexity theory has become one of those products that have been packaged by consultants to spread like a quick burning fire and fizzle out.
I don't think complexity science is a fad in the sciences. There is a lot that it offers and, if we follow Bill McKelvey's advice and implement a model-based approach, complexity science will have good long term staying power and can reveal its ability to explain the natural world in ways other approaches could not.
Although, I'm glad to hear that even those who think it's a fad in business management, that it still has the power to transform different processes on its fiery run.
"There's no doubt this is a fad, and the fad will pass," says Robert R. MacDonald over lunch in downtown Santa Fe. But Mr. MacDonald, a venture capitalist and executive who has been president of four start-up companies, thinks there will be much of value left when the fad passes. He is counting on it, in fact. He moved from Boston to Santa Fe a week before this conference began, to become president of the Bios Group L.P., a new venture that he started with Dr. Kauffman. The new company is aiming to market practical applications to businesses and consumers, Mr. MacDonald says.
"Some of them are concrete, like plant scheduling and dynamic feedback control of chaotic systems, and others are more abstract, such as sizing an organization, structuring for innovation and interactions of competitors, suppliers and customers in economic webs," he e-mails after the conference.
Science can go through fads, especially sciences on the border of the humanities (critical theory, for instance, which is almost like fashion for knowledge). Major CEOs and presidents of companies rarely connect with the academic world. Instead the hire consultants who briefly foray into the sciences or were formally part of them. They push a consulting product and so are always trying to hoc the latest and greatest product. The fear is that complexity theory has become one of those products that have been packaged by consultants to spread like a quick burning fire and fizzle out.
I don't think complexity science is a fad in the sciences. There is a lot that it offers and, if we follow Bill McKelvey's advice and implement a model-based approach, complexity science will have good long term staying power and can reveal its ability to explain the natural world in ways other approaches could not.
Although, I'm glad to hear that even those who think it's a fad in business management, that it still has the power to transform different processes on its fiery run.
Monday, May 11, 2009
Complex Adaptive Systems: an introduction to computational models of social life
Complex Adaptive Systems: an introduction to computational models of social life
John H. Miller and Scott E. Page
I would say this is a required text for anyone doing or curious about computational modeling. Miller and Page host a workshop each summer at the Santa Fe Institute for graduate students from a number of fields interested in computational social science - the deadline is past, but the book is here! This book is not the how, in fact it de-emphasizes the programming and computer part of the whole process and puts a large emphasis on the theory and science. The book is to tell the reader why someone would want to build a computational model, what sorts of questions they can help answer, and a number of aphoristic bits to advise the computational modeler.
Before I read this book I had a bit of a falling out with social simulation. I just didn't see the point, and it felt like it was going nowhere. Miller and Page make a very good case for computational modeling. They address a number of concerns researchers have, such as lack of discipline in simulation, and cover a number of key concepts, such as agent-based objects and the edge of chaos.
Miller and Page hesitate to dive into technical details, they don't give a lick of code or even pseudo-code in the whole book. The emphasis is on science. One really gets the feeling that the computational models really are tools for advancing knowledge and improving our understanding of complex systems. The tools make systems like wild-fires or culture moving through a social space that once seemed intractable to model seem comprehensible and maybe even predictable. The authors also take the 'introduction' seriously whereas many other texts on the topic assume too much about their readership. Through a detailed step-by-step they move through a simple 1-dimenional cellular automata model of forest fires. They use the example model to help illuminate ideas like the edge of chaos and emergence (for instance, the growing pattern of 'trees' in the model 'learns' to create a firewall to prevent mass spreads of fires, an interesting, emergent outcome). The authors then show how we can progressively introduce complexity to the model and examine the results.
The rest of the book takes off from the introductory concepts and moves into more complex topics such as evolving automata, organizational decision-making, and social dynamics. The end of the book is where you will find the "Open Agenda for Complex Adaptive Social Systems" where a number of the big broad questions are posed that the study of CAS hopes to answer. A number of the questions that interest me in the back are "What makes a system robust?", "Causality in complex systems?", "When does heterogeneity matter?", and "When do organizations arise?". I feel this book gives a fantastic, well-written introduction to possible new directions of addressing these and other questions - an utter necessity to anyone who wants to understand or use computational modeling to explore complex social systems.
For reference, here is Langton's Edge of Chaos, the original paper. It's actually very interesting, he defines a cellular automata model and shows how it responds to changes in a certain parameter. A particular range in the parameter produces complex behavior.
John H. Miller and Scott E. Page
I would say this is a required text for anyone doing or curious about computational modeling. Miller and Page host a workshop each summer at the Santa Fe Institute for graduate students from a number of fields interested in computational social science - the deadline is past, but the book is here! This book is not the how, in fact it de-emphasizes the programming and computer part of the whole process and puts a large emphasis on the theory and science. The book is to tell the reader why someone would want to build a computational model, what sorts of questions they can help answer, and a number of aphoristic bits to advise the computational modeler.
Before I read this book I had a bit of a falling out with social simulation. I just didn't see the point, and it felt like it was going nowhere. Miller and Page make a very good case for computational modeling. They address a number of concerns researchers have, such as lack of discipline in simulation, and cover a number of key concepts, such as agent-based objects and the edge of chaos.
Miller and Page hesitate to dive into technical details, they don't give a lick of code or even pseudo-code in the whole book. The emphasis is on science. One really gets the feeling that the computational models really are tools for advancing knowledge and improving our understanding of complex systems. The tools make systems like wild-fires or culture moving through a social space that once seemed intractable to model seem comprehensible and maybe even predictable. The authors also take the 'introduction' seriously whereas many other texts on the topic assume too much about their readership. Through a detailed step-by-step they move through a simple 1-dimenional cellular automata model of forest fires. They use the example model to help illuminate ideas like the edge of chaos and emergence (for instance, the growing pattern of 'trees' in the model 'learns' to create a firewall to prevent mass spreads of fires, an interesting, emergent outcome). The authors then show how we can progressively introduce complexity to the model and examine the results.
The rest of the book takes off from the introductory concepts and moves into more complex topics such as evolving automata, organizational decision-making, and social dynamics. The end of the book is where you will find the "Open Agenda for Complex Adaptive Social Systems" where a number of the big broad questions are posed that the study of CAS hopes to answer. A number of the questions that interest me in the back are "What makes a system robust?", "Causality in complex systems?", "When does heterogeneity matter?", and "When do organizations arise?". I feel this book gives a fantastic, well-written introduction to possible new directions of addressing these and other questions - an utter necessity to anyone who wants to understand or use computational modeling to explore complex social systems.
For reference, here is Langton's Edge of Chaos, the original paper. It's actually very interesting, he defines a cellular automata model and shows how it responds to changes in a certain parameter. A particular range in the parameter produces complex behavior.
Friday, May 08, 2009
Social networks that matter: Twitter under the microscope
Social networks that matter: Twitter under the microscope
Bernardo A. Huberman, Daniel M. Romero, and Fang Wu
First, I'd like to point that this article comes from a free online peer-reviewed journal called First Monday. There's a lot of internet research on this journal - a lot of it is pretty good. Anywho, I came across this article while watching a lecture from Jon Kleinberg where he discussed saturation curves in human behavior online (and perhaps elsewhere). Saturation curves like this intrigue me and you can find this phenomenon in number of places.
What we see here is that you post more on Twitter if you have more followers, that is, until about 300 followers. Then you reach saturation and there is suddenly a lot of variation in the number of posts (although, that might just be from a smaller sample size at those scales, there are a lot fewer people with 500 followers than there are with 20 followers). The point of the article was that the type of links on Twitter are important. There are two different types of links between people on Twitter, friendships and followship - friendship establishes you as a friend with a higher degree of sharing, while following just means you're listening to the tweets of the person followed.
There is no saturation point with respect the number of friends. Of course, there might be a saturation point, but there aren't very many people with more than 300 friends. We can say across the empirical range of numbers of friends, there is no saturation point with respect to number of tweets. So the authors suggest that some networks based on some kinds of links are more important to people than other networks. This is an unsurprising result, but the way that it was discovered was neat. I'm in love with saturation curves, did I mention that?
Bernardo A. Huberman, Daniel M. Romero, and Fang Wu
First, I'd like to point that this article comes from a free online peer-reviewed journal called First Monday. There's a lot of internet research on this journal - a lot of it is pretty good. Anywho, I came across this article while watching a lecture from Jon Kleinberg where he discussed saturation curves in human behavior online (and perhaps elsewhere). Saturation curves like this intrigue me and you can find this phenomenon in number of places.
What we see here is that you post more on Twitter if you have more followers, that is, until about 300 followers. Then you reach saturation and there is suddenly a lot of variation in the number of posts (although, that might just be from a smaller sample size at those scales, there are a lot fewer people with 500 followers than there are with 20 followers). The point of the article was that the type of links on Twitter are important. There are two different types of links between people on Twitter, friendships and followship - friendship establishes you as a friend with a higher degree of sharing, while following just means you're listening to the tweets of the person followed.
There is no saturation point with respect the number of friends. Of course, there might be a saturation point, but there aren't very many people with more than 300 friends. We can say across the empirical range of numbers of friends, there is no saturation point with respect to number of tweets. So the authors suggest that some networks based on some kinds of links are more important to people than other networks. This is an unsurprising result, but the way that it was discovered was neat. I'm in love with saturation curves, did I mention that?
Thursday, May 07, 2009
Social emergence: societies as complex systems
Social emergence: societies as complex systems
R. Keith Sawyer
This book is awesome because it has Sim City 4 on the cover... well more seriously this book is exactly what my list this semester is about. One of the things I'm primarily interested in with society is the macro-micro causation. A macro-level structure is created from the behavior of micro-level behavior, but the structure can constrain micro-level behavior. I tend to agree with the central aim of this book, methodological individualism misses a lot of the causal power of including system level causes.
Emergence is a very tricky subject. Definitions and understandings of it vary wildly. It is used a number of different ways. Sometimes it's difficult to distinguish between self-organization and emergence and many people accept them as the same thing. But it's not just self-organization, a recent paper by Alex Ryan on Mobius strips demonstrates that. A bunch of triangles topologically have two sides, but if combined into a Mobius strip the strip will have only one side (*poof* emergence!). He also suggests that emergence relates to scope and resolution instead of scale, per se', which I totally agree with*.
Before I read this book, I read a page on emergent properties on Cosma Shalizi's notebooks. The idea Shalizi puts forward is that a theory of emergence should be useful if anything at all. Many believe that emergent properties intrinsicly cannot be predicted from knowledge of the individual agents (a property Sawyer referred to as multiple realizability, many different micro-state can produce the same emergent property), but this almost completely eradicates the utility of a theory of emergent properties. If however, emergent properties can be modeled and predicted, then it is possible to make a model that makes predictions of behavior not yet observed and we can go out and search for them (*poof* science!).
This book provides a nice history of emergentism and the thinkers involved in cooking it up. It discusses the major disagreements, such as the one I hinted at in the previous paragraph. Supervenience was a key term I was introduced to. It means that if two higher level properties A and B have the exact same properties then the lower-level properties that created them are the same as well. In addition, if the lower level properties that create B ever occur again, then B is sure to emerge again. That term is the most easy to agree with as a materialist who rejects vitalism**.
The book moves from there into a discussion of emergentism and psychology, but I didn't spend much time in that chapter, maybe some other time. I focused on the chapters about emergentism and sociology, Durkheim's emergentism, and modeling using agent-based models. It seemed the point that needed the most justification was the feedback from emergent structure back on individual behavior (downward causation), and that is what much of the section on emergentism in social theory focused on. To me emergentism is useful since it suggests that individuals create and perpetuate a social structure that has a causal influence on the behavior of individuals.
I don't feel like the book was argumentative at all. Sawyer seemed more intent on describing emergence and its importance in social science than tearing down other theories (kudos, let it stand on its own). He seems to present a straw-man of methodological individualism (MI), like most theorists do of theories they criticize. Most people I've met who subscribe to MI aren't unreceptive to emergentism or it's implications. I feel it's just a Kuhnian thing, that much of sociology, psychology, economics, etc. has been in practice for at least a hundred years doing what they've been doing. And it's not like MI is incorrect, it just misses the forest in favor of the trees.
I don't think I'd ever give this to family or friends. This book is made of serious theory and it does not have the feel of being popularized at all.
If all my friends and family were social scientists though, I might recommend it. I would recommend 1,000 Years of Nonlinear History by DeLanda first, then this second.
* - I was playing with cinnamon bears where I could bite the heads off and use the stickiness to attach them to each other. Then I thought, "could I make a larger bear from small bears?" Then I thought, "at what size and resolution of small bears could we say a large bear can be created?" That is, I could take 5 bears and make it vaguely resemble a larger bear, but if I used 2,000 I could make a very high-fidelity larger bear. What what scope and resolution can a new larger bear be made?
** - vitalism is a form of dualism, which suggests that life requires some 'vital' force in addition to physical forces in order to exist and is not supervenient on lower-level interactions.
R. Keith Sawyer
This book is awesome because it has Sim City 4 on the cover... well more seriously this book is exactly what my list this semester is about. One of the things I'm primarily interested in with society is the macro-micro causation. A macro-level structure is created from the behavior of micro-level behavior, but the structure can constrain micro-level behavior. I tend to agree with the central aim of this book, methodological individualism misses a lot of the causal power of including system level causes.
Emergence is a very tricky subject. Definitions and understandings of it vary wildly. It is used a number of different ways. Sometimes it's difficult to distinguish between self-organization and emergence and many people accept them as the same thing. But it's not just self-organization, a recent paper by Alex Ryan on Mobius strips demonstrates that. A bunch of triangles topologically have two sides, but if combined into a Mobius strip the strip will have only one side (*poof* emergence!). He also suggests that emergence relates to scope and resolution instead of scale, per se', which I totally agree with*.
Before I read this book, I read a page on emergent properties on Cosma Shalizi's notebooks. The idea Shalizi puts forward is that a theory of emergence should be useful if anything at all. Many believe that emergent properties intrinsicly cannot be predicted from knowledge of the individual agents (a property Sawyer referred to as multiple realizability, many different micro-state can produce the same emergent property), but this almost completely eradicates the utility of a theory of emergent properties. If however, emergent properties can be modeled and predicted, then it is possible to make a model that makes predictions of behavior not yet observed and we can go out and search for them (*poof* science!).
This book provides a nice history of emergentism and the thinkers involved in cooking it up. It discusses the major disagreements, such as the one I hinted at in the previous paragraph. Supervenience was a key term I was introduced to. It means that if two higher level properties A and B have the exact same properties then the lower-level properties that created them are the same as well. In addition, if the lower level properties that create B ever occur again, then B is sure to emerge again. That term is the most easy to agree with as a materialist who rejects vitalism**.
The book moves from there into a discussion of emergentism and psychology, but I didn't spend much time in that chapter, maybe some other time. I focused on the chapters about emergentism and sociology, Durkheim's emergentism, and modeling using agent-based models. It seemed the point that needed the most justification was the feedback from emergent structure back on individual behavior (downward causation), and that is what much of the section on emergentism in social theory focused on. To me emergentism is useful since it suggests that individuals create and perpetuate a social structure that has a causal influence on the behavior of individuals.
I don't feel like the book was argumentative at all. Sawyer seemed more intent on describing emergence and its importance in social science than tearing down other theories (kudos, let it stand on its own). He seems to present a straw-man of methodological individualism (MI), like most theorists do of theories they criticize. Most people I've met who subscribe to MI aren't unreceptive to emergentism or it's implications. I feel it's just a Kuhnian thing, that much of sociology, psychology, economics, etc. has been in practice for at least a hundred years doing what they've been doing. And it's not like MI is incorrect, it just misses the forest in favor of the trees.
I don't think I'd ever give this to family or friends. This book is made of serious theory and it does not have the feel of being popularized at all.
If all my friends and family were social scientists though, I might recommend it. I would recommend 1,000 Years of Nonlinear History by DeLanda first, then this second.
* - I was playing with cinnamon bears where I could bite the heads off and use the stickiness to attach them to each other. Then I thought, "could I make a larger bear from small bears?" Then I thought, "at what size and resolution of small bears could we say a large bear can be created?" That is, I could take 5 bears and make it vaguely resemble a larger bear, but if I used 2,000 I could make a very high-fidelity larger bear. What what scope and resolution can a new larger bear be made?
** - vitalism is a form of dualism, which suggests that life requires some 'vital' force in addition to physical forces in order to exist and is not supervenient on lower-level interactions.
Wednesday, May 06, 2009
Nobility and stupidity: modeling the evolution of class endogamy
Nobility and stupidity: modeling the evolutin of class endogamy
Theodore C. Belding
What we have here is a suggestion of the mechanism of status disparity caused by marrying and reproducing based on status or class. Further the models Belding uses create a step clustering effect, meaning status isn't uniformly distributed but bunches up in certain ranges. His aim to create a computational model from Marcus and Flannery's verbal model of 'class endogamy'. A computational model is incredibly powerful since it's outcomes are "follow rigorously from the model's assumptions. Furthermore, the model's assumptions and mechanisms can be specified explicitly" (3).
He uses the simplest agent-based model there is, a one-dimensional model, an agents status is their position, and there is only one status. Each male and female choose someone to marry relative to some tolerance. They will only marry someone that is only so much higher or lower than their own status. Originally everything is totally random, the distributions of status is practically uniform. Next the agents can learn and keep a list of their most recently encounters. This creates somewhat modular groups, but still a uniform distribution of status. The mechanism that tends to create status classes, really, with multinomial distributions of status is a mechanism of inheritance. That is, when a child is born from an interaction it inherits the mean of the parent's status.
In all I think that the computational model, although simple, provides support that the mechanism expressed in the verbal model is sufficient to create classes of status. I don't tend to agree with the assessment of the agents as "stupid" but rather I would say they are boundedly rational - or rather they are simple and mechanistic. There doesn't have be some intentionality towards raising one's status to create the distributions we see, the agents can just be following simple rules.
Theodore C. Belding
What we have here is a suggestion of the mechanism of status disparity caused by marrying and reproducing based on status or class. Further the models Belding uses create a step clustering effect, meaning status isn't uniformly distributed but bunches up in certain ranges. His aim to create a computational model from Marcus and Flannery's verbal model of 'class endogamy'. A computational model is incredibly powerful since it's outcomes are "follow rigorously from the model's assumptions. Furthermore, the model's assumptions and mechanisms can be specified explicitly" (3).
He uses the simplest agent-based model there is, a one-dimensional model, an agents status is their position, and there is only one status. Each male and female choose someone to marry relative to some tolerance. They will only marry someone that is only so much higher or lower than their own status. Originally everything is totally random, the distributions of status is practically uniform. Next the agents can learn and keep a list of their most recently encounters. This creates somewhat modular groups, but still a uniform distribution of status. The mechanism that tends to create status classes, really, with multinomial distributions of status is a mechanism of inheritance. That is, when a child is born from an interaction it inherits the mean of the parent's status.
In all I think that the computational model, although simple, provides support that the mechanism expressed in the verbal model is sufficient to create classes of status. I don't tend to agree with the assessment of the agents as "stupid" but rather I would say they are boundedly rational - or rather they are simple and mechanistic. There doesn't have be some intentionality towards raising one's status to create the distributions we see, the agents can just be following simple rules.
Tuesday, May 05, 2009
Hierarchical organization of modularity in metabolic networks
Hierarchical organization of modularity in metabolic networks
E. Ravasz, A. L. Somera, D. A. Mongru, Z. N. Oltvai, A.-L. Barabasi
I chose this paper as an example of complexity and community structure having an effect in another field other than social science. In this case we look at the network of different metabolic interactions in E. Coli. They found that while the degree distributions of these networks have a power-law distribution, the clustering coefficient is an order of magnitude larger than what would be expected in a scale-free network. This suggests a high degree of modularity in the network.
In order to look at the groups of nodes they use standard hierarchical clustering. I would suggest more sophisticated methods of finding community structure in networks, but this works for their point. They find that the clusters correspond to functional groups. Meaning that if a chain of interactions cascades into a cluster then it would initiate a complex function into action.
Given that cell biology is very distant from my own field, I probably couldn't fully grasp what was so amazing about this paper. To me, it was useful as another reference to hierarchical structures in networks other than social networks.
E. Ravasz, A. L. Somera, D. A. Mongru, Z. N. Oltvai, A.-L. Barabasi
I chose this paper as an example of complexity and community structure having an effect in another field other than social science. In this case we look at the network of different metabolic interactions in E. Coli. They found that while the degree distributions of these networks have a power-law distribution, the clustering coefficient is an order of magnitude larger than what would be expected in a scale-free network. This suggests a high degree of modularity in the network.
In order to look at the groups of nodes they use standard hierarchical clustering. I would suggest more sophisticated methods of finding community structure in networks, but this works for their point. They find that the clusters correspond to functional groups. Meaning that if a chain of interactions cascades into a cluster then it would initiate a complex function into action.
Given that cell biology is very distant from my own field, I probably couldn't fully grasp what was so amazing about this paper. To me, it was useful as another reference to hierarchical structures in networks other than social networks.
Monday, May 04, 2009
Effective leadership and decision making in animal groups on the move
Effective leadership and decision making in animal groups on the move
This paper is another that would rank in my top-ten. Again we have a simple fact of social organization. We could think of this side by side with work on fractal scaling in group sizes at different levels. The powerful insight, I thought, was that groups that behave like swarms or flocks can find some resource if only a small proportion of the the individuals have the knowledge. Furthermore, the ratio between those who know and those who follow scales logarithmically with respect to the size of the total group. That means the larger the group, the group of knowledgeable individuals does not have to rise linearly.
When I say agents who behave like a swarm I mean they obey a small set of simple rules. Match direction and speed of those near you and don't get too close. Why is this powerful to me, as a social scientist? Well, this is a simple model. The agents and space they move through could be a number of things. For me, I saw it as a high-dimensional conceptual space, people adopt ideas of those they interact with frequently. Informed individuals could be thought of specialists who work in a system that employs a division of labor. A logarithmically number of specialists are needed to expend time towards the study of some topic or the creation of some good in order to draw the entire population towards the things they find. Therefore, the number of specialties that a population can reliably handle is a function of its population size.
There are of course other effects, social network complexity, physical distance, language barriers, etc... But in the search of universal properties of social organization and collective decision making, this is a firm step.
This paper is another that would rank in my top-ten. Again we have a simple fact of social organization. We could think of this side by side with work on fractal scaling in group sizes at different levels. The powerful insight, I thought, was that groups that behave like swarms or flocks can find some resource if only a small proportion of the the individuals have the knowledge. Furthermore, the ratio between those who know and those who follow scales logarithmically with respect to the size of the total group. That means the larger the group, the group of knowledgeable individuals does not have to rise linearly.
When I say agents who behave like a swarm I mean they obey a small set of simple rules. Match direction and speed of those near you and don't get too close. Why is this powerful to me, as a social scientist? Well, this is a simple model. The agents and space they move through could be a number of things. For me, I saw it as a high-dimensional conceptual space, people adopt ideas of those they interact with frequently. Informed individuals could be thought of specialists who work in a system that employs a division of labor. A logarithmically number of specialists are needed to expend time towards the study of some topic or the creation of some good in order to draw the entire population towards the things they find. Therefore, the number of specialties that a population can reliably handle is a function of its population size.
There are of course other effects, social network complexity, physical distance, language barriers, etc... But in the search of universal properties of social organization and collective decision making, this is a firm step.
Sunday, May 03, 2009
Experimental study of inequality and unpredictability in an artificial cultural market
Experimental study of inequality and unpredictability in an artificial cultural market
Matthew J. Salganik, Peter Sheridan Dodds, Duncan J Watts
I'd rank this paper somewhere in my top-ten right now. I think the results have an effect on ideas of aesthetics and social influence. The quality of a song is only slightly influential on the spread and success of a song. The experiment itself is also revolutionary for its methods since it puts web technology to good use, which, it could be argued, hasn't been done to any great effect to date.
Users came to a website that claimed to be distributing free music from unknown artists. There were 14,341 participants who were unknowingly divided into a number of 'worlds' that had different experimental conditions. Many were in a world where the number of times a song had been downloaded was not displayed. In eight other worlds the number of times songs were downloaded were displayed to the participants. I can't find it (I might have read about it elsewhere), but there was also a world wherein the number of times downloaded was displayed, but the number was randomly distributed and didn't actually represent downloads.
The researchers found it was much easier to predict whether a song would be successful when the number of downloads was not displayed. There were still some songs that were much more successful than others though. The social influence, mediated by the number of time a song was downloaded, had a large effect on the success of a song. There was little correlation between the which songs were highly successful in each social influence world. In the world where the number of downloads were random there was a much higher rate of attrition and less downloads overall. Someone might be drawn to a song with a lot of 'downloads' and find that it sucks, and quit thinking that everyone downloading songs here were daft.
This suggests that quality is important, but that social influence can significantly trump that. If a song is at least marginal, it can be a success if it gathers enough initial followers and aggregates.
Anywho - I agree with the researchers. I look forward to seeing more web-based experiments where large numbers of participants are necessary for proper results.
Matthew J. Salganik, Peter Sheridan Dodds, Duncan J Watts
I'd rank this paper somewhere in my top-ten right now. I think the results have an effect on ideas of aesthetics and social influence. The quality of a song is only slightly influential on the spread and success of a song. The experiment itself is also revolutionary for its methods since it puts web technology to good use, which, it could be argued, hasn't been done to any great effect to date.
Users came to a website that claimed to be distributing free music from unknown artists. There were 14,341 participants who were unknowingly divided into a number of 'worlds' that had different experimental conditions. Many were in a world where the number of times a song had been downloaded was not displayed. In eight other worlds the number of times songs were downloaded were displayed to the participants. I can't find it (I might have read about it elsewhere), but there was also a world wherein the number of times downloaded was displayed, but the number was randomly distributed and didn't actually represent downloads.
The researchers found it was much easier to predict whether a song would be successful when the number of downloads was not displayed. There were still some songs that were much more successful than others though. The social influence, mediated by the number of time a song was downloaded, had a large effect on the success of a song. There was little correlation between the which songs were highly successful in each social influence world. In the world where the number of downloads were random there was a much higher rate of attrition and less downloads overall. Someone might be drawn to a song with a lot of 'downloads' and find that it sucks, and quit thinking that everyone downloading songs here were daft.
This suggests that quality is important, but that social influence can significantly trump that. If a song is at least marginal, it can be a success if it gathers enough initial followers and aggregates.
Anywho - I agree with the researchers. I look forward to seeing more web-based experiments where large numbers of participants are necessary for proper results.
Saturday, May 02, 2009
Network scaling reveals consistent fractal pattern in hierarchical mammalian societies
Network scaling reveals consistent fractal pattern in hierarchical mammalian societies
Russell A. Hill, R. Alexander Bently, and Robin I. M. Dunbar
First, this seems like a cool article just because Dunbar is in it. I find her research on group size in primates very interesting. Scaling laws are an interesting topic. In this case he look at a scaling ratio, meaning what is the ratio between group sizes at one level, say families, to group sizes at an adjacent level, like a 'bond group'. And they suggest the ratio is about 3.15, and what's more is that this scaling law exists in human organizations as well. The only mammalian species studied that had a significant difference were killer whales with 3.8.
It suggests that social organization in mammals is very fractal with one level sharing many structural features of a lower or higher level. They leave the paper asking what sort of mechanisms would cause such a scaling law? Is it a result of bounded rationality (they don't say it, but I am) such as there are cognitive limits on the number of contacts individuals or groups can maintain relationships with? Or is it a time constraint where individuals and groups can only devote so much time to socialization?
I would suggest a lecture by Jeffery West on scaling laws. It's interesting and surprising.
Russell A. Hill, R. Alexander Bently, and Robin I. M. Dunbar
First, this seems like a cool article just because Dunbar is in it. I find her research on group size in primates very interesting. Scaling laws are an interesting topic. In this case he look at a scaling ratio, meaning what is the ratio between group sizes at one level, say families, to group sizes at an adjacent level, like a 'bond group'. And they suggest the ratio is about 3.15, and what's more is that this scaling law exists in human organizations as well. The only mammalian species studied that had a significant difference were killer whales with 3.8.
It suggests that social organization in mammals is very fractal with one level sharing many structural features of a lower or higher level. They leave the paper asking what sort of mechanisms would cause such a scaling law? Is it a result of bounded rationality (they don't say it, but I am) such as there are cognitive limits on the number of contacts individuals or groups can maintain relationships with? Or is it a time constraint where individuals and groups can only devote so much time to socialization?
I would suggest a lecture by Jeffery West on scaling laws. It's interesting and surprising.
A simple model of bipartite cooperation for ecological and organizational networks
A simple model of bipartite cooperation for ecological and organizational networks
Serguei Ssavedra, Felix Reed-Tsochas, and Brian Uzzi
This is a very interesting network growth model which attempts to explain the emergence of community structure and position in bipartite networks using two mechanisms: specialization and interaction. They test their model on networks of relationships between businesses and distributors and between networks of insects and flowers. The authors also happened to introduce me to Guimera's classification universal roles based on community structure rather than role based on structural equivalence.
The only inputs taken from real-world networks are the number of links and the sizes of the two classes of nodes (in a bipartite network, I thought of the two classes as Active and Passive nodes). I felt that was a draw back since there really no other parameters. Perhaps something that could tweak some of the distributions in such a way that the parameter could be measured from real world networks.
There were two mechanisms they claimed are responsible for the development of networks like the ones they studied. Specialization is how many active nodes a passive node will link up with. Interaction helps determine which passive nodes the active nodes will interact with. This is done by pulling from a uniform distribution for each node then sorting according to the roll.
What is important about this article? What is the model useful for? The important part of this article is how well replicates the community structure and nestedness of real world networks. That's useful since it suggests that this model can be used for
simulation purposes to test things like game theory, or develop access structures for models that may have a modular structure. I think further directions could be developing he model, possibly with new parameters to adjust the distributions that can better match real world networks, and testing the model with known processes like resilience (how vulnerable is this model network to attack?) or games (how well do agents cooperate in a public goods game on this model?).
Serguei Ssavedra, Felix Reed-Tsochas, and Brian Uzzi
This is a very interesting network growth model which attempts to explain the emergence of community structure and position in bipartite networks using two mechanisms: specialization and interaction. They test their model on networks of relationships between businesses and distributors and between networks of insects and flowers. The authors also happened to introduce me to Guimera's classification universal roles based on community structure rather than role based on structural equivalence.
The only inputs taken from real-world networks are the number of links and the sizes of the two classes of nodes (in a bipartite network, I thought of the two classes as Active and Passive nodes). I felt that was a draw back since there really no other parameters. Perhaps something that could tweak some of the distributions in such a way that the parameter could be measured from real world networks.
There were two mechanisms they claimed are responsible for the development of networks like the ones they studied. Specialization is how many active nodes a passive node will link up with. Interaction helps determine which passive nodes the active nodes will interact with. This is done by pulling from a uniform distribution for each node then sorting according to the roll.
What is important about this article? What is the model useful for? The important part of this article is how well replicates the community structure and nestedness of real world networks. That's useful since it suggests that this model can be used for
simulation purposes to test things like game theory, or develop access structures for models that may have a modular structure. I think further directions could be developing he model, possibly with new parameters to adjust the distributions that can better match real world networks, and testing the model with known processes like resilience (how vulnerable is this model network to attack?) or games (how well do agents cooperate in a public goods game on this model?).
Friday, May 01, 2009
The structure and function of complex networks
The structure and function of complex networks - M E J Newman
I remember reading a brief two pages on network analysis in a sociological methods book which described it as "qualitative" since the author understood network analysis as mapping the nodes and looking at them. But a number of networks that one might study are so large and complex that little can be gained from looking at the mess of nodes and edges.
Complex networks aren't your typical graphs. The history of graph theory typically doesn't study networks larger than 20 or so nodes, and even a 20 node graph would be pretty big. Complex networks are often many orders of magnitude larger than the networks of graph theory and display properties on the edge of chaos (not completely erratic, nor perfectly structured). In this article Newman surveys the different methods and findings that computer scientists and physicists have discovered about complex networks.
You get the feeling from this paper of the incredible weight of the literature on this topic. A number of my favorite topics are squeezed into a rampant listing of different topics and papers that address them. This paper serves as a really good jumping off point for someone who may be interested in the study of complex networks since it provides such a wide survey of work. The focus is on statistical properties, modeling of networks and processes on networks.
Statistical properties of networks are important since, due to the complexity, visualization is limited in its utility. He covers small-worlds, clustering coefficients, degree distributions, community structure and a number of other measures. His bit on modeling networks starts with the original work of Rapoport and Erdos and Renyi. I really like the small-world model in that it's very simple, but has such interesting properties. A lot of attention is also paid to Albert & Barabasi's scale-free model of preferential attachment.
There is a large divide between the work on this paper though and empirical study of real-world networks. For instance, a number of real-world networks have power-law distributions in their degree distibutions. If Barabasi-Albert's preferential attachment model has a power-law distribution, to what degree can we say that real-world networks develope using preferential attachment based on the degree of other nodes in the network? This paper is an excellent launching point to understanding how the structure of networks develops and behaves, but it should not be considered a point of arrival. The large list of references is where one should go next when they find something that interests them.
I remember reading a brief two pages on network analysis in a sociological methods book which described it as "qualitative" since the author understood network analysis as mapping the nodes and looking at them. But a number of networks that one might study are so large and complex that little can be gained from looking at the mess of nodes and edges.
Complex networks aren't your typical graphs. The history of graph theory typically doesn't study networks larger than 20 or so nodes, and even a 20 node graph would be pretty big. Complex networks are often many orders of magnitude larger than the networks of graph theory and display properties on the edge of chaos (not completely erratic, nor perfectly structured). In this article Newman surveys the different methods and findings that computer scientists and physicists have discovered about complex networks.
You get the feeling from this paper of the incredible weight of the literature on this topic. A number of my favorite topics are squeezed into a rampant listing of different topics and papers that address them. This paper serves as a really good jumping off point for someone who may be interested in the study of complex networks since it provides such a wide survey of work. The focus is on statistical properties, modeling of networks and processes on networks.
Statistical properties of networks are important since, due to the complexity, visualization is limited in its utility. He covers small-worlds, clustering coefficients, degree distributions, community structure and a number of other measures. His bit on modeling networks starts with the original work of Rapoport and Erdos and Renyi. I really like the small-world model in that it's very simple, but has such interesting properties. A lot of attention is also paid to Albert & Barabasi's scale-free model of preferential attachment.
There is a large divide between the work on this paper though and empirical study of real-world networks. For instance, a number of real-world networks have power-law distributions in their degree distibutions. If Barabasi-Albert's preferential attachment model has a power-law distribution, to what degree can we say that real-world networks develope using preferential attachment based on the degree of other nodes in the network? This paper is an excellent launching point to understanding how the structure of networks develops and behaves, but it should not be considered a point of arrival. The large list of references is where one should go next when they find something that interests them.
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