The latest issue of Swarm Intelligence is now available featuring this paper “A speculative approach to parallelization in particle swarm optimization.” The original formulation of PSO is due to Kennedy, J., Eberhart, R. C., with Shi, Y. (2001). Swarm Intelligence. Morgan Kaufmann.
This article from The Atlantic.
A FEW YEARS AFTER Philip Rosedale graduated from college with a degree in physics, he joined RealNetworks, then an audio-streaming company. It was a top-down, command-and-control kind of place, where difficult software projects were outlined in advance and executed according to carefully conceived plans.
Rosedale hated it. As a teenager more interested in programming than partying, he had experimented with simulations of flocking birds and other leaderless systems. He marveled at how order could emerge in the absence of hierarchy. “You think that they have a leader and a command architecture, and of course they don’t,” he tells me, going on to describe his “almost spiritual belief” in group self-organization.
Simon died this day in 2001. Check out these two books – Models of a Man (as with most edited books this is uneven, but there is still much to recommend it) and Herbert A. Simon: The Bounds of Reason in Modern America, an excellent intellectual biography. Speaking of Simon, I have a paper coming out entitled “Mindscapes and Landscapes: Hayek and Simon on Cognitive Extension” to be found in a collection edited by Roger Frantz and Robert Leeson Hayek and Behavioural Economics” Vol 4 of Archival Insights into the Evolution of Economics with an introduction by non-other than Vernon Smith (whom I met in Tucson last May) and a host of other luminaries such as Herb Gintis, Deirdre McCloskey, Gerry Steele and others. Here is the abstract for my paper:
Hayek’s and Simon’s social externalism runs on a shared presupposition: mind is constrained in its computational capacity to detect, harvest, and assimilate “data” generated by the infinitely fine-grained and perpetually dynamic characteristic of experience in complex social environments. For Hayek, mind and sociality are co-evolved spontaneous orders, allowing little or no prospect of comprehensive explanation, trapped in a hermeneutically sealed, i.e. inescapably context bound, eco-system. For Simon, it is the simplicity of mind that is the bottleneck, overwhelmed by the ambient complexity of the environmental. Since on Simon’s account complexity is unidirectional, Simon is far more ebullient about the prospects of explanation. Hayek’s social externalism functions as a kind of distributed “extra-neural” memory store manifest as dynamic spontaneous orders. Simon’s organizational rule-governed externalism negotiates the “inner” world (the mind) with the “outer” world through a homeostatic interface that offloads the cognitive burden into the environment. Their respective externalisms may differ in detail but not in spirit in that it ameliorates their shared presupposition of cognitive constraint. Even though any “optimization talk” for Hayek and Simon is objectionable, knowledge acquisition can be represented by a contextualized stigmergic swarm optimization algorithm that gives due emphasis to both the individual and the environment. The key insight is that “perfect” knowledge is both unnecessary, impracticable and indeed irrelevant if one understands the mechanism at work in complex sociality, a stigmergic sociality that in effect augments or scaffolds cognition.
This article is really creating a buzz (sorry!!) The idea has some resonance to an aspect of Hayek’s social epistemology (see the article that I just today uploaded).
In much the same way that synapses are strengthened while unused linkages weaken and wither away, so too are paths to salient social knowledge strengthened or weakened – “social connectionism,” if you will.
Here’s an interview conducted by Howard Rheingold with Mark Elliott who like myself has been promoting the virtues of stigmergy. The application of the concept to city planning and consultation is especially interesting since it’s a world I have recently come into contact with.
Check out this new paper from Swarm Intelligence.
Stigmergy yet again – well, there is no escaping it. While I wouldn’t call stigmergy a new paradigm (that’s too pompous – in any event, paradigms only have shape in a historical sense), stigmergy is one of the most fruitful mechanisms around that speaks to distributed cognition.
Check out this paper nicely titled “Standing on the shoulders of ants: stigmergy in the Web” by a student who is working on the stigmergic properties of the Web. A nice overture to further more technical work highlighting some important issues that need to be addressed. Two points are in order.
(1) I disagree that ”we don‟t yet have a clear definition of stigmergy” – I don’t think that one can prescribe necessary and sufficient conditions but we sure can specify typical features.
(2) And “combining bio-inspired designs and algorithms based on stigmergy with social network analysis might facilitate the creation of a more sophisticated web application.” This is already here, the preeminent example being Amazon’s recommendation algorithm.
Recommendation algorithms generally come in two varieties – collaborative filtering (CF) and cluster models (CM). CF attempts to mimic the process of ‘‘word-of-mouth’’ by which people recommend products or services to one another. CF runs on the notion that people who agreed in the past will agree in the future. CF aggregates ratings of items to recognize similarities between users, and generates a new recommendation of an item by weighting the ratings of similar users for the same item. But this technique is computationally expensive because ‘‘the average customer vector is extremely sparse’’ (Linden, Smith, & York, 2003, p. 77). By contrast CM divides the agent base into segments, treating the task as a classificatory problem. An agent is assigned a category comprised of similar agent profiles. Only then are recommendations generated. CM is computationally efficient since it only searches segments, rather than the complete database. Amazon.com’s recommendation algorithm is a derivative form of CF and CM. Consider an example. A search on Amazon for ‘‘stigmergy’’ returns 176 items, the default sort being by relevance (as opposed to price, reviews, publication date). Also given some prominence is a category ‘‘Customers who bought items in your Recent History also bought x, y, z . . ..’’ supplemented by Listmania, lists of salient material compiled by agents (all-comers as in Wikipedia) who ostensibly have some intimacy with the topic. There are also so-called ‘‘reviews’’ of a given title. All this over and above a record of my recent purchases which included stigmergy related material, assuming one hasn’t expunged Amazon’s cookies from one’s browser. Even on offer is the opportunity, for many titles, to peruse the contents page, read an excerpt and even be enticed by the dustjacket hyperbole. Furthermore, one can be alerted by email when a new title or new edition of a book matching one’s previous trails of interest, will become available: a preorder entitling the buyer to a discount. This all adds up to a highly bespoke experience that is better tailored than being in a bookstore, because it is unlikely the bookstore even stocks a title you have yet to discover as one scans the shelves – there is no ‘‘pheromone’’ trail. The Amazon algorithm rather than matching user-to-user finds items that customers tend to purchase together. It is computationally efficient (and easily scalable) because much of the computation has already been done off-line. The stigmergic interest of Amazon’s algorithm is patently clear: an item-to-item search generates a trail that gives rise to novel patterns of behavior. CF’s great virtue is that suppliers can be finely attuned to consumer behavior. The downside is that there runs the risk of ‘‘a kind of dysfunctional communal narrowing of attention’’ that can be self-fulfilling (Clark, 2003, p. 158; Gureckis & Goldstone, 2006, p. 296). Excerpt from Stigmergic epistemology, stigmergic cognition.
Of the External Senses
Why we still need a mark of the cognitive
Steps to a “Properly Embodied” Cognitive Science
Intersubjectivity and Objectivity in Adam Smith and Edmund Husserl
Epistemological Problems of Privacy and Secrecy
Science, the Market and Iterative Knowledge
The Goldilocks problem and extended cognition
Is external memory memory? Biological memory and extended mind
Brain (call) box
A liberal arts education
