Other things equal: Samuelsonian Economics

Deirdre McClosky argues that we need to get beyond the Age of Samuelsonianism in economics and get back to theorizing and observing. Economics, especially mainstream American economics, for all its promise, is in very bad shape because it has fallen into a cargo-cult version of “science” in which qualitative theorem-making runs the “theory” and statistical significance without a loss function runs the “empirical work.” Consequently, none of the high-prestige “work” in the journals is to be taken seriously. Most (say 95 percent) of its alleged “results” have to be done all over again, by economic scientists using—in preference to the mumbo-jumbo that has passed for scientific method among economists since 1947— real scientific methods (such as serious simulation disciplined by the world’s facts; and functional-form math; and statistical significance, when relevant, with loss functions; and economic history; and inquiry into all the other human sciences we economists have been invited so long to ignore). A real science—or a real inquiry into anything about the actual world—should both think and watch, theorize and observe.Economics

Inertial Range Scaling, Karman-Howarth Theorem and Intermittency for Forced and Decaying Lagrangian Averaged MHD in 2D

We present an extension of the Karman-Howarth theorem to the Lagrangian averaged magnetohydrodynamic (LAMHD-alpha) equations. The scaling laws resulting as a corollary of this theorem are studied in numerical simulations, as well as the scaling of the longitudinal structure function exponents indicative of intermittency. Numerical simulations for a magnetic Prandtl number equal to unity are presented both for freely decaying and for forced two dimensional MHD turbulence, solving directly the MHD equations, and employing the LAMHD-alpha equations at 1/2 and 1/4 resolution. Linear scaling of the third-order structure function with length is observed. The LAMHD-alpha equations also capture the anomalous scaling of the longitudinal structure function exponents up to order 8.Comment: 34 pages, 7 figures author institution addresses added magnetic Prandtl number stated clearl

Motif counting beyond five nodes

Counting graphlets is a well-studied problem in graph mining and social network analysis. Recently, several papers explored very simple and natural algorithms based on Monte Carlo sampling of Markov Chains (MC), and reported encouraging results. We show, perhaps surprisingly, that such algorithms are outperformed by color coding (CC) [2], a sophisticated algorithmic technique that we extend to the case of graphlet sampling and for which we prove strong statistical guarantees. Our computational experiments on graphs with millions of nodes show CC to be more accurate than MC; furthermore, we formally show that the mixing time of the MC approach is too high in general, even when the input graph has high conductance. All this comes at a price however. While MC is very efficient in terms of space, CC’s memory requirements become demanding when the size of the input graph and that of the graphlets grow. And yet, our experiments show that CC can push the limits of the state-of-the-art, both in terms of the size of the input graph and of that of the graphlets

Relating Knowledge and Coordinated Action: The Knowledge of Preconditions Principle

The Knowledge of Preconditions principle (KoP) is proposed as a widely applicable connection between knowledge and action in multi-agent systems. Roughly speaking, it asserts that if some condition is a necessary condition for performing a given action A, then knowing that this condition holds is also a necessary condition for performing A. Since the specifications of tasks often involve necessary conditions for actions, the KoP principle shows that such specifications induce knowledge preconditions for the actions. Distributed protocols or multi-agent plans that satisfy the specifications must ensure that this knowledge be attained, and that it is detected by the agents as a condition for action. The knowledge of preconditions principle is formalised in the runs and systems framework, and is proven to hold in a wide class of settings. Well-known connections between knowledge and coordinated action are extended and shown to derive directly from the KoP principle: a "common knowledge of preconditions" principle is established showing that common knowledge is a necessary condition for performing simultaneous actions, and a "nested knowledge of preconditions" principle is proven, showing that coordinating actions to be performed in linear temporal order requires a corresponding form of nested knowledge.Comment: In Proceedings TARK 2015, arXiv:1606.0729