scholarly journals Token-Weighted Crowdsourcing

2020 ◽  
Vol 66 (9) ◽  
pp. 3843-3859 ◽  
Author(s):  
Gerry Tsoukalas ◽  
Brett Hemenway Falk
Keyword(s):  
Revenue Management ◽  
Predictive Power ◽  
Weighted Voting ◽  
Aggregation Mechanism ◽  
Wide Range ◽  
Marketing Materials ◽  
Optimality Gap

Blockchain-based platforms often rely on token-weighted voting (“τ-weighting”) to efficiently crowdsource information from their users for a wide range of applications, including content curation and on-chain governance. We examine the effectiveness of such decentralized platforms for harnessing the wisdom and effort of the crowd. We find that τ-weighting generally discourages truthful voting and erodes the platform’s predictive power unless users are “strategic enough” to unravel the underlying aggregation mechanism. Platform accuracy decreases with the number of truthful users and the dispersion in their token holdings, and in many cases, platforms would be better off with a “flat” 1/n mechanism. When, prior to voting, strategic users can exert effort to endogenously improve their signals, users with more tokens generally exert more effort—a feature often touted in marketing materials as a core advantage of τ-weighting—however, this feature is not attributable to the mechanism itself, and more importantly, the ensuing equilibrium fails to achieve the first-best accuracy of a centralized platform. The optimality gap decreases as the distribution of tokens across users approaches a theoretical optimum, which we derive, but tends to increase with the dispersion in users’ token holdings. This paper was accepted by Gabriel Weintraub, revenue management and market analytics.

An Extension of the Theory of Planned Behavior Model for Tourists

2010 ◽  
Vol 36 (3) ◽  
pp. 390-417 ◽  
Author(s):  
Cathy H. C. Hsu ◽  
Songshan (Sam) Huang
Keyword(s):  
Theory Of Planned Behavior ◽  
Planned Behavior ◽  
Behavioral Intention ◽  
Predictive Power ◽  
Actual Behavior ◽  
Tourist Behavior ◽  
Comprehensive Literature Review ◽  
Wide Range ◽  
Tpb Model ◽  
Stage 1

The sufficiency of theory of planned behavior (TPB) is still being questioned although the model was validated in predicting a wide range of intentions and behaviors. Based on a comprehensive literature review, an extended TPB model of tourists was proposed to investigate relations among constructs of the model with the addition of motivation and actual behavior. An instrument was developed based on previous tourism and marketing studies as well as focus groups. A two-wave data collection was implemented, with data collected from 1,524 Beijing, Shanghai, and Guangzhou residents in Stage 1 and 311 respondents from the same cohort in Stage 2. Results of the study demonstrated that the extended TPB model with tourist motivation fit the data relatively well, explaining 5% more of the variation in behavioral intention in comparison with a base model without motivation. However, the model with both tourist motivation and actual behavior was not tenable, despite a marginal relationship found between behavioral intention and actual tourist behavior using regression analysis. The findings warrant further research examining the predictive power of behavioral intention on actual behavior.

scholarly journals An analytical approach to bistable biological circuit discrimination using real algebraic geometry

2014 ◽  
Author(s):  
Dan Siegal-Gaskins ◽  
Elisa Franco ◽  
Tiffany Zhou ◽  
Richard M Murray
Keyword(s):  
Algebraic Geometry ◽  
Circuit Design ◽  
Biological Networks ◽  
Predictive Power ◽  
Real Algebraic Geometry ◽  
Ease Of Use ◽  
Topological Properties ◽  
Essential Components ◽  
Wide Range ◽  
Geometric Techniques

Biomolecular circuits with two distinct and stable steady states have been identified as essential components in a wide range of biological networks, with a variety of mechanisms and topologies giving rise to their important bistable property. Understanding the differences between circuit implementations is an important question, particularly for the synthetic biologist faced with determining which bistable circuit design out of many is best for their specific application. In this work we explore the applicability of Sturm?s theorem--a tool from 19th-century real algebraic geometry--to comparing ?functionally equivalent? bistable circuits without the need for numerical simulation. We first consider two genetic toggle variants and two different positive feedback circuits, and show how specific topological properties present in each type of circuit can serve to increase the size of the regions of parameter space in which they function as switches. We then demonstrate that a single competitive monomeric activator added to a purely-monomeric (and otherwise monostable) mutual repressor circuit is sufficient for bistability. Finally, we compare our approach with the Routh-Hurwitz method and derive consistent, yet more powerful, parametric conditions. The predictive power and ease of use of Sturm?s theorem demonstrated in this work suggests that algebraic geometric techniques may be underutilized in biomolecular circuit analysis.

Does the Capitalization of Intangible Assets Increase the Predictability of Corporate Failure?

2011 ◽  
Vol 25 (1) ◽  
pp. 41-70 ◽  
Author(s):  
Stewart Jones
Keyword(s):  
Default Risk ◽  
Predictive Power ◽  
Intangible Assets ◽  
Value Relevance ◽  
Extended Period ◽  
Failure Model ◽  
Statistical Association ◽  
Corporate Failure ◽  
Wide Range ◽  
Reporting Environment

SYNOPSIS: The value relevance of intangible assets is now well documented in the literature, leading to calls for standard setters to adopt more flexible reporting rules for these assets. In this study, I evaluate the merits of intangibles capitalization from a bankruptcy and default risk perspective, which has not been previously considered in the literature. The study is conducted in a unique reporting environment, where managers have had considerable discretion to capitalize a wide range of intangibles over an extended period. Three main results are reported. First, failing firms capitalize intangible assets more aggressively than non-failed firms over the 16-year sample period, but particularly over the five-year period leading up to firm failure. Second, drawing on the accounting choice literature, I find that managers’ propensity to capitalize intangible assets has a strong statistical association with earnings management proxies, particularly among failing firms. Finally, voluntary capitalization of intangibles has strong discriminating and predictive power in a firm failure model, even after controlling for several other factors.

scholarly journals Predicting Resistivity and Permeability of Porous Media Using Minkowski Functionals

2019 ◽  
Vol 131 (2) ◽  
pp. 705-722 ◽  
Author(s):  
Per Arne Slotte ◽  
Carl Fredrik Berg ◽  
Hamid Hosseinzade Khanamiri
Keyword(s):  
Surface Roughness ◽  
Predictive Power ◽  
Pore Space ◽  
Random Systems ◽  
Minkowski Functional ◽  
Two Dimensional ◽  
Formation Factor ◽  
Minkowski Functionals ◽  
Functional Value ◽  
Wide Range

AbstractPermeability and formation factor are important properties of a porous medium that only depend on pore space geometry, and it has been proposed that these transport properties may be predicted in terms of a set of geometric measures known as Minkowski functionals. The well-known Kozeny–Carman and Archie equations depend on porosity and surface area, which are closely related to two of these measures. The possibility of generalizations including the remaining Minkowski functionals is investigated in this paper. To this end, two-dimensional computer-generated pore spaces covering a wide range of Minkowski functional value combinations are generated. In general, due to Hadwiger’s theorem, any correlation based on any additive measurements cannot be expected to have more predictive power than those based on the Minkowski functionals. We conclude that the permeability and formation factor are not uniquely determined by the Minkowski functionals. Good correlations in terms of appropriately evaluated Minkowski functionals, where microporosity and surface roughness are ignored, can, however, be found. For a large class of random systems, these correlations predict permeability and formation factor with an accuracy of 40% and 20%, respectively.

scholarly journals An analytical approach to bistable biological circuit discrimination using real algebraic geometry

2015 ◽  
Vol 12 (108) ◽  
pp. 20150288 ◽  
Author(s):  
Dan Siegal-Gaskins ◽  
Elisa Franco ◽  
Tiffany Zhou ◽  
Richard M. Murray
Keyword(s):  
Algebraic Geometry ◽  
Circuit Design ◽  
Biological Networks ◽  
Predictive Power ◽  
Real Algebraic Geometry ◽  
Ease Of Use ◽  
Topological Properties ◽  
Essential Components ◽  
Wide Range ◽  
Geometric Techniques

Biomolecular circuits with two distinct and stable steady states have been identified as essential components in a wide range of biological networks, with a variety of mechanisms and topologies giving rise to their important bistable property. Understanding the differences between circuit implementations is an important question, particularly for the synthetic biologist faced with determining which bistable circuit design out of many is best for their specific application. In this work we explore the applicability of Sturm's theorem—a tool from nineteenth-century real algebraic geometry—to comparing ‘functionally equivalent’ bistable circuits without the need for numerical simulation. We first consider two genetic toggle variants and two different positive feedback circuits, and show how specific topological properties present in each type of circuit can serve to increase the size of the regions of parameter space in which they function as switches. We then demonstrate that a single competitive monomeric activator added to a purely monomeric (and otherwise monostable) mutual repressor circuit is sufficient for bistability. Finally, we compare our approach with the Routh–Hurwitz method and derive consistent, yet more powerful, parametric conditions. The predictive power and ease of use of Sturm's theorem demonstrated in this work suggest that algebraic geometric techniques may be underused in biomolecular circuit analysis.

The Writing Essential Skill Screener–Upper Elementary Version: A Technical Analysis

1999 ◽  
Vol 84 (3) ◽  
pp. 917-926
Author(s):  
Bradley T. Erford ◽  
Elizabeth A. Ivey ◽  
Stacy L. Dorman
Keyword(s):  
Predictive Power ◽  
Reliability And Validity ◽  
Written Language ◽  
Upper Elementary ◽  
Written Expression ◽  
Diagnostic Efficiency ◽  
Wide Range Achievement Test ◽  
Wide Range ◽  
Test Retest Reliability ◽  
Wide Range Achievement

The Writing Essential Skill Screener–Upper Elementary Version was designed to assess students in Grades 3 to 6 at risk for difficulties in written expression. Four independent samples were used to study the Screener's reliability and validity. Thirty-day test-retest reliability was .83 and concurrent validity coefficients ( n = 275) were .66 with the Woodcock-Johnson: Tests of Achievement–Revised Written Language domain and .80 with the Wide-Range Achievement Test–Revised Spelling subtest. Exploratory factor analytic procedures suggest the Screener has a single dimension, Written Expression. A diagnostic efficiency study indicated the total predictive value was 86%, sensitivity was .91, specificity was .80, positive predictive power was .82, and negative predictive power was .90.

scholarly journals Deciphering signatures of natural selection via deep learning

2021 ◽  
Author(s):  
Xinghu Qin ◽  
Charleston W.K. Chiang ◽  
Oscar E Gaggiotti
Keyword(s):  
Deep Learning ◽  
Natural Selection ◽  
Quantitative Traits ◽  
Predictive Power ◽  
Spatial Location ◽  
Human Genetic Variation ◽  
Environmental Attributes ◽  
Wide Range ◽  
Candidate Regions ◽  
Genomic Regions

Identifying genomic regions influenced by natural selection provides fundamental insights into a wide range of problems including human health, animal and plant breeding, and the understanding of local adaptation. We propose a new method, DeepGenomeScan, that can be used to address all these problems. It is based on the principle that the genotypes of individuals can be used to predict any associated trait; not only their phenotype but also their spatial location or the environmental attributes of the habitat they live in. We, therefore,implemented a deep learning method to detect candidate regions under selection by identifying loci that contribute the most to the predictive power of the deep neural network. Using simulations, we show that our method can successfully identify loci underlying quantitative traits subject to complex spatial patterns of selection. We apply DeepGenomeScan to a European human genetic variation dataset and posit that the loci that contribute the most to the prediction of latitude and longitude are located in genomic regions under selection. Using this approach, we identified many SNPs located within well-known genes, some of which were not identified using existing population genetics approaches, e.g. MCM6, MGAT5, TMEM163.

Predicting Turning Points of the Business Cycle: Do Financial Sector Variables Help?

2013 ◽  
pp. 63-81 ◽  
Author(s):  
A. Pestova
Keyword(s):  
Business Cycle ◽  
Predictive Power ◽  
Choice Model ◽  
Turning Points ◽  
Financial Sector ◽  
Leading Indicators ◽  
Macroeconomic Dynamics ◽  
Wide Range ◽  
The Business Cycle ◽  
Binary Choice Model

The objective of this study is to develop a system of leading indicators of the business cycle turning points for a wide range of countries, including Russia, over a period of more than thirty years. We use a binary choice model with the dependent variable of the state of economy: the recession, there is no recession. These models allow us to assess how likely is the change of macroeconomic dynamics from positive to negative and vice versa. Empirical analysis suggests that the inclusion of financial sector variables into equation can significantly improve the predictive power of the models of the turning points of business cycles. At the same time, models with financial and real sector variables obtained in the paper outperform the "naive" models based only on the leading indicator of GDP in the OECD methodology due to either a lower level of noise (recession model) or a higher predictive power (model of the recovery from recession).

scholarly journals A Family of Heuristic-Based Inequalities for Maximizing Overall Safety Margins in Aircraft Parking Stands Arrangement Problems

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Yichen Qin ◽  
Z. X. Wang ◽  
Felix T. S. Chan ◽  
S. H. Chung ◽  
T. Qu
Keyword(s):  
Mixed Integer Linear Programming ◽  
Programming Model ◽  
Mixed Integer ◽  
Weighted Sum ◽  
Stopping Criterion ◽  
Binary Variables ◽  
Safety Margins ◽  
Wide Range ◽  
The Individual ◽  
Optimality Gap

We consider the problem of arranging a set of aircraft in a maintenance hangar operated by an independent aircraft service provider. The overall safety margins of the parking layout need to be maximized within the limited available space, measured by the weighted sum of the individual discrete safety margins of each aircraft. A mixed-integer linear programming model is developed, and the positions of the aircraft are determined by the position-controlling binary variables associated with a set of revised No-Fit Polygons (NFPs). Due to the nonconvex irregular shape of aircraft, the model involves a great number of binary variables associated with the revised NFP. The default branch-and-bound algorithm is inefficient in solving such a model as the infeasibility information of the precedent visited solution cannot be directly utilized by the default method to update the bounds. A heuristic algorithm is developed to provide practical solutions, and the intermediate infeasible solutions identified during searching are utilized to develop valid and approximate inequalities, tightening the optimality gap. The computational results demonstrate that the addition of inequalities improves the computational efficiency in solving a wide range of instances and in tightening the optimality gap while the stopping criterion is met.

scholarly journals AI and social theory

AI & Society ◽  
2021 ◽  
Author(s):  
Jakob Mökander ◽  
Ralph Schroeder
Keyword(s):  
Artificial Intelligence ◽  
Social Theory ◽  
Predictive Power ◽  
Digital Data ◽  
Wide Range ◽  
Social Theories ◽  
The World ◽  
The Future ◽  
State Breakdown ◽  
Breakdown Model

AbstractIn this paper, we sketch a programme for AI-driven social theory. We begin by defining what we mean by artificial intelligence (AI) in this context. We then lay out our specification for how AI-based models can draw on the growing availability of digital data to help test the validity of different social theories based on their predictive power. In doing so, we use the work of Randall Collins and his state breakdown model to exemplify that, already today, AI-based models can help synthesise knowledge from a variety of sources, reason about the world, and apply what is known across a wide range of problems in a systematic way. However, we also find that AI-driven social theory remains subject to a range of practical, technical, and epistemological limitations. Most critically, existing AI-systems lack three essential capabilities needed to advance social theory in ways that are cumulative, holistic, open-ended, and purposeful. These are (1) semanticisation, i.e., the ability to develop and operationalize verbal concepts to represent machine-manipulable knowledge; (2) transferability, i.e., the ability to transfer what has been learned in one context to another; and (3) generativity, i.e., the ability to independently create and improve on concepts and models. We argue that if the gaps identified here are addressed by further research, there is no reason why, in the future, the most advanced programme in social theory should not be led by AI-driven cumulative advances.

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