Michael J. Fox

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— Existing work on programmable self-assembly has focused on deterministic performance guarantees—stability of desirable states. In particular, for any acyclic target graph a binary rule set can be synthesized such that the target graph is the uniquely stable assembly. If the number of agents is finite, communication and consensus algorithms are necessary(More)
We analyze a novel model of the co-evolution of linguistic community structure and language. Intuitively, agents want to communicate well with others in their linguistic community, and similarly , linguistic communities consist of those agents that can communicate effectively amongst themselves. Absent the effects of comunity structure, the model suffers(More)
In distributed self-assembly, a multitude of nodes, or agents, seek to form copies of a given target graph. The target graph is identified as the vertices of a labeled graph. Agents encounter each other in spontaneous pairwise interactions and decide whether or not to form or sever edges based on their two labels and a fixed set of local interaction rules(More)
We study an atomic signaling game under stochastic evolutionary dynamics. There are a finite number of players who repeatedly update from a finite number of available languages/signaling strategies. Players imitate the most fit agents with high probability or mutate with low probability. We analyze the long-run distribution of states and show that, for(More)
— We study a simple game-theoretical model of language evolution in finite populations. This model is of particular interest due to a surprising recent result for the infinite population case: under replicator dynamics, the population game converges to socially inefficient outcomes from a set of initial conditions with non-zero Lesbegue measure. If finite(More)
We present an algorithm that, given any target tree, synthesizes reversible self-assembly rules that provide a maximum yield in the sense of stochastic stability. If the reversibility constraint is relaxed then the same algorithm can be trivially modified so that it converges to a maximum yield almost surely. The proof of correctness in both cases relies on(More)
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