You can use an ABM to test hypotheses for how something works, like a thought experiment. But building, running, and analyzing a model is science, in fact like an empirical science. So the relationship between the model and a philosophical point is similar to the relationship between some conceptual issue and the science of that issue. So what role does philosophy play in making and/or interpreting models? How can the results be philosophically relevant/important/implicating? Can science ever decide something in philosophy?

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The structure of systems at different scales is often referred to as a hierarchy, and traditional thinking implies that this is so. Molecules make up organelles, which make up cells, which make up tissues, which make up organs, which make up organ systems, which make up bodies, and beyond. I refer to any scale at which recognizable coherent patterns in behavior are observable and describable as a level of organization. This is an epistemic notion because it relates to what we can capture in models, not to the structure of reality. Hierarchies of scale are a mixed ontological and epistemic notion: the levels are still based on what people can discern, but there is an added ontological assumption that a higher level exhaustively includes the elements of a lower level. Now I will show why levels of organization, not hierarchies, are the domains and ranges of scientifically useful reduction and emergence relationships.

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One of the oft-cited features of complex systems is their ability to adapt to environmental changes and shocks. This is often contrasted with human-made engineered systems which are typically specialized and optimized to work in only the limited conditions for which they were designed. The point being underlined in these discussions is that complex systems are self-organizing (and often self-perpetuating) and thus their behaviors are contingent on inputs in ways that purpose-built systems are typically not. What these discussions often leave out is the crucial fragility that many complex systems exhibit to specific inputs and disruptions.

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Michigan State University will host the 13th International Artificial Life Conference (Alife13) July 19 to 22, 2012. This year’s major conference theme is "Evolution in Action". They encourage submissions by biologists, computer scientists, and especially interdisciplinary groups projects that explore the many ways that evolution and artificial life research intersect. The current paper submission is Feb. 26, 2012 and the conference page is here

The University of North Carolina at Charlotte is sponsoring their 1st annual conference on Complexity and Human Experience from May 30th to June 1st, 2012. This conference is geared specifically toward complexity research in the humanities and social sciences. Submissions of 5000-word papers are due February 5th. More information can be found on their website.

Complexity science purports to shift the focus from describing states to describing processes, yet this conceptual shift has been much slower and more difficult than many would let on. The amount of self-anointed complexity research that still relies on equilibrium analysis reveals the limited degree to which the field has actually broken free from its methodological roots. Complex adaptive systems are exactly those that self organize in a way that allows them to maintain functionality, cohesion, or other such properties while being continuously in flux. If a system really reaches equilibrium then it’s a bad candidate for being a complex system. But we don’t need to throw out all our old concepts in the pursuit of new, dynamic ones; we can use them as a springboard for developing complexity science. This post is an attempt to analogize the equilibrium concept in a way that directly shifts the focus from states to processes.

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A new project I'm working on aims to establish a formalism to convert between and combine (update) beliefs measured in just about any way available. Probabilistic, Boolean-based beliefs with Bayesian updating is so much the dominant approach that anything else seems like a niche belief representation. Alternatives such as fuzzy truth and Dempster-Shafer beliefs (incorporating uncertainty) have their subdomain applications for specialized information-system components, but then these components cannot work with other components representing beliefs in a different way. Furthermore, several of these alternative representations do not yet have consistent updating rules, or clear guidelines for how to apply and interpret combined probabilities. So I want to do this.

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Distance, as it is usually thought of, is between two points (aka dyadic). This can be discreet as in the minimal number of grid spaces an agent needs to traverse to travel from location A to location B (the Chebyshev distance). For standard real-valued spaces it is the length of the shortest line connecting the two points. We can general this into higher dimensions by measuring the distances of manifolds (like strings, curves, solids, etc.), but to be a metric they always have to satisfy the same criteria: 1) positive or 0, 2) symmetric, and 3) the triangle inequality. Now, let's say I want to measure how far apart the elements of a set are, say three points in a 2D plane. Can we come up with a formula for the distance among these three points?

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The idea of layered networks is quite straight forward: the objects in your model are related to each other in more than one kind of way. This distinguishes a layered network from a multi-graph in which there are multiple connections of the same type. In some cases types of edges represent different relational features; for example, one could have a model with people as nodes and the ability to see each other (i.e. in line of sight) as being one kind of edge and the ability to hear each other (i.e. within natural hearing range) as another. Clearly these are different information paths with different properties on the kinds of information, speeds, distances possible, reciprocity (directedness), etc. Combining a city's road, power, and water networks on the same graph is another straightforward example. For these sorts of heterogeneous communication networks many of the common properties (such as path length and out-component) have already been adapted. But others (such as community structure and betweenness centrality) need a deeper look.

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The dominant discrete geometry for 2D simulation environments is the square grid; mostly because it's the default and conceptually and methodologically simple. There are, however, some advantages to using a hexagonal grid in 2D: hexes also tessellate (tile) in two dimensions and the centers of all six neighboring hexes are equidistant (see Hex in Netlogo). In three dimensions, however, only the cube tessellates and hence the corner effects are inescapable…until now! By considering discrete geometries as networks of connections, it is possible to build a 3D hexagonal-like (actually dodecahedral) geometry in which all neighbors are equidistant.

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