A Parsimonious Model of Treasury Futures 2

2017 ◽  
pp. 23-46
Author(s):  
Joel Clarke Gibbons
Keyword(s):  
Parsimonious Model ◽  
Treasury Futures

Narrative as active inference

2020 ◽  
Author(s):  
Nabil Bouizegarene ◽  
maxwell ramstead ◽  
Axel Constant ◽  
Karl Friston ◽  
Laurence Kirmayer
Keyword(s):  
Well Being ◽  
Social Practices ◽  
Human Adaptation ◽  
Active Inference ◽  
Future Projection ◽  
Future Developments ◽  
The Social ◽  
Parsimonious Model ◽  
Living Organisms ◽  
Theory Research

The ubiquity and importance of narratives in human adaptation has been recognized by many scholars. Research has identified several functions of narratives that are conducive to individuals’ well-being and adaptation as well as to coordinated social practices and enculturation. In this paper, we characterize the social and cognitive functions of narratives in terms of the framework of active inference. Active inference depicts the fundamental tendency of living organisms to adapt by creating, updating, and maintaining inferences about their environment. We review the literature on the functions of narratives in identity, event segmentation, episodic memory, future projection, storytelling practices, and enculturation. We then re-cast these functions of narratives in terms of active inference, outlining a parsimonious model that can guide future developments in narrative theory, research, and clinical applications.

scholarly journals Testing Todd: family types and development

2021 ◽  
pp. 1-18
Author(s):  
Jerg Gutmann ◽  
Stefan Voigt
Keyword(s):  
Rule Of Law ◽  
Civil Society Organizations ◽  
The Rule Of Law ◽  
State Fragility ◽  
Family Types ◽  
Low Levels ◽  
Parsimonious Model ◽  
The One ◽  
High Level

Abstract Many years ago, Emmanuel Todd came up with a classification of family types and argued that the historically prevalent family types in a society have important consequences for its economic, political, and social development. Here, we evaluate Todd's most important predictions empirically. Relying on a parsimonious model with exogenous covariates, we find mixed results. On the one hand, authoritarian family types are, in stark contrast to Todd's predictions, associated with increased levels of the rule of law and innovation. On the other hand, and in line with Todd's expectations, communitarian family types are linked to racism, low levels of the rule of law, and late industrialization. Countries in which endogamy is frequently practiced also display an expectedly high level of state fragility and weak civil society organizations.

Determining the Number of Factors When Population Models Can Be Closely Approximated by Parsimonious Models

2021 ◽  
pp. 001316442199283
Author(s):  
Yan Xia
Keyword(s):  
Factor Model ◽  
Population Level ◽  
Parallel Analysis ◽  
Model Data ◽  
Behavioral Studies ◽  
Number Of Factors ◽  
Parsimonious Models ◽  
Parsimonious Model ◽  
Fit Index ◽  
Tucker Lewis Index

Despite the existence of many methods for determining the number of factors, none outperforms the others under every condition. This study compares traditional parallel analysis (TPA), revised parallel analysis (RPA), Kaiser’s rule, minimum average partial, sequential χ2, and sequential root mean square error of approximation, comparative fit index, and Tucker–Lewis index under a realistic scenario in behavioral studies, where researchers employ a closing–fitting parsimonious model with K factors to approximate a population model, leading to a trivial model-data misfit. Results show that while traditional and RPA both stand out when zero population-level misfits exist, the accuracy of RPA substantially deteriorates when a K-factor model can closely approximate the population. TPA is the least sensitive to trivial misfits and results in the highest accuracy across most simulation conditions. This study suggests the use of TPA for the investigated models. Results also imply that RPA requires further revision to accommodate a degree of model–data misfit that can be tolerated.

scholarly journals Cross-Sectional Model-Building for Research on Subjective Well-Being: Gaining Clarity on Control Variables

2021 ◽  
Author(s):  
David Bartram
Keyword(s):  
Model Building ◽  
Well Being ◽  
Directed Acyclic Graphs ◽  
Subjective Well Being ◽  
Social Scientists ◽  
Cross Sectional ◽  
Acyclic Graphs ◽  
Standard Set ◽  
Substantial Bias ◽  
Parsimonious Model

AbstractHappiness/well-being researchers who use quantitative analysis often do not give persuasive reasons why particular variables should be included as controls in their cross-sectional models. One commonly sees notions of a “standard set” of controls, or the “usual suspects”, etc. These notions are not coherent and can lead to results that are significantly biased with respect to a genuine causal relationship.This article presents some core principles for making more effective decisions of that sort.  The contribution is to introduce a framework (the “causal revolution”, e.g. Pearl and Mackenzie 2018) unfamiliar to many social scientists (though well established in epidemiology) and to show how it can be put into practice for empirical analysis of causal questions.  In simplified form, the core principles are: control for confounding variables, and do not control for intervening variables or colliders.  A more comprehensive approach uses directed acyclic graphs (DAGs) to discern models that meet a minimum/efficient criterion for identification of causal effects.The article demonstrates this mode of analysis via a stylized investigation of the effect of unemployment on happiness.  Most researchers would include other determinants of happiness as controls for this purpose.  One such determinant is income—but income is an intervening variable in the path from unemployment to happiness, and including it leads to substantial bias.  Other commonly-used variables are simply unnecessary, e.g. religiosity and sex.  From this perspective, identifying the effect of unemployment on happiness requires controlling only for age and education; a small (parsimonious) model is evidently preferable to a more complex one in this instance.

Acute respiratory distress syndrome phenotypes with distinct clinical outcomes in PHARLA trial cohort

2021 ◽  
Vol 23 (2) ◽  
pp. 163-170
Author(s):  
Shailesh Bihari ◽  
◽  
Andrew Bersten ◽  
Eldho Paul ◽  
Shay McGuinness ◽  
...  
Keyword(s):  
Acute Respiratory Distress Syndrome ◽  
Respiratory Distress Syndrome ◽  
Respiratory Distress ◽  
Clinical Outcomes ◽  
Distress Syndrome ◽  
Lung Ventilation ◽  
Tumour Necrosis Factor Receptor ◽  
Open Lung ◽  
Inflammatory Phenotype ◽  
Parsimonious Model

Background: The Permissive Hypercapnia, Alveolar Recruitment and Low Airway Pressure (PHARLAP) randomised controlled trial compared an open lung ventilation strategy with control ventilation, and found that open lung ventilation did not reduce the number of ventilator-free days (VFDs) or mortality in patients with moderate-to-severe acute respiratory distress syndrome (ARDS). Parsimonious models can identify distinct phenotypes of ARDS (hypo-inflammatory and hyperinflammatory) which are associated with different outcomes and treatment responses. Objective: To test the hypothesis that a parsimonious model would identify patients with distinctly different clinical outcomes in the PHARLAP study. Design, setting and participants: Blood and lung lavage samples were collected in a subset of PHARLAP patients who were recruited in Australian and New Zealand centres. A previously validated parsimonious model (interleukin-8, soluble tumour necrosis factor receptor-1 and bicarbonate) was used to classify patients with blood samples into hypo-inflammatory and hyperinflammatory groups. Generalised linear modelling was used to examine the interaction between inflammatory phenotype and treatment group (intervention or control). Main outcome measure: The primary outcome was number of VFDs at Day 28. Results: Data for the parsimonious model were available for 56 of 115 patients (49%). Within this subset, 38 patients (68%) and 18 patients (32%) were classified as having hypo-inflammatory and hyperinflammatory phenotypes, respectively. Patients with the hypo-inflammatory phenotype had more VFDs at Day 28 when compared with those with the hyperinflammatory phenotype (median [IQR], 19.5 [11–24] versus 8 [0–21]; P = 0.03). Patients with the hyperinflammatory phenotype had numerically fewer VFDs when managed with an open lung strategy than when managed with control “protective” ventilation (median [IQR], 0 [0–19] versus 16 [8–22]). Conclusion: In the PHARLAP trial, ARDS patients classified as having a hyperinflammatory phenotype, with a parsimonious three-variable model, had fewer VFDs at Day 28 compared with patients classified as having a hypo-inflammatory phenotype. Future clinical studies of ventilatory strategies should consider incorporating distinct ARDS phenotypes into their trial design.

Sign in / Sign up

Export Citation Format

Share Document