Beans as a Medium of Exchange

This note describes an experiment, which is an extension of the experiment proposed by Levy and Bergen (1993). The experiment is designed to simulate an environment where something that is very similar to fiat money (i.e., is homogenous, durable, portable, storable, divisible, has no intrinsic value of its own, etc.) will be accepted in market transactions and thus will have a “value.” This is accomplished through an implementation of a taxation mechanism in the spirit of legal restriction theory of monetary economics.Roles of Money, Functions of Money, Barter, Exchange Economy, Medium of Exchange, Store of Value, Unit of Account, Experiment, Efficient and Inefficient Medium of Exchange, Types of Money, Fiat Money, Commodity Money, Features of Money, Homogeneity, Divisibility, Durability, Storability, Portability, Scarcity, Efficiency versus Equity, Information Cost

Spatial Sign Correlation

A new robust correlation estimator based on the spatial sign covariance matrix (SSCM) is proposed. We derive its asymptotic distribution and influence function at elliptical distributions. Finite sample and robustness properties are studied and compared to other robust correlation estimators by means of numerical simulations.Comment: 20 pages, 7 figures, 2 table

The spatial sign covariance matrix and its application for robust correlation estimation

8 pages, 2 figures, to be published in the conference proceedings of 11th international conference "Computer Data Analysis & Modeling 2016" http://www.ajs.or.at/index.php/ajs/about/editorialPolicies#openAccessPolicyPeer reviewedPublisher PD

Improving Neural Parsing by Disentangling Model Combination and Reranking Effects

Recent work has proposed several generative neural models for constituency parsing that achieve state-of-the-art results. Since direct search in these generative models is difficult, they have primarily been used to rescore candidate outputs from base parsers in which decoding is more straightforward. We first present an algorithm for direct search in these generative models. We then demonstrate that the rescoring results are at least partly due to implicit model combination rather than reranking effects. Finally, we show that explicit model combination can improve performance even further, resulting in new state-of-the-art numbers on the PTB of 94.25 F1 when training only on gold data and 94.66 F1 when using external data.Comment: ACL 2017. The first two authors contributed equall

Unified Pragmatic Models for Generating and Following Instructions

We show that explicit pragmatic inference aids in correctly generating and following natural language instructions for complex, sequential tasks. Our pragmatics-enabled models reason about why speakers produce certain instructions, and about how listeners will react upon hearing them. Like previous pragmatic models, we use learned base listener and speaker models to build a pragmatic speaker that uses the base listener to simulate the interpretation of candidate descriptions, and a pragmatic listener that reasons counterfactually about alternative descriptions. We extend these models to tasks with sequential structure. Evaluation of language generation and interpretation shows that pragmatic inference improves state-of-the-art listener models (at correctly interpreting human instructions) and speaker models (at producing instructions correctly interpreted by humans) in diverse settings.Comment: NAACL 2018, camera-ready versio