"Influence systems form a large class of multiagent systems designed to model how influence, broadly defined, spreads across a dynamic network. We build a general analytical framework which we then use to prove that, while sometimes chaotic, influence dynamics is almost always asymptotically periodic. Besides resolving the dynamics of a popular family of multiagent systems, the other contribution of this work is to introduce a new type of renormalization-based bifurcation analysis for multiagent systems."

"This article is concerned with the Axelrod model, a stochastic process which similarly to the voter model includes social influence, but unlike the voter model also accounts for homophily. Each vertex of the network of interactions is characterized by a set of F cultural features, each of which can assume q states. Pairs of adjacent vertices interact at a rate proportional to the number of features they share, which results in the interacting pair having one more cultural feature in common. The Axelrod model has been extensively studied during the past ten years, based on numerical simulations and simple mean-field treatments, while there is a total lack of analytical results for the spatial model itself. Simulation results for the one-dimensional system led physicists to formulate the following conjectures. When the number of features F and the number of states q both equal two, or when the number of features exceeds the number of states, the system converges to a monocultural equilibrium in the sense that the number of cultural domains rescaled by the population size converges to zero as the population goes to infinity. In contrast, when the number of states exceeds the number of features, the system freezes in a highly fragmented configuration in which the ultimate number of cultural domains scales like the population size. In this article, we prove analytically for the one-dimensional system convergence to a monocultural equilibrium in terms of clustering when F = q = 2, as well as fixation to a highly fragmented configuration when the number of states is sufficiently larger than the number of features. Our first result also implies clustering of the one-dimensional constrained voter model."

"A discrete agent-based model on a periodic lattice of arbitrary dimension is considered. Agents move to nearest-neighbor sites by a motility mechanism accounting for general interactions, which may include volume exclusion. The partial differential equation describing the average occupancy of the agent population is derived systematically. A diffusion equation arises for all types of interactions and is nonlinear except for the simplest interactions. In addition, multiple species of interacting subpopulations give rise to an advection-diffusion equation for each subpopulation. This work extends and generalizes previous specific results, providing a construction method for determining the transport coefficients in terms of a single conditional transition probability, which depends on the occupancy of sites in an influence region. These coefficients characterize the diffusion of agents in a crowded environment in biological and physical processes."

Isn't this all obvious?

"Just as neurons interconnect in networks that create structured thoughts beyond the ken of any individual neuron, so people spontaneously organize themselves into groups to create emergent organizations that no individual may intend, comprehend, or even perceive. ... two experimental paradigms in which we attempt to build predictive bridges between the beliefs, goals, and cognitive capacities of individuals and patterns of behavior at the group level, showing how the members of a group dynamically allocate themselves to resources and how innovations diffuse through a social network. Agent-based computational models have provided useful explanatory and predictive accounts. Together, the models and experiments point to tradeoffs between exploration and exploitation—that is, compromises between individuals using their own innovations and using innovations obtained from their peers—and the emergence of group-level organizations..."

Looks interesting, and co-edited by my old office mate Troy.  (Oh, and Blake has a paper as well!)  ETA: We don't subscribe. :(

I have liked DeLanda's recent books, though I still find _War in the Age of Intelligent Machines_ bad, and don't get what he sees in Deleuze.  I look forward to this one with interest.

"NetLogo is a software platform for agent-based modelling that is increasingly used in ecological and environmental modelling. So far, for comprehensive analyses of agent-based models (ABMs) implemented in NetLogo, results needed to be written to files and evaluated by using external software, for example R. Ideally, however, it would be possible to call any R function from within a NetLogo program. This would allow sophisticated interactive statistical analysis of model structure and dynamics, using R functions and packages for generating certain statistical distributions and experimental design, and for implementing complex descriptive submodels within ABMs. Here we present an R extension of NetLogo. It consists of only nine new NetLogo primitives for sending data between NetLogo and R and for calling R functions (six additional primitives for debugging). We demonstrate the usage of the R extension with three short examples."

"Static estimation treats variation in the dependent data as noise, or error. In simulations using agent-based models, however — especially with dynamic responses — such variation in the simulated output may well possess valuable information from the simulation. We explore previous methods of estimating simulation models before examining the simulated output from an early agent-based model in marketing, and asking whether these methods allow the modeller to conclude with some degree of confidence that the simulated output is generated by essentially the same process that generated the historical output by measuring the degree of similarity between two sets of time-series. We introduce a new measure, the State Similarity Measure (SSM), to measure the distance beween two sets of time-series that embody dynamic responses." --- Not read yet, no idea if it's useful.

"In the list of horrible waste of tax-payer’s hard earned money exposed by Fox News, I saw this: '$50,000 to build a computer model of an ancient city in Pakistan complete with “animated and interactive ‘inhabitants’.' If history is our guide, it won’t be long before these inhabitants fall to radical ideologies and turn back on the NEH! Agent-based computing models are already rife with terrorists and terrorist-sympathizers. We know this."

"We use agent-based modeling to investigate the effect of conservatism and partisanship on the efficiency with which large populations solve the density classification task--a paradigmatic problem for information aggregation and consensus building. We find that conservative agents enhance the populations' ability to efficiently solve the density classification task despite large levels of noise in the system. In contrast, we find that the presence of even a small fraction of partisans holding the minority position will result in deadlock or a consensus on an incorrect answer. Our results provide a possible explanation for the emergence of conservatism and suggest that even low levels of partisanship can lead to significant social costs."

My reaction to the first half is "preach it, brothers and sisters!" Perhaps inevitably, the constructive proposals of the 2nd half are less compelling.

Ummm... maybe my memory is playing tricks with me, but this really sounds like the Brock & Hommes models from the mid-1990s...

"...two market agent-based model [of] how global portfolio managers affect global financial crises and stability. The Markowitz model [plus] several insights from behavioral finance. Simulation results ... reveal that global financial crises do not occur when global managers are added to the model. However, when risk is determined based on investors’ historical losses and exponential averaging, slight global manager losses can trigger a widening of both markets’ risk premium, accelerating the decline in asset prices worldwide. ... global managers are ... stabilizing
... in smaller numbers [but] destabilizing in larger numbers. The ability to reduce risk by diversifying across markets results in excessive risk taking. If global managers exist in larger numbers, systematic over leverage may result such that a deleveraging process can lead to the spreading of financial crises."

Laws of large numbers and central limit theorems for (simple) agent-based models.

See http://bactra.org/reviews/computational-beauty-of-nature/

there isn't any real statistics in this...

Source code for simulations - can I get them to work? hard to assign if not

Required text for my "chaos, complexity and inference" class