A Bubble on the Mighty Mississippi: An Application of a General Model of Speculative Bubbles to the Mississippi Bubble of 1716-1720

Speculative economic bubbles are a common phenomenon but are not wholly understood. Bubbles progress through a series of nine stages. A shock triggers a profitable redistribution of assets, credit creation and new investments; speculation fuels the economy; doubts burst the bubble. The Mississippi Bubble, in early 18th century France, presents an example to which this model may be applied. The history of the Mississippi Bubble, and a brief IS-LM analysis, show that speculative bubbles can be like a lively party: loud and boisterous, but with a mess to clean up afterwards. The French economy was left with a national hangover which stunted business activity and scarred the communal psyche. The model of speculative bubbles applies well to the Mississippi Bubble

Why electing former governors may help ease the partisan gridlock in the US Senate

The US Senate now hosts more than 20 former state governors, a group which played an important role in ending the recent government shutdown. In new research, Alex Keena and Misty Knight-Finley examine the bipartisan role that former governors play in the US' upper house. They find that former governors were 8 percent more likely to vote with the other side, compared to legislators who had not previously been in charge of a state

New Zealand Working For Families programme: Methodological considerations for evaluating MSD programmes

The methodological review is the second part of the evaluation research commissioned by the Ministry of Social Development (MSD) in 2005 to help in the preparation of the evaluation of the Working for Families (WFF) programme. This review enumerates the key evaluation questions identified by MSD as central to their policy concerns and considers how the features of WFF could affect evaluation. It details the methodological and data requirements that must be addressed in order to meet the four key evaluation objectives, namely: (1) tracking and evaluating the implementation and delivery of WFF (2) identifying changes in entitlement take-up and reasons for it (3) establishing the impact of WFF on employment-related outcomes (4) assessing WFF’s effect on net income and quality of life more generally. The methodological review complements the literature review by reviewing evaluations from around the world that are pertinent to WFF. An overview of evaluation methods is provided, concentrating on particular issues that arise within the WFF context. Section 2 focuses on implementation and delivery. Section 3 covers the issues related to take-up and entitlement and their evaluation. Section 4 discusses the evaluation methodologies that can be used in evaluating programmes such as WFF and introduces the data requirements they entail. Making work pay is the focus of section 5. Finally, section 6 examines hardship and poverty, living standards and wellbeing.

Analysis of a Standardized Perioperative Pain Management Order Set in Highly Opioid-Tolerant Patients

Objective: The aim was to assess a standardized order set for perioperative pain management in highly opioid-tolerant patients undergoing elective orthopedic surgery.Methods: This retrospective chart review evaluated a pain order set in highly opioid-tolerant patients undergoing elective total knee or total hip arthroplasty from January 2010 through August 2012. Based on the date of the surgery, patients were allocated into preimplementation or postimplementation order set groups. The primary outcome assessed whether an adjustment in daily opioid dosage was required within the first 48 hours postoperatively. Secondary outcomes included pain scores, length of hospitalization, and safety outcomes.Results: Sixty patients were included in the analysis. An adjustment to postoperative opioid therapy occurred in 62% of the patients in the preimplementation group and in 56% of postimplementation group patients (P = 0.786). There were no differences in median pain scores 48 hours postoperatively (P = 0.348). Cumulative toxicity was increased after order set implementation compared with previous patients (44% versus 5%, P \u3c 0.005); however, opioid doses held for sedation was the only individual toxicity to reach statistical significance (P = 0.011).Conclusions: This study is the first to evaluate a standardized order set for pain management in highly opioid-tolerant patients undergoing elective orthopedic surgery. The order set demonstrated similar efficacy to previous treatment modalities, but opioid-induced sedation was of concern with the order set. After the initial analysis, the order set was modified to minimize opioid-induced sedation. Continual safety analysis is warranted for quality improvement to enhance perioperative pain management in highly opioid-tolerant patients

Insect-inspired navigation: Smart tricks from small brains

Small-brained insects are expert at many tasks that are currently difficult for robots, but especially in the speed and robustness of their learning abilities. In contrast to AI methods which generally take long times to train and large amounts of labelled data, insects are rapid learners of visual and olfactory information and are capable of long distance navigation, exploration and spatial learning. What if we could give robots these abilities, by mimicking the sensors, circuits and behaviours of insects? This is the goal of the Brains on Board project (brainsonboard.co.uk). In this talk, we will discuss the Brains on Board project and our work on insect-inspired visual navigation in particular. The use of visual information for navigation is a universal strategy for sighted animals, amongst whom ants are particular experts despite have small brains and low-resolution vision [1]. To understand how they achieve this, we combine behavioural experiments with modelling and robotics to show how ants directly acquire and use task-specific information through specialised sensors, brains and behaviours, enabling complex behaviour to emerge without complex processing. In this spirit, we will show that an agent – insect or robot – can robustly navigate without ever knowing where it is, without specifying when or what it should learn, nor requiring it to recognise specific objects, places routes or maps. This leads to an algorithm in which visual information specifies actions not locations in which route navigation is recast as a search for familiar views allowing routes through visually complex worlds to be encoded by a single layer artificial neural network (ANN) after a single training run with only low resolution vision [2]. As well as meaning that the algorithms are plausible in terms of memory load and computation for a small-brained insect, it also makes them very well-suited to a small, power-efficient, robot. We thus demonstrate that this algorithm, with all computation performed on a small low-power robot, is capable of delivering reliable direction information along outdoor routes, even when scenes contain few local landmarks and have high-levels of noise (from variable lighting and terrain) [3]. Indeed, routes can be precisely recapitulated and we show that the required computation does not increase with the number of training views. Thus the ANN provides a compact representation of the knowledge needed to traverse a route. In fact, rather than the compact representation losing information, there are instances where the use of an ANN ameliorates the problems of sub optimal paths caused by tortuous training routes. Our results suggest the feasibility of familiarity-based navigation for long-range autonomous visual homing. [1] Shettleworth, S. (2010) Clever animals and killjoy explanations in comparative psychology. Trends in Cognitive Sciences 14 (11):477-481 [2] Baddeley, B., Graham, P., Husbands, P., & Philippides, A. (2012). A model of ant route navigation driven by scene familiarity. PLoS computational biology, 8(1), e1002336. [3] Knight, J, Sakhapov, D., Domcsek, A., Dewar, A., Graham, P., Nowotny, T., Philippides, A. (2019) Insect-Inspired Visual Navigation On-Board an Autonomous Robot: Real-World Routes Encoded in a Single Layer Network. Proc. Artificial Life 19. In Press

A network science-based assessment methodology for robust modular system architectures during early conceptual design

This article describes a methodology to assess, during the early conceptual design stage, the robustness, and modularity of engineering system architectures, which integrates concepts from network science with engineering systems. The application specifically focuses on the architecture of the power, propulsion, and cooling systems of a naval ship. The methodology incorporates a binary Design Structure Matrix as the basis for an assessment of redundancy and modularity effects on robustness, in response to disruption of modules in the architecture. Robustness is used to drive the module selection, which supports the formulation of a robust module configuration subject to the level of redundancy in the system architecture. The case study results demonstrated: redundancy promotes robustness of the architecture and enables modularity; however, high levels of redundancy in comparison to medium level redundancy does not significantly improve robustness. The novel contribution of this article relates to the combined quantitative assessment of redundancy, modularity and robustness in a collective methodology. This methodology supports conceptual design decision making, allowing early prediction of compliance of requirements that enable cost, development time and survivability targets to be achieved

A network tool to analyse and improve robustness of system architectures

The architecture of a system is decided at the initial stage of the design. However, the robustness of the system is not usually assessed in detail during the initial stages, and the exploration of alternative system architectures is limited due to the influence of previous designs and opinions. This article presents a novel network generator that enables the analysis of the robustness of alternative system architectures in the initial stages of design. The generator is proposed as a network tool for system architectures dictated by their configuration of source and sink components structured in a way to deliver a particular functionality. Its parameters allow exploration with theoretical patterns to define the main structure and hub structure, vary the number, size, and connectivity of hub components, define source and sink components and directionality at the hub level and adapt a redundancy threshold criterion. The methodology in this article assesses the system architecture patterns through robustness and modularity network based metrics and methods. Two naval distributed engineering system architectures are examined as the basis of reference for the simulated networks. The generator provides the capacity to create alternative complex system architecture options with identifiable patterns and key features, aiding in a broader explorative and analytical, in-depth, time and cost-efficient initial design process

Predicting Precedent: A Psycholinguistic Artificial Intelligence in the Supreme Court

Since the proliferation of analytic methodologies and ‘big data’ in the 1980s, there have been multiple studies claiming to offer consistent predictions for Supreme Court behavior. Political scientists focus on analyzing the ideology of judges, with prediction accuracy as high as 70%. Institutionalists, such as Kaufmann (2019), seek to make predictions on verdicts based on a thorough, qualitative analysis of rules and structures, with predictive accuracy as high as 75%. We argue that a psycholinguistic model utilizing machine learning (SCOTUS_AI) can best predict Court outcomes. Extracting sentiment features from parsed briefs through the Linguistic Inquiry and Word Count (LIWC), our results indicate SCOTUS_AI (AUC = .8087; Top K=.9144) outcompetes traditional analysis in both class-controlled accuracy and range of possible, specific outcomes. Moreover, unlike traditional models, SCOTUS_AI can also predict the procedural outcome of the case as one-hot encoded by remand (AUC=.76). Our findings support a psycholinguistic paradigm of case analysis, suggesting that the framing of arguments is a relatively strong predictor of case results. Finally, we cast predictions for the Supreme Court docket, demonstrating that SCOTUS_AI can be practically deployed in the field for individual cases

Network-based metrics for assessment of naval distributed system architectures

The architecture of a system is generally established at the end of the conceptual design phase where sixty to eighty percent of the lifetime system costs are committed. The architecture influences the system’s complexity, integrality, modularity and robustness. However, such properties of system architecture are not typically analytically evaluated early on during the conceptual process. System architectures are defined using qualitative experience, and the early stage decisions are subject to the judgement of stakeholders. This article suggests a set of network-based metrics that can potentially function as early evaluation indicators to assess complexity, integrality, modularity and robustness of distributed system architectures during conceptual design. A new robustness metric is proposed that assesses the ability of architecture to support a level functional requirement of the system after a disruption. The new robustness metric is evaluated by an electrical simulation software (MATPOWER). A ship vulnerability assessment software (SURVIVE) was used to find potential disruptive events. Two technical case studies examining existing naval distributed system architectures are elaborated. Conclusions on the network modelling and metrics as early aids to assess system architectures and to choose among alternatives during the conceptual decision phase are presented