Imperfect Knowledge and Monetary Policy

2006 ◽  
Author(s):  
Vítor Gaspar ◽  
Otmar Issing ◽  
Oreste Tristani ◽  
David Vestin
Keyword(s):  
Monetary Policy ◽  
Imperfect Knowledge

Regular estimates of paddock pasture mass can improve profitability on New Zealand dairy farms

2015 ◽  
Vol 77 ◽  
pp. 29-34 ◽  
Author(s):  
P.C. Beukes ◽  
S. Mccarthy ◽  
C.M. Wims ◽  
A.J. Romera
Keyword(s):  
Supply And Demand ◽  
Grazing Management ◽  
Average Error ◽  
Value Proposition ◽  
Perfect Knowledge ◽  
Operating Profit ◽  
A Value ◽  
Main Driver ◽  
Imperfect Knowledge ◽  
Farm Scale

Paddock selection is an important component of grazing management and is based on either some estimate of pasture mass (cover) or the interval since last grazing for each paddock. Obtaining estimates of cover to guide grazing management can be a time consuming task. A value proposition could assist farmers in deciding whether to invest resources in obtaining such information. A farm-scale simulation exercise was designed to estimate the effect of three levels of knowledge of individual paddock cover on profitability: 1) "perfect knowledge", where cover per paddock is known with perfect accuracy, 2) "imperfect knowledge", where cover per paddock is estimated with an average error of 15%, 3) "low knowledge", where cover is not known, and paddocks are selected based on longest time since last grazing. Grazing management based on imperfect knowledge increased farm operating profit by approximately $385/ha compared with low knowledge, while perfect knowledge added a further $140/ha. The main driver of these results is the level of accuracy in daily feed allocation, which increases with improving knowledge of pasture availability. This allows feed supply and demand to be better matched, resulting in less incidence of under- and over-feeding, higher milk production, and more optimal post-grazing residuals to maximise pasture regrowth. Keywords: modelling, paddock selection, pasture cover

Cognitive Skills You Need for the 21st Century

2020 ◽  
Author(s):  
Stephen K. Reed
Keyword(s):  
21St Century ◽  
Cognitive Skills ◽  
Computational Thinking ◽  
World Economic Forum ◽  
Action Categorization ◽  
Visuospatial Reasoning ◽  
The World ◽  
Imperfect Knowledge ◽  
World Economic ◽  
Knowledge Strategies

Cognitive Skills You Need for the 21st Century begins with the Future of Jobs Report 2018 of the World Economic Forum that describes trending skills through the year 2022. To assist with the development of these skills, the book describes techniques that should benefit everyone. The 20 chapters occupy 6 sections on acquiring knowledge (comprehension, action, categorization, abstraction), organizing knowledge (matrices, networks, hierarchies), reasoning (visuospatial reasoning, imperfect knowledge, strategies), problem-solving (problems, design, dynamics), artificial intelligence (data sciences, explainable AI, information sciences, general AI), and education (complex systems, computational thinking, continuing education). Classical research, recent research, personal anecdotes, and a few exercises provide a broad introduction to this critical topic.

scholarly journals A Method for Integration of Preferences to a Multi-Objective Evolutionary Algorithm Using Ordinal Multi-Criteria Classification

2021 ◽  
Vol 26 (2) ◽  
pp. 27
Author(s):  
Alejandro Castellanos-Alvarez ◽  
Laura Cruz-Reyes ◽  
Eduardo Fernandez ◽  
Nelson Rangel-Valdez ◽  
Claudia Gómez-Santillán ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Selective Pressure ◽  
Optimization Problems ◽  
Region Of Interest ◽  
Decision Makers ◽  
Multiple Objective ◽  
Objective Functions ◽  
Multi Objective ◽  
Imperfect Knowledge ◽  
Real World Problems

Most real-world problems require the optimization of multiple objective functions simultaneously, which can conflict with each other. The environment of these problems usually involves imprecise information derived from inaccurate measurements or the variability in decision-makers’ (DMs’) judgments and beliefs, which can lead to unsatisfactory solutions. The imperfect knowledge can be present either in objective functions, restrictions, or decision-maker’s preferences. These optimization problems have been solved using various techniques such as multi-objective evolutionary algorithms (MOEAs). This paper proposes a new MOEA called NSGA-III-P (non-nominated sorting genetic algorithm III with preferences). The main characteristic of NSGA-III-P is an ordinal multi-criteria classification method for preference integration to guide the algorithm to the region of interest given by the decision-maker’s preferences. Besides, the use of interval analysis allows the expression of preferences with imprecision. The experiments contrasted several versions of the proposed method with the original NSGA-III to analyze different selective pressure induced by the DM’s preferences. In these experiments, the algorithms solved three-objectives instances of the DTLZ problem. The obtained results showed a better approximation to the region of interest for a DM when its preferences are considered.

Decision making involving imperfect knowledge

1993 ◽  
Vol 23 (3) ◽  
pp. 840-851 ◽  
Author(s):  
C.F. Eick ◽  
N.N. Mehta
Keyword(s):  
Decision Making ◽  
Imperfect Knowledge

scholarly journals DataStorm: Coupled, Continuous Simulations for Complex Urban Environments

2021 ◽  
Vol 2 (3) ◽  
pp. 1-37
Author(s):  
Hans Walter Behrens ◽  
K. Selçuk Candan ◽  
Xilun Chen ◽  
Yash Garg ◽  
Mao-Lin Li ◽  
...  
Keyword(s):  
Parameter Space ◽  
Simulation Analysis ◽  
Urban System ◽  
Decision Criterion ◽  
Urban Environments ◽  
Parallel Execution ◽  
Effective Density ◽  
Urban Systems ◽  
Space Partitioning ◽  
Imperfect Knowledge

Urban systems are characterized by complexity and dynamicity. Data-driven simulations represent a promising approach in understanding and predicting complex dynamic processes in the presence of shifting demands of urban systems. Yet, today’s silo-based, de-coupled simulation engines fail to provide an end-to-end view of the complex urban system, preventing informed decision-making. In this article, we present DataStorm to support integration of existing simulation, analysis and visualization components into integrated workflows. DataStorm provides a flow engine, DataStorm-FE , for coordinating data and decision flows among multiple actors (each representing a model, analytic operation, or a decision criterion) and enables ensemble planning and optimization across cloud resources. DataStorm provides native support for simulation ensemble creation through parameter space sampling to decide which simulations to run, as well as distributed instantiation and parallel execution of simulation instances on cluster resources. Recognizing that simulation ensembles are inherently sparse relative to the potential parameter space, we also present a density-boosting partition-stitch sampling scheme to increase the effective density of the simulation ensemble through a sub-space partitioning scheme, complemented with an efficient stitching mechanism that leverages partial and imperfect knowledge from partial dynamical systems to effectively obtain a global view of the complex urban process being simulated.

scholarly journals Classification of Cancer Recurrence with Alpha-Beta BAM

2009 ◽  
Vol 2009 ◽  
pp. 1-14 ◽  
Author(s):  
María Elena Acevedo ◽  
Marco Antonio Acevedo ◽  
Federico Felipe
Keyword(s):  
Breast Cancer ◽  
Stable State ◽  
Cancer Recurrence ◽  
Breast Cancer Surgery ◽  
Associative Memories ◽  
Training Set ◽  
Perfect Recall ◽  
Alpha Beta ◽  
Leave One Out

Bidirectional Associative Memories (BAMs) based on first model proposed by Kosko do not have perfect recall of training set, and their algorithm must iterate until it reaches a stable state. In this work, we use the model of Alpha-Beta BAM to classify automatically cancer recurrence in female patients with a previous breast cancer surgery. Alpha-Beta BAM presents perfect recall of all the training patterns and it has a one-shot algorithm; these advantages make to Alpha-Beta BAM a suitable tool for classification. We use data from Haberman database, and leave-one-out algorithm was applied to analyze the performance of our model as classifier. We obtain a percentage of classification of 99.98%.

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