Theodore C. Belding

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When the same set of people interact frequently with one another, they grow to think more and more along the same lines, a phenomenon we call " collective cognitive convergence " (C 3). In this paper, we discuss instances of this phenomenon and why it is advantageous or disadvantageous; review previous work in sociology, computational social science, and(More)
Stigmergy is usually used to model semantically simple problems such as routing. It can be applied to more complex problems by encoding them in the stigmergic environment. We demonstrate this approach by showing how stigmergic agents can plan over a hierarchical task network, specifically a resource-oriented dialect of the TAEMS language. 1 Introduction 1(More)
Swarming agents often operate in benign geographic topologies that let them explore alternative trajectories with minor variations that the agent dynamics then amplify for improved performance. In this paper we demonstrate the deployment of swarming agents in the non-metric and discontinuous topology of a process graph. We align our research with(More)
When a set of people interact frequently with one another, they often grow to think more and more along the same lines, a phenomenon we call " collective cognitive convergence " (C 3). We discuss instances of C 3 and why it is advantageous or disadvantageous; review previous work in sociology, computational social science, and evolutionary biology that(More)
It is still unclear how an evolutionary algorithm (EA) searches a fitness landscape, and on what fitness landscapes a particular EA will do well. The validity of the building-block hypothesis , a major tenet of traditional genetic algorithm theory, remains controversial despite its continued use to justify claims about EAs. This paper outlines a research(More)
Many agent-based models predict the future. Nonlinear interactions in most non-trivial domains make predictions useless beyond a certain point (the "prediction horizon"), as agent trajectories diverge. We exhibit this behavior in a simple agent-based model, and discuss how a single agent in such a model can estimate the prediction horizon locally and use(More)
One motivation for many agent-based models is to predict the future. The nonlinearity of agent interactions in most non-trivial domains mean that the usefulness of such predictions will be limited beyond a certain point (the " prediction horizon "), due to unbounded divergence of their trajectories. The model's predictions are increasingly useful out to the(More)