The dynamics of critical Kauffman networks under asynchronous stochastic update

We show that the mean number of attractors in a critical Boolean network under asynchronous stochastic update grows like a power law and that the mean size of the attractors increases as a stretched exponential with the system size. This is in strong contrast to the synchronous case, where the number of attractors grows faster than any power law.Comment: submitted to PR

Comment on "Quantum Decoherence in Disordered Mesoscopic Systems"

In a recent paper, Phys. Rev. Lett. 81, 1074 (1998), Golubev and Zaikin (GZ) found that ``zero-point fluctuations of electrons'' contribute to the dephasing rate extracted from the magnetoresistance. As a result, the dephasing rate remains finite at zero temperature. GZ claimed that their results ``agree well with the experimental data''. We point out that the GZ results are incompatible with (i) conventional perturbation theory of the effects of interaction on weak localization (WL), and (ii) with the available experimental data. More detailed criticism of GZ findings can be found in cond-mat/9808053.Comment: 1 page, no figure

What Differences a Day Can Make: Quantile Regression Estimates of the Distribution of Daily Learning Gains

Recent research exploits a variety of natural experiments that create exogenous variation in annual school days to estimate the average effect of formal schooling on students' academic achievement. However, the extant literature's focus on average effects masks potentially important variation in the effect of formal schooling across the achievement distribution. We address this gap in the literature by estimating quantile regressions that exploit quasi-random variation in the number of school days between kindergarten students' fall and spring tests in the nationally representative Early Childhood Longitudinal Study Kindergarten Cohort (ECLS-K). The marginal effect of a typical 250-day school-year on kindergarten students' math and reading gains varies significantly, and monotonically, across the achievement distribution. For example, the marginal effect on the 10th percentile of the reading achievement distribution is 0.9 test score standard deviation (SD), while the marginal effect on the 90th percentile is 2.1 test score SD. We find analogous results for math achievement

Short-Run Externalities of Civic Unrest: Evidence from Ferguson, Missouri

We document externalities of the civic unrest experienced in Ferguson, MO following the police shooting of an unarmed black teenager. Difference-in-differences and synthetic control method estimates compare Ferguson-area schools to neighboring schools in the greater St. Louis area and find that the unrest led to statistically significant, arguably causal declines in students' math and reading achievement. Attendance is one mechanism through which this effect operated, as chronic absence increased by five percent in Ferguson-area schools. Impacts were concentrated in elementary schools and at the bottom of the achievement distribution and spilled over into majority black schools throughout the area

Learning, Social Intelligence and the Turing Test - why an "out-of-the-box" Turing Machine will not pass the Turing Test

The Turing Test (TT) checks for human intelligence, rather than any putative general intelligence. It involves repeated interaction requiring learning in the form of adaption to the human conversation partner. It is a macro-level post-hoc test in contrast to the definition of a Turing Machine (TM), which is a prior micro-level definition. This raises the question of whether learning is just another computational process, i.e. can be implemented as a TM. Here we argue that learning or adaption is fundamentally different from computation, though it does involve processes that can be seen as computations. To illustrate this difference we compare (a) designing a TM and (b) learning a TM, defining them for the purpose of the argument. We show that there is a well-defined sequence of problems which are not effectively designable but are learnable, in the form of the bounded halting problem. Some characteristics of human intelligence are reviewed including it's: interactive nature, learning abilities, imitative tendencies, linguistic ability and context-dependency. A story that explains some of these is the Social Intelligence Hypothesis. If this is broadly correct, this points to the necessity of a considerable period of acculturation (social learning in context) if an artificial intelligence is to pass the TT. Whilst it is always possible to 'compile' the results of learning into a TM, this would not be a designed TM and would not be able to continually adapt (pass future TTs). We conclude three things, namely that: a purely "designed" TM will never pass the TT; that there is no such thing as a general intelligence since it necessary involves learning; and that learning/adaption and computation should be clearly distinguished.Comment: 10 pages, invited talk at Turing Centenary Conference CiE 2012, special session on "The Turing Test and Thinking Machines

The properties of attractors of canalyzing random Boolean networks

We study critical random Boolean networks with two inputs per node that contain only canalyzing functions. We present a phenomenological theory that explains how a frozen core of nodes that are frozen on all attractors arises. This theory leads to an intuitive understanding of the system's dynamics as it demonstrates the analogy between standard random Boolean networks and networks with canalyzing functions only. It reproduces correctly the scaling of the number of nonfrozen nodes with system size. We then investigate numerically attractor lengths and numbers, and explain the findings in terms of the properties of relevant components. In particular we show that canalyzing networks can contain very long attractors, albeit they occur less often than in standard networks.Comment: 9 pages, 8 figure

Traffic flow on realistic road networks with adaptive traffic lights

We present a model of traffic flow on generic urban road networks based on cellular automata. We apply this model to an existing road network in the Australian city of Melbourne, using empirical data as input. For comparison, we also apply this model to a square-grid network using hypothetical input data. On both networks we compare the effects of non-adaptive vs adaptive traffic lights, in which instantaneous traffic state information feeds back into the traffic signal schedule. We observe that not only do adaptive traffic lights result in better averages of network observables, they also lead to significantly smaller fluctuations in these observables. We furthermore compare two different systems of adaptive traffic signals, one which is informed by the traffic state on both upstream and downstream links, and one which is informed by upstream links only. We find that, in general, both the mean and the fluctuation of the travel time are smallest when using the joint upstream-downstream control strategy.Comment: 41 pages, pdflate